Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “Geospatial Modelling”
Geospatial Modelling of Australia's National Electricity Market - Dataset
<p>This dataset contains information relating to the topology of Australia's largest electricity transmission network, along with details pertaining to the technical and economic characteristics of generators operating within this grid. Information has been compiled from publicly available datasets released by the Australian Energy Market Operator (AEMO) [1, 2] and Geoscience Australia (GA) [3, 4, 5]. Potential applications include the development of economic dispatch, power-flow, and unit commitment models.</p> <p>The network is comprised of 912 nodes, 1406 AC edges, and three HVDC links. Information regarding forward and reverse power-flow limits for two AC interconnectors is also provided. Latitude and longitude coordinates are given for each node, with the network based off of geospatial datasets obtained from GA [3, 4, 5]. Signals for electricity demand at each node were derived using regional load profiles in combination with population data obtained from the Australian Bureau of Statistics (ABS) [6]. Allocation methods outlined in [7, 8] were used to disaggregate regional load profiles according to the geospatial distribution of Australia's population. Construction of the generator dataset involved compiling information obtained from AEMO's Market Management System Data Model (MMSDM) [1] and National Transmission Network Development Plan (NTNDP) [2] datasets. Historic generator dispatch signals were also obtained from AEMO [1], allowing the output of market models to be compared with realised outcomes.</p> <p>For further information regarding the contents of each csv file please refer to <code>dataset_summary.pdf</code>. Jupyter Notebooks at [9] contain the Python code necessary to reproduce these datasets.</p> <p><strong>Version history:</strong></p> <p><strong>v1.3 - Documentation update:</strong></p> <ul> <li>The document summarising datasets, <code>dataset_summary.pdf</code>, has been updated.</li> </ul> <p><strong>v1.2 - Startup cost correction:</strong></p> <ul> <li>Startup cost column labels in <code>generators.csv</code> were mistakenly switched (specifically, SU_COST_WARM and SU_COST_HOT). This has now been corrected.</li> </ul> <p><strong>v1.1 - Transmission line parameters and demand allocation update</strong></p> <ul> <li><strong>Transmission lines: </strong>Line resistance and shunt susceptance values have been updated. Transmission line lengths and voltages have also been added to <code>network_edges.csv</code>. AC interconnector information is split over two files to better capture aggregate flow limits defined over the New South Wales - Victoria interconnector. Connection points for these interconnectors are described in <code>network_ac_interconnector_links.csv</code>, while <code>network_ac_interconnector_flow_limits.csv</code> contains aggregate forward and reverse power flow limits.</li> <li><strong>Demand allocation: </strong>The algorithm used to approximate demand at different nodes has been updated. The new method constructs a Voronoi tessellation based on network nodes. These cells are then overlapped with geospatial ABS population data, which are used to estimate the number of people served by each node.</li> </ul> <p><strong>v1.0 - First release</strong></p>
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania
<div> <p><strong>Background</strong>: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.</p> </div> <p><strong>Methods</strong>: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability.</p> <p><strong>Results</strong>: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area.</p> <p><strong>Conclusion</strong>: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions.</p>
Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania
Open the record for dataset details and reuse information.
Merged HLS2 (L30), ERA5-Land inputs and sample predictions of land surface temperature for the IBM granite-geospatial-land-surface-temperature model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and ERA5-Land data following the UTM CRS:WGS84. It has been assembled for predicting land surface temperature with a fine-tuned granite geospatial foundation model developed by IBM Research. In addition, we include sample predictions of land surface temperature derived from this model. Please see https://huggingface.co/ibm-granite/granite-geospatial-land-surface-temperature for more information on data preparation and model use.</p> <p><strong>HLS:</strong></p> <p>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <h4><strong>ERA5-Land:<br></strong></h4> <p>Copernicus Climate Change Service, Climate Data Store, (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p> <h4><strong>LST-predictions:</strong></h4> <p>These predictions of land surface temperature are derived from the IBM granite-geospatial-land-surface-temperature model and have been made available for Abidjan, Côte d’Ivoire and Johannesburg, South Africa for the period 2013-2023. </p> <h4>Attribution</h4> <p>Copernicus programme:</p> <p>Contains modified Copernicus Climate Change Service information [2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Data</strong></p> <p>Muñoz Sabater, J., Comyn-Platt, E., Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N., Cagnazo, C., Cucchi, M. (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p>
Merged HLS2 and GEDI data for estimating canopy height with IBM's granite-geospatial-canopyheight model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and Global Ecosystem Dynamics Investigation (GEDI) L2A data following CRS:4326. It has been assembled for estimating canopy height with a fine-tuned granite geospatial foundation model developed by IBM Research. Please see https://huggingface.co/ibm-granite/granite-geospatial-canopyheight for more information on data preparation and model use.</p> <p><strong>HLS2—</strong>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <p><strong>GEDI L2A—</strong>Lee, J., S. Favrichon, S. Mauceri, Y. Yang, J. Armston, and S. Saatchi. 2023. Addressing underestimation in global forest structure mapping. <a href="https://doi.org/10.22541/essoar.167276451.10705079/v1">https://doi.org/10.22541/essoar.167276451.10705079/v1</a></p>
Data from: The role of geospatial hotspots in the spatial spread of tuberculosis in rural Ethiopia: a mathematical modelling
Open the record for dataset details and reuse information.
Merged HLS2 and GEDI data for estimating above ground biomass with IBM's granite-geospatial-biomass model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and Global Ecosystem Dynamics Investigation (GEDI) L4A data following CRS:4326. It has been assembled for estimating above ground biomass with a fine-tuned granite geospatial foundation model developed by IBM Research. Please see https://huggingface.co/ibm-granite/granite-geospatial-biomass for more information on data preparation and model use.</p> <p><strong>HLS2—</strong>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <p><strong>GEDI L4A—</strong>Dubayah, R.O., J. Armston, J.R. Kellner, L. Duncanson, S.P. Healey, P.L. Patterson, S. Hancock, H. Tang, M.A. Hofton, J.B. Blair, and S.B. Luthcke. 2021. GEDI L4A Footprint Level Aboveground Biomass Density, Version 1. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1907</p>
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.