From 85bc5cee07cac4f67507da7cf12183089e0b1347 Mon Sep 17 00:00:00 2001 From: github-actions Date: Sun, 29 Sep 2024 00:18:17 +0000 Subject: [PATCH] update catalog --- .../Daily_Chlorophyll_a/models/USGSHABs1.json | 2 +- .../models/cb_prophet.json | 2 +- .../models/climatology.json | 2 +- .../models/persistenceRW.json | 2 +- .../models/procBlanchardMonod.json | 2 +- .../models/procCTMIMonod.json | 2 +- .../models/procEppleyNorbergMonod.json | 2 +- .../models/procEppleyNorbergSteele.json | 2 +- .../models/procHinshelwoodMonod.json | 2 +- .../models/procHinshelwoodSteele.json | 2 +- .../Daily_Chlorophyll_a/models/tg_arima.json | 2 +- .../Daily_Chlorophyll_a/models/tg_ets.json | 2 +- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 2 +- .../Daily_Chlorophyll_a/models/tg_lasso.json | 4 +- .../models/tg_precip_lm.json | 4 +- .../models/tg_precip_lm_all_sites.json | 4 +- .../models/tg_randfor.json | 4 +- .../Daily_Chlorophyll_a/models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- .../Daily_Dissolved_oxygen/collection.json | 24 +++--- .../models/AquaticEcosystemsOxygen.json | 12 +-- .../models/GLEON_lm_lag_1day.json | 2 +- .../models/air2waterSat_2.json | 2 +- .../models/cb_prophet.json | 2 +- .../models/climatology.json | 2 +- .../models/hotdeck.json | 8 +- .../models/persistenceRW.json | 52 +++++------ .../models/tg_arima.json | 2 +- .../Daily_Dissolved_oxygen/models/tg_ets.json | 2 +- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 2 +- .../models/tg_lasso.json | 2 +- .../models/tg_precip_lm.json | 2 +- .../models/tg_precip_lm_all_sites.json | 24 +++--- .../models/tg_randfor.json | 28 +++--- .../models/tg_tbats.json | 2 +- .../models/tg_temp_lm.json | 2 +- .../models/tg_temp_lm_all_sites.json | 2 +- .../Daily_Water_temperature/collection.json | 54 ++++++------ .../models/GAM_air_wind.json | 2 +- .../models/GLEON_JRabaey_temp_physics.json | 2 +- .../models/GLEON_lm_lag_1day.json | 2 +- .../models/GLEON_physics.json | 2 +- .../models/TSLM_seasonal_JM.json | 2 +- .../models/acp_fableLM.json | 2 +- .../models/air2waterSat_2.json | 2 +- .../models/baseline_ensemble.json | 2 +- .../models/bee_bake_RFModel_2024.json | 16 ++-- .../models/cb_prophet.json | 2 +- .../models/climatology.json | 2 +- .../models/fARIMA_clim_ensemble.json | 8 +- .../models/fTSLM_lag.json | 2 +- .../models/flareGLM.json | 2 +- .../models/flareGLM_noDA.json | 2 +- .../models/flareGOTM_noDA.json | 2 +- .../models/flareSimstrat_noDA.json | 2 +- .../models/flare_ler.json | 2 +- .../models/flare_ler_baselines.json | 2 +- .../models/hotdeck.json | 8 +- .../models/lm_AT_WTL_WS.json | 2 +- .../models/mkricheldorf_w_lag.json | 2 +- .../models/mlp1_wtempforecast_LF.json | 2 +- .../models/persistenceRW.json | 2 +- .../models/precip_mod.json | 2 +- .../models/tg_arima.json | 2 +- .../models/tg_ets.json | 2 +- .../models/tg_humidity_lm.json | 2 +- .../models/tg_humidity_lm_all_sites.json | 2 +- .../models/tg_lasso.json | 2 +- .../models/tg_precip_lm.json | 2 +- .../models/tg_precip_lm_all_sites.json | 2 +- .../models/tg_randfor.json | 12 +-- .../models/tg_tbats.json | 2 +- .../models/tg_temp_lm.json | 20 ++--- .../models/tg_temp_lm_all_sites.json | 2 +- .../models/zimmerman_proj1.json | 12 +-- .../collection.json | 10 +-- .../models/tg_arima.json | 4 +- .../models/tg_ets.json | 66 +++++++------- .../models/tg_humidity_lm.json | 4 +- .../models/tg_humidity_lm_all_sites.json | 4 +- .../models/tg_lasso.json | 4 +- .../models/tg_precip_lm.json | 4 +- .../models/tg_precip_lm_all_sites.json | 4 +- .../models/tg_randfor.json | 4 +- .../models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- .../collection.json | 6 +- .../models/tg_arima.json | 4 +- .../models/tg_ets.json | 4 +- .../models/tg_humidity_lm.json | 4 +- .../models/tg_humidity_lm_all_sites.json | 4 +- .../models/tg_lasso.json | 4 +- .../models/tg_precip_lm.json | 4 +- .../models/tg_precip_lm_all_sites.json | 4 +- .../models/tg_randfor.json | 74 ++++++++-------- .../models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- .../collection.json | 28 +++--- .../models/ChlorophyllCrusaders.json | 4 +- .../models/PEG.json | 4 +- .../models/cb_prophet.json | 4 +- .../models/climatology.json | 14 +-- .../models/persistenceRW.json | 4 +- .../models/tg_arima.json | 4 +- .../models/tg_ets.json | 4 +- .../models/tg_humidity_lm.json | 78 ++++++++--------- .../models/tg_humidity_lm_all_sites.json | 62 ++++++------- .../models/tg_lasso.json | 4 +- .../models/tg_precip_lm.json | 4 +- .../models/tg_precip_lm_all_sites.json | 4 +- .../models/tg_randfor.json | 46 +++++----- .../models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- .../collection.json | 28 +++--- .../models/PEG.json | 4 +- .../models/baseline_ensemble.json | 4 +- .../models/cb_prophet.json | 4 +- .../models/climatology.json | 14 +-- .../models/persistenceRW.json | 82 +++++++++--------- .../models/tg_arima.json | 86 +++++++++---------- .../models/tg_ets.json | 4 +- .../models/tg_humidity_lm.json | 4 +- .../models/tg_humidity_lm_all_sites.json | 4 +- .../models/tg_lasso.json | 58 ++++++------- .../models/tg_precip_lm.json | 70 +++++++-------- .../models/tg_precip_lm_all_sites.json | 58 ++++++------- .../models/tg_randfor.json | 74 ++++++++-------- .../models/tg_tbats.json | 26 +++--- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- .../models/climatology.json | 4 +- .../models/climatology.json | 4 +- .../collection.json | 10 +-- .../models/USUNEEDAILY.json | 4 +- .../models/bookcast_forest.json | 4 +- .../models/cb_prophet.json | 4 +- .../models/climatology.json | 4 +- .../models/persistenceRW.json | 4 +- .../models/tg_arima.json | 4 +- .../models/tg_ets.json | 4 +- .../models/tg_humidity_lm.json | 4 +- .../models/tg_humidity_lm_all_sites.json | 4 +- .../models/tg_precip_lm.json | 4 +- .../models/tg_precip_lm_all_sites.json | 4 +- .../models/tg_randfor.json | 4 +- .../models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- .../Daily_latent_heat_flux/collection.json | 18 ++-- .../models/cb_prophet.json | 4 +- .../models/climatology.json | 4 +- .../models/tg_arima.json | 4 +- .../Daily_latent_heat_flux/models/tg_ets.json | 4 +- .../models/tg_humidity_lm.json | 4 +- .../models/tg_humidity_lm_all_sites.json | 4 +- .../models/tg_precip_lm.json | 4 +- .../models/tg_precip_lm_all_sites.json | 82 +++++++++--------- .../models/tg_randfor.json | 4 +- .../models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- .../collection.json | 14 +-- .../models/tg_arima.json | 22 ++--- .../models/tg_ets.json | 4 +- .../models/tg_humidity_lm.json | 4 +- .../models/tg_humidity_lm_all_sites.json | 4 +- .../models/tg_lasso.json | 4 +- .../models/tg_precip_lm.json | 4 +- .../models/tg_precip_lm_all_sites.json | 4 +- .../models/tg_randfor.json | 4 +- .../models/tg_tbats.json | 4 +- .../models/tg_temp_lm.json | 4 +- .../models/tg_temp_lm_all_sites.json | 4 +- catalog/summaries/collection.json | 4 +- 180 files changed, 882 insertions(+), 882 deletions(-) diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json index 5108379bd6..4a025efc18 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json @@ -197,7 +197,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json index 3d175ad795..d3ecda03c7 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json index e976f53cd2..7029100112 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -220,7 +220,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index 4e174d2643..c643ea5e72 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json index 3656e10497..06e915a718 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json index c477f9c6f2..76d1cc0387 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json index d9e73e6b33..d9104320c2 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index b6add7aae3..e13de86eb1 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 479afa4503..bbdba60315 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json index a0a2887002..50da913174 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index c4c9b9eabb..06b5d7a0bf 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index ca94eaa52c..ebe910e58a 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json index 1fd700ff62..b975f9dd68 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json index 005d33e943..6dddeb279a 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json index 5e16940bce..c85a53f11f 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json index a9e8e33559..0c3c6c761b 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json index f70cd79f43..90a9b875c7 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index 00785b9be4..189c48a131 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index f0217f3a2a..d999a6a513 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json index f1007a6eff..3f8e10c55b 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json index ea9fd489ac..a4e5f5dbdf 100644 --- a/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json @@ -24,7 +24,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -211,7 +211,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Chlorophyll_a", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json index e2a772c9f7..28dfb0ba29 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -26,7 +26,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/cb_prophet.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/climatology.json" }, { "rel": "item", @@ -38,6 +43,11 @@ "type": "application/json", "href": "./models/tg_arima.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_lasso.json" + }, { "rel": "item", "type": "application/json", @@ -66,12 +76,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", @@ -83,11 +88,6 @@ "type": "application/json", "href": "./models/tg_temp_lm_all_sites.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" - }, { "rel": "item", "type": "application/json", diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index c798dbfba4..18c8846d86 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -10,13 +10,13 @@ "type": "MultiPoint", "coordinates": [ [-82.0084, 29.676], - [-105.9154, 39.8914], - [-102.4471, 39.7582] + [-102.4471, 39.7582], + [-105.9154, 39.8914] ] }, "properties": { "title": "AquaticEcosystemsOxygen", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: BARC, WLOU, ARIK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: BARC, ARIK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", "end_datetime": "2024-08-04T00:00:00Z", @@ -49,8 +49,8 @@ "Daily", "P1D", "BARC", - "WLOU", - "ARIK" + "ARIK", + "WLOU" ], "table:columns": [ { @@ -197,7 +197,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=AquaticEcosystemsOxygen?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=AquaticEcosystemsOxygen?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=AquaticEcosystemsOxygen?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json index 1577e57f70..f521c16a5a 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json index 89671c00b5..c3de43e928 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index 8c38daac5e..accbe28bb8 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -255,7 +255,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 9197426123..ea88dca1b9 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index e2811d6396..74c54e14b5 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -16,9 +16,9 @@ [-119.2575, 37.0597], [-122.1655, 44.2596], [-111.7979, 40.7839], + [-111.5081, 33.751], [-89.4737, 46.2097], [-89.7048, 45.9983], - [-111.5081, 33.751], [-97.7823, 33.3785], [-78.1473, 38.8943], [-87.4077, 32.9604], @@ -27,7 +27,7 @@ }, "properties": { "title": "hotdeck", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, BLDE, BIGC, MCRA, REDB, CRAM, LIRO, SYCA, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, BLDE, BIGC, MCRA, REDB, SYCA, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", @@ -66,9 +66,9 @@ "BIGC", "MCRA", "REDB", + "SYCA", "CRAM", "LIRO", - "SYCA", "PRIN", "POSE", "MAYF", @@ -219,7 +219,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=hotdeck?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=hotdeck?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=hotdeck?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index 7b806f2485..0b5f0e8628 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -19,35 +19,35 @@ [-111.7979, 40.7839], [-82.0177, 29.6878], [-111.5081, 33.751], + [-87.4077, 32.9604], + [-96.443, 38.9459], + [-122.1655, 44.2596], + [-149.143, 68.6698], + [-78.1473, 38.8943], + [-96.6242, 34.4442], + [-87.7982, 32.5415], + [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], [-110.5871, 44.9501], - [-96.6242, 34.4442], - [-83.5038, 35.6904], - [-77.9832, 39.0956], - [-89.7048, 45.9983], - [-121.9338, 45.7908], - [-87.4077, 32.9604], [-119.0274, 36.9559], [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-87.7982, 32.5415], - [-105.9154, 39.8914], [-84.4374, 31.1854], [-66.7987, 18.1741], [-72.3295, 42.4719], [-96.6038, 39.1051], - [-96.443, 38.9459], - [-122.1655, 44.2596], - [-149.143, 68.6698], - [-78.1473, 38.8943] + [-83.5038, 35.6904], + [-77.9832, 39.0956], + [-89.7048, 45.9983], + [-121.9338, 45.7908] ] }, "properties": { "title": "persistenceRW", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, ARIK, BARC, BIGC, BLDE, BLUE, LECO, LEWI, LIRO, MART, MAYF, TECR, TOMB, TOOK, WALK, BLWA, WLOU, FLNT, GUIL, HOPB, KING, MCDI, MCRA, OKSR, POSE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", @@ -89,30 +89,30 @@ "REDB", "SUGG", "SYCA", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "BLUE", + "BLWA", + "WLOU", "ARIK", "BARC", "BIGC", "BLDE", - "BLUE", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", "TECR", "TOMB", "TOOK", "WALK", - "BLWA", - "WLOU", "FLNT", "GUIL", "HOPB", "KING", - "MCDI", - "MCRA", - "OKSR", - "POSE" + "LECO", + "LEWI", + "LIRO", + "MART" ], "table:columns": [ { @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index 131cd4ebbb..fa46f97bba 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 8745facac1..da1e2f29a4 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json index 8d9cbddced..ba4b5506b2 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json index 4cae315b0f..610afedde5 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json index 5a157a30b0..68ef933093 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json index 86984c4368..f27d4541e9 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json index 2e4c24d76a..2bdcb663ff 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json @@ -9,10 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -42,12 +38,16 @@ [-111.7979, 40.7839], [-82.0177, 29.6878], [-111.5081, 33.751], - [-119.0274, 36.9559] + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", @@ -79,10 +79,6 @@ "oxygen", "Daily", "P1D", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +108,11 @@ "REDB", "SUGG", "SYCA", - "TECR" + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json index f5559cb53f..043daa4ec5 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -9,11 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-119.0274, 36.9559], - [-88.1589, 31.8534], - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -42,12 +37,17 @@ [-99.2531, 47.1298], [-111.7979, 40.7839], [-82.0177, 29.6878], - [-111.5081, 33.751] + [-111.5081, 33.751], + [-119.0274, 36.9559], + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", @@ -79,11 +79,6 @@ "oxygen", "Daily", "P1D", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +107,12 @@ "PRPO", "REDB", "SUGG", - "SYCA" + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index fa35840c75..8e0c0163ea 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json index f9a6d5dc38..f84bf7b5fe 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json index 0318835346..461c016f79 100644 --- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Dissolved_oxygen", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json b/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json index 6e962b5a72..3385ba1625 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json @@ -21,102 +21,102 @@ { "rel": "item", "type": "application/json", - "href": "./models/fTSLM_lag.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM_noDA.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM_noDA.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat_noDA.json" + "href": "./models/fTSLM_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler.json" + "href": "./models/flareGLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler_baselines.json" + "href": "./models/flareGLM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/flareGOTM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/flareSimstrat_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/flare_ler.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/flare_ler_baselines.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fARIMA_clim_ensemble.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/fARIMA_clim_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", @@ -151,37 +151,37 @@ { "rel": "item", "type": "application/json", - "href": "./models/zimmerman_proj1.json" + "href": "./models/lm_AT_WTL_WS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bee_bake_RFModel_2024.json" + "href": "./models/mlp1_wtempforecast_LF.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GAM_air_wind.json" + "href": "./models/zimmerman_proj1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/TSLM_seasonal_JM.json" + "href": "./models/hotdeck.json" }, { "rel": "item", "type": "application/json", - "href": "./models/hotdeck.json" + "href": "./models/GAM_air_wind.json" }, { "rel": "item", "type": "application/json", - "href": "./models/lm_AT_WTL_WS.json" + "href": "./models/TSLM_seasonal_JM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mlp1_wtempforecast_LF.json" + "href": "./models/bee_bake_RFModel_2024.json" }, { "rel": "item", diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index 3ba8683f66..5ed6284271 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GAM_air_wind?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=GAM_air_wind?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GAM_air_wind?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index 32e5df8099..2cc73c6b68 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index f8d0117b24..cfe0ecf2be 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json index 2ec8a3ae1f..9d5d735a92 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json @@ -203,7 +203,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json index 26c0437c72..212d699bf6 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=TSLM_seasonal_JM?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=TSLM_seasonal_JM?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=TSLM_seasonal_JM?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json index 8941cfcdfd..695fa65b81 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/acp_fableLM.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=acp_fableLM?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=acp_fableLM?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=acp_fableLM?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json index e5e38c40d5..5569e64827 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index af0c44a1cb..2088a4e729 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index 5d73926e18..cb8c68d3b9 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -9,18 +9,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-82.0084, 29.676], - [-99.2531, 47.1298], - [-89.4737, 46.2097], [-99.1139, 47.1591], + [-99.2531, 47.1298], [-89.7048, 45.9983], + [-82.0084, 29.676], + [-89.4737, 46.2097], [-82.0177, 29.6878], [-149.6106, 68.6307] ] }, "properties": { "title": "bee_bake_RFModel_2024", - "description": "All summaries for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: BARC, PRPO, CRAM, PRLA, LIRO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: PRLA, PRPO, LIRO, BARC, CRAM, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", @@ -52,11 +52,11 @@ "temperature", "Daily", "P1D", - "BARC", - "PRPO", - "CRAM", "PRLA", + "PRPO", "LIRO", + "BARC", + "CRAM", "SUGG", "TOOK" ], @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=bee_bake_RFModel_2024?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=bee_bake_RFModel_2024?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=bee_bake_RFModel_2024?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json index 3bdc31352b..bcfa8bd985 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json @@ -255,7 +255,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json index 765bf24629..5f7d95d2cd 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/climatology.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index 4b454d7080..85af118a96 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -34,8 +34,8 @@ [-88.1589, 31.8534], [-119.2575, 37.0597], [-110.5871, 44.9501], - [-89.4737, 46.2097], [-84.4374, 31.1854], + [-89.4737, 46.2097], [-111.5081, 33.751], [-89.7048, 45.9983], [-99.1139, 47.1591], @@ -47,7 +47,7 @@ }, "properties": { "title": "fARIMA_clim_ensemble", - "description": "All summaries for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, CRAM, FLNT, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, FLNT, CRAM, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2023-11-10T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", @@ -104,8 +104,8 @@ "TOMB", "BIGC", "BLDE", - "CRAM", "FLNT", + "CRAM", "SYCA", "LIRO", "PRLA", @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index 133df21036..3a4b1c3025 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json index eea62c5c86..176ea7ff87 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index 3bd059799d..27455695be 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json index e8863e85f0..39c74c90b0 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json index 1f8a1871e1..85ff6f11c9 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -203,7 +203,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json index ecd77ad8ab..8fd8dff83a 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -203,7 +203,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json index 59c7f1c3d1..3774baea63 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json @@ -195,7 +195,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json index e62903ce1d..be12fd833b 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -14,7 +14,6 @@ [-88.1589, 31.8534], [-87.7982, 32.5415], [-84.4374, 31.1854], - [-122.1655, 44.2596], [-96.6038, 39.1051], [-111.5081, 33.751], [-78.1473, 38.8943], @@ -22,6 +21,7 @@ [-87.4077, 32.9604], [-77.9832, 39.0956], [-83.5038, 35.6904], + [-122.1655, 44.2596], [-102.4471, 39.7582], [-72.3295, 42.4719], [-111.7979, 40.7839], @@ -43,7 +43,7 @@ }, "properties": { "title": "hotdeck", - "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, MCRA, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", @@ -80,7 +80,6 @@ "TOMB", "BLWA", "FLNT", - "MCRA", "KING", "SYCA", "POSE", @@ -88,6 +87,7 @@ "MAYF", "LEWI", "LECO", + "MCRA", "ARIK", "HOPB", "REDB", @@ -251,7 +251,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=hotdeck?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=hotdeck?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=hotdeck?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index 507543e588..690513f99a 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=lm_AT_WTL_WS?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=lm_AT_WTL_WS?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=lm_AT_WTL_WS?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index 16b39484db..99cb0abd74 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=mkricheldorf_w_lag?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=mkricheldorf_w_lag?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=mkricheldorf_w_lag?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index 7212259265..b898653f01 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=mlp1_wtempforecast_LF?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=mlp1_wtempforecast_LF?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=mlp1_wtempforecast_LF?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json index 8165ea14f1..add88cbfe9 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json index b0966b1a0a..f471a8e83c 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=precip_mod?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=precip_mod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=precip_mod?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json index cf1263d911..8599a0060a 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json index 0e7571c13f..3d073ecaad 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json index 85c32a8280..a95ff0a628 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json index 05f2681fa3..fcb01b0c23 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json index 28b79fed99..f69e0c32f5 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json index 65a37b6957..e3547ef3a4 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json index 8102f08bb3..8a5e3449f2 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json index 1e18677437..44461c99e2 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json @@ -9,6 +9,7 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], [-110.5871, 44.9501], @@ -41,13 +42,12 @@ [-88.1589, 31.8534], [-149.6106, 68.6307], [-84.2793, 35.9574], - [-105.9154, 39.8914], - [-102.4471, 39.7582] + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", @@ -79,6 +79,7 @@ "temperature", "Daily", "P1D", + "ARIK", "BARC", "BIGC", "BLDE", @@ -111,8 +112,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK" + "WLOU" ], "table:columns": [ { @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json index 624ce0031f..4d9da3658b 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json index ccd0dba895..b6f77f7763 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json @@ -9,9 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.6106, 68.6307], - [-84.2793, 35.9574], - [-105.9154, 39.8914], [-102.4471, 39.7582], [-82.0084, 29.676], [-119.2575, 37.0597], @@ -42,12 +39,15 @@ [-82.0177, 29.6878], [-111.5081, 33.751], [-119.0274, 36.9559], - [-88.1589, 31.8534] + [-88.1589, 31.8534], + [-149.6106, 68.6307], + [-84.2793, 35.9574], + [-105.9154, 39.8914] ] }, "properties": { "title": "tg_temp_lm", - "description": "All summaries for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", @@ -79,9 +79,6 @@ "temperature", "Daily", "P1D", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -112,7 +109,10 @@ "SUGG", "SYCA", "TECR", - "TOMB" + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json index dbdff11677..707259ea9d 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json @@ -259,7 +259,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index d6999a6dd7..a007065a82 100644 --- a/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/catalog/summaries/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -9,18 +9,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-149.6106, 68.6307], [-82.0084, 29.676], [-89.4737, 46.2097], [-89.7048, 45.9983], [-99.1139, 47.1591], [-99.2531, 47.1298], - [-82.0177, 29.6878] + [-82.0177, 29.6878], + [-149.6106, 68.6307] ] }, "properties": { "title": "zimmerman_proj1", - "description": "All summaries for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-28T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", @@ -52,13 +52,13 @@ "temperature", "Daily", "P1D", - "TOOK", "BARC", "CRAM", "LIRO", "PRLA", "PRPO", - "SUGG" + "SUGG", + "TOOK" ], "table:columns": [ { @@ -205,7 +205,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Water_temperature", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=zimmerman_proj1?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=temperature/model_id=zimmerman_proj1?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=temperature/model_id=zimmerman_proj1?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json index 82ef8b026f..d5caaad663 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json @@ -8,6 +8,11 @@ ], "type": "Collection", "links": [ + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_arima.json" + }, { "rel": "item", "type": "application/json", @@ -43,11 +48,6 @@ "type": "application/json", "href": "./models/tg_temp_lm_all_sites.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_arima.json" - }, { "rel": "item", "type": "application/json", diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index f07aec6d63..0694860b44 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index ef2d69c100..f434e0bdba 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -9,6 +9,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], @@ -41,27 +55,13 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_ets", - "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -92,6 +92,20 @@ "abundance", "Weekly", "P1W", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", "HEAL", "JERC", "JORN", @@ -124,21 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV" + "YELL" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json index 1d43f90dd1..87ea052c7f 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json index f3e934fce4..52164edfdc 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json index 7c415361ca..b11cd1a44d 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -281,7 +281,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json index 31831215d1..9f3fa55904 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json index 301f9e8b27..511c1f9952 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json index 93c17ab243..809a6957cd 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index ae05de97cf..aeafdd5fbd 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json index d96623b687..0275195836 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 22a4f284a9..b0c1028502 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_abundance", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=abundance/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json index 15d296b98f..9c09826e62 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json @@ -21,7 +21,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", @@ -41,12 +41,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index 41ef896946..22379b5138 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 60c799f4c3..5e466ef3a3 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json index a13c6cb54b..f9a37ad28c 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json index a574e86aec..cd2642ca4b 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json index 201256dd2b..4ebc1cddc3 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -281,7 +281,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json index bb3577dee1..0161f9c418 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json index 6f73038f5a..c51c7aa395 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json index 174f540469..f911e6b3c0 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json @@ -9,22 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], @@ -55,13 +39,29 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -92,22 +92,6 @@ "richness", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -138,7 +122,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index d5e3cd10c5..4980863deb 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json index de7e333e46..c11959d134 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json index 9df9ee17a1..36540bf62c 100644 --- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly beetle_community_richness", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=richness/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json index ce0e7ccf4f..271a14713a 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,72 +11,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 127c6606df..827798a679 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -16,7 +16,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ @@ -195,7 +195,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=ChlorophyllCrusaders?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=ChlorophyllCrusaders?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=ChlorophyllCrusaders?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json index 1d154ec108..7628857551 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json @@ -61,7 +61,7 @@ "properties": { "title": "PEG", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=PEG?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=PEG?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=PEG?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json index 6025cceafc..ced975237c 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json @@ -61,7 +61,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index 641d565b48..424ab77699 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -54,14 +54,14 @@ [-147.5026, 65.154], [-145.7514, 63.8811], [-149.2133, 63.8758], - [-156.6194, 71.2824], - [-149.3705, 68.6611] + [-149.3705, 68.6611], + [-156.6194, 71.2824] ] }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -137,8 +137,8 @@ "BONA", "DEJU", "HEAL", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index c8fdb76f42..61c00f4e77 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -61,7 +61,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, SJER, SOAP, SRER, STEI, STER, TALL, NOGP, OAES, ONAQ, ORNL, OSBS, TEAK, TOOL, TREE, UKFS, UNDE, LAJA, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index afc6b6e7b1..97afd92896 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index 170cb1f95a..7ce93b98e3 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json index f28e14ccef..0ef835a791 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json @@ -9,6 +9,23 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104], + [-96.5631, 39.1008], + [-67.0769, 18.0213], + [-88.1612, 31.8539], + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], [-119.7323, 37.1088], @@ -38,30 +55,13 @@ [-81.4362, 28.1251], [-83.5019, 35.689], [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], - [-96.5631, 39.1008], - [-67.0769, 18.0213], - [-88.1612, 31.8539], - [-80.5248, 37.3783], - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759] + [-72.1727, 42.5369] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,23 @@ "gcc_90", "Daily", "P1D", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", "SCBI", "SERC", "SJER", @@ -121,24 +138,7 @@ "DSNY", "GRSM", "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP" + "HARV" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 113cedc3d7..80073db3f0 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -9,6 +9,19 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -42,26 +55,13 @@ [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-119.7323, 37.1088] ] }, "properties": { "title": "tg_humidity_lm_all_sites", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,6 +92,19 @@ "gcc_90", "Daily", "P1D", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -125,20 +138,7 @@ "RMNP", "SCBI", "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SJER" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json index 621ccaae1b..aede2cf565 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json index f4281ba632..f2bef6d1bf 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json index ef712dd85c..714357d075 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json index 1047c1898f..bb98f46ac8 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json @@ -9,15 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-109.3883, 38.2483], - [-105.5824, 40.0543], - [-100.9154, 46.7697], - [-99.0588, 35.4106], - [-112.4524, 40.1776], - [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], [-78.1395, 38.8929], [-76.56, 38.8901], [-119.7323, 37.1088], @@ -55,13 +46,22 @@ [-96.5631, 39.1008], [-67.0769, 18.0213], [-88.1612, 31.8539], - [-80.5248, 37.3783] + [-80.5248, 37.3783], + [-109.3883, 38.2483], + [-105.5824, 40.0543], + [-100.9154, 46.7697], + [-99.0588, 35.4106], + [-112.4524, 40.1776], + [-84.2826, 35.9641], + [-81.9934, 29.6893], + [-155.3173, 19.5531], + [-105.546, 40.2759] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,15 +92,6 @@ "gcc_90", "Daily", "P1D", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", "SCBI", "SERC", "SJER", @@ -138,7 +129,16 @@ "KONZ", "LAJA", "LENO", - "MLBS" + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index db2bc64f26..33f6dd7793 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json index 9dea84edcb..2f3fd078ff 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 88cebbb9c6..50a3d783ec 100644 --- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Green_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json index 972d138984..063aeef91b 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -11,72 +11,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json index 8912b43399..a651b8bd84 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -61,7 +61,7 @@ "properties": { "title": "PEG", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=PEG?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=PEG?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=PEG?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index 47e262b536..731eda7f38 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -61,7 +61,7 @@ "properties": { "title": "baseline_ensemble", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json index c28be43c5c..b88488f478 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json @@ -61,7 +61,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 612d057420..dc6c4a5780 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -54,14 +54,14 @@ [-145.7514, 63.8811], [-149.2133, 63.8758], [-147.5026, 65.154], - [-156.6194, 71.2824], - [-149.3705, 68.6611] + [-149.3705, 68.6611], + [-156.6194, 71.2824] ] }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -137,8 +137,8 @@ "DEJU", "HEAL", "BONA", - "BARR", - "TOOL" + "TOOL", + "BARR" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index b6b88cbc33..8a82f1f24d 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -9,6 +9,15 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-84.4686, 31.1948], + [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], @@ -16,34 +25,21 @@ [-99.2413, 47.1282], [-121.9519, 45.8205], [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], [-104.7456, 40.8155], [-99.1066, 47.1617], [-145.7514, 63.8811], [-87.8039, 32.5417], [-81.4362, 28.1251], + [-99.0588, 35.4106], [-112.4524, 40.1776], [-84.2826, 35.9641], [-81.9934, 29.6893], [-155.3173, 19.5531], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-99.0588, 35.4106], - [-84.4686, 31.1948], - [-106.8425, 32.5907], [-80.5248, 37.3783], [-109.3883, 38.2483], [-105.5824, 40.0543], @@ -52,6 +48,10 @@ [-89.5857, 45.4937], [-95.1921, 39.0404], [-89.5373, 46.2339], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], [-83.5019, 35.689], [-66.8687, 17.9696], [-72.1727, 42.5369], @@ -60,8 +60,8 @@ }, "properties": { "title": "persistenceRW", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, RMNP, SCBI, SERC, SJER, SOAP, JERC, JORN, KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, SRER, STEI, STER, TALL, TEAK, CPER, DCFS, DEJU, DELA, DSNY, OAES, ONAQ, ORNL, OSBS, PUUM, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, BART, BLAN, BONA, CLBJ, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -92,6 +92,15 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -99,34 +108,21 @@ "WOOD", "WREF", "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", "CPER", "DCFS", "DEJU", "DELA", "DSNY", + "OAES", "ONAQ", "ORNL", "OSBS", "PUUM", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "OAES", - "JERC", - "JORN", "MLBS", "MOAB", "NIWO", @@ -135,6 +131,10 @@ "TREE", "UKFS", "UNDE", + "BART", + "BLAN", + "BONA", + "CLBJ", "GRSM", "GUAN", "HARV", @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index e7de3827fd..733e74625a 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -9,6 +9,25 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-155.3173, 19.5531], + [-105.546, 40.2759], + [-78.1395, 38.8929], + [-76.56, 38.8901], + [-119.7323, 37.1088], + [-119.2622, 37.0334], + [-110.8355, 31.9107], + [-89.5864, 45.5089], + [-103.0293, 40.4619], + [-87.3933, 32.9505], + [-119.006, 37.0058], + [-149.3705, 68.6611], + [-89.5857, 45.4937], + [-95.1921, 39.0404], + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], + [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], [-78.0418, 39.0337], @@ -36,32 +55,13 @@ [-99.0588, 35.4106], [-112.4524, 40.1776], [-84.2826, 35.9641], - [-81.9934, 29.6893], - [-155.3173, 19.5531], - [-105.546, 40.2759], - [-78.1395, 38.8929], - [-76.56, 38.8901], - [-119.7323, 37.1088], - [-119.2622, 37.0334], - [-110.8355, 31.9107], - [-89.5864, 45.5089], - [-103.0293, 40.4619], - [-87.3933, 32.9505], - [-119.006, 37.0058], - [-149.3705, 68.6611], - [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624] + [-81.9934, 29.6893] ] }, "properties": { "title": "tg_arima", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -92,6 +92,25 @@ "rcc_90", "Daily", "P1D", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", + "ABBY", "BARR", "BART", "BLAN", @@ -119,26 +138,7 @@ "OAES", "ONAQ", "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", - "ABBY" + "OSBS" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index dc2464ff59..db1a3e3e5a 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index f6bf22dd19..8be10ae14c 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 4d8f8fa4cd..64a1f8b108 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json index 19b6c1a5b7..db1e00b831 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json @@ -9,18 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], [-66.8687, 17.9696], [-72.1727, 42.5369], [-149.2133, 63.8758], @@ -55,13 +43,25 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689] ] }, "properties": { "title": "tg_lasso", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,18 +92,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -138,7 +126,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json index 1a2c7c0e99..217ee5f0a1 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json @@ -9,6 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], @@ -40,28 +55,13 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_precip_lm", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -92,6 +92,21 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -123,22 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 1a9ae4645a..f1302d3d48 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -9,18 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], [-66.8687, 17.9696], [-72.1727, 42.5369], [-149.2133, 63.8758], @@ -55,13 +43,25 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,18 +92,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", "GUAN", "HARV", "HEAL", @@ -138,7 +126,19 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json index 6a2de9d9bc..b08b7d2fcd 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json @@ -9,6 +9,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], [-106.8425, 32.5907], [-96.6129, 39.1104], [-96.5631, 39.1008], @@ -39,29 +55,13 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535], - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948] + [-110.5391, 44.9535] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -92,6 +92,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -122,23 +138,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index d35a0999de..cfefe73405 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -9,6 +9,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [-89.5373, 46.2339], + [-99.2413, 47.1282], + [-121.9519, 45.8205], + [-110.5391, 44.9535], [-122.3303, 45.7624], [-156.6194, 71.2824], [-71.2874, 44.0639], @@ -51,17 +55,13 @@ [-119.006, 37.0058], [-149.3705, 68.6611], [-89.5857, 45.4937], - [-95.1921, 39.0404], - [-89.5373, 46.2339], - [-99.2413, 47.1282], - [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-95.1921, 39.0404] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -92,6 +92,10 @@ "rcc_90", "Daily", "P1D", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -134,11 +138,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UKFS" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json index d71f4e1f96..2fab766339 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 3d654b6efe..5225ac855f 100644 --- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Red_chromatic_coordinate", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json b/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json index 4a2054325f..923028332c 100644 --- a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -61,7 +61,7 @@ "properties": { "title": "climatology", "description": "All summaries for the 30min_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for 30min Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json b/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json index 0a9d8a8491..3f2b516bdd 100644 --- a/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/catalog/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -61,7 +61,7 @@ "properties": { "title": "climatology", "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, STER, TALL, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for 30min latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index f81eb5e3fa..20111ac780 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -21,27 +21,27 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json index 14ff1ee41b..06512ca081 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json @@ -15,7 +15,7 @@ "properties": { "title": "USUNEEDAILY", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the USUNEEDAILY model. Information for the model is provided as follows: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates..\n The model predicts this variable at the following sites: PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-12-12T00:00:00Z", "end_datetime": "2024-01-16T00:00:00Z", "providers": [ @@ -193,7 +193,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=USUNEEDAILY?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=USUNEEDAILY?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=USUNEEDAILY?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index 7c21ce0ce9..617c01245c 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -16,7 +16,7 @@ "properties": { "title": "bookcast_forest", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: TALL, OSBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2024-01-10T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ @@ -195,7 +195,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=bookcast_forest?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=bookcast_forest?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=bookcast_forest?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json index 8f2c0c896e..0a5779e71f 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json @@ -60,7 +60,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: PUUM, GUAN, OSBS, SCBI, MOAB, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, ONAQ, DSNY, BONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -283,7 +283,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index 465af4367f..bda143aa30 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -61,7 +61,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index 351501f0fe..0d634c8208 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -61,7 +61,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, UNDE, WOOD, WREF, YELL, BONA, CLBJ, CPER, DCFS, DEJU, DELA, HEAL, JERC, JORN, KONA, KONZ, LAJA, SJER, SOAP, SRER, STEI, STER, TALL, DSNY, GRSM, GUAN, HARV, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index 932ac598f8..6df21d6aa7 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index 396e8d950c..bc83eac521 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json index 4b1c53ae08..1593dd6e8e 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json index ffe32d8422..d41630cacd 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json index 847aaee181..5f69efa0d0 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json index 61aecca70c..98d18352b0 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index 5033a93e6b..ba9e8a55e3 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index 702a514970..4e2f94446d 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json index 494e23d714..1867ac7840 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json index 6cc9908713..77d1ebff4b 100644 --- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily Net_ecosystem_exchange", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json index 9b0abdb3c2..a983504c40 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json @@ -21,27 +21,27 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", @@ -51,22 +51,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "parent", diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json index dc89f42f4b..013ec8408a 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json @@ -60,7 +60,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DSNY, SCBI, MOAB, PUUM, GUAN, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, OSBS, BONA, ONAQ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -283,7 +283,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json index fddee21d53..9e30add6f2 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -61,7 +61,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index 89cabd7aef..7ca1768e66 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index edfd525768..b0bd05506b 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json index 4f7ee37064..6f3d97d180 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json index 4556613085..ea2ad57328 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json index 46653a2b91..2040830138 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json index a467823c4a..432b9c64f5 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json @@ -9,24 +9,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-122.3303, 45.7624], - [-156.6194, 71.2824], - [-71.2874, 44.0639], - [-78.0418, 39.0337], - [-147.5026, 65.154], - [-97.57, 33.4012], - [-104.7456, 40.8155], - [-99.1066, 47.1617], - [-145.7514, 63.8811], - [-87.8039, 32.5417], - [-81.4362, 28.1251], - [-83.5019, 35.689], - [-66.8687, 17.9696], - [-72.1727, 42.5369], - [-149.2133, 63.8758], - [-84.4686, 31.1948], - [-106.8425, 32.5907], - [-96.6129, 39.1104], [-96.5631, 39.1008], [-67.0769, 18.0213], [-88.1612, 31.8539], @@ -55,13 +37,31 @@ [-89.5373, 46.2339], [-99.2413, 47.1282], [-121.9519, 45.8205], - [-110.5391, 44.9535] + [-110.5391, 44.9535], + [-122.3303, 45.7624], + [-156.6194, 71.2824], + [-71.2874, 44.0639], + [-78.0418, 39.0337], + [-147.5026, 65.154], + [-97.57, 33.4012], + [-104.7456, 40.8155], + [-99.1066, 47.1617], + [-145.7514, 63.8811], + [-87.8039, 32.5417], + [-81.4362, 28.1251], + [-83.5019, 35.689], + [-66.8687, 17.9696], + [-72.1727, 42.5369], + [-149.2133, 63.8758], + [-84.4686, 31.1948], + [-106.8425, 32.5907], + [-96.6129, 39.1104] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -92,24 +92,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", "KONZ", "LAJA", "LENO", @@ -138,7 +120,25 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA" ], "table:columns": [ { @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json index 453503f472..8b16f2f651 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index c77d946e7f..2af445957c 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json index 493cd94fcf..d430ede5d7 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json index 988c229f25..1eef7fffd3 100644 --- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json @@ -61,7 +61,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ @@ -285,7 +285,7 @@ "type": "application/x-parquet", "title": "Database Access for Daily latent_heat_flux", "href": "s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json index e3a2fb305d..c8233867d6 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json @@ -21,17 +21,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", @@ -41,22 +41,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json index 274969b661..90d822badc 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json @@ -9,21 +9,21 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [-78.0418, 39.0337], - [-96.5631, 39.1008], - [-88.1612, 31.8539], [-84.2826, 35.9641], [-81.9934, 29.6893], [-78.1395, 38.8929], [-76.56, 38.8901], [-87.3933, 32.9505], - [-95.1921, 39.0404] + [-95.1921, 39.0404], + [-78.0418, 39.0337], + [-96.5631, 39.1008], + [-88.1612, 31.8539] ] }, "properties": { "title": "tg_arima", - "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ @@ -54,15 +54,15 @@ "amblyomma_americanum", "Weekly", "P1W", - "BLAN", - "KONZ", - "LENO", "ORNL", "OSBS", "SCBI", "SERC", "TALL", - "UKFS" + "UKFS", + "BLAN", + "KONZ", + "LENO" ], "table:columns": [ { @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json index deddd31e5f..b92ef7bf15 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json index 68dfdf9620..b42049c01b 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json index df058b0ab2..c33231e2ad 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json index d2b3d53be4..77a1d685bb 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json @@ -22,7 +22,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -207,7 +207,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json index ed0ceb1360..f232a47b56 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json index 68a63f8da6..eb37076bec 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json index e1d62c34ab..05869254ec 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json @@ -22,7 +22,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -207,7 +207,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json index 67cbb74bba..9c289b2ee0 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json index 25352f9df8..4271c28bf5 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json index 7714f0a069..c9d72f0f4b 100644 --- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json +++ b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json @@ -23,7 +23,7 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-28T00:00:00Z", + "datetime": "2024-09-29T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ @@ -209,7 +209,7 @@ "type": "application/x-parquet", "title": "Database Access for Weekly Amblyomma_americanum_population", "href": "s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/collection.json b/catalog/summaries/collection.json index 432cda883a..6f32ee504f 100644 --- a/catalog/summaries/collection.json +++ b/catalog/summaries/collection.json @@ -170,13 +170,13 @@ ], "assets": { "data": { - "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/bundled-summaries/?endpoint_override=sdsc.osn.xsede.org", + "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/bundled-summaries//project_id=neon4cast?endpoint_override=sdsc.osn.xsede.org", "type": "application/x-parquet", "title": "Database Access", "roles": [ "data" ], - "description": "Use `arrow` for remote access to the database. This R code will return results for the Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/bundled-summaries/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "description": "Use `arrow` for remote access to the database. This R code will return results for the Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/bundled-summaries//project_id=neon4cast?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "thumbnail": { "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_desert.jpg",