skills.Rmd

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title: "skills"
site: distill::distill_website
css: ["post.css","container.css"]
---

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<h3 style="border-bottom:2px solid #77C66E;margin:10;width:80vh;">Multidisciplinary workflows</h3>
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I built my career with multidisciplinary projects where I interacted with collaborators of different scientific backgrounds. As a result, I acquired advanced skills in the development of multidisciplinary workflows, where I integrate feedback from different actors. This is a critical and difficult skill, as different sciences have unique perspectives, and contrasting priorities and expectations.
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<h3 style="border-bottom:2px solid #77C66E;margin:10;width:80vh;">Team and student mentoring</h3>
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I am involved in projects with a strong spatial dimension. Here, my expertise as a geographer are valuable, and my feedback is frequently requested in the design of sophisticated workflows and design research projects.
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<h3 style="border-bottom:2px solid #77C66E;margin:10;width:80vh;">Coding and data management</h3>

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My daily work involves the mobilization of large datasets with Gigabytes - and even Terabytes! - of data, making coding a critical tool. I often combine R and Python, using the first for generic data management and the latter for large-scale spatial data analysis. In this process, I developed several R packages to make my job more efficient, such as <a href="https://github.com/RRemelgado/rsMove">**rsMove**</a>, <a href="https://github.com/RRemelgado/CAWaR">**CAWaR**</a>, and <a href="https://github.com/RRemelgado/fieldRS">**fieldRS**</a>. More recently, I developed <a href="">**masDMT**</a>, whcih offers a light system to manage and access data within the Macroecology and Society (MAS) lab, build standardized reports and plots, and submit jobs to iDiv's cloud computing resources.
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<h3 style="border-bottom:2px solid #77C66E;margin:10;width:80vh;">Data integration</h3>
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I built my career within multidisciplinary settings, and this motivated me to develop skills in data integration. In the past, I mobilized large amounts of data such as satellite imagery (e.g. Landsat, MODIS, Sentinel), climate reanalysis and meteorological observations (e.g. <a href="https://www.ecad.eu/dailydata/">**ECAD**</a>), high-resolution GPS tracking data (e.g. <a href="https://www.movebank.org/cms/movebank-main">**MoveBank**</a>), species observations (e.g. <a href="https://www.gbif.org/">**GBIF**</a>), and land statistics (e.g. <a href="http://www.fao.org/faostat/en/#data/QC">**FAOSTAT**</a>). Integrating multiple data sources is a common aspect of my daily work, and these data frequently differ in type, spatial and temporal scale, and thematic scope.
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