diff --git a/pyleoclim/core/series.py b/pyleoclim/core/series.py
index ef3bd0b1..d30860f8 100644
--- a/pyleoclim/core/series.py
+++ b/pyleoclim/core/series.py
@@ -2431,7 +2431,8 @@ def annualize(self, months=list(range(1, 13)), min_res=0.25, frac_req_months=2/3
         
         Examples
         --------
-        1) Annual average
+        1) Calendar average
+    
         .. jupyter-execute::
  
             soi = pyleo.utils.load_dataset('SOI')
@@ -2444,7 +2445,8 @@ def annualize(self, months=list(range(1, 13)), min_res=0.25, frac_req_months=2/3
             fig, ax = soi.plot(title='Jan-Dec averaging')
             soi_a.plot(marker='o',ax=ax)
 
-        2) JJA average : straightforward
+        2) JJA average (straightforward)
+    
         .. jupyter-execute::
             
             soi_jja = soi.annualize(months=[6, 7 , 8])
@@ -2452,6 +2454,7 @@ def annualize(self, months=list(range(1, 13)), min_res=0.25, frac_req_months=2/3
             soi_jja.plot(marker='o',ax=ax, label='JJA average')
 
         3) DJF average : straddles a year; handles it gracefully
+    
         .. jupyter-execute::
             
             soi_djf = soi.annualize(months=[12, 1 , 2])
@@ -2459,17 +2462,18 @@ def annualize(self, months=list(range(1, 13)), min_res=0.25, frac_req_months=2/3
             soi_djf.plot(marker='o',ax=ax, label='DJF average')
 
         4) Varying the fraction of required months
+    
         .. jupyter-execute::
             
             AprMar = [4,5,6,7,8,9,10,11,12,1,2,3]
             soi_am_default = soi.annualize(months=AprMar)
-            soi_am_stringent = soi.annualize(months=AprMar,frac_req_months=0.9)
+            soi_am_stringent = soi.annualize(months=AprMar,frac_req_months=1.0)
 
-            fig, ax = soi.plot(title='Apr-Mar averaging')
+            fig, ax = soi.plot(title='Apr-Mar averaging', xlim = [2000, 2026])
             soi_am_default.plot(marker='o',ax=ax, label='Apr-Mar, $f=2/3$')
-            soi_am_stringent.plot(marker='o',ax=ax, label='Apr-Mar, $f=0.9$')
+            soi_am_stringent.plot(marker='o',ax=ax, label='Apr-Mar, $f=1.0$')
         
-        We see that insisting on a very high fraction of available months will result in dropped years 
+        The last year is incomplete, so insisting on complete coverage results in dropping it 
         
         Notes
         -----
@@ -3458,11 +3462,11 @@ def wavelet(self, method='cwt', settings=None, freq=None, freq_kwargs=None, verb
         Examples
         --------
 
-        Wavelet analysis on the evenly-spaced SOI record. The CWT method will be applied by default.
+        Wavelet analysis on the evenly-spaced NINO3 record. The CWT method will be applied by default.
 
         .. jupyter-execute::
 
-            ts = pyleo.utils.load_dataset('SOI')
+            ts = pyleo.utils.load_dataset('NINO3')
             scal1 = ts.wavelet()
             scal_signif = scal1.signif_test(number=20)  # for research-grade work, use number=200 or larger
             fig, ax = scal_signif.plot()
diff --git a/pyleoclim/data/metadata.yml b/pyleoclim/data/metadata.yml
index 472f1f45..e5024013 100644
--- a/pyleoclim/data/metadata.yml
+++ b/pyleoclim/data/metadata.yml
@@ -1,9 +1,12 @@
 SOI:
-  filename: 'soi_data'
+  filename: 'soi.long'
   file_extension: 'csv'
   pandas_kwargs:
     skiprows: 0
-    header: 1
+    header: 0
+    parse_dates: [0]
+    index_col: 0
+    skipfooter: 1
   pyleo_kwargs:
     time_name: 'time'
     time_unit: 'year C.E.'
@@ -11,8 +14,8 @@ SOI:
     value_unit: 'mb'
     label: 'Southern Oscillation Index'
     archiveType: 'Instrumental'
-  time_column: 1
-  value_column: 2
+  time_column: 0
+  value_column: ' SOI from CRU  missing value -99.99 https://psl.noaa.gov/data/timeseries/month/'
 
 NINO3:
   filename: 'wtc_test_data_nino_even'
diff --git a/pyleoclim/data/soi.long.csv b/pyleoclim/data/soi.long.csv
new file mode 100644
index 00000000..a5efc695
--- /dev/null
+++ b/pyleoclim/data/soi.long.csv
@@ -0,0 +1,1921 @@
+Date, SOI from CRU  missing value -99.99 https://psl.noaa.gov/data/timeseries/month/
+1866-01-01,-0.62
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+2020-05-01,0.71
+2020-06-01,-1.36
+2020-07-01,0.44
+2020-08-01,0.85
+2020-09-01,0.98
+2020-10-01,0.27
+2020-11-01,0.85
+2020-12-01,1.76
+2021-01-01,1.64
+2021-02-01,1.02
+2021-03-01,-0.2
+2021-04-01,0.3
+2021-05-01,0.49
+2021-06-01,0.22
+2021-07-01,1.61
+2021-08-01,0.27
+2021-09-01,0.86
+2021-10-01,0.51
+2021-11-01,1.12
+2021-12-01,1.43
+2022-01-01,0.34
+2022-02-01,0.76
+2022-03-01,1.35
+2022-04-01,2.69
+2022-05-01,1.9
+2022-06-01,2.45
+2022-07-01,0.91
+2022-08-01,0.81
+2022-09-01,1.73
+2022-10-01,1.73
+2022-11-01,0.31
+2022-12-01,2.11
+2023-01-01,1.14
+2023-02-01,0.93
+2023-03-01,-0.38
+2023-04-01,-0.03
+2023-05-01,-1.6
+2023-06-01,-0.12
+2023-07-01,-0.38
+2023-08-01,-1.6
+2023-09-01,-1.36
+2023-10-01,-0.6
+2023-11-01,-0.9
+2023-12-01,-0.39
+2024-01-01,0.31
+2024-02-01,-1.47
+2024-03-01,-0.2
+2024-04-01,-0.69
+2024-05-01,0.57
+2024-06-01,-0.43
+2024-07-01,-0.65
+2024-08-01,0.59
+2024-09-01,-0.12
+2024-10-01,0.35
+2024-11-01,0.61
+2024-12-01,1.09
+2025-01-01,0.26
+2025-02-01,0.21
+,
+,
+,
+,
+,
+,
+,
+,
+,
+,
\ No newline at end of file
diff --git a/pyleoclim/data/soi_data.csv b/pyleoclim/data/soi_data.csv
deleted file mode 100644
index 3872db4c..00000000
--- a/pyleoclim/data/soi_data.csv
+++ /dev/null
@@ -1,830 +0,0 @@
-Southern Oscillation Index (SOI),,
-Date,Year,Value
-195101,1951,1.5
-195102,1951.083333,0.9
-195103,1951.166667,-0.1
-195104,1951.25,-0.3
-195105,1951.333333,-0.7
-195106,1951.416667,0.2
-195107,1951.5,-1
-195108,1951.583333,-0.2
-195109,1951.666667,-1.1
-195110,1951.75,-1
-195111,1951.833333,-0.8
-195112,1951.916667,-0.7
-195201,1952,-0.9
-195202,1952.083333,-0.6
-195203,1952.166667,0.5
-195204,1952.25,-0.2
-195205,1952.333333,0.8
-195206,1952.416667,0.7
-195207,1952.5,0.5
-195208,1952.583333,0.1
-195209,1952.666667,-0.2
-195210,1952.75,0.4
-195211,1952.833333,0
-195212,1952.916667,-1.2
-195301,1953,0.3
-195302,1953.083333,-0.5
-195303,1953.166667,-0.2
-195304,1953.25,0.2
-195305,1953.333333,-1.7
-195306,1953.416667,0.1
-195307,1953.5,0
-195308,1953.583333,-1.2
-195309,1953.666667,-1.2
-195310,1953.75,0.1
-195311,1953.833333,-0.3
-195312,1953.916667,-0.5
-195401,1954,0.7
-195402,1954.083333,-0.3
-195403,1954.166667,0.3
-195404,1954.25,0.6
-195405,1954.333333,0.5
-195406,1954.416667,0.1
-195407,1954.5,0.4
-195408,1954.583333,1.1
-195409,1954.666667,0.2
-195410,1954.75,0.3
-195411,1954.833333,0.1
-195412,1954.916667,1.4
-195501,1955,-0.5
-195502,1955.083333,1.9
-195503,1955.166667,0.6
-195504,1955.25,-0.1
-195505,1955.333333,1
-195506,1955.416667,1.3
-195507,1955.5,1.6
-195508,1955.583333,1.5
-195509,1955.666667,1.3
-195510,1955.75,1.5
-195511,1955.833333,1.2
-195512,1955.916667,1
-195601,1956,1.3
-195602,1956.083333,1.6
-195603,1956.166667,1.3
-195604,1956.25,0.9
-195605,1956.333333,1.4
-195606,1956.416667,1.1
-195607,1956.5,1.1
-195608,1956.583333,1.2
-195609,1956.666667,0.1
-195610,1956.75,1.8
-195611,1956.833333,0.2
-195612,1956.916667,1.1
-195701,1957,0.6
-195702,1957.083333,-0.1
-195703,1957.166667,0.2
-195704,1957.25,0.2
-195705,1957.333333,-0.7
-195706,1957.416667,0.2
-195707,1957.5,0.2
-195708,1957.583333,-0.5
-195709,1957.666667,-0.9
-195710,1957.75,0
-195711,1957.833333,-1
-195712,1957.916667,-0.3
-195801,1958,-1.9
-195802,1958.083333,-0.5
-195803,1958.166667,0.3
-195804,1958.25,0.4
-195805,1958.333333,-0.5
-195806,1958.416667,0.3
-195807,1958.5,0.4
-195808,1958.583333,0.9
-195809,1958.666667,-0.3
-195810,1958.75,0.1
-195811,1958.833333,-0.4
-195812,1958.916667,-0.6
-195901,1959,-0.9
-195902,1959.083333,-1.4
-195903,1959.166667,1.3
-195904,1959.25,0.4
-195905,1959.333333,0.5
-195906,1959.416667,-0.1
-195907,1959.5,-0.3
-195908,1959.583333,-0.1
-195909,1959.666667,0
-195910,1959.75,0.5
-195911,1959.833333,0.9
-195912,1959.916667,0.9
-196001,1960,0.1
-196002,1960.083333,0.1
-196003,1960.166667,1
-196004,1960.25,0.8
-196005,1960.333333,0.5
-196006,1960.416667,0.1
-196007,1960.5,0.5
-196008,1960.583333,0.8
-196009,1960.666667,0.7
-196010,1960.75,0.1
-196011,1960.833333,0.5
-196012,1960.916667,0.8
-196101,1961,-0.3
-196102,1961.083333,0.9
-196103,1961.166667,-1.8
-196104,1961.25,0.8
-196105,1961.333333,0.3
-196106,1961.416667,0.1
-196107,1961.5,0.2
-196108,1961.583333,0.2
-196109,1961.666667,0.1
-196110,1961.75,-0.3
-196111,1961.833333,0.5
-196112,1961.916667,1.5
-196201,1962,2
-196202,1962.083333,-0.3
-196203,1962.166667,0.1
-196204,1962.25,0.2
-196205,1962.333333,1.1
-196206,1962.416667,0.7
-196207,1962.5,0.1
-196208,1962.583333,0.6
-196209,1962.666667,0.4
-196210,1962.75,1
-196211,1962.833333,0.3
-196212,1962.916667,0.2
-196301,1963,1
-196302,1963.083333,0.6
-196303,1963.166667,1.1
-196304,1963.25,0.8
-196305,1963.333333,0.4
-196306,1963.416667,-0.5
-196307,1963.5,-0.1
-196308,1963.583333,0
-196309,1963.666667,-0.6
-196310,1963.75,-1.2
-196311,1963.833333,-0.8
-196312,1963.916667,-1.2
-196401,1964,-0.4
-196402,1964.083333,0
-196403,1964.166667,1.1
-196404,1964.25,1.1
-196405,1964.333333,0.2
-196406,1964.416667,0.8
-196407,1964.5,0.6
-196408,1964.583333,1.5
-196409,1964.666667,1.3
-196410,1964.75,1.3
-196411,1964.833333,0.2
-196412,1964.916667,-0.3
-196501,1965,-0.4
-196502,1965.083333,0.4
-196503,1965.166667,0.8
-196504,1965.25,-0.5
-196505,1965.333333,0.2
-196506,1965.416667,-0.6
-196507,1965.5,-1.8
-196508,1965.583333,-0.7
-196509,1965.666667,-1.3
-196510,1965.75,-0.9
-196511,1965.833333,-1.5
-196512,1965.916667,0.2
-196601,1966,-1.3
-196602,1966.083333,-0.2
-196603,1966.166667,-0.9
-196604,1966.25,-0.2
-196605,1966.333333,-0.4
-196606,1966.416667,0.3
-196607,1966.5,0.1
-196608,1966.583333,0.6
-196609,1966.666667,-0.2
-196610,1966.75,-0.1
-196611,1966.833333,0
-196612,1966.916667,-0.3
-196701,1967,1.7
-196702,1967.083333,1.7
-196703,1967.166667,1.2
-196704,1967.25,0
-196705,1967.333333,0
-196706,1967.416667,0.6
-196707,1967.5,0.2
-196708,1967.583333,0.7
-196709,1967.666667,0.5
-196710,1967.75,0.1
-196711,1967.833333,-0.4
-196712,1967.916667,-0.6
-196801,1968,0.5
-196802,1968.083333,1.3
-196803,1968.166667,0.1
-196804,1968.25,0
-196805,1968.333333,1.2
-196806,1968.416667,1.1
-196807,1968.5,0.7
-196808,1968.583333,0.3
-196809,1968.666667,-0.3
-196810,1968.75,-0.1
-196811,1968.833333,-0.3
-196812,1968.916667,0.2
-196901,1969,-1.5
-196902,1969.083333,-0.5
-196903,1969.166667,0.4
-196904,1969.25,-0.4
-196905,1969.333333,-0.2
-196906,1969.416667,0.2
-196907,1969.5,-0.5
-196908,1969.583333,-0.1
-196909,1969.666667,-1
-196910,1969.75,-0.9
-196911,1969.833333,-0.1
-196912,1969.916667,0.4
-197001,1970,-1.1
-197002,1970.083333,-1
-197003,1970.166667,0.6
-197004,1970.25,-0.1
-197005,1970.333333,0.4
-197006,1970.416667,1
-197007,1970.5,-0.4
-197008,1970.583333,0.6
-197009,1970.666667,1.2
-197010,1970.75,1
-197011,1970.833333,1.6
-197012,1970.916667,1.9
-197101,1971,0.4
-197102,1971.083333,2
-197103,1971.166667,2.3
-197104,1971.25,1.7
-197105,1971.333333,0.9
-197106,1971.416667,0.4
-197107,1971.5,0.2
-197108,1971.583333,1.5
-197109,1971.666667,1.4
-197110,1971.75,1.7
-197111,1971.833333,0.5
-197112,1971.916667,0.3
-197201,1972,0.5
-197202,1972.083333,1.1
-197203,1972.166667,0.6
-197204,1972.25,-0.1
-197205,1972.333333,-1.6
-197206,1972.416667,-0.5
-197207,1972.5,-1.4
-197208,1972.583333,-0.5
-197209,1972.666667,-1.4
-197210,1972.75,-0.9
-197211,1972.833333,-0.3
-197212,1972.916667,-1.3
-197301,1973,-0.3
-197302,1973.083333,-1.4
-197303,1973.166667,0.7
-197304,1973.25,0.1
-197305,1973.333333,0.4
-197306,1973.416667,1.1
-197307,1973.5,0.6
-197308,1973.583333,1.3
-197309,1973.666667,1.2
-197310,1973.75,0.8
-197311,1973.833333,2.6
-197312,1973.916667,1.8
-197401,1974,2.4
-197402,1974.083333,2.1
-197403,1974.166667,2.4
-197404,1974.25,0.9
-197405,1974.333333,1
-197406,1974.416667,0.4
-197407,1974.5,1.1
-197408,1974.583333,0.8
-197409,1974.666667,1.1
-197410,1974.75,0.9
-197411,1974.833333,-0.1
-197412,1974.916667,0.2
-197501,1975,-0.5
-197502,1975.083333,0.8
-197503,1975.166667,1.6
-197504,1975.25,1.2
-197505,1975.333333,0.6
-197506,1975.416667,1.3
-197507,1975.5,1.9
-197508,1975.583333,2
-197509,1975.666667,2.1
-197510,1975.75,1.7
-197511,1975.833333,1.2
-197512,1975.916667,2.1
-197601,1976,1.4
-197602,1976.083333,1.7
-197603,1976.166667,1.7
-197604,1976.25,0.3
-197605,1976.333333,0.4
-197606,1976.416667,0.3
-197607,1976.5,-0.9
-197608,1976.583333,-0.8
-197609,1976.666667,-1.1
-197610,1976.75,0.4
-197611,1976.833333,0.7
-197612,1976.916667,-0.3
-197701,1977,-0.4
-197702,1977.083333,1.2
-197703,1977.166667,-0.5
-197704,1977.25,-0.4
-197705,1977.333333,-0.5
-197706,1977.416667,-0.9
-197707,1977.5,-1.1
-197708,1977.583333,-0.8
-197709,1977.666667,-0.8
-197710,1977.75,-1
-197711,1977.833333,-1.3
-197712,1977.916667,-1.1
-197801,1978,-0.3
-197802,1978.083333,-2.7
-197803,1978.166667,-0.2
-197804,1978.25,-0.3
-197805,1978.333333,1.4
-197806,1978.416667,0.7
-197807,1978.5,0.6
-197808,1978.583333,0.4
-197809,1978.666667,0.1
-197810,1978.75,-0.4
-197811,1978.833333,0
-197812,1978.916667,-0.1
-197901,1979,-0.4
-197902,1979.083333,1
-197903,1979.166667,0.1
-197904,1979.25,-0.1
-197905,1979.333333,0.5
-197906,1979.416667,0.6
-197907,1979.5,1.3
-197908,1979.583333,-0.2
-197909,1979.666667,0.1
-197910,1979.75,-0.1
-197911,1979.833333,-0.4
-197912,1979.916667,-0.7
-198001,1980,0.4
-198002,1980.083333,0.3
-198003,1980.166667,-0.4
-198004,1980.25,-0.6
-198005,1980.333333,0
-198006,1980.416667,0
-198007,1980.5,0
-198008,1980.583333,0.4
-198009,1980.666667,-0.5
-198010,1980.75,0
-198011,1980.833333,-0.3
-198012,1980.916667,-0.1
-198101,1981,0.4
-198102,1981.083333,-0.2
-198103,1981.166667,-1.3
-198104,1981.25,-0.1
-198105,1981.333333,0.8
-198106,1981.416667,1.2
-198107,1981.5,0.8
-198108,1981.583333,0.7
-198109,1981.666667,0.3
-198110,1981.75,-0.4
-198111,1981.833333,0.2
-198112,1981.916667,0.5
-198201,1982,1.2
-198202,1982.083333,0.3
-198203,1982.166667,0.6
-198204,1982.25,0.1
-198205,1982.333333,-0.3
-198206,1982.416667,-1
-198207,1982.5,-1.5
-198208,1982.583333,-1.7
-198209,1982.666667,-1.7
-198210,1982.75,-1.7
-198211,1982.833333,-2.6
-198212,1982.916667,-2.2
-198301,1983,-3.5
-198302,1983.083333,-3.6
-198303,1983.166667,-2.4
-198304,1983.25,-0.9
-198305,1983.333333,0.6
-198306,1983.416667,0
-198307,1983.5,-0.6
-198308,1983.583333,0.1
-198309,1983.666667,0.9
-198310,1983.75,0.4
-198311,1983.833333,-0.1
-198312,1983.916667,0
-198401,1984,0.2
-198402,1984.083333,0.9
-198403,1984.166667,-0.2
-198404,1984.25,0.3
-198405,1984.333333,0.2
-198406,1984.416667,-0.3
-198407,1984.5,0.2
-198408,1984.583333,0.4
-198409,1984.666667,0.1
-198410,1984.75,-0.3
-198411,1984.833333,0.3
-198412,1984.916667,-0.1
-198501,1985,-0.3
-198502,1985.083333,1.2
-198503,1985.166667,0.8
-198504,1985.25,1.2
-198505,1985.333333,0.4
-198506,1985.416667,-0.4
-198507,1985.5,-0.1
-198508,1985.583333,1
-198509,1985.666667,0
-198510,1985.75,-0.4
-198511,1985.833333,-0.2
-198512,1985.916667,0.2
-198601,1986,1
-198602,1986.083333,-1
-198603,1986.166667,0.5
-198604,1986.25,0.3
-198605,1986.333333,-0.2
-198606,1986.416667,1
-198607,1986.5,0.3
-198608,1986.583333,-0.4
-198609,1986.666667,-0.5
-198610,1986.75,0.6
-198611,1986.833333,-1.2
-198612,1986.916667,-1.4
-198701,1987,-0.7
-198702,1987.083333,-1.2
-198703,1987.166667,-1.3
-198704,1987.25,-1.4
-198705,1987.333333,-1.3
-198706,1987.416667,-1.1
-198707,1987.5,-1.4
-198708,1987.583333,-0.9
-198709,1987.666667,-1
-198710,1987.75,-0.4
-198711,1987.833333,0
-198712,1987.916667,-0.5
-198801,1988,-0.1
-198802,1988.083333,-0.4
-198803,1988.166667,0.6
-198804,1988.25,0.1
-198805,1988.333333,0.9
-198806,1988.416667,0.1
-198807,1988.5,1
-198808,1988.583333,1.5
-198809,1988.666667,1.8
-198810,1988.75,1.4
-198811,1988.833333,1.7
-198812,1988.916667,1.2
-198901,1989,1.5
-198902,1989.083333,1.2
-198903,1989.166667,1.1
-198904,1989.25,1.6
-198905,1989.333333,1.2
-198906,1989.416667,0.7
-198907,1989.5,0.9
-198908,1989.583333,-0.3
-198909,1989.666667,0.5
-198910,1989.75,0.8
-198911,1989.833333,-0.2
-198912,1989.916667,-0.5
-199001,1990,-0.1
-199002,1990.083333,-1.8
-199003,1990.166667,-0.4
-199004,1990.25,0.2
-199005,1990.333333,1.2
-199006,1990.416667,0.3
-199007,1990.5,0.5
-199008,1990.583333,-0.2
-199009,1990.666667,-0.7
-199010,1990.75,0.3
-199011,1990.833333,-0.5
-199012,1990.916667,-0.2
-199101,1991,0.6
-199102,1991.083333,0.3
-199103,1991.166667,-0.7
-199104,1991.25,-0.6
-199105,1991.333333,-1
-199106,1991.416667,-0.1
-199107,1991.5,0
-199108,1991.583333,-0.4
-199109,1991.666667,-1.5
-199110,1991.75,-1
-199111,1991.833333,-0.7
-199112,1991.916667,-1.8
-199201,1992,-2.9
-199202,1992.083333,-0.9
-199203,1992.166667,-2
-199204,1992.25,-1
-199205,1992.333333,0.3
-199206,1992.416667,-0.6
-199207,1992.5,-0.6
-199208,1992.583333,0.4
-199209,1992.666667,0.1
-199210,1992.75,-1.4
-199211,1992.833333,-0.7
-199212,1992.916667,-0.6
-199301,1993,-0.9
-199302,1993.083333,-0.7
-199303,1993.166667,-0.5
-199304,1993.25,-1.2
-199305,1993.333333,-0.3
-199306,1993.416667,-0.8
-199307,1993.5,-0.8
-199308,1993.583333,-0.9
-199309,1993.666667,-0.7
-199310,1993.75,-1.1
-199311,1993.833333,-0.1
-199312,1993.916667,0.2
-199401,1994,-0.1
-199402,1994.083333,0.3
-199403,1994.166667,-0.7
-199404,1994.25,-1.3
-199405,1994.333333,-0.7
-199406,1994.416667,-0.4
-199407,1994.5,-1.3
-199408,1994.583333,-1.2
-199409,1994.666667,-1.6
-199410,1994.75,-1.1
-199411,1994.833333,-0.6
-199412,1994.916667,-1.2
-199501,1995,-0.4
-199502,1995.083333,-0.1
-199503,1995.166667,0.8
-199504,1995.25,-0.7
-199505,1995.333333,-0.4
-199506,1995.416667,0.1
-199507,1995.5,0.4
-199508,1995.583333,0.3
-199509,1995.666667,0.3
-199510,1995.75,0
-199511,1995.833333,0
-199512,1995.916667,-0.5
-199601,1996,1
-199602,1996.083333,0.3
-199603,1996.166667,1.1
-199604,1996.25,0.8
-199605,1996.333333,0.3
-199606,1996.416667,1.2
-199607,1996.5,0.7
-199608,1996.583333,0.7
-199609,1996.666667,0.6
-199610,1996.75,0.6
-199611,1996.833333,-0.1
-199612,1996.916667,0.9
-199701,1997,0.5
-199702,1997.083333,1.7
-199703,1997.166667,-0.4
-199704,1997.25,-0.6
-199705,1997.333333,-1.3
-199706,1997.416667,-1.4
-199707,1997.5,-0.8
-199708,1997.583333,-1.4
-199709,1997.666667,-1.4
-199710,1997.75,-1.5
-199711,1997.833333,-1.2
-199712,1997.916667,-1
-199801,1998,-2.7
-199802,1998.083333,-2
-199803,1998.166667,-2.4
-199804,1998.25,-1.4
-199805,1998.333333,0.3
-199806,1998.416667,1
-199807,1998.5,1.2
-199808,1998.583333,1.2
-199809,1998.666667,1
-199810,1998.75,1.1
-199811,1998.833333,1
-199812,1998.916667,1.4
-199901,1999,1.8
-199902,1999.083333,1
-199903,1999.166667,1.3
-199904,1999.25,1.4
-199905,1999.333333,0.2
-199906,1999.416667,0.3
-199907,1999.5,0.5
-199908,1999.583333,0.4
-199909,1999.666667,-0.1
-199910,1999.75,1
-199911,1999.833333,1
-199912,1999.916667,1.4
-200001,2000,0.7
-200002,2000.083333,1.7
-200003,2000.166667,1.3
-200004,2000.25,1.2
-200005,2000.333333,0.4
-200006,2000.416667,-0.2
-200007,2000.5,-0.2
-200008,2000.583333,0.7
-200009,2000.666667,0.9
-200010,2000.75,1.1
-200011,2000.833333,1.8
-200012,2000.916667,0.8
-200101,2001,1
-200102,2001.083333,1.7
-200103,2001.166667,0.9
-200104,2001.25,0.2
-200105,2001.333333,-0.5
-200106,2001.416667,0.3
-200107,2001.5,-0.2
-200108,2001.583333,-0.4
-200109,2001.666667,0.2
-200110,2001.75,0
-200111,2001.833333,0.7
-200112,2001.916667,-0.8
-200201,2002,0.4
-200202,2002.083333,1.1
-200203,2002.166667,-0.2
-200204,2002.25,-0.1
-200205,2002.333333,-0.8
-200206,2002.416667,-0.2
-200207,2002.5,-0.5
-200208,2002.583333,-1
-200209,2002.666667,-0.6
-200210,2002.75,-0.4
-200211,2002.833333,-0.5
-200212,2002.916667,-1.1
-200301,2003,-0.2
-200302,2003.083333,-0.7
-200303,2003.166667,-0.3
-200304,2003.25,-0.1
-200305,2003.333333,-0.3
-200306,2003.416667,-0.6
-200307,2003.5,0.3
-200308,2003.583333,0.1
-200309,2003.666667,-0.1
-200310,2003.75,0
-200311,2003.833333,-0.3
-200312,2003.916667,1.1
-200401,2004,-1.3
-200402,2004.083333,1.2
-200403,2004.166667,0.4
-200404,2004.25,-0.9
-200405,2004.333333,1
-200406,2004.416667,-0.8
-200407,2004.5,-0.5
-200408,2004.583333,-0.3
-200409,2004.666667,-0.3
-200410,2004.75,-0.1
-200411,2004.833333,-0.7
-200412,2004.916667,-0.8
-200501,2005,0.3
-200502,2005.083333,-3.1
-200503,2005.166667,0.3
-200504,2005.25,-0.6
-200505,2005.333333,-0.8
-200506,2005.416667,0.4
-200507,2005.5,0.2
-200508,2005.583333,-0.3
-200509,2005.666667,0.4
-200510,2005.75,1.2
-200511,2005.833333,-0.2
-200512,2005.916667,0
-200601,2006,1.7
-200602,2006.083333,0.1
-200603,2006.166667,1.8
-200604,2006.25,1.1
-200605,2006.333333,-0.5
-200606,2006.416667,-0.2
-200607,2006.5,-0.6
-200608,2006.583333,-1
-200609,2006.666667,-0.6
-200610,2006.75,-1.3
-200611,2006.833333,0.1
-200612,2006.916667,-0.3
-200701,2007,-0.8
-200702,2007.083333,-0.1
-200703,2007.166667,0.2
-200704,2007.25,-0.1
-200705,2007.333333,-0.1
-200706,2007.416667,0.5
-200707,2007.5,-0.3
-200708,2007.583333,0.4
-200709,2007.666667,0.2
-200710,2007.75,0.7
-200711,2007.833333,0.9
-200712,2007.916667,1.7
-200801,2008,1.8
-200802,2008.083333,2.6
-200803,2008.166667,1.4
-200804,2008.25,0.7
-200805,2008.333333,-0.1
-200806,2008.416667,0.6
-200807,2008.5,0.3
-200808,2008.583333,1
-200809,2008.666667,1.2
-200810,2008.75,1.3
-200811,2008.833333,1.3
-200812,2008.916667,1.4
-200901,2009,1.1
-200902,2009.083333,1.9
-200903,2009.166667,0.4
-200904,2009.25,0.8
-200905,2009.333333,-0.1
-200906,2009.416667,0.1
-200907,2009.5,0.2
-200908,2009.583333,-0.2
-200909,2009.666667,0.3
-200910,2009.75,-1.2
-200911,2009.833333,-0.6
-200912,2009.916667,-0.7
-201001,2010,-1.1
-201002,2010.083333,-1.5
-201003,2010.166667,-0.7
-201004,2010.25,1.2
-201005,2010.333333,0.9
-201006,2010.416667,0.4
-201007,2010.5,1.8
-201008,2010.583333,1.8
-201009,2010.666667,2.2
-201010,2010.75,1.7
-201011,2010.833333,1.3
-201012,2010.916667,2.9
-201101,2011,2.3
-201102,2011.083333,2.7
-201103,2011.166667,2.5
-201104,2011.25,1.9
-201105,2011.333333,0.4
-201106,2011.416667,0.2
-201107,2011.5,1
-201108,2011.583333,0.4
-201109,2011.666667,1
-201110,2011.75,0.8
-201111,2011.833333,1.1
-201112,2011.916667,2.5
-201201,2012,1.1
-201202,2012.083333,0.5
-201203,2012.166667,0.7
-201204,2012.25,-0.3
-201205,2012.333333,0
-201206,2012.416667,-0.4
-201207,2012.5,0
-201208,2012.583333,-0.2
-201209,2012.666667,0.2
-201210,2012.75,0.3
-201211,2012.833333,0.3
-201212,2012.916667,-0.6
-201301,2013,-0.1
-201302,2013.083333,-0.2
-201303,2013.166667,1.5
-201304,2013.25,0.2
-201305,2013.333333,0.8
-201306,2013.416667,1.2
-201307,2013.5,0.8
-201308,2013.583333,0.2
-201309,2013.666667,0.3
-201310,2013.75,-0.1
-201311,2013.833333,0.7
-201312,2013.916667,0.1
-201401,2014,1.4
-201402,2014.083333,0.1
-201403,2014.166667,-0.9
-201404,2014.25,0.8
-201405,2014.333333,0.5
-201406,2014.416667,0.2
-201407,2014.5,-0.2
-201408,2014.583333,-0.7
-201409,2014.666667,-0.7
-201410,2014.75,-0.6
-201411,2014.833333,-0.9
-201412,2014.916667,-0.6
-201501,2015,-0.8
-201502,2015.083333,0.2
-201503,2015.166667,-0.7
-201504,2015.25,0
-201505,2015.333333,-0.7
-201506,2015.416667,-0.6
-201507,2015.5,-1.1
-201508,2015.583333,-1.4
-201509,2015.666667,-1.6
-201510,2015.75,-1.7
-201511,2015.833333,-0.5
-201512,2015.916667,-0.6
-201601,2016,-2.2
-201602,2016.083333,-2
-201603,2016.166667,-0.1
-201604,2016.25,-1.2
-201605,2016.333333,0.4
-201606,2016.416667,0.6
-201607,2016.5,0.4
-201608,2016.583333,0.7
-201609,2016.666667,1.2
-201610,2016.75,-0.3
-201611,2016.833333,-0.1
-201612,2016.916667,0.3
-201701,2017,0.2
-201702,2017.083333,-0.1
-201703,2017.166667,0.9
-201704,2017.25,-0.2
-201705,2017.333333,0.3
-201706,2017.416667,-0.4
-201707,2017.5,0.8
-201708,2017.583333,0.5
-201709,2017.666667,0.6
-201710,2017.75,0.9
-201711,2017.833333,0.9
-201712,2017.916667,-0.1
-201801,2018,1.1
-201802,2018.083333,-0.5
-201803,2018.166667,1.5
-201804,2018.25,0.5
-201805,2018.333333,0.4
-201806,2018.416667,-0.1
-201807,2018.5,0.2
-201808,2018.583333,-0.3
-201809,2018.666667,-0.9
-201810,2018.75,0.4
-201811,2018.833333,-0.1
-201812,2018.916667,1
-201901,2019,0
-201902,2019.083333,-1.4
-201903,2019.166667,-0.3
-201904,2019.25,0.1
-201905,2019.333333,-0.4
-201906,2019.416667,-0.5
-201907,2019.5,-0.4
-201908,2019.583333,-0.1
-201909,2019.666667,-1.2
-201910,2019.75,-0.4
-201911,2019.833333,-0.8
-201912,2019.916667,-0.6
\ No newline at end of file
diff --git a/pyleoclim/tests/test_core_Series.py b/pyleoclim/tests/test_core_Series.py
index 4351e93e..7d10e1aa 100644
--- a/pyleoclim/tests/test_core_Series.py
+++ b/pyleoclim/tests/test_core_Series.py
@@ -585,13 +585,13 @@ def test_annualize_retrograde_series(self):
     def test_annualize_no_complete_years(self):
         """Test when no complete years exist with specified months"""
         soi = pyleo.utils.load_dataset("SOI")
-        inc = soi.sel(time=slice(1951, 1953))
+        inc = soi.sel(time=slice(2025, 2026))
         # Create a scenario where no complete years exist
         # Use a very restrictive month selection that's unlikely to have complete coverage
         
         with pytest.raises(ValueError, match="No years found with sufficient data coverage"):
             # This should fail for some edge case datasets
-            tsa = inc.annualize(months=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], frac_req_months=1.0)
+            inc.annualize(months=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], frac_req_months=1.0)
     
     @pytest.mark.parametrize("ds_name", ["SOI", "NINO3"])
     def test_annualize_preserve_metadata(self, ds_name):
@@ -1556,7 +1556,7 @@ def test_ssa_t3(self):
         """Test Series.ssa() with Knee truncation"""
         ts = pyleo.utils.load_dataset("SOI")
         ssa = ts.ssa(trunc="knee")
-        knee = 12
+        knee = 32
         assert_array_equal(ssa.mode_idx, np.arange(knee + 1))
 
     def test_ssa_t4(self):
@@ -1569,7 +1569,7 @@ def test_ssa_t4(self):
         soi_m.value[missing] = np.nan  # put NaNs at the randomly chosen locations
         miss_ssa = soi_m.ssa()
         assert all(miss_ssa.eigvals >= 0)
-        assert np.square(miss_ssa.RCseries.value - soi.value).mean() < 0.3
+        assert np.square(miss_ssa.RCseries.value - soi.value).mean() < 0.35
 
 
 class TestUISeriesPlot:
diff --git a/pyleoclim/utils/datasets.py b/pyleoclim/utils/datasets.py
index 804d731f..29395811 100644
--- a/pyleoclim/utils/datasets.py
+++ b/pyleoclim/utils/datasets.py
@@ -97,14 +97,15 @@ def load_dataset(name):
     
     Returns
     -------
-    pyleoclim_util.Series of the dataset
+    Series object
 
     Examples
     --------
 
     .. jupyter-execute::
-        from pyleoclim.utils.datasets import load_dataset
-        pyleo_series = load_dataset('LR04')
+        
+        LR04 = pyleo.utils.load_dataset('LR04')
+        LR04.view()
 
     """
     # load the metadata for this dataset
@@ -123,46 +124,87 @@ def load_dataset(name):
         # load into pandas
         df = pd.read_csv(path, **pandas_kwargs)
 
-        # use iloc if we're given an int
-        if isinstance(time_column, int):
-            time=df.iloc[:, time_column]
-        # use column name otherwise
+        # Check if the index is a DatetimeIndex - if so, use from_pandas()
+        if isinstance(df.index, pd.DatetimeIndex):
+            # Filter out rows with NaT (Not a Time) values and missing data
+            valid_mask = df.index.notna()
+            if isinstance(value_column, int):
+                value_series = df.iloc[:, value_column]
+            else:
+                value_series = df[value_column]
+            
+            # Also filter out rows where values are NaN
+            valid_mask = valid_mask & value_series.notna()
+            
+            # Apply the mask to get clean data
+            clean_index = df.index[valid_mask]
+            clean_values = value_series[valid_mask]
+            
+            # Create a pandas Series with the clean DatetimeIndex
+            pandas_series = pd.Series(clean_values.values, index=clean_index)
+            
+            # Use from_pandas() method which handles DatetimeIndex properly
+            if 'lat' in pyleo_kwargs.keys() and 'lon' in pyleo_kwargs.keys():
+                if pyleo_kwargs['lat'] is not None and pyleo_kwargs['lon'] is not None:
+                    ts = pyleo.GeoSeries.from_pandas(
+                        pandas_series,
+                        metadata=pyleo_kwargs,
+                        verbose=False
+                    )
+                else:
+                    ts = pyleo.Series.from_pandas(
+                        pandas_series,
+                        metadata=pyleo_kwargs,
+                        verbose=False
+                    )
+            else:
+                ts = pyleo.Series.from_pandas(
+                    pandas_series,
+                    metadata=pyleo_kwargs,
+                    verbose=False
+                )
         else:
-            # if its a column
-            if time_column in df.columns:
-                time = df[time_column]
+            # Original logic for non-DatetimeIndex data
+            # use iloc if we're given an int
+            if isinstance(time_column, int):
+                time=df.iloc[:, time_column]
+            # use column name otherwise
+            else:
+                # if its a column
+                if time_column in df.columns:
+                    time = df[time_column]
+                else:
+                    time = df.Index
+
+            if isinstance(value_column, int):
+                value=df.iloc[:, value_column]
             else:
-                time = df.Index
+                value = df[value_column]
+            
+            if 'lat' in pyleo_kwargs.keys() and 'lon' in pyleo_kwargs.keys():
+                if pyleo_kwargs['lat'] is not None and pyleo_kwargs['lon'] is not None:
+                    ts=pyleo.GeoSeries(
+                        time=time, 
+                        value=value,
+                        **pyleo_kwargs, 
+                        verbose=False
+                    )
+                else:
+                   ts=pyleo.Series(
+                       time=time, 
+                       value=value,
+                       **pyleo_kwargs, 
+                       verbose=False
+                   ) 
 
-        if isinstance(value_column, int):
-            value=df.iloc[:, value_column]
-        else:
-            value = df[value_column]
-        
-        if 'lat' in pyleo_kwargs.keys() and 'lon' in pyleo_kwargs.keys():
-            if pyleo_kwargs['lat'] is not None and pyleo_kwargs['lon'] is not None:
-                ts=pyleo.GeoSeries(
+            else:
+                # convert to pyleo.Series
+                ts=pyleo.Series(
                     time=time, 
                     value=value,
                     **pyleo_kwargs, 
                     verbose=False
                 )
-            else:
-               ts=pyleo.Series(
-                   time=time, 
-                   value=value,
-                   **pyleo_kwargs, 
-                   verbose=False
-               ) 
-
-        else:
-            # convert to pyleo.Series
-            ts=pyleo.Series(
-                time=time, 
-                value=value,
-                **pyleo_kwargs, 
-                verbose=False
-            )
     # if this is a json
     elif metadata['file_extension'] == 'json':
         ts=jsonutils.json_to_PyleoObj(str(path), 'Series')
diff --git a/pyleoclim/utils/tsutils.py b/pyleoclim/utils/tsutils.py
index 28831df2..740dcbb8 100644
--- a/pyleoclim/utils/tsutils.py
+++ b/pyleoclim/utils/tsutils.py
@@ -2067,6 +2067,7 @@ def custom_year_averages(
     
         import pandas as pd
         import numpy as np
+        import pyleoclim.utils.tsutils as ut
         
         # Create sample monthly data
         dates = pd.date_range('2020-01-01', '2023-12-31', freq='M')
@@ -2075,35 +2076,35 @@ def custom_year_averages(
         
         # Example 1: Single year (int)
         # Compute Jan-Mar average for 2021 only
-        avg_single = custom_year_averages(ts, 1, 3, years=2021)
+        avg_single = ut.custom_year_averages(ts, 1, 3, years=2021)
         print("Single year (2021):")
         print(avg_single)
         print()
         
         # Example 2: List of specific years
         # Compute Apr-Mar averages for selected years
-        avg_list = custom_year_averages(ts, 4, 3, years=[2021, 2023])
+        avg_list = ut.custom_year_averages(ts, 4, 3, years=[2021, 2023])
         print("Specific years [2021, 2023]:")
         print(avg_list)
         print()
         
         # Example 3: Range of years
         # Compute Oct-Sep averages for consecutive years
-        avg_range = custom_year_averages(ts, 10, 9, years=range(2021, 2024))
+        avg_range = ut.custom_year_averages(ts, 10, 9, years=range(2021, 2024))
         print("Range of years (2021-2023):")
         print(avg_range)
         print()
         
         # Example 4: All available years (None - default)
         # Compute Jan-Dec averages for all years in data
-        avg_all = custom_year_averages(ts, 1, 12, years=None)
+        avg_all = ut.custom_year_averages(ts, 1, 12, years=None)
         print("All available years (None):")
         print(avg_all)
         print()
         
         # Example 5: Straddling periods with different year specifications
         # Apr-Mar periods: year refers to the March year
-        avg_straddle = custom_year_averages(ts, 4, 3, years=range(2021, 2024))
+        avg_straddle = ut.custom_year_averages(ts, 4, 3, years=range(2021, 2024))
         print("Straddling periods (Apr-Mar), years 2021-2023:")
         print(avg_straddle)
     
@@ -2119,7 +2120,7 @@ def custom_year_averages(
         daily_ts = pd.Series(daily_values, index=daily_dates, name='daily_data')
         
         # Compute weighted average (accounts for uneven spacing)
-        daily_avg = custom_year_averages(daily_ts, 1, 3, years=2021)
+        daily_avg = ut.custom_year_averages(daily_ts, 1, 3, years=2021)
         print("Daily data with uneven spacing (Jan-Mar 2021):")
         print(f"Weighted average: {daily_avg.iloc[0]:.2f}")
         print(f"Simple mean: {daily_ts.mean():.2f}")