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CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format. Version 2.
<p><em><strong>CoastSeg: global 30-m shoreline in 5x5 degree chunks</strong></em></p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): global_5x5grid.geojson</p>
2.8M SNPs Chinese Spring RefSeq v2.1 dataset
<p>VCF file of 2,799,166 single nucleotide polymorphism (SNP) markers positioned onto the Chinese Spring reference assembly RefSeq v2.1 developed by the International Wheat Genome Sequence Consortium (IWGSC; Zhu et al., 2021). These SNPs were lifted from the 1,000 wheat exome project, originally positioned onto RefSeq v1.0 (He et al., 2019). The SNP projection from RefSeq v1.0 onto RefSeq v2.1 was accomplished using LiftOff (Shumate and Salzberg, 2021).</p> <p>References</p> <p>He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, Forrest K, Fritz A, Hucl P, Wiebe K, et al: <strong>Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome.</strong> <em>Nature Genetics </em>2019, <strong>51:</strong>896-904.</p> <p>Shumate A, Salzberg SL: <strong>Liftoff: accurate mapping of gene annotations.</strong> <em>Bioinformatics </em>2021, <strong>37:</strong>1639-1643.</p> <p>Zhu T, Wang L, Rimbert H, Rodriguez JC, Deal KR, De Oliveira R, Choulet F, Keeble-Gagnère G, Tibbits J, Rogers J, et al: <strong>Optical maps refine the bread wheat Triticum aestivum cv. Chinese Spring genome assembly.</strong> <em>The Plant Journal </em>2021, <strong>107:</strong>303-314..</p>
ESA WorldCereal 10 m 2021 v100
<p><strong>ESA WorldCereal 2021 products v100</strong></p> <p>The European Space Agency (ESA) WorldCereal 10m 2021 product suite consist of global-scale annual and seasonal crop maps and (where applicable) their related confidence. Every file in this repository contains up to 106 agro-ecological zone (AEZ) products which were all processed with respect to <a href="https://www.tandfonline.com/doi/full/10.1080/15481603.2022.2079273">their own regional seasonality</a> and should be considered as independent products.</p> <p>Naming convention of the ZIP files is as follows:</p> <p><strong>WorldCereal_{year}_{season}_{product}_{classification|confidence}.zip</strong></p> <p>The actual AEZ-based GeoTIFF files inside each ZIP are named according to following convention:</p> <p><strong>{AEZ_id}_{season}_{product}_{startdate}_{enddate}_{classification|confidence}.tif</strong></p> <p>The seasons are defined in Table 1. Note that <strong>cereals</strong> as described by WorldCereal include wheat, barley and rye, which belong to the <em>Triticeae</em> tribe. Next to the actual WorldCereal products, this repository contains the files "<strong>WorldCereal_AEZ.geojson</strong>" that contains the AEZ description and outline, as well as "<strong>QGIS_stylefiles.zip</strong>"<strong> </strong>which contains QGIS style files (.qml) for product visualization purposes.</p> <table> <tbody> <tr> <th>Season</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>tc-annual</td> <td>A one-year cycle being defined in a region by the end of the last considered growing season</td> </tr> <tr> <td>tc-wintercereals</td> <td>The main cereals season defined in a region</td> </tr> <tr> <td>tc-springcereals</td> <td>Optional springcereals season, only defined in certain AEZ</td> </tr> <tr> <td>tc-maize-main</td> <td>The main maize season defined in a region</td> </tr> <tr> <td>tc-maize-second</td> <td>Optional second maize season, only defined in certain AEZ.</td> </tr> </tbody> </table> <p><strong>Note</strong>: AEZs for which no irrigation product is available were not processed because of the unavailability of thermal Landsat data.</p> <p>A scientific paper describing the WorldCereal products and the methodology behind them is available through the link below:</p> <p><a href="https://essd.copernicus.org/articles/15/5491/2023/essd-15-5491-2023.html" target="_blank" rel="noopener">Van Tricht, K., Degerickx, J., Gilliams, S., Zanaga, D., Battude, M., Grosu, A., Brombacher, J., Lesiv, M., Bayas, J. C. L., Karanam, S., Fritz, S., Becker-Reshef, I., Franch, B., Mollà-Bononad, B., Boogaard, H., Pratihast, A. K., Koetz, B., and Szantoi, Z.: WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping, Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, 2023.</a></p> <p><em>This work was supported by the European Space Agency under contract N°4000130569/20/I-NB.</em></p>
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</p>
Waves Hindcast on the Senegalese Coast over the Last Four Decades (from 1980 to 2021). [A Dataset use in : SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. Atmosphere 2023]
<p>Computed from the WW3 Model, the last Four Decades Wave Hindcast is available on the Senegalese Coast through this present Dataset. Covering the period 1980 to 2021, this high resolution hindcast, both spatial (0.05x0.05) and temporal (1 h) provided all the wave parameters such as: the significant wave heights, the mean wave periods, the wave directions and the peak wave periods (to compute from wave frequencies) with an hourly interval.</p> <p>More details on this data (e.g., model implementation and validation) can be obtained in: SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. <em>Journal Atmosphere 2023</em>].</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
Supplementary Data for Low-loss stable storage of 1.2 Angstrom X-ray pulses in a 14 m Bragg cavity
<p>Supplementary Data for Margraf, R. et al. "Low-loss stable storage of 1.2 Angstrom X-ray pulses in a 14 m Bragg cavity," Nature Photonics, 2023.</p>
CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.
<p><strong>CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.</strong></p> <p>This is a shoreline atlas of California at 30m resolution, to support analysis of CoastSat/CoastSeg-derived shoreline time-series and other shoreline data, and miscellaneous analyses of coastal shoreline data. The dataset consists of a GeoJSON files containing a 30-m shoreline estimate for California, based on an analysis of 2014 Landsat imagery (Sayre et al., 2019). This shoreline vector has been attributed with the following fields that may be useful in analyses of shoreline patterns and regional variability:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT (m)</li> <li>TIDAL_RANGE (m)</li> <li>CHLOROPHYLL (mg/L)</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE (descriptive)</li> <li>EMU_PHYSICAL (descriptive)</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE (%)</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY (descriptive)</li> <li>LENGTH_GEO</li> <li>ch_label (descriptive)</li> <li>river_label (descriptive)</li> <li>sinuosity_label (descriptive)</li> <li>slope_label (descriptive)</li> <li>tidal_label (descriptive)</li> <li>turbid_label (descriptive)</li> <li>wave_label (descriptive)</li> <li>CSU_Descriptor (descriptive)</li> <li>CSU_ID</li> <li>elevation (m)</li> <li>aspect (degrees N)</li> <li>slope (degrees)</li> </ol> <p>Fields 1 to 21 inclusive originally come from raw data https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk, which is described in Sayre et al (2019)</p> <p>Fields 22 and 24 come from raw data originally in the U.S. Geological Survey Elevation Derivatives for National Applications (EDNA) database (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-elevation-derivatives-national), accessed through Earth Explorer and processed in QGIS.</p> <p>The figure shows distributions of selected quantities. A python script to reproduce this plot is provided</p> <p>A subset of numeric-only variables and descriptive-only variables has also been prepared and made available. A CSV version of the full dataset is also provided</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></li> <li><a href="https://doi.org/10.5066/F7TD9VTQ">Elevation Derivatives for National Applications (EDNA) Seamless Three-Dimensional Hydrologic Database Digital Object Identifier (DOI) number: /10.5066/F7TD9VTQ</a></li> </ol> <p> </p>
AgriCarbon-EO Winter wheat Net Ecosystem Exchange and Biomass over South-west France at 10 m resolution
<p>Dataset contains the outputs of the AgriCarbon-EO</p> <p>An agronomical modeling tool for the carbon and water flux estimates by Bayesian assimilation of S2 and LandSat8 remote sensing data into the Prosail radiative transfer model and the SAFYE-CO2 crop model.<br> -----------------------<br> -for TILE : T31TCJ <br> -for year: 2017<br> -for Winter wheat crops<br> - at 10 m resolution</p> <p> </p> <p>Maps:<br> -file: "GLA_statmap.tif"<br> Description: A raster with 4 bands containing respectively:<br> *The R2 of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The RMSE of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The Bias of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The number of images that are assimilated into SAFYE-CO2 from 2016/11/01 until 2017/08/01</p> <p>-file: "emerg_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of emerg retrieved by the SAFYE-CO2 inversion in days of simulation (the simulation begins the 01/01/2016).<br> *The standard deviation of emerg retrieved by the SAFYE-CO2 inversion.<br> <br> -file: "LUEa_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ.<br> *The standard deviation of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ.</p> <p>-file: "SENa_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of Sena retrieved by the SAFYE-CO2 inversion in °C.<br> *The standard deviation of Sena retrieved by the SAFYE-CO2 inversion in °C.</p> <p>-file: "SENb_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of SENb retrieved by the SAFYE-CO2 inversion.<br> *The standard deviation of SENb retrieved by the SAFYE-CO2 inversion.</p> <p>-file: "PRT_La_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of DAM retrieved by the SAFYE-CO2 inversion.<br> *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion.</p> <p>-file: "DAM_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of DAM retrieved by the SAFYE-CO2 inversion in g/m2.<br> *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NEP_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NEP retrieved by the SAFYE-CO2 inversion in g/m2.<br> *The standard deviation of NEP retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NECB_exportG_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2 , considering an export scénario with grains export only.<br> *The standard deviation of NECB_exportG retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NECB_exportGLS_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2, considering an export scénario with grains, stems and leaves.<br> *The standard deviation of NECB_exportGLS retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p> </p> <p>Shapefiles a GIS: <br> -file: "S2_TILE_T31TCJ.shp"<br> Description: shape file of the contour of the T231 TCJ sentinel2 tile <br> -file: "FR_AUR.shp"<br> Description: shape file of the contour of AURADE experimental field <br> -file: "FR_AUR_TOWER.shp"<br> Description: shape file of the location of the AURADE eddy covariance flux tower<br> -file: "POI_2017.shp"<br> Description: shape file of the location of points of interest that illustrate the ... paper<br> -file: "ESU_DAM.shp"<br> Description: shape file of the contour of the plots where dry biomass samples were taken.<br> -file: "ESU_DAM_points.shp"<br> Description: shape file of the location of the points where dry biomass samples were taken.<br> -file: "mapT31TCJ_spamaps.qgz"<br> QGIS project file for the visualisation of the NEP maps.<br> </p>
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</p>
Cipher Machine Hagelin M-209_surface meshes (Inv. 2017-397)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</p>
Cipher Machine Fialka M-125_surface meshes (Inv. 2018-659)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</p>
Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys
<p>This repository contains the data released in the paper "Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology measurements for bright (<em>r </em>< 19) galaxies in the DESI Legacy Imaging Surveys footprint. These measurements estimate the presence of bars, spirals arms, ongoing mergers, and more.</p> <p>---</p> <p><strong>GZ DESI Detailed Morphology Catalogs</strong></p> <p>These catalogs are created by training deep learning models on Galaxy Zoo volunteer responses, to predict what volunteers might say for new galaxies. The models are available at [www.github.com/mwalmsley/zoobot](www.github.com/mwalmsley/zoobot). Our measurements are predicted vote fractions i.e. the fraction of volunteers expected to select a given answer for a given question.</p> <p>We share two catalog versions containing the same morphology measurements but presented in different ways.</p> <p>gz_desi_deep_learning_catalog_friendly.parquet contains the morphology measurements</p> <p>gz_desi_deep_learning_catalog_advanced.parquet contains the same measurements, and additional information:</p> <p>- _friendly includes only relevant vote fractions, defined as vote fractions to answers of questions that a majority of volunteers would have been asked. This removes predicted vote fractions for e.g. the fraction of volunteers answering "2 spiral arms" to a galaxy with no spiral arms. _advanced includes all vote fractions and instead reports the (column "proportion_asked"). The user must select which vote fractions they consider relevant (we suggest proportion_asked > 0.5, which recovers the _friendly fractions).</p> <p>- _advanced includes columns with estimated credible intervals (error bars) around each vote fraction. These are calculated from the vote fraction posterior predicted by our models.</p> <p>Finally, we separately present volunteer votes collected for 96k galaxies during the GZD-8 campaign, i.e. after the release of GZ DECaLS but before this (GZ DESI) release. These are split into the _core and _extended catalogs, where _extended includes galaxies which received five or more votes for "artifact". The models above were trained on these votes as well as votes from GZ DECaLS.</p> <p>---</p> <p><strong>External Catalog</strong></p> <p>For convenience, we also include an additional catalog of non-morphology measurements created by other authors (external_catalog.parquet) cross-matched to our morphology catalogs. Please credit those authors if you use this catalog (references are in the GZ DESI paper).</p> <p>A particularly important external measurement is redshift. Morphology is increasingly hard to resolve at higher redshift and so <strong>distant galaxies appear less featured</strong>. external_catalog.parquet includes the column "redshift", which is the SDSS spectroscopic redshift where available and a photometric redshift estimate otherwise (again, see the GZ DESI paper for references and credit). You may want to select only galaxies at lower redshifts.</p> <p>---</p> <p><strong>Data Notes</strong></p> <p>Parquet is a fast csv-like format which can be read with pd.read_parquet(loc, columns=[some columns]). Parquet files are read column-by-column (rather than row-by-row) and so you can chose which columns to load. You can easily check which columns are available using columns=['foo'] and reading the error message. We suggest loading only the columns you need when working with the larger catalogs. This will require much less memory than loading every column.</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI to follow on publication) when using the data in this repository.</p> <p>---</p> <p><strong>History</strong></p> <p>v0.0.1 - closed pre-release for internal review</p> <p>v1.0.0 - draft public release. Removed low-z pre-filtered catalogs.</p> <p>v1.0.1 - first public release. Added .csv version of _friendly catalog. Tweaked catalog formatting for clarity and consistency.</p>
X-ray and optical light curves of the M dwarf dipper star TIC 234284556
<p>We observed the star TIC 234284556 with XMM-Newton in soft X-rays and in the optical for ca. 35 hours (127.8 ks), starting 2022-04-16 22:58:46, ObsID 0881050101.</p> <p>We provide here two extracted soft X-ray light curves (energy band 0.2-2 keV) collected with XMM-Newton's PN camera, namely for a circular extraction region with 20 arcsec radius centered on the position of the M dwarf star TIC 234284556 (pn_lca_02_2.fits) with 100 seconds time binning, and a background light curve with the same energy range and time binning extracted for a PN background region with a three times larger radius (pn_lcabg_02_2.fits). We also provide optical light curves in the V band, collected with XMM-Newton's Optical Monitor with 10 seconds cadence (file names P0881050101OMS0**TIMESR0000.FIT).</p> <p>A barycentric correction, using the XMM-SAS task "barycen", has been applied to the PN and OM time columns. The time coordinate is given in seconds since BJD 2450814.5 (1998-01-01 00:00:00).</p>
30 m resolution global forest burned area dataset 2018
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<sup>°</sup>(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2016
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<span>°</span> (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2014
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
30 m resolution global forest burned area dataset 2020
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
Habitat availability estimates of LTER 100 m stream sites at the Coweeta Hydrologic Lab from 1991 to 1998
Habitat availability measurements were recorded biannually along with electrofishing (different Project) starting in late summer 1991. Data was collected each spring and late summer from 1991-1998, in order to examine changes in fish assemblage structure along the habitat gradient.
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm
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Understand access before you commit
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.