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.

datasets available to search

ShareScore release 0.9.0

Dataset results

16 results for “Socioeconomic pathways”

Learn how ShareScore rates datasets ↗

zenodo44/100

Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.

<h2>Description (V2 - Latest):</h2> To enable easy integration in the workflows, we have provided the main datasets in the following formats:   <ul> <li>Vector dataset:<code> Folder - Vector</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>Geopackage (.gpkg)</code> file (Results_Vis.gpkg) with polygon geometries at 1/8-degree spatial resolution in an EPSG:4326 coordinate system. The attribute table of this file contains FN_ID column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul>   <ul> <li>Raster datasets: <code> Folder - Raster</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>geotiff (.tif)</code> files with LZW compression in an EPSG:4326 coordinate system. The assessed gross rooftop area datasets are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units.</li> </ul>   <ul> <li>Numerical dataset: <code> Folder - Numerical</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>parquet (.parquet)</code> file (Results.parquet). This file contains FN_ID column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul>   In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <code> Folder - Models</code> <ul> <li>M2_Model.json: This file contains the frozen parameters of the M2 model in <code>.json</code> format generated from <code>XGBoost version 2.0.3</code></li> <li>SSP_drivers.parquet: This file contains the driver data used for generating the main dataset in our study</li> <li>FN_MAP.parquet: This file contains the boundary information for each fishnet grid tile in a Well Known Text (WKT) format.</li> <li>Prediction.ipynb: This file provides a python notebook interface to generate inferencing from <code>M2_Model.json</code> using <code>SSP_drivers.parquet</code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code>FN_MAP.parquet</code><code> </code>file.</li> <li>environment.yaml: This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul>   <h2>Version history:</h2> This version corresponds to the revised journal submission (Round 1). The version will be updated upon the completion of the review of the main manuscript. <ul> <li>This version V2 is supersedes V1 to correspond with round 1 of review.</li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 ", submitted to Scientific Reports Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul>   <h2>Changelog:</h2> The following files from version V1 of this dataset are now archived based on the reviews (Round 1). <ol> <li> <blockquote>1_Geospatial_Dataset_V1.gpkg</blockquote> </li> <li> <blockquote>2_Countrylevel_gross_rooftop_area_V1.parquet</blockquote> </li> <li> <blockquote>3_Analytics_Scripts_V1.ipynb</blockquote> </li> </ol>

opencc-by-4.0Sep 2023View details →

zenodo44/100

Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways

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).

opencc-by-4.0Jul 2023View details →

zenodo40/100

Data and literature repository for "Climate Futures are Political Futures: Integrating Political Development Into the Shared Socioeconomic Pathways (SSPs)"

The datasets provided in the repository (listed in Table 1 of the manuscript): <ul> <li>Governance (Andrijevic et al., 2020)*</li> <li>Government effectiveness (Andrijevic et al., 2020)*</li> <li>Violent conflict (Hegre et al., 2016)</li> <li>Rule of law (update to the Soergel et al., 2021)</li> </ul> *Please note that these two variables can be found in the same data file. The indicators can also be retrieved through the <a href="https://ssp-extensions.apps.ece.iiasa.ac.at/">SSP Extensions Explorer.</a>  For applications of the projections of political indicators in further analyses, please consult the following references:   Brutschin, E., Pianta, S., Tavoni, M., Riahi, K., Bosetti, V., Marangoni, G., & Van Ruijven, B. J. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abf0ce/meta">A multidimensional feasibility evaluation of low-carbon scenarios.</a> Environmental Research Letters 2021, 16(6), 064069. Gidden MJ, Brutschin E, Ganti G, Unlu G, Zakeri B, Fricko O, et al. <a title="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5" href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5">Fairness and feasibility in deep mitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate</a>. Environmental Research Letters 2023, 18(7): 074006.   Hoch JM, de Bruin SP, Buhaug H, Von Uexkull N, van Beek R, Wanders N. <a title="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2" href="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2">Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach</a>. Environmental Research Letters 2021, 16(12): 124068.   Joshi DK, Hughes BB, Sisk TD. <a title="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145" href="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145">Improving governance for the Post-2015 Sustainable Development Goals: Scenario forecasting the next 50 years</a>. World Development 2015, 70: 286-302.   Moyer JD. <a title="https://www.sciencedirect.com/science/article/pii/S0305750X23000062" href="https://www.sciencedirect.com/science/article/pii/S0305750X23000062">Blessed are the peacemakers: The future burden of intrastate conflict on poverty</a>. World Development 2023, 165: 106188.   Moyer JD, Turner SD, Meisel CJ. <a title="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740" href="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740">What are the drivers of diplomacy? Introducing and testing new annual dyadic data measuring diplomatic exchange</a>. Journal of Peace Research 2021, 58(6): 1300-1310.   Petrova, K, Olafsdottir, G, Hegre, H, Gilmore, EA (2023). <a title="https://iopscience.iop.org/article/10.1088/1748-9326/acb163" href="https://iopscience.iop.org/article/10.1088/1748-9326/acb163">The ‘conflict trap’ reduces economic growth in the shared socioeconomic pathways</a>. Environmental Research Letters, 2023, 18(2), 024028.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)

This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (Earth Syst. Dynam., 2023). For details see the README.md file.

opencc-zeroJul 2023View details →

zenodo36/100

Population scenarios for U.S. states consistent with Shared Socioeconomic Pathways

This is a data record of the input and output data associated with the following publication: Jiang, L., B.C. O'Neill, H. Zoraghein, and S. Dahlke. 2020. Population scenarios for U.S. states consistent with Shared Socioeconomic Pathways. Environmental Research Letters, <a href="https://doi.org/10.1088/1748-9326/aba5b1">https://doi.org/10.1088/1748-9326/aba5b1</a>. The accompanying code can be found here:   Zoraghein, H., R. Nawrotzki, L. Jiang, and S. Dahlke (2020). IMMM-SFA/statepop: v0.1.0 (Version v0.1.0). Zenodo. <a href="https://protect2.fireeye.com/v1/url?k=dbf32d36-8746128f-dbf30723-0cc47adc5fce-abb2658d1d55b41e&q=1&e=1c8eff6c-b006-48cb-8ff3-fd95c41a8980&u=https%3A%2F%2Fprotect-us.mimecast.com%2Fs%2FbxLNCXD7VRHXoAlVcVcXsa%3Fdomain%3Dprotect2.fireeye.com">http://doi.org/10.5281/zenodo.3956703</a>  The following detail the contents: <ul> <li>SSP<ssp_number>.zip <ul> <li><state_number>_<state_abbreviation> <ul> <li><state_number>_<state_abbreviation>_In_Mig.jpg <ul> <li>output figure for in-migration change through time for the target state</li> </ul> </li> <li><state_number>_<state_abbreviation>_Net_Mig.jpg <ul> <li>output figure for net-migration change through time for the target state</li> </ul> </li> <li><state_number>_<state_abbreviation>_Out_Mig.jpg <ul> <li>output figure for out-migration change through time for the target state</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_in_mig.csv <ul> <li>projected age and gender specific number of domestic in-migration for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_net_mig.csv <ul> <li>projected age and gender specific number of domestic net-migration for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_out_mig.csv <ul> <li>projected age and gender specific number of domestic out-migration for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_pop.csv <ul> <li>projected age and gender specific number of population for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_total_in_mig.csv <ul> <li>projected age and gender specific in-migration from other states to the current state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_total_out_mig.csv <ul> <li>projected age and gender specific out-migration from the current state to other states over time</li> </ul> </li> <li><state_number>_<state_abbreviation>.jpg <ul> <li>figure for projected change of population for the state</li> </ul> </li> </ul> </li> </ul> </li> <li>State_inputs.zip <ul> <li><state_number>_<state_abbreviation> <ul> <li><state_number>_<state_abbreviation>_in_mig.csv <ul> <li>a table containing gender- and age-specific migration rates from all states to the current state at the base year.</li> </ul> </li> <li><state_number>_<state_abbreviation>_out_mig.csv <ul> <li>a table containing gender- and age-specific migration rates from the current state to all other states at the base year.</li> </ul> </li> <li>basePop.csv <ul> <li>a table containing gender- and age-specific counts of people in the current state at the base year (2010).</li> </ul> </li> <li>Constant_rate.csv <ul> <li>a table that constructs a constant scenario for fertility, mortality (life expectancy), urbanization level (required if we differentiate urban and rural), sex ratio at birth and net estimates of international migrants for the current state. The table is from 2010 to 2100 at 5 year intervals used for scenario building. The presented scenario, for instance, keeps everything constant at its historical record estimated from the data.</li> </ul> </li> <li>fertility.csv <ul> <li>a table containing age-specific fertility rates for the current state at the base year.</li> </ul> </li> <li>intMig.csv <ul> <li>a table containing gender- and age-specific proportions of international migrants to the current state at the base year.</li> </ul> </li> <li>mortality.csv <ul> <li>a table containing gender- and age-specific life expectancy and mortality rates for the current state at the base year (life table).</li> </ul> </li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Jul 2020View details →

zenodo36/100

Current and Future Flood maps for Flood risk assessment under the Shared Socioeconomic Pathways in the Greater Accra region, Ghana

The study used 15 flood conditioning factors in simulating current and future flood conditions under the SSP scenarios using the Frequency Ratio (FR) model

opencc-by-4.0Feb 2024View details →

zenodo36/100

Improved estimation of global soil moisture of different shared socioeconomic pathways for 2016-2099

We design a novel Transformer SM Simulation Net (TSMSNet) to conduct global monthly 0.5°×0.5° SM simulation of SSP1-2.6, SSP2-4.5, and SSP5-8.5 from 2016 to 2099. Nine qualified future SM datasets, along with corresponding spatial distribution dataset of error parameters, and geographic background data are selected as model inputs. The learning target is calculated through merging the merits from Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM. The results indicate the TSMSNet simulated SM (R = 0.68, ubRMSE = 0.045 m3/m3) performs notable superiority in matching both temporal variation and absolute value of in-situ measurements across different landcover and climate regions. Besides, the TSMSNet simulated SM could favorably match the long-term trend of learning target. TSMSNet simulated SM has an overwhelming drying trend during 2016 to 2099. The decline magnitude rises accompanied by SSP changing from sustainable pathway to fossil-fueled development. The areas with significant drying trend mainly distributed in plateau and mid-latitude region. In terms of land cover types, evident drying trends are found in cropland and forest. SM shows faster descent rate in livable areas than unlivable areas. In summary, we develop a reliable future SM dataset, that is expected to act as a valuable reference for understanding future water cycle patterns.

opencc-by-4.0Sep 2024View details →

zenodo32/100

Dateset The "blue eyed" in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups

Dateset The “blue eyed” in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups

opencc-by-4.0Jan 2020View details →

zenodo32/100

Data Supplement: U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways.

These data are to supplement the following in-press publication:   Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability. The data herein were generated using the `population_gravity` model which can be found here:  <a href="https://github.com/IMMM-SFA/population_gravity">https://github.com/IMMM-SFA/population_gravity</a> CONTENTS: zoraghein-oneill_population_gravity_inputs_outputs.zip <ul> <li>contains a directory for each U.S. state for inputs and outputs</li> <li>inputs contain the following: <ul> <li><state-name>_<urban or rural>_<yr>_1km.tif:  Urban and Rural population GeoTIF rasters at a 1km resolution <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value:  -3.40282e+38</li> </ul> </li> <li><state-name>_mask_short_term.tif:  Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics <ul> <li>value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value:  -3.40282e+38</li> </ul> </li> <li><state-name>_<ssp>_popproj.csv:  Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100</li> <li><state-name>_coordinates.csv:  CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)</li> <li><state-name>_within_indices.txt:  text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.</li> <li><state-name>_<ssp>_params.csv:  CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the `population_gravity` model for each year from 2010-2100 in 10-year time-steps as described in the companion publication</li> </ul> </li> <li>outputs contain the following: <ul> <li>jones_oneill directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication <ul> <li>contains three directories:  SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100. <ul> <li><state-name>_1km_<ssp>_total_<year>_jones_oneill.tif: <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value:  -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> <li>model directory; these are the model outputs from `population_gravity` for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps. <ul> <li><state-name>_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif: <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value:  -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> </ul>   zoraghein-oneill_population_gravity_national-ssp-maps.zip <ul> <li>Results of the `population_gravity` model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research.  These are used to generate Figure 6 of the companion paper <ul> <li>National_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif: <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value:  -3.40282e+38</li> </ul> </li> <li>National_1km_<ssp>_<urban, rural, or total>_<year>.tif: <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value:  -3.40282e+38</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Apr 2020View details →

zenodo32/100

Global terrestrial moisture recycling in Shared Socioeconomic Pathways

Data generated for the preprint "Global terrestrial moisture recycling in Shared Socioeconomic Pathways" by Staal et al.: Staal, A., Meijer, P., Nyasulu, M.K., Tuinenburg, O.A. & Dekker, S.C. (preprint). Global terrestrial moisture recycling in Shared Socioeconomic Pathways. [link and info to be added later]  Data are stored as netcdf files and zipped. The global files contain monthly global terrestrial precipitation recycling in mm/month. The basin files contain monthly basin precipitation recycling in mm/month. For SSPs 1-2.6, 2-4.5, 3-7.0, and 5-8.5 the monthly results are given for 2050-2059 and 2090-2099. For SSP2-4.5, also monthly results for 2015-2024 are provided. Please see Staal et al. for further details.

opencc-by-4.0Feb 2024View details →

zenodo32/100

Global Transportation Demand Dataset using the Shared Socioeconomic Pathways (SSPs) Scenario Framework

We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand source: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. <a href="https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en">https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en</a> We collect the historical socioeconomic variables from the World Bank’s global open data bank source: World Bank. (2020). Data Bank: World Development Indicators. <a href="https://databank.worldbank.org/source/world-development-indicators">https://databank.worldbank.org/source/world-development-indicators</a> For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). source: Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. <a href="https://doi.org/10.1016/j.gloenvcha.2016.05.009">https://doi.org/10.1016/j.gloenvcha.2016.05.009</a> Available Online: <a href="https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about">https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about</a> The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.

opencc-by-4.0Feb 2021View details →

ClinicalTrials.gov32/100

Race and Socioeconomic Position: Examining Common Social Pathways to Disease Risk

ClinicalTrials.gov study NCT06552702. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

nasa24/100

Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019

The Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019 consists of biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019 that used the SSPs. The database was generated from a Google Scholar search, followed by a manual examination of the results for papers that made substantial use of the SSPs. Each paper was then coded along a number of different dimensions, including categories of types of papers or analysis, number of subcategories for SSP Applications and SSP Extensions, particular Shared Socioeconomic Pathways (SSPs) used, particular Representative Concentration Pathways (RCPs) used, and particular SSP-RCP combinations used. Over the past ten years, the climate change research commUnity developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. This framework consists of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), which together describe alternative visions of how society and climate may evolve over the coming decades, while providing a framework for combining these pathways in integrated studies. The tracking of the use of this framework in the literature allows for assessment of how it is being used, whether it is achieving its original goals, and what improvements to the framework would benefit future research.

restrictednotspecifiedApr 2025View details →

nasa24/100

Global One-Eighth Degree Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01

The Global One-Eighth Degree Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total population data for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes), consistent both quantitatively and qualitatively with the SSPs. Spatial demographic data are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

restrictednotspecifiedApr 2025View details →

nasa24/100

Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01

The Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total populaton for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds), consistent both quantitatively and qualitatively with the SSPs. This 1-km data set is a downscaled version of the one-eighth degree (7.5 arc-minutes) data published in Jones and O'Neill (2016). The downscaling methods were published in Gao (2017). Spatial demographic data are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

restrictednotspecifiedApr 2025View details →

zenodo8/100

Oscillatory gamma activity mediates the pathway from socioeconomic status to language acquisition in infancy

Representative subset of the raw infant EEG data included in the original manuscript. Baseline EEG data were acquired from Italian infants at age six months and 15 days ( ± 2 weeks). A 4-min block of baseline EEG was collected while a research assistant was blowing bubbles to engage infant’s attention. The EEG was recorded from 60 scalp electrodes using Geodesic Sensor Net (Geodesic EEG System 300, Electrical Geodesics, Inc., Eugene, OR; USA), with impedances below 50 Hz. Vertex was used as an online reference. EEG was sampled at 250 Hz and bandpassfiltered (0.1–100 Hz) online. After recording, EEG data were exported for processing through lab-internal Matlab (MathWorks, Natick, MA) routines and EEGLAB toolbox (Delorme & Makeig, 2004).

restrictedOct 2019View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record