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Monthly aggregated GLASS FAPAR V6 (250 m): 95th percentile monthly time-series (2011)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Monthly aggregated GLASS FAPAR V6 (250 m): 5th percentile monthly time-series (2011)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Near-surface, soil, and air temperature data acquired across multiple locations on the San Joaquin Experimental Range, California, 2011-2017
These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies at the San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Temperature sensors were located at 23 sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. To characterize surface temperature variation within a site, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens. An additional 18 sensors were placed along three transects over the landscape running E-W. They were placed strategically to sample topographic inflection points (hill tops and valley bottoms) as well as north and south facing slopes. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor, using HOBO (Onset, www.onsetcomp.com) devices.
Weather station data acquired across multiple locations on the San Joaquin Experimental Range, California, 2011-2017
These weather station records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These weather station records are for studies at the San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Weather stations were located at six sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. Three full weather stations (north slope, south slope, and valley floor) monitored precipitation, wind, insolation, temperature, relative humidity, and soil moisture. Three micro stations (west slope, east slope, and ridge) measured soil moisture at -20 cm. Data was recorded on a 10-minute interval using HOBO (Onset, www.onsetcomp.com) devices.
Near-surface, soil, and air temperature data acquired across multiple locations in the Teakettle Experimental Forest, California, 2011-2017
These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies at the Teakettle Experimental Forest (Lat 36.967, Long -119.017, elevation 2000-2800 m, www.fs.fed.us/psw/ef/teakettle/). Temperature sensors were located at 44 sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges and valleys. To characterize surface temperature variation within select sites, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens (see garden schematic for details). An additional 33 sites were located across the site by way of a stratified sampling scheme which targeted low, medium, and high elevation areas, low, medium, and high radiation areas, and cold air pooling areas. In June 2012, in order to concentrate sensors in a smaller study area (ease of access and to make this more similar to other sites, 22 sites were "retired," and 7 new sites were installed, for a total of 18 during the remainder of the study. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor. using HOBO (Onset, www.onsetcomp.com) devices.
State Water Project, Genetic Determination of Population of Origin 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring
Central Valley Project, Genetic Determination of Population of Origin 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring
Temporal patterns of leaf litter inputs into a stream over a four-year period (2011-2014), Arbúcies, Catalonia, Spain.
Data based on estimations of leaf litter inputs from riparian trees into a stream reach over a 4 years period (2011-2014). Data was collected in Arbucies, Barcelona is a forested stream with no human pressure (i.e., pristine). Data contains values from 4 riparian tree species: AL (alder), AS (ash), BL (Black Locust) and BP (Black Poplar). Units are in mg. Estimations were extracted from sampling leaf litter input into the stream during the study period (30 samplings per year) and fitting Gaussian-type models (P<0.001, r2>0.60). Data also includes daily-basis discharge flow estimations based on discrete measure of flow using salt dilution technique and water level sensor data.
Soil organic carbon and nutrient dynamics in response to anaerobic digestate application to farm fields, Eastern Iowa, 2011-2023
This dataset documents a long-term, field-scale study of anaerobic digestate application on commercial croplands in eastern Iowa, USA. It includes detailed records of digestate composition, application rates, and timing, as well as soil test results collected over a 12-year period (2011–2023) from 14 agricultural fields. The dataset supports analysis of soil organic carbon (SOC), nutrient dynamics, and isotopic composition in response to digestate inputs. It contains 421 georeferenced soil samples, digestate nutrient profiles, field management histories, and spatial boundaries. The data were collected as part of a collaborative effort between researchers at Iowa State University and Sievers Family Farms to evaluate the agronomic and environmental implications of integrating anaerobic digestion into row crop and livestock systems.
Genetic assignments for Spring Evolutionary Significant Unit reanalysis, Central Valley Chinook Salmon populations, CA, 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is difficult to visually distinguish individuals from the different Evolutionarily Significant Units (ESU). As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to genetic lineage; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program compliance monitoring programs. The genetic lineage was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central V
Soil and root-associated fungal response to nitrogen and phosphorus addition from grasslands worldwide: 2011-2012.
Ecosystems across the globe receive elevated inputs of nutrients, but the consequences of this for soil fungal guilds that mediate key ecosystem functions remain unclear. We found that nitrogen and phosphorus addition to 25 grasslands distributed across four continents promoted the relative abundance of fungal pathogens, suppressed mutualists, but did not affect saprotrophs. Structural equation models suggested that responses were often indirect and primarily mediated by nutrient-induced shifts in plant communities. Nutrient addition also reduced co-occurrences within and among fungal guilds, which could have important consequences for belowground interactions. Focusing only on plots that received no nutrient addition, soil properties influenced pathogen abundance globally, whereas plant community characteristics influenced mutualists, and climate influenced saprotrophs. These guild-level responses enhance our ability to predict soil functional responses to anthropogenic eutrophication and the associated longer-term responses of plant communities to this important global change factor.
Marcell Experimental Forest 15-minute precipitation, 2011 - ongoing
This data publication contains 15-minute precipitation data collected from 2011-ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. The data come from three long-term meteorological monitoring stations.
Stream cross-section profiles in the Andrews Experimental Forest and Hagan Block RNA 1978 to 2011
This database consists of periodically resurveyed cross-section profiles for 5 sites (stream reaches) located in the H.J. Andrews Experimental Forest on 3rd to 5th-order stream channels over the period 1978-present, and also includes historical data from Shorter Creek within the Andrews and two sites (North and South) on the Hagan Creek in the Hagan Block RNA. Each of these sites includes 10-20 cross sections spaced about 10-50 m apart. Observations include cross-sectional geometry based on surveys relative to a horizontal line between permanent stations on opposite sides of the stream, substrate type based on pebble counts and notes on extent of bedrock, and photo-documentation of the sites. Sampling was primarily done annually until 2000 and opportunistically since then depending on funding and noted geomorphic changes in response to floods. Sampling at least every 5 years is desired. Data tables cover 1. elevation and distance measurements between fixed end points, 2. pebble counts for particle size, 3. measures of extent of exposed bedrock, 4. vegetation (1984 only) in terms of % cover by spp., % cover of substrate types, and plant measurements for making biomass estimates with allometric equations, but such estimates are not presented. The original raw cross-section data for these sites has been adjusted to align profiles for consecutive year comparisons at each cross-section location to correct for imprecision in the survey methodology. John Faustini, in 1998, further corrected this cross-section data from 1978-1998 for the five main sites in database GS019. Arianna Goodman, in 2021, performed additional corrections on the data, including more recent collections, 1978-2011, also in database GS019. The Shorter Creek cross-section surveys took place in 1978-1982 only. The Shorter Creek cross-section surveys took place in 1978-1982 only.
Plant Pollinator data at HJ Andrews Experimental Forest, 2011 to 2021
Despite the importance of plant-pollinator interactions for ecological communities, few long-term observational studies have been conducted of plant-pollinator networks. This study involves the annual collection of plant-pollinator interaction data from up to 18 meadows in the Willamette National Forest, Oregon, including 12 meadows in the HJ Andrews Forest. This study addresses how meadow size, the amount of nearby meadow habitat, weather, degree days, and soil moisture are related to the flowering of meadow plants and the frequency of plant-pollinator interactions.
Hourly weather data from the Arctic LTER Moist Acidic Tussock Experimental plots from 2011 to present, Toolik Filed Station, North Slope, Alaska.
Hourly weather data from the LTER Moist Acidic Tussock Experimental plots. The station was installed in 1990 in block 2 of the Toolik LTER experimental moist acidic tussock plots. The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Global solar radiation, photosynthetic active radiation, unfrozen precipitation, air temperature, relative humidity, wind speed, and wind direction are measured at 3 meters. Additional sensors in greenhouses and shade houses plots measure air temperature, relative humidity and photosynthetic active radiation during the growing season. The sensors are read every minute and averaged or totaled every hour.
Mass per tiller, nitrogen concentration, stable isotope ratios for carbon and nitrogen from the 1980-82 Eriophorum vaginatum reciprocal transplant experiment along a latitudinal gradient in interior Alaska collected in July, 2011
In 1980-1982, six transplant gardens were established along a latitudinal gradient in interior Alaska from Eagle Creek, AK in the south to Prudhoe Bay, AK in the north. Three sites, Toolik Lake (TL), Sagwon (SAG), and Prudhoe Bay (PB) are north of the continental divide and the remaining three, Eagle Creek (EC), No Name Creek (NN), and Coldfoot (CF), are south of the continental divide. Each garden consisted of 10 individual Eriophorum vaginatum tussocks transplanted back to their home-site, as well as 10 individuals from each of the other transplant sites. The gardens were harvested in 2011. Important variables are garden name, source population, mass per tiller, nitrogen concentration, and stable isotope ratios for Carbon and Nitrogen.
Eddy Flux Measurements, Tussock Station, Imnavait Creek, Alaska - 2011
The Biocomplexity Station was established in 2005 to measure landscape-level carbon, water and energy balances at Imnavait Creek, Alaska. The station is now contributing valuable data to the Arctic Observing Network that was established at two nearby stations. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system.
Eddy Flux Measurements, Fen Station, Imnavait Creek, Alaska - 2011
In contribution to the Arctic Observing Network, the researchers have established two observatories of landscape-level carbon, water and energy balances at Imnavait Creek, Alaska and at Pleistocene Park near Cherskii, Russia. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system. In support of these objectives, a 3m eddy covariance station was established on a low ridge adjacent to Imnavait Creek, Alaska. This station has been continuously monitoring carbon dioxide, water vapor, energy fluxes and various micro-meteorological variables.
Eddy Flux Measurements, Ridge Station, Imnavait Creek, Alaska - 2011
In contribution to the Arctic Observing Network, the researchers have established two observatories of landscape-level carbon, water and energy balances at Imnavait Creek, Alaska and at Pleistocene Park near Cherskii, Russia. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system. In support of these objectives, a 3m eddy covariance station was established on a low ridge adjacent to Imnavait Creek, Alaska. This station has been continuously monitoring carbon dioxide, water vapor, energy fluxes and various micro meteorological variables.
Eddy Flux Measurements, Pleistocene Park, Cherskii, Russia - 2011
In contribution to the Arctic Observing Network, the researchers have established two observatories of landscape-level carbon, water and energy balances at Imnaviat Creek, Alaska and at Pleistocene Park near Cherskii, Russia. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. In support of these objectives, a new 32m tower was deployed in Pleistocene Park, about 20km south of North-East Science Station in Cherskii, Russia. This station is currently measuring fluxes of carbon dioxide, methane, water vapor and energy in addition to other meteorological variables.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.