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.
12,237
datasets available to search
ShareScore release 0.9.0
Dataset results
12,237 results for “2019”
RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)
<p>This is an RDFied version of the dataset published by Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1021/acsnano.8b07562">https://doi.org/10.1021/acsnano.8b07562</a></p> <p>The Original publication authors: Hagar I. Labouta, Nasimeh Asgarian, Kristina Rinker, and David T. Cramb</p>
Montserrat Digital Surface Model (1 metre) - March 2019 - Pleiades Photogrammetry (raw, not co-registered)
<p>This tiff file contains a Digital Surface Model (DSM) of the southern portion of Montserrat, Eastern Caribbean, including the Soufrière Hills Volcano. This is the raw DSM, generated via leverage of Pleiades tri-stereo imagery using the DSM-OPT software platform; it has not been co-registered. </p>
Belham River Valley Digital Surface Model (1 metre) - March 2019 - Pleiades Photogrammetry (co-registered)
<p>This tiff contains a Digital Surface Model (DSM) of the Belham River Valley in Montserrat, Eastern Caribbean. This was generated via leverage of Pleiades tri-stereo imagery throught the DSM-OPT software platform. This DSM has been co-registered to a LiDAR DSM (generated in 2010, vertical error 0.15 m) and found to have a vertical error of 2.3 m.</p>
ChinaHighO₃: Hourly Seamless 1 km Ground-Level O₃ Dataset for China (2019)
<p>ChinaHighO<sub>3</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) hourly 1 km (i.e., H1K) O<sub>3</sub> dataset for China for the year<strong> 2019</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.89, a root-mean-square error (RMSE) of 16.35 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 11.53 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighO<sub>3</sub> dataset in your scientific research, please cite the following reference (Cheng et al., RSE, 2025):</p> <ul> <li> <p>Cheng, F., Li, Z., Yang, Z., Li, R., Wang, D., Jia, A., Li, K., Zhao, B., Wang, S., Yin, D., Li, S., Xue, W., Cribb, M., and Wei, J. <a href="https://weijing-rs.github.io/publications/Cheng_et_al-RSE-2025.pdf" target="_blank" rel="noopener">First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O<sub>3</sub>) from space in China using artificial intelligence: diurnal variations and implications for air quality and phytotoxicity</a>. <em>Remote Sensing of Environment</em>, 2025, 316, 114482. https://doi.org/10.1016/j.rse.2024.114482</p> </li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
Aggregate dataset on descriptive representation in the Hellenic Parliament (2019-2023)
<p>This is the dataset provided by the AUTH team on descriptive representation in the Hellenic Parliament for WP4 of the ActEU project. </p>
The Guardian headlines 2019-2024
<p>This dataset contains the headlines of the british newspaper "The Guardian" from the 1st of January 2019 until the 1st of November 2024.</p> <p>It is related to this github repository on web scrapping: https://github.com/ulisesrey/web_scrapping_uoc</p>
AI-related patents (WIPO, category G06N) and market capitalisation by companies registering at least 2 new ones in 2019, sorted into four global regions (China, USA, EEA, rest of the world)
<p>NOTE: for some reason the pptx and previews keep getting munged on this supposedly permanent arxiv, but the data is still there, unchanged, and you can see how the pptx should look in either the jpg, or the the article.</p> <p>Datasets and presentations concerning the strength of the EU and "the rest of the world" relative to China and the USA, for the purpose of illustrating and counteracting / better informing narratives concerning a "new AI cold war". The materials authored by us may be freely used under the terms of the MIT License, which appears in its entirety in both the dataset and the presentation. The other materials are only curated by us, taken from Twitter as examples of misinformation pertaining to this concern.</p> <p>As of 28 June, this work now also appears in a formal publication: Joanna J. Bryson, Helena Malikova; Is There an AI Cold War?. <em><em>Global Perspectives</em></em> 2021; 2 (1): 24803. doi: <a href="https://doi.org/10.1525/gp.2021.24803">https://doi.org/10.1525/gp.2021.24803</a></p> <p>Authors: The original analysis was conducted primarily by Malikova in collaboration with Bryson. An associated publication is anticipated where Bryson is the lead author.</p> <p>Contributors: independently followed Malikova's procedures to check her work. Inconsistencies were triple checked and resolved.</p>
Number of BEV (a battery electric vehicle) and PHEV (a plug-in hybrid electric vehicle) vehicles for each Country (2019)
<p>According to the Global E.V. Outlook 2020, China ranks first in vehicles in operation with electric or hybrid engines. In second place in the U.S. and third place in Norway.</p>
Result data related to "Tröndle et al (2019) -- Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe"
<p>The files include results to out study investigating the possibility for renewable electricity autarky in Europe. For each administrative unit on the continental, national, regional, and municipal levels these files include:</p> <ol> <li>Name, country, population, current electricity demand, land cover statistics, shared coast with exclusive economic zone</li> <li>Potential in terms of area [km2], installable capacity [MW], annual electricity yield [TWh]</li> </ol> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Pfenninger, S., & Lilliestam, J. (2019). Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe. <em>Energy Strategy Reviews</em>, <em>26</em>.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 3 (2021-07-19)</p> <p>* Fix ID of EEZ in shared-coast.csv files.</p> <p>Version 2 (2019-11-08)</p> <p>* Add land cover statistics for each unit.</p>
Processed data for the manuscript, entitled "Substantial increase in heavy precipitation events preceded by moist heatwaves over China during 1961–2019"
<p>This is the dataset on annual frequency of heatwaves, heavy precipitation, and heatwave-heavy precipitation events during 1961-2019 at 1776 stations across China. This dataset is the processed results based on daily observations that are provided by the National Meteorological Science Data Center (<a href="http://data.cma.cn/en">http://data.cma.cn/en</a>). In this processed dataset, the "HW_HI" column is the annual frequency of heat-index-based heatwaves; "HW_TW" column is the annual frequency of wet-bulb temperature based heatwaves; "HP" is the annual frequency of heavy precipitation with taking the 95<sup>th</sup> percentile of non-zero precipitation at the threshold. "HWHP_HI"/"HWHP_TW" column indicate annual frequency of heavy precipitation preceded by HW_HI/HW_TW. All the results shown in the manuscript entitled "<strong>Substantial increase in heavy precipitation events preceded by moist heatwaves over China during 1961</strong>–<strong>2019</strong>", are obtained based on this processed dataset.</p>
Real-time ZTD and gradients from 20 IGS stations; year 2019
<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute over the entire year 2019 for 20 IGS stations: ALGO, BIK0, CAS1, CHPG, CPVG, FAA1, JPLM, KERG, KZN2, LHAZZ, LMMF, NKLG, NNOR, PNGM, POVE, REYK, RGDG, ULAD, WROC, YEL2.</p> <p>Two subsets are available that correspond to two processing strategies, i.e. common and advanced. A detailed description, validation and comparison of both strategies can be found in: https://link.springer.com/article/10.1007/s10291-020-01014-w</p> <p>Data are stored in standard Matlab MAT files. Each file contains a single table array (Matlab format) with the complete set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using in-build Matlab function "writetable.m".</p>
Lithuanian Household Energy Expenditure and Energy Poverty Data, 2019
<p>This dataset provides data about household energy expenditure and energy poverty in Lithuania. The dataset contains detailed data about 5031 households and is based on the Lithuanian Survey on Income and Living Conditions (2019) micro dataset provided by Statistics Lithuania. It includes additional data derived from original survey data and energy poverty calculation results at household level.</p> <p>Duomenų rinkinyje pateikiami duomenys apie namų ūkių energijos išlaidas ir energijos nepriteklių Lietuvoje 2019 metais. Duomenų rinkinys apima 5131 namų ūkį. Rinkinio pagrindas - Pajamų ir gyvenimo sąlygų statistinio tyrimo duomenys, skelbiami Lietuvos Statistikos departamento. Duomenų rinkinys apima ir papildomus duomenis gautus remiantis originalios apklausos duomenimis bei energijos nepritekliaus skaičiavimų rezultatus namų ūkio lygmenyje.</p>
Daily temperature data at Adelaide, Australia taken in a Glaisher thermometer stand (1856–1952) and a homogenised daily temperature dataset for Adelaide (1856–2019)
<p>23000_Adelaide_Glaisherstand_Tx_data.tsv and 23000_Adelaide_Glaisherstand_Tn_data.tsv: Daily maximum and minimum temperature observations for Adelaide,South Australia, taken in a Glaisher thermometer stand from November 1856 to July 1947. Data are given in Station Exchange Format (SEF, https://github.com/C3S-Data-Rescue-Lot1-WP3/SEF/wiki).</p> <p>23000_combined_homogenised_data.tsv: A homogenised daily temperature dataset for Adelaide, South Australia, from January 1859 to December 2019. Data are provided in the format Year, Month, Day, Maximum temperature (degrees Celcius), Minimum temperature (degrees Celcius).</p> <p>Images of the data source for File 1 are available from the Australian Meteorological Association at https://www.met-acre.net/MERIT/AMETA.html. </p> <p>The data are shared under Attribution-NonCommercial 4.0 International licence (CC BY-NC 4.0)</p>
Data from the National Prioritisation of Australian plant species after the 2019-2020 bushfires
<p>Data for 26,062 native Australian plant species assessed against ten post-fire recovery criteria. Details of criteria and methods available in Gallagher, R. V. (2020) <em>National prioritisation of Australian plants affected by the 2019–2020 bushfire season.</em> Report to the Commonwealth Dartement of Agriculture, Water and Environment. https://www.environment.gov.au/system/files/pages/289205b6-83c5-480c-9a7d-3fdf3cde2f68/files/final-national-prioritisation-australian-plants-affected-2019-2020-bushfire-season.pdf </p>
Vides satraukuma indekss (piecu Rīgas pieostas apkaimju iedzīvotāju aptauja, 2019)
<p>Datu avots: projekta <em>Dzīve līdzās ostai</em> piecu Rīgas pieostas apkaimju iedzīvotāju aptauja (2019). Tā aprakstīta grāmatā: Dace Bula (zin. red.). <em>Dzīve līdzās ostai</em>. Rīga: LU LFMI, 2022. 184. lpp. (tabula publicēta kā 3.pielikums, 168-173.lpp.).</p> <p>Vides satraukuma indekss parāda, cik satrauktas par vairākiem vidi apdraudošiem un piesārņojošiem aspektiem ir konkrētas iedzīvotāju grupas, piemēram, vīrieši un sievietes; jaunieši, vidējā vecuma iedzīvotāji un seniori. Indekss aprēķināts, ņemot vērā katra respondenta atbildes uz jautājumu par satraukumu par astoņiem vides piesārņojuma veidiem (putekļi, smakas, troksnis, vibrācijas, liegta piekļuve Daugavai, ūdens piesārņojums, gaismas piesārņojums, intensīva satiksme), katrai viņa atbildei piešķirot noteiktu vērtību un tad tās summējot. Zemāk ir seši iespējamie atbilžu varianti uz katru no indeksā iekļautajiem astoņiem rādītājiem, kā arī to svars jeb summēja­mā vērtība. Maksimālā indeksa vērtība ir 24, mini­mālā: -8 (“ļoti satrauc” =3, “drīzāk satrauc”=2, “drīzāk nesatrauc”= 1, “nemaz nesatrauc”=0, “[apkaimē] nav šīs problēmas”=-1). Detalizētāk indeksa izveide un analīze aprakstīta nodaļā «Iedzīvotāji vērtē dzīves vidi līdzās ostai» (sadaļa «Kuras iedzīvotāju grupas ir vissatrauktākās?», 98.lpp.).</p> <p>Ja konkrētā respondentu grupā aptaujāto skaits ir mazāks par 30, tad indekss netiek attēlots (tā vietā ir “..”), tāpēc tabulā atsevišķi nav izdalīti Kundziņsalas iedzīvotāji, jo kopējais aptaujāto kundziņsaliešu skaits ir 30, un to salīdzināšanai mazākās sociāli demogrāfiskajās šķērsgriezumu grupās nav statistiska pamatojuma.</p> <p>«Atšķirību būtiskums» parāda konkrētā respondentu dalījuma atšķirību statistisko nozīmīgumu («0.000» nozīmē lielākās, bet «0.005» — mazākās atšķirības; ja rādītājs ir lielāks nekā «0.005», atšķirības jāuzskata par statistiski nenozīmīgām, un tad rādītājs netiek attēlots). Piemēram, salīdzinot piecu apkaimju iedzīvotāju vides satraukuma indeksu, ir konstatējamas būtiskas statistiskās atšķirības starp dažādu apkaimju iedzīvotāju vērtējumiem, jo atšķirību būtiskuma rādītājs ir «0.000» (satrauktākie ir Mangaļsalas iedzīvotāji, indekss «17.2», bet vismazāk satrauktie — Daugavgrīvas iedzīvotāji, indekss «14.4»), bet nav atšķirību starp visu apkaimju vīriešu un sieviešu satraukumu (atšķirību būtiskuma rādītājs ir bijis lielāks nekā «0.005», tātad atšķirības nav statistiski nozīmīgas un tabulā atšķirību būtiskuma vērtība nav attēlota).</p> <p> </p> <p>«Dzīve līdzās ostai: ekonaratīvi, vietējā vēsture un vides aktīvisms Daugavas lejtecē» (Nr. lzp-2018/1-0446). Projekta vadītāja Dr. philol. Dace Bula.</p> <p>Dzīve līdzās ostai: ekonaratīvi, vietējā vēsture un vides aktīvisms Daugavas lejtecē // Living Next to the Port: Eco-Narratives, Local Histories and Environmental Activism in the Daugava Delta (<a href="https://zenodo.org/communities/ekohum">https://zenodo.org/communities/ekohum</a>)</p> <p>Kā <em>google sheets</em> publicēta: <a href="https://docs.google.com/spreadsheets/d/1k4o1cuNXLolHwqVKM1m2tR-5Xgb0Uk0T9OyusYUtUyo/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1k4o1cuNXLolHwqVKM1m2tR-5Xgb0Uk0T9OyusYUtUyo/edit?usp=sharing</a></p>
WUSA surface wave amplification measurements (Schardong et al. 2019)
<p>#Western U.S. surface wave amplification measurements</p> <p>If using this data please cite: <br> Schardong, L., Ferreira, A.M., Berbellini, A. and Sturgeon, W., 2019. The anatomy of uppermost mantle shear-wave speed anomalies in the western US from surface-wave amplification. Earth and Planetary Science Letters, 528, p.115822.</p> <p>Directory format:<br> USA.RAV... = vertical-component Rayleigh wave amplification measurements<br> USA.RAH... = horizontal-component Rayleigh wave amplification measurements<br> USA.TOR... = Love wave amplification measurements</p> <p>File format:<br> motion type index -- mode order (n) -- angular order (l) -- period -- relative local amplification w.r.t. PREM -- estimated error on local amplification</p> <p>The number of data points varies depending on the available data in Hendrik van Heijst's database, and the various selection steps. </p>
Hydrological simulations for Costa Rica from 1985 to 2019 using HYPE CR 1.0
<p>This dataset corresponds to the hydrological simulations of <strong>605 catchments</strong> in <strong>Costa Rica</strong> using the<strong> HYPE CR</strong> 1.0 for the period from <strong>1985 to 2019</strong>.</p> <p>The conceptual <a href="https://www.smhi.se/en/research/research-departments/hydrology/hype-our-hydrological-model-1.7994">HYPE</a> (Hydrological Predictions for the Environment) model was used to assess the water resources of tropical Costa Rica at a national scale at daily scale using adjusted global topography, soil, land use, and remotely-sensed climate products to force, calibrate and independently evaluate the model.</p> <p> A bias corrected temperature data from <a href="https://psl.noaa.gov/data/gridded/data.ghcncams.html">NOAA</a> (National Oceanic and Atmospheric Administration) and <a href="https://www.chc.ucsb.edu/data/chirps">CHIRPS</a> (Climate Hazards Group InfraRed Precipitation with Station data) precipitation were used as model forcings. Daily streamflow from 13 gauges for the period 1990-2003 and monthly <a href="https://www.ntsg.umt.edu/project/modis/mod16.php">MODIS</a> (Moderate Resolution Imaging Spectroradiometer) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000-2014 were used to calibrate and evaluate the model.</p> <p><strong>Here, we presented the results of our calibrated HYPE model for Costa Rica (HYPE CR) version 1.0.</strong></p> <p> </p> <p>The files contained in this repository are described below :</p> <ul> <li><strong>CHIRPS_bias_corrected.zip</strong>: contains the daily gridded CHIRPS bias corrected rainfall using a linear bias correction method using ground stations in NetCDF4 files.</li> <li><strong>Catchments_CostaRica.geojson</strong>: simulated catchments' boundaries in GIS format. The columns "subid" correspond to the identifier of the catchments.</li> <li><strong>Streams_CostaRica.geojson</strong>: streams network used to delineate the catchments.</li> <li><strong>AET_daily_mm.csv</strong>: time series of daily actual evapotranspiration in mm.</li> <li><strong>AET_month_mm.csv</strong>: time series of monthly actual evapotranspiration in mm (sum of daily).</li> <li><strong>AET_year_mm.csv</strong>: time series of annual actual evapotranspiration in mm (sum of monthly).</li> <li><strong>Baseflow_daily_mm.csv</strong>: time series of daily baseflow in mm, as the sum of discharge of three soil layers. Represents the contribution of the subcatchment area.</li> <li><strong>Baseflow_month_mm.csv</strong>: time series of monthly baseflow in mm (sum of daily). Represents the contribution of the subcatchment area.</li> <li><strong>Baseflow_year_mm.csv</strong>: time series of annual baseflow in mm (sum of monthly). Represents the contribution of the subcatchment area.</li> <li><strong>Infiltration_daily_mm.csv</strong>: time series of daily infiltration in the upper layer, in mm.</li> <li><strong>Infiltration_month_mm.csv</strong>: time series of monthly infiltration in the upper layer, in mm (sum od daily).</li> <li><strong>Infiltration_year_mm.csv</strong>: time series of annual infiltration in the upper layer, in mm (sum of monthly).</li> <li><strong>PET_daily_mm.csv</strong>: time series of daily potential evapotranspiration in mm.</li> <li><strong>PET_month_mm.csv</strong>: time series of monthly potential evapotranspiration in mm (sum of daily).</li> <li><strong>PET_year_mm.csv</strong>: time series of annual potential evapotranspiration in mm (sum of monthly).</li> <li><strong>Prec_daily_mm.csv</strong>: time series of daily bias corrected precipitation in mm.</li> <li><strong>Prec_month_mm.csv</strong>: time series of monthly bias corrected precipitation in mm (sum of daily).</li> <li><strong>Prec_year_mm.csv</strong>: time series of annual bias corrected precipitation in mm (sum of monthly).</li> <li><strong>Qsim_daily_m3-s.csv</strong>: time series of daily streamflow in m3-s. Represents the contribution of all upstream area.</li> <li><strong>Qsim_month_m3-s.csv</strong>: time series of monthly streamflow in m3-s (average of daily). Represents the contribution of all upstream area.</li> <li><strong>Qsim_year_m3-s.csv</strong>: time series of annual streamflow in m3-s (average of monthly). Represents the contribution of all upstream area.</li> <li><strong>Runoff_daily_mm.csv</strong>: time series of daily runoff in mm. Represents the contribution of the subcatchment area.</li> <li><strong>Runoff_month_mm.csv</strong>: time series of monthly runoff in mm (sum of daily). Represents the contribution of the subcatchment area.</li> <li><strong>Runoff_year_mm.csv</strong>: time series of annual runoff in mm (sum of monthly). Represents the contribution of the subcatchment area.</li> <li><strong>SM_daily_mm.csv</strong>: time series of daily soil moisture in mm. Represents the contributionof three soil layers.</li> <li><strong>SM_month_mm.csv</strong>: time series of monthly soil moisture in mm (average of daily). Represents the contributionof three soil layers.</li> <li><strong>SM_year_mm.csv</strong>: time series of annual soil moisture in mm (average of monthly). Represents the contributionof three soil layers.</li> <li><strong>Streamflow_daily_mm.csv</strong>: time series of daily streamflow in mm. Represents the contribution of the subcatchment area.</li> <li><strong>Streamflow_month_mm.csv</strong>: time series of monthly streamflow in mm (sum of daily). Represents the contribution of the subcatchment area.</li> <li><strong>Streamflow_year_mm.csv</strong>: time series of annual streamflow in mm (sum of monthly). Represents the contribution of the subcatchment area.</li> <li><strong>Tmean_daily_deg.csv</strong>: time series of daily mean air temperature in degrees.</li> <li><strong>Tmean_month_deg.csv</strong>: time series of monthly mean air temperature in degrees.</li> <li><strong>Tmean_year_deg.csv</strong>: time series of yearly mean air temperature in degrees.</li> </ul>
Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019
<p>Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019. Described in more detail in Brekke, C., Espeseth, M. M., Dagestad, K.-F., Röhrs, J., Hole, L. R., & Reigber, A. (2021). Integrated analysis of multisensor datasets and oil drift simulations - a free-floating oil experiment in the open ocean. Journal of Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499</p> <p>Work is funded by grant no. 237906 (CIRFA) of the Norwegian Research Council.</p>
HIdden fishing activity hotspots in the Adriatic Sea in 2019
<p>The animated image shows the unreported fishing activity hotspots in the Adriatic in 2019. The datasets report the fishing activity ban areas in the Adriatic over the months and the reported, unreported, total, unreported/total fishing hour distributions across the months estimated by our workflow for vessel data processing (Coro et al., 2023).</p>
Dataset of traffic dynamics during the 2019 Kincade Wildfire Evacuation
<p>This dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented and summarized in the paper: <em>Rohaert et al., ‘Traffic dynamics during the 2019 Kincade wildfire evacuation’, [Submitted for peer-review to an international journal.], 2022.</em></p> <p><strong>CRediT author statement</strong></p> <p><strong>Arthur Rohaert: </strong>Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. <strong>Erica D. Kuligowski: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Adam Ardinge: </strong>Conceptualization, Formal analysis, Investigation, Methodology, Resources, Validation, Writing - review & editing. <strong>Jonathan Wahlqvist:</strong> Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Steven M.V. Gwynne: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Amanda Kimball: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing - review & editing. <strong>Noureddine Bénichou: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Enrico Ronchi: </strong>Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review & editing</p> <p><strong>Acknowledgements</strong></p> <p>This work has been funded under award 60NANB20D191 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce. The authors would like to thank the WUI-NITY team (Guillermo Rein, Nikolaos Kalogeropoulos, Harry Mitchell, Max Kinateder, Maxime Berthiaume). The authors also acknowledge the technical panel of the project for their support and guidance: Carole Adam, Amy Christianson, Tom Cova, Lauren Folk, Abishek Gaur, Paolo Intini, Justice Jones, Bryan Klein, Chris Lautenberger, Ruggiero Lovreglio, Jerry McAdams, Ruddy Mell, Elise Miller-Hooks, Cathy Stephens, Steve Taylor, Sandra Vaiciulyte, Xilei Zhao, Rita Fahy, Lucian Deaton, and Michele Steinberg. </p> <p> </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.