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5,155 results for “resolution”
Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space
<p>The products are the first <strong>regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from remote sensing observations</strong>. They cover three major river basins: the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project (<a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>). The irrigation amounts have been estimated through the <strong>SM-based (Soil-Moisture-based) inversion approach</strong>. The satellite-derived irrigation products referring to the European sites have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. The three irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. Details on the data sets development and on their performance assessment can be found in:</p> <p><strong>Dari, J.</strong>, Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Volden, E. <strong>Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space</strong>. <em>Earth System Science Data, </em>15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023.</p> <p> </p> <p><strong>Novelties in v1.1 with respect to v1.0:</strong></p> <p>v1.1 of irrigation estimates through the SM-based inversion approach are currently available for the Ebro basin and the Po valley only. The novelties with respect to the previous version are: (i) the use of high-resolution (1 km) potential evapotranspiration rates in the algorithm and (ii) temporal extension as now the data sets cover a 6-year period from January 2016 to December 2021.</p> <p><strong>Acknowledgements</strong>:</p> <p>ESA Irrigation+ project, <a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>, (contract n. 4000129870/20/I-NB).</p> <p>ESA 4DMED-Hydrology project, <a href="https://esairrigationplus.org/">https://www.4dmed-hydrology.org/</a>, (contract n. 4000136272/21/I-EF).</p>
The first 10m resolution thermokarst lake and pond data set in the Lena basin during 2020 thawing season
<p>We present the first 10m resolution thermokarst lake and pond data set in the Lena basin during 2020 thawing season. A mapping workflow was proposed and implemented on the Google Earth Engine (GEE) platform. The accuracy assessment demonstrates a satisfactory overall accuracy of 93.63%, and comparing with several land cover and waterbody products, our results exhibited better consistency with TLPs under real conditions.</p>
Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"
<p><strong>File 1:</strong> raw_data_BOULDERING.zip</p> <p><strong>Size:</strong> 8.8 GB</p> <p><strong>Summary: </strong>It contains all of the rasters (planetary images) and labeled boulders (raw data):</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches within a raster.</p> </li> </ul> <p>There are multiple locations/images per planetary body.</p> <p><strong>Structure:</strong></p> <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ ├── <image_name>-ROM.shp │ ├── <image_name>-boulder-mapping.shp │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</pre> <p> </p> <p><strong>File 2:</strong> best_model.zip</p> <p><strong>Size:</strong> 624.7 MB</p> <p><strong>Summary:</strong></p> <p>This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics):</p> <ul> <li> <p>augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information.</p> </li> </ul> <ul> <li> <p>Base-RCNN-FPN.yaml (base model setup file).</p> </li> <li> <p>config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file).</p> </li> <li> <p>Mars-MoonEarth-v050...yaml (model setup file).</p> </li> <li> <p>log.txt (log during training of the algorithm).</p> </li> <li> <p>model_0055999.pth (model weights at second last saving step)</p> </li> <li> <p>model_0063999.pth (model weights at last saving step)</p> </li> </ul> <p>We advice the use of model weights model_0055999.pth (to avoid slight overfitting).</p> <p><strong>File 3:</strong> Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images)</p> <p><strong>Size:</strong> 252.8 MB</p> <p><strong>Summary:</strong></p> <p>This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files.</p> <ul> <li> <p>The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json".</p> </li> <li> <p>The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset.</p> </li> <li> <p>The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles.</p> </li> <li> <p>The shapefile folder is actually empty (it should not be there!).</p> </li> </ul> <p><strong>Structure:</strong></p> <pre>. └── preprocessed_inputs/ ├── json ├── pkl ├── preprocessing/ │ ├── train/ │ │ ├── images │ │ └── labels │ ├── validation/ │ │ ├── images │ │ └── labels │ └── test/ │ ├── images │ └── labels └── shp</pre> <p> </p>
A 500m-Resolution Agricultural Drought Area Dataset for the period 2006–2019 in the North China Plain
<p>Agricultural drought has caused huge loss in crop production in the recent decades and will continue to occur with increased frequency and severity in the upcoming future period. However, lack of reliable historic agricultural drought information at finer spatiotemporal resolutions constrains the effective formulation strategy for prevention and mitigation of agricultural drought, especially for monitoring and early/warming. Hence, we generated a 500 m-resolution agricultural drought area dataset with three degrees (drought covered, drought damaged, and crop failure) for summer-harvest crops and autumn-harvest crops encompassing the whole NCP.</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>
High-resolution surface wind observations over complex terrain: Big Southern Butte, Salmon River Canyon, Birch Creek
<p>This dataset contains high-resolution wind observations from three field campaigns that took place during 2010-2014 at Big Southern Butte, Salmon River Canyon, and Birch Creek, Idaho. There are three SQLite databases containing 30-s averaged 3-m wind speed, wind direction, and wind gust data from 30-90 cup-and-vane anemometers over a period of 2-4 months at each field site.</p>
High Temporal Resolution Records of Hansbreen Ice Flow Velocity for Years 2006-2019
<p>This repository contains the datasets of the positions of 16 mass balance stakes, horizontal velocity (m/yr) and accuracy of velocity (m/yr) for Hansbreen, a tidewater glacier in southern Svalbard. Data were derived from GNSS measurements conducted in the period 2006-2019. Stake positions are given in UTM zone 33X, and elevation in geoidal height (EGM96). Additionally, we provide files with annual, summer and winter velocities (m/yr) with a standard deviation of velocity, estimated for the hydrological year. The file „Hansbreen_preprocessing_code_stakes.zip” contains the code used for the velocity estimation.</p>
High resolution WRF simulation of Hurricane Florence (2018) and Harvey (2017) during Landfall
<p>A high-resolution simulation of Hurricane Florence (2018) and Harvey (2017) is performed using the Weather Research and Forecasting (WRF) model version 4.3. The WRF model was configured by three nested domains with a horizontal grid spacing of 12, 4, and 1.33 km, respectively, and 60 vertical levels. The common physics options used in the simulations are the Thompson microphysics scheme (Thompson et al., 2008); RRTMG as shortwave and long-wave schemes (Iacono et al., 2008); Mellor–Yamada–Janjic Scheme as Planetary Boundary Layer (Janjic, 1994;Mesinger, 1993); Unified Noah land surface model (Tewari et al., 2004); Tiedtke Scheme (Tiedtke, 1989; Zhang et al., 2011) as cumulus parameterization scheme only for the first domain. The simulations were performed considering (a) a control experiment with an urban slab model (NUCM), (b) a single-layer UCM, and (c) multi-layer BEP urban physics (BEP). All other physics schemes remain the same for the simulations. The simulations for Hurricane Florence start on 2018-09-12 at 12:00 UTC and end on 2018-09-18 at 00:00 UTC, and for Hurricane Harvey simulations start on 2017-08-24 at 12:00:00 UTC and end on 2017-08-29 at 00:00:00 UTC.</p> <p>The data herein presents the model output of 3-hour for domain one and 1-hour for domain three. The format is NetCDF, containing detailed metadata. Three-dimensional meteorological field variables include three wind components (u, v, and w), potential temperature, water vapour mixing ratio, and atmospheric pressure. Two-dimensional variables include horizontal wind components at 10 m AGL, potential temperature and water vapour mixing ratio at 2 m AGL, atmospheric pressure at the surface, terrain height, planetary boundary layer height, total accumulated rainfall, latent heat flux, sensible heat flux, and the Coriolis sine latitude term. The outputs are provided for NUCM, UCM and BEP simulations.</p>
ACCESS-OM2 1° resolution global repeat decade full forcing interannual simulation data for 1972-2018
<p>This data set contains the <strong>full forcing</strong> interannual simulation output from the global ocean-sea ice model ACCESS-OM2 in the 1° horizontal configuration over the period 1972-2018.</p> <p>This simulation was branched off from the repeat decade forcing spin-up and alongside the control simulation (see light blue and black lines in Fig. 1c in the publication linked below).</p> <p>The control simulation output can be found here: https://zenodo.org/record/8339578 The output here as well as in the control simulation is saved in sets of ten years (output200, output201, ...) in the ocean/ and ice/ folders as netcdf files.</p> <p>The last output folder contains the data for 2012-2018 with the last four years of this output folder (output204) are again the 1972-1975 period and should be omitted from any analysis.</p> <p>For more information on the spin-up and the model configuration, see the Methods section and Fig. 1 in Huguenin, M.F., Holmes, R.M. & England, M.H. Drivers and distribution of global ocean heat uptake over the last half century. <em>Nat Commun</em> 13, 4921 (2022). https://doi.org/10.1038/s41467-022-32540-51</p> <p> </p>
Genotyping of European Toxoplasma gondii strains by a new high-resolution next-generation sequencing-based method
<p>The data set comprises 164 FASTQ files generated with an Ion AmpliSeq-based genotyping method for <em>Toxoplasma gondii </em>and<em> </em>a BED file used for the design of the Ion AmpliSeq primer panel. The FASTA file named as "AmpliSeq-ME49-Reference" was used as a reference for mapping and data analysis of the FASTQ files. The GZ file named as "Tgondii_IonAmpliSeq_Results_SNPs_VCF" is a VCF file, which contains all SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference. The VCF file was converted into a FASTA file named as "Tgondii_IonAmpliSeq_Results_SNPs", which also contains the SNPs identified within the 164 FASTQ files relative to the AmpliSeq-ME49-Reference.</p> <p>The work is published in the European Journal of Clinical Microbiology & Infectious Diseases with the title "Genotyping of European <em>Toxoplasma gondii</em> strains by a new high‑resolution next‑generation sequencing‑based method"; https://doi.org/10.1007/s10096-023-04721-7</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>
Fire Season Multi-Resolution Database
<p>In the realm of wildfire research and analysis, the need for comprehensive and adaptable datasets has never been more critical. To address this imperative, we introduce the "Fire Season Multi-Resolution Database." This meticulously curated dataset represents a significant advancement in the realm of wildfire data, offering a versatile, multi-resolution approach to understanding the intricate dynamics of fire seasons.</p> <p>The Fire Season Multi-Resolution Database is a set of geospatial earth grids, thoughtfully designed to accommodate diverse research needs by providing information at varying levels of detail. Within each grid, a wealth of data is embedded, concerning fire seasons and their comprehensive characterizations at every individual pixel.</p> <p>Within each pixel of these multi-resolution grids, a set of nine variables about their respective fire seasons can be found.<br> </p> <ul> <li> FsOrNot (int): a binary variable (0,1) that contains information whether a pixel has a fire season or not </li> <li> MainStart (int): Starting month of the fire season </li> <li> MainEnd (int) : Ending month of the fire season </li> <li> SecondStart (int): Start month of a possible secondary fire seasson (if it is bimodal) </li> <li> SecondEnd (int) : End month of the secondary fire seasson </li> <li> Length (int): Months that lasts the fire season </li> <li> C (float): Seasonal concentration of the fire seasson </li> <li> P (float): Seasonal phase or timming of the fire season </li> <li> FBA (float): Fraction of Burneable Area </li> </ul> <p> </p> <p>Github used to build these files: </p> <p>https://github.com/delatorre96/Development-of-a-multi-resolution-fire-season-database-on-a-global-scale</p> <p>Finally, the sources of information used to build this dataset is the Copernicus project, specifically the web page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-fire-burned-area?tab=overview. This dataset has been constructed with the aim of enhancing wildfire research and aiding in the development of effective wildfire management and mitigation strategies. </p>
High-resolution T2M and RH simulated using WRF-ARW over Cyprus for 2015
<p>Meteorological data generated over Cyprus for the year 2015 using the open-source, community-based, state-of-the-art Weather Research and Forecasting (WRF-ARW) Model. The model was configured according to the operational numerical weather forecasts of the Cyprus Department of Meteorology (DoM). The meteorological fields, generated with a nested configuration setup, are at an ultra-fine spatiotemporal resolution (<em>i.e.,</em> 2 km horizontal grid spacing and 1-hour temporal frequency).</p>
High-resolution spatiotemporal modelling of sand fly abundance in Cyprus in 2015
<p>The expected population size of <em>P. papatasi</em> in Cyprus in 2015 was simulated using the stochastic climate-driven population dynamics model of the species presented in Erguler <em>et al.</em> (2019). The model was simulated with air temperature and relative humidity obtained from WRF-ARW. Two sets of parameters, one for Steni and one for Geri - each with 1000 alternative configurations - were used to simulate the average number of adult females per day per trap (a proxy to expected population size).</p>
High-resolution maps of rubber and rubber-related deforestation for Southeast Asia
<p>This dataset contains maps of rubber plantations in 2021, and maps of rubber-related deforestation between 1993-2016 for Southeast Asia. The rubber maps have a 10 m pixel size, and the deforestation maps have a 30 m pixel size. The dataset and the methods for generating it are described in Wang et al. 2023. High-resolution maps show that rubber causes substantial deforestation. <em>Nature</em>. <strong>Please note that an update of this dataset will follow in September 2025.</strong> </p>
Typology of agricultural land systems of Germany at a resolution of 100 hectares.
The decline of farmland biodiversity has widely been recognized in society and politics. Many factors that negatively affect biodiversity are associated with agriculture. European policy instruments and measures, which aimed at mitigating these impacts, have not been successful in counteracting the negative trends of farmland biodiversity. There is a growing recognition that conservation policy instruments need to be spatially targeted, given the heterogeneity of agricultural landscapes and extent of agricultural intensification in Europe. For Germany, we developed a typology of agricultural land systems (ALS) that captures the regional characteristics of agricultural intensification. For this purpose, we applied a cluster-analysis integrating indicators for land cover, landscape structure, land-use intensity, climate and relief at a resolution of with a spatial resolution of 1 km². As a result, we present a typology of eight ALS ranging from large-scale, intensive arable farming to extensive grassland/forest mosaics in mountains. The data included in this package contain the typology ALS and the corresponding values for the indicators for each hexagonal grid cell of 1 km²cell size. The typology of ALS could be used as a spatial framework for regional targeting of conservation policy instruments and for monitoring regional-specific trends of biodiversity indicators and their drivers. The data are supplement to the publication: Pingel, M; Sietz, D; Röder, N; Klimek, S and Golla, B. (2025) Typology of Agricultural Land Systems to Support Tailored Agri-Environmental Schemes for Farmland Biodiversity: A Case Study from Germany. [Preprint]. http://dx.doi.org/10.2139/ssrn.5162566.
Flow direction grid at 1 kilometer resolution for North Slope drainage basins, Alaska
We derived and evaluated a 1 kilometer spatial resolution flow direction grid for the terrestrial drainage of the North Slope of Alaska. The region is resolved by 182,722 grid cells and the associated connectivity. It is bounded by the Brooks Range and Beaufort Sea coast, and extends from the northern Chukchi Sea coast eastward to the small rivers near 140 degrees West. The dataset is provided in raster and tabular format, with the latter including coordinates, river basin identifier, and downstream reach and direction for each grid cell in the region. Over three dozen river basins are identified by name in an associated lookup table. This new mapping resolves the terrestrial drainages for rivers exporting freshwater, nutrients, and other materials to Elson, Simpson, Jago, Kaktovik, and other coastal lagoons at a resolution that captures important processes linked to surface and subsurface hydrological flows. Our analysis suggests that the mapping exhibits notable similarity in basin area boundaries relative to the benchmark USGS National Hydrography Dataset.
ScienceDex guides
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.