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Cascade Project at North Temperate Lakes LTER cross-lakes comparison carbon Data 1988 - 2007
Data on dissolved organic and inorganic carbon as well as particulate organic matter and the partial pressure of CO2. Samples were collected with a Van Dorn bottle. Organic samples were collected from the epilimnion, metalimnion, and hypolimnion. Inorganic samples were collected at depths corresponding to 100%, 50%, 25%, 10%, 5%, and 1% of surface irradiance, as well as one sample from the hypolimnion. Samples for the partial pressure of CO2 were collected from two meters above the lake surface (air) and just below the lake surface (water).
Fluxes project at North Temperate Lakes LTER: Spatial Metabolism Study 2007
Data from a lake spatial metabolism study by Matthew C. Van de Bogert for his Phd project, "Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The goal of this study was to capture the spatial heterogeneity of within-lake processes in effort to make robust estimates of daily metabolism metrics such as gross primary production (GPP), respiration (R), and net ecosystem production (NEP). In pursuing this goal, multiple sondes were placed at different locations and depths within two stratified Northern Temperate Lakes, Sparkling Lake (n=35 sondes) and Peter Lake (n=27 sondes), located in the Northern Highlands Lake District of Wisconsin and the Upper Peninsula of Michigan, respectively.Dissolved oxygen and temperature measurements were made every 10 minutes over a 10 day period for each lake in July and August of 2007. Dissolved oxygen measurements were corrected for drift. In addition, conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were measured by a subset of sondes in each lake. Two data tables list the spatial information regarding sonde placement in each lake, and a single data table lists information about the sondes (manufacturer, model, serial number etc.). Documentation:Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156. Also see Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
Cascade Project at North Temperate Lakes LTER: Physical and Chemical Limnology 1984 - 2007
Physical and chemical variables are measured at one central station near the deepest point of each lake. In most cases these measurements are made in the morning (0800 to 0900). Vertical profiles are taken at varied depth intervals. Chemical measurements are sometimes made in a pooled mixed layer sample (PML); sometimes in the epilimnion, metalimnion, and hypolimnion; and sometimes in vertical profiles. In the latter case, depths for sampling usually correspond to the surface plus depths of 50percent, 25percent, 10percent, 5percent and 1percent of surface irradiance. The 1991-1995 chemistry data obtained from the Lachat auto-analyzer. Like the process data, there are up to seven samples per sampling date due to Van Dorn collections across a depth interval according to percent irradiance. Voichick and LeBouton (1994) describe the autoanalyzer procedures in detail. Methods for 1984-1990 were described by Carpenter and Kitchell (1993) and methods for 1991-1997 were described by Carpenter et al. (2001). Carpenter, S.R. and J.F. Kitchell (eds.). 1993. The Trophic Cascade in Lakes. Cambridge University Press, Cambridge, England. Carpenter, S.R., J.J. Cole, J.R. Hodgson, J.F. Kitchell, M.L. Pace,D. Bade, K.L. Cottingham, T.E. Essington, J.N. Houser and D.E. Schindler. 2001. Trophic cascades, nutrients and lake productivity: whole-lake experiments. Ecological Monographs 71: 163-186. Number of sites: 8
Cascade Project at North Temperate Lakes LTER: Process Data 1984 - 2007
Data on chlorophyll, primary productivity, and alkaline phosphatase activity from 1984-95. Samples were collected with a Van Dorn bottle at 6 depths determined from the percent of surface irradiance (100%, 50%, 25%, 10%, 5% and 1%) and in the hypolimnion (12 m in Peter, East Long, West Long, and Tuesday lakes; 9 m in Paul Lake; and 4.5 m in Central Long Lake). Sampling Frequency: varies Number of sites: 8
Microbial Community Composition in Lakes - Taxonomic/Ecological characteristics of the sample at North Temperate Lakes LTER 2000 - 2007
Microbial community composition is inferred by a combination of automated ribosomal intergenic spacer analysis (ARISA) and PCR-generated clone library analysis. Clone libraries include both the 16S rRNA gene and the 16S-23S ribosomal intergenic spacer fragment. Phylogenetic assignments for individual ARISA fragments are obtained by comparing the ARISA fragment length from each clone to all of the profiles stored in our database. We have analyzed over 3900 clones obtained from 41 lakes that represent the range of trophic types found in temperate landscapes. Querying by a combination of taxonomic and ecological characteristics of the sample allows the user to retrieve sample information [sample IDs, sample dates, lake information (region, type, size, depth) and physical/chemical data (water temperature, clarity, pH, DOC, SUVA, TN, TP, nitrates/nitrites)] and clone information [clone IDs, sequence data, and characteristics of the sequence (length, chimera status, accession number, taxonomic affiliation)]. The data can be filtered by ecological characteristics of the sample [lake name, sample date, lake information (region, type, size, depth)] and taxonomic characteristics of the community members [clone ID, ARISA fragment length (raw or binned), and/or taxonomic characteristics (Phylum and Phylum-Class)]. The output can include links to individual sample records, which contain links to the taxonomic composition of the sample inferred by dynamically matching clones to ARISA fragments in the individual sample. The output can also include links to clone records directly (though this creates a very large number of lines in the output and is not recommended). Project ID's 30 Lakes - Survey of 30 lakes in northern and southern Wisconsin. June, August and October, 2002. See http://microbes.limnology.wisc.edu/lakes30.html. Lake Characteristics. CB0000 - Time series monitoring microbial community composition in Crystal Bog. 2000-2002. CBX_02 - Food web manipulation experiment i
Cascade Project at North Temperate Lakes LTER: Nutrients 1991 - 2007
Physical and chemical variables are measured at one central station near the deepest point of each lake. In most cases these measurements are made in the morning (0800 to 0900). Vertical profiles are taken at varied depth intervals. Chemical measurements are sometimes made in a pooled mixed layer sample (PML); sometimes in the epilimnion, metalimnion, and hypolimnion; and sometimes in vertical profiles. In the latter case, depths for sampling usually correspond to the surface plus depths of 50percent, 25percent, 10percent, 5percent and 1percent of surface irradiance. The 1991-1999 chemistry data obtained from the Lachat auto-analyzer. Like the process data, there are up to seven samples per sampling date due to Van Dorn collections across a depth interval according to percent irradiance. Voichick and LeBouton (1994) describe the autoanalyzer procedures in detail. Nutrient samples were sent to the Cary Institute of Ecosystem Studies for analysis beginning in 2000. The Kjeldahl method for measuring nitrogen is not used at IES, and so measurements reported from 2000 onwards are Total Nitrogen.
Cascade Project at North Temperate Lakes LTER: Zooplankton 1984 - 2007
Zooplankton data from 1984-1995. Sampled approximately weekly with two net hauls through the water column (30 cm diameter net, 80 um mesh). There have been 5 zooplankton counters during this period, so species-level identifications (TAX, below) are not as consistent as those for some of the other datasets. To standardize across counters, I have assigned higher-level taxonomic categories for a few "confusing" taxa; these identifications can be found in the column LLTAX, below. Sampling Frequency: varies Number of sites: 5
Microbial Community Composition in lakes - Ecological characteristics of the sample at North Temperate Lakes LTER 2002 - 2007
Microbial community composition is inferred by a combination of automated ribosomal intergenic spacer analysis (ARISA) and PCR-generated clone library analysis. Clone libraries include both the 16S rRNA gene and the 16S-23S ribosomal intergenic spacer fragment. Phylogenetic assignments for individual ARISA fragments are obtained by comparing the ARISA fragment length from each clone to all of the profiles stored in our database. We have analyzed over 3900 clones obtained from 41 lakes that represent the range of trophic types found in temperate landscapes. Querying by ecological characteristics of the sample allows the user to retrieve sample IDs, sample dates, lake information (region, type, size, depth) and physical/chemical data (water temperature, clarity, pH, DOC, SUVA, TN, TP, nitrates/nitrites). The data can be filtered by lake name, sample date, lake information (region, type, size, depth), and physical/chemical data (water temperature, clarity, pH, DOC, SUVA, TN, TP, nitrates/nitrites). The output includes links to individual sample records, which contain links to the taxonomic composition of the sample inferred by dynamically matching clones to ARISA fragments in the individual sample
Microbial Community Composition in Lakes - Taxonomic characteristics of the clones at North Temperate Lakes LTER 2000 - 2007
Microbial community composition is inferred by a combination of automated ribosomal intergenic spacer analysis (ARISA) and PCR-generated clone library analysis. Clone libraries include both the 16S rRNA gene and the 16S-23S ribosomal intergenic spacer fragment. Phylogenetic assignments for individual ARISA fragments are obtained by comparing the ARISA fragment length from each clone to all of the profiles stored in our database. We have analyzed over 3900 clones obtained from 41 lakes that represent the range of trophic types found in temperate landscapes. Querying by taxonomic characteristics of the clone allows the user to retrieve clone IDs, sequence data, and characteristics of the sequence (length, chimera status, accession number, taxonomic affiliation). The data can be filtered by clone ID, ARISA fragment length (raw or binned), and/or taxonomic characteristics (Phylum and Phylum-Class). The output includes links to individual clone records, which contain more detailed information about how the clone was generated (researcher, library ID, project ID, primer sets used, etc.).
High and Low Tides of Hog Island Bay, Redbank, VA, and Oyster, VA for the Virginia Coast Reserve 2007-2025
This dataset is a summary version of the 12-minute tide data. It records the date, time and level of tides at high and low tide (as indicated by the data). Water temperatures are also recorded.
Flux Tower Data for Fowling Point Marsh at the Virginia Coast Reserve 2007-2008
This dataset contains measurements of CO2 and H2O concentrations, temperature and wind speed made every 1/10th of a second. Important note: due to the high frequency of measurements, this dataset is very large (>25 GB) and may take a long time to download. For this reason the main vcrflux_cpole.csv file is provided as well in compressed (.gz and .zip) forms which are only about 6 GB. There are two additional files. Tide_PPT_temp_2007_2008.csv contains 1-per-minute data on tide level, temperature at different heights on the tower and precipitation. Wind_light_temp_2007_2008.csv contains 1-minute summaries of wind, light, and temperature. The research was conducted at the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The flux tower site (37deg 24'39.85"N, 75deg 50'0.53"W) a lagoonal salt marsh which is located near the area of Fowling point. The site is located at about 2.2 kilometers away from the mainland and 10.7 kilometers away from Hog Island, the nearest barrier island. The flux tower is situated at about 80 meters away from the creek edge, on the lagoonal salt marsh.
Groundwater Levels on Hog Island, VA. 2007-2021
This dataset contains hourly measurements of groundwater levels on Hog Island, VA - off the Atlantic Coast of the Delmarva Peninsula. It continues measurements of groundwater levels that were initiated in by Frank Day in July 1990 (dataset VCR99066) and by Mark Brinson in October 1997 (dataset VCR05130). Refer to these datasets for previous measurements. The groundwater measurements use the same wells and locations, but differ in sensor technology and frequency of measurement. In August 2007, a system of wirelessly-connected data loggers designed by Thomas Williams was installed with help from Arthur Schwarzschild, Chris Buck, John Hayward and John Porter. The system was decomissioned December 31, 2021
Density of Seagrass in Virginia Coastal Bays, 2007-2021
This dataset contains measurements of seagrass shoot density in restored Z. marina meadows in the Virginia coastal bays. Measurements were made annually in June-July at plots in Hog Island Bay and South Bay, VA. GPS locations of sampling plots are available in the companion data set VCR11180.
Above- and Below-Ground Biomass and Canopy Height of Seagrass in Virginia Coastal Bays 2007-2021
This data set contains measurements of above and belowground biomass and canopy height in restored Z. marina meadows in the Virginia coastal bays. Samples were collected annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Organic Matter of Seagrass Sediment in Virginia Coastal Bays 2007-2021
This data set contains measurements of sediment organic matter and bulk density from plots in the restored Z. marina meadows in Hog Island Bay and South Bay, VA. Samples were collected annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Sediment Carbon and Nitrogen of Seagrass Restoration in Virginia Coastal Bays 2007-2021
This data set contains measurements of sediment carbon and nitrogen content in restored Z. marina meadows in Hog Island Bay and South Bay, VA. Sediments were sampled annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Tide Data for Hog Island (1991-), Redbank (1992-), Oyster (2007-)
Tide data from VCR/LTER tide stations. It is available as a single large comma-separated-value file (size >50MB) or as individual years. Zip files contain annually-segmented data in two forms. The file tideYY.dat contains the data for year YY in a column format. The file tideYY.csv contains the same data in a comma-delimited format. For several sites and time periods, water temperatures are also recorded. All dates and times are in Eastern Standard Time.
SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs
<p>This is a GeoTIFF version of the <a href="http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/">SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture</a> data set v1.1 (Brocca et al. 2019). Conversion steps are available <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Few important notes:</p> <ul> <li>Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place.</li> <li>The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted).</li> <li>P05, P50 and P95 indicate quantiles derived per pixel.</li> </ul> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>Monthly averages and s.d. of precipitation are available in the files:</p> <ul> <li>clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm,</li> <li>clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used),</li> </ul> <p>Downscaled monthly averages (1 km) are also available (<a href="https://doi.org/10.5281/zenodo.1435912">https://doi.org/10.5281/zenodo.1435912</a>).</p> <p>To cite this data set please refer to the <strong><a href="https://doi.org/10.5281/zenodo.2591214">original copy</a></strong> of the data set.</p> <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong><a href="https://doi.org/10.5194/essd-11-1583-2019">SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</a></strong>. Earth Syst. Sci. Data, 11, 1583–1601. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a></li> </ul>
Urban material ground truth data for the 2007 HyMap hyperspectral image of Munich
<p><span>This dataset entails a spectral library file (.sli file with matching .hdr text file) with 12028 labeled spectra derived from the 4m resolution airborne hyperspectral HyMap image of Munich (Germany) that was acquired during the summer of 2007 (June 17 and 25 2007). The labeled image spectra are retrieved from pixels of the HyMap dataset that has been processed to level 2A surface reflectance in 119 bands ranging between the wavelengths of 455 nm and 2496 nm. The preprocessing performed on this image data is explained in Heldens et al. (2008) and Heiden et al. (2012). See the "Related works" section of this data publication.</span></p> <p><span>The ground truth (GT) data have been used in previous research (again, see the "Related works" section) and they were likewise used for the remote sensing-based mapping experiments with a generic urban spectral library performed in the frame of the GENLIB research project. The data set contains reflectance spectra of typical urban surface materials and their spectral variations.</span></p> <p><span>The spectra included in this dataset were sampled from the above mentioned HyMap image by (1) using the methodology described in Jilge et al. (2017) and (2) through the delineation of manually digitized regions of interest. The image spectra are <span> </span>labeled based on the method mentioned above and using ancillary reference data, already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to the image spectra. These labels cover:</span></p> <ul> <li><span>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the </span><span><a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener"><span>website of the EAGLE framework</span></a></span><span> for more information.</span></li> <li><span>Material Groups (MG).</span></li> <li><span>Artificial Material Types (AMT).</span></li> </ul> <p><span>The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication.</span></p> <p><span>While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.</span></p>
Monthly aggregated GLASS FAPAR V6 (250 m): 5th percentile monthly time-series (2007)
<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>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.