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.
11,394
datasets available to search
ShareScore release 0.9.0
Dataset results
11,394 results for “Surveys”
cldf-datasets/afbo: AfBo: A world-wide survey of affix borrowing
<p>Seifart, Frank. 2013. AfBo: A world-wide survey of affix borrowing. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at <a href="https://afbo.info">https://afbo.info</a>)</p>
An Experiences Survey about Sprint Feedback for Teams
<p>This survey elicits thoughts and experiences from researchers and practitioners towards the relevance of sprint feedback for teams in agile software development. The survey covers the following aspects: demographics, team-driven factors, information usages for Sprint planning, and the overall experiences on lived team feedback, also Information demands by teams.</p> <p>Available under: <a href="http://survey.se.uni-hannover.de/index.php/57192?lang=en">http://survey.se.uni-hannover.de/index.php/57192?lang=en</a></p> <p> </p> <p> </p>
Dataset for Creating a Scholarly Knowledge Graph from Survey Article Tables
<ul> <li><strong>Selected papers.csv</strong><br> This file lists all selected survey papers used to create the knowledge graph. The file contains paper titles, table references (of the tables that are extracted), sources and the reference to the survey paper (either a DOI, or a full textual reference)<br> </li> <li><strong>ORKG comparisons.csv</strong><br> All comparisons imported in the ORKG are listed in this file. Per survey paper, multiple tables could be extracted, and therefore multiple comparisons are created. The files lists the internal IDs and the URL to the comparisons. <br> </li> <li><strong>Ingested papers.csv</strong><br> A full list of all individual papers extracted from the survey articles. The file contains paper titles and their respective URL in the ORKG. </li> </ul>
Modern Fortran Survey 2019 Raw Data
<p>We've organized an online survey about usage of Modern Fortran features (2003/2008 standard) on https://modernfortran.limequery.org</p> <p>The survey was online from June 2019 to March 2020 and includes 140 complete responses and 85 incomplete.</p> <p>This package contains the raw data and the automatically generated statistics from it:</p> <ul> <li>html-forms.zip .. the survey exported as a static HTML page (the original survey hid subquestions based on previous answers)</li> <li>limesurvey-archive.lsa .. the complete survey (including the results) in LimeSurvey archive format (ZIP file with proprietary files)</li> <li>printed-forms.pdf .. the survey as PDF</li> <li>results-{all,complete-only,incomplete-only}.pdf .. the automatically generated statistics of the survey results including plots. "all": include all surveys, "complete-only": only surveys which were completed, "incomplete-only": only surveys which were not completed</li> <li>results-{complete,incomplete}_only.csv .. the results in CSV format for the respective subsets</li> </ul>
Survey data of xylem-sap feeding Auchenorrhyncha
<p>The recent outbreaks of the xylem-limited bacterium <em>Xylella fastidiosa</em> in Europe require the improvement of prevention measures and of integrated management strategies for the diseases caused by this destructive pathogen. These needs are adressed by the XF-ACTORS consortium. Within the workpackage 5 of this project, faunistic surveys have been carried out in various habitats of southern and central Europe and in olive groves in Brazil to monitor the presence of xylem-sap feeding Auchenorrhyncha as confirmed or potential vectors of <em>X. fastidiosa</em>. The metadata of the collection are reported in this database. It provides general information about the occurrence and abundance of xylem-sap feeder species in Europe.</p> <p>This work has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 727987 - XF-ACTORS “Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy”</p>
CitieS-Health Barcelona Survey Results
<p>This dataset contains the data collected using an online survey on knowledge, perceptions and preferences on topics to be investigated around the theme of air pollution and health in Barcelona, Spain. The data collected are for the CitieS-Health project in Barcelona. A scientific paper based on the online survey results is under review. </p>
A survey of Hogna carolinensis burrows on Nantucket Island, MA
<p>This dataset contains the locations of <em>Hogna carolinensis</em> burrows in the Quaker Cemetery on Nantucket Island, MA. This population is probably near where James Emerton collected specimens in 1928/1929. (<a href="https://www.mariamitchell.org/wp-content/uploads/Publication_Johnson1930_Insects.pdf">https://www.mariamitchell.org/wp-content/uploads/Publication_Johnson1930_Insects.pdf</a>).<br> Students from Matt Liddle's Nantucket New School 8th grade class measured and recorded these data under the guidance of Andrew Mckenna-Foster on 22 April and 13 May 2016.<br> To calculate georeferenced locations for each burrow, we chose a convenient base point that is 38 meters at 280 degrees from the hydrant at the SE corner of the Quaker Cemetery. Using QGIS and data from MassGIS (<a href="http://www.mass.gov/orgs/massgis-bureau-of-geographic-information">https://www.mass.gov/orgs/massgis-bureau-of-geographic-information</a>), we found the UTM coordinates of the hydrant and calculated the coordinates of the base point: 407230.04E 4570493.1N UTM 19N NAD83.<br> For each burrow we collected a bearing and distance from base point to calculate a UTM coordinate. We also measured the turret height, full turret width, and interior turret width.<br> These data do not represent all the burrows in the area but cover the main population center.<br> We hope that this dataset acts as a reference point for follow up surveys.</p>
User requirements of Big Earth Data - Survey 2019
<p>The survey was conducted between November 2018 and May 2019 with the aim to find out how users working with large volumes of environmental data interact with data, what challenges they face and how they would like to use cloud-based data services in the future.</p> <p>The term Big Earth Data in this context refers to digital information about Earth, including observations, imagery, derived higher-level products, forecasts and analyses produced by computer models.</p> <p>The survey was conducted in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF) and as part of a PhD thesis on "Big Data technologies for environmental and climate data" at University of Marburg, Germany.</p> <p>The results are published in form of two articles:</p> <ul> <li>Wagemann, J., Siemen, S., Seeger, B. and J. Bendix (2021): Users of open Big Earth data - An analysis of the current state. Computers and Geosciences 2021. <a href="https://doi.org/10.1016/j.cageo.2021.104916">doi:10.1016/j.cageo.2021.104916</a></li> <li>Wagemann, J. Siemen, S., Seeger, B. and J. Bendix (2021): A user perspective on future cloud-based services for Big Earth data. International Journal of Digital Earth 2021. doi: <a href="http://doi.org/10.1080/17538947.2021.1982031">10.1080/17538947.2021.1982031</a></li> </ul> <p> </p>
A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey
<p>Open access to curated datasets positively impacts on scientific research of machine learning and deep learning techniques. It is a fact that benchmarks and public datasets prepared for data science assist researchers interested in evaluating, testing, and building new data-driven methodologies for specific domain areas.</p> <p>In geosciences, there has been a remarkable growth of public datasets arranged to address machine learning challenges related to the oil and gas industry, particularly for reserves exploration and data interpretation. </p> <p>For these reasons, we present the Taranaki dataset, which is a collection of seismic horizons interpreted for a seismic stratigraphic interpretation study in the Taranaki Basin, offshore New Zealand. This data comprises fourteen seismic horizons that mark stratigraphic discordances in the Tui-3D seismic dataset. We annotated five seismic horizons on 33 inline sections and nine horizons on 19 crossline sections.</p> <p>Besides, we present the results of a series of experiments that compare a method of interpolation and a method of deep learning for seismic segmentation. The deep learning experiments evaluated the result of different image tile sizes to train the model, which is presented separately in this dataset. </p> <p>Finally, we evaluated both methodologies to interpret the horizons of this dataset in selected seismic sections. Also, we assessed the absolute error of each method with the ground truth interpretations proposed in this dataset.</p>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
2020 Stakeholder Survey of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) - Quantitative Dataset
<p>This dataset is the outcome of a survey of IPBES stakeholders that was conducted in May-June 2020. The aim of this survey is to better understand stakeholder engagement with IPBES, to improve implementation of the IPBES stakeholder engagement strategy (decision IPBES-3/4 presented in document IPBES/3/18), and to further increase the inclusivity and effectiveness of the IPBES work programme. Results will help, among others, to better align communication and outreach, and to strengthen collaborative processes within the IPBES work programme.</p> <p><br> This dataset presents only the quantitative data of the complete dataset of responses. It has been anonymised and all personal comments in response to open questions have been removed. For information, the full anonymised dataset has been published on Zenodo, with restricted access (see DOI: <a href="http://doi.org/10.5281/zenodo.4121916">10.5281/zenodo.4121916</a>).</p> <p>This dataset is under restricted access and embargoed until after the eighth session of IPBES Plenary. For any inquiry, please contact IPBES Head of Communications (stakeholders@ipbes.net). </p>
ClairCity Skylines Game Player Survey Data
<p>This dataset contains the responses of players of the ClairCity Skylines Game to a post-game survey to evaluate the player experience and changes in knowledge / future actions. The dataset has been cleaned and verified to ensure no privacy or ethics issues. </p>
Deliverable D-IA.2.2.OH-Harmony-Cap.2.1: Completed Pilot Survey
<p>This is a public deliverable of One Health EJP Joint Research Project,<strong><em> </em></strong><strong><em>Integrative Action-2.2,</em></strong> <strong>OH-HARMONY-CAP: </strong>One Health Harmonisation of Protocols for the Detection of Foodborne Pathogens and AMR Determinants. <a href="https://onehealthejp.eu/jip-oh-harmony-cap/"><strong>https://onehealthejp.eu/jip-oh-harmony-cap/</strong></a></p> <p>The purpose is to develop an integrated One Health map (OHLabCap ) of the levels of system capability/capacity/interoperability for each of the EU MS that is repeatable and sustainable. The first step is to develop of a pilot survey, targeting NRLs and the primary diagnostic services (primary sector). The pilot survey covers: 1. six priority bacteria and ten priority parasites have been chosen, as model organisms, together with the antimicrobial resistance (AMR) testing of <em>Salmonella</em> and <em>Campylobacter</em>. 2. 63 questions incorporating capability, capacity, and interoperability</p>
2018 Carr Wildfire Evacuation Survey Data
<p>Following the 2018 Carr Wildfire, an online survey was distributed by researchers from the University of California, Berkeley to collect information on the individual choices of those impacted by the fires in the Redding, California area. Collected from March to April 2019, the data includes questions regarding risk perceptions, communications, evacuation decisions, potential usage of the sharing economy in disasters, opinions of evacuation management, and demographic information. The survey was distributed with the assistance of local partners (i.e., transportation agencies, emergency management agencies, local city and county governments, CBOs, and news outlets). Partners were allowed to post the survey using electronic communication methods including but not limited to: Facebook, Twitter, Nextdoor, agency websites, news websites, email listservs, and alert subscription services. The survey received 647 valid responses, of which 338 were completed. Subsequent papers using this data retained 284 cleaned survey responses for discrete choice modeling, based on the respondents' completion of key choice and demographic questions. The survey was incentivized with the chance to win one of ten $200 gift cards. The survey questions are included in a separate Word document.</p> <p>We request that those who download the data send a courtesy email to the lead author, Dr. Stephen Wong (swong1392@gmail.com). To ensure that any new research makes unique contributions to knowledge and does not duplicate past analyses, users are requested to read and cite publications using this data including:</p> <p>Wong, S., Broader, J., Walker, J. & Shaheen, S. (2021). Understanding California Wildfire Evacuee Behavior and Joint Choice-Making. Retrieved from <a href="https://escholarship.org/uc/item/4fm7d34j">https://escholarship.org/uc/item/4fm7d34j</a></p> <p>Wong, S., Walker, J., & Shaheen, S. (2020). Role of Trust and Compassion in Resource Sharing in Evacuations: A Case Study of the 2017 and 2018 California Wildfire. <em>International Journal of Disaster Risk Reduction. </em><a href="https://escholarship.org/content/qt1zm0q2qc/qt1zm0q2qc.pdf">https://www.sciencedirect.com/science/article/abs/pii/S2212420920314023</a></p> <p>Wong, S., Broader, J., Shaheen, S. (2020). Review of California Wildfire Evacuations from 2017 to 2019. Retrieved from <a href="https://escholarship.org/uc/item/5w85z07g">https://escholarship.org/uc/item/5w85z07g</a></p> <p>Wong, S. & Shaheen, S. (2019). Current State of the Sharing Economy and Evacuations: Lessons from California. SB 1 Report. Retrieved from <a href="https://escholarship.org/uc/item/16s8d37x">https://escholarship.org/uc/item/16s8d37x</a></p> <p> </p> <p>Additional framing work on evacuations can be found here:</p> <p>Wong, S. (2020). Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization. University of California, Berkeley. Dissertation. <a href="https://escholarship.org/uc/item/9b51w7h6">https://escholarship.org/uc/item/9b51w7h6</a></p>
2017 December California Wildfires Evacuation Survey Data
<p>Following the December Southern California Wildfires in 2017, an online survey was distributed by researchers from the University of California, Berkeley to collect information on the individual choices of those impacted by the fires in California. Collected from March to July 2018, the data includes questions regarding risk perceptions, communications, evacuation decisions, potential usage of the sharing economy in disasters, opinions of evacuation management, and demographic information. The survey was distributed with the assistance of local partners (i.e., transportation agencies, emergency management agencies, local city and county governments, CBOs, and news outlets). Partners were allowed to post the survey using electronic communication methods including but not limited to: Facebook, Twitter, Nextdoor, agency websites, news websites, email listservs, and alert subscription services. The survey received 552 valid responses, of which 303 were completed. Subsequent papers using this data retained 226 cleaned survey responses for discrete choice modeling, based on the respondents' completion of key choice and demographic questions. The survey was incentivized with the chance to win one of five $200 gift cards. The survey questions are included in a separate PDF document.</p> <p>We request that those who download the data send a courtesy email to the lead author, Dr. Stephen Wong (swong1392@gmail.com). To ensure that any new research makes unique contributions to knowledge and does not duplicate past analyses, users are requested to read and cite publications using this data including:</p> <p>Wong, S., Broader, J., Walker, J. & Shaheen, S. (2021). Understanding California Wildfire Evacuee Behavior and Joint Choice-Making. Retrieved from <a href="https://escholarship.org/uc/item/4fm7d34j">https://escholarship.org/uc/item/4fm7d34j</a></p> <p>Wong, S., Walker, J., & Shaheen, S. (2020). Role of Trust and Compassion in Resource Sharing in Evacuations: A Case Study of the 2017 and 2018 California Wildfire. <em>International Journal of Disaster Risk Reduction. </em><a href="https://escholarship.org/content/qt1zm0q2qc/qt1zm0q2qc.pdf">https://www.sciencedirect.com/science/article/abs/pii/S2212420920314023</a></p> <p>Wong, S., Chorus, C., Shaheen, S. & Walker, J. (2020). A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study. <em>Travel Behaviour and Society.</em> Retrieved from <a href="https://www.sciencedirect.com/science/article/pii/S2214367X19303291"> https://www.sciencedirect.com/science/article/pii/S2214367X19303291</a></p> <p>Wong, S., Broader, J., Shaheen, S. (2020). Review of California Wildfire Evacuations from 2017 to 2019. Retrieved from <a href="https://escholarship.org/uc/item/5w85z07g">https://escholarship.org/uc/item/5w85z07g</a></p> <p>Wong, S. & Shaheen, S. (2019). Current State of the Sharing Economy and Evacuations: Lessons from California. SB 1 Report. Retrieved from <a href="https://escholarship.org/uc/item/16s8d37x">https://escholarship.org/uc/item/16s8d37x</a></p> <p> </p> <p>Additional framing work on evacuations can be found here:</p> <p>Wong, S. (2020). Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization. University of California, Berkeley. Dissertation. <a href="https://escholarship.org/uc/item/9b51w7h6">https://escholarship.org/uc/item/9b51w7h6</a></p>
EPSRC HEED Data Repository: Surveys
<p>The HEED project aims at understanding energy needs of refugees and displaced populations to improve access to clean energy. The focus of HEED is on the lived experiences of refugees living for protracted periods of time in three refugee camps in Rwanda (Nyabiheke, Gihembe and Kigeme) and internally displaced persons (IDPs) forced to leave their homes as a result of the 2015 earthquake in Nepal. As part of the project, an energy assessment survey of households in both countries was undertaken using quantitative and qualitative research methods with households living in different parts of the camps/settlements, entrepreneurs running small businesses, and those responsible for community facilities, such as schools and health clinics. In the first phase, a questionnaire-based survey targeting displaced populations was conducted with households living in three refugee camps in Rwanda and four displaced sites in Nepal (see tables 2.1 and 2.2 respectively). The second phase of the field research involved a series of interviews and focus group discussions with various stakeholders in Nepal and Rwanda. The surveys were designed and delivered between March and April 2018 by the project partner, Practical Action. In both countries, the enumerators for the survey received a two-day training on research methods, data collection and ethics. </p> <p>With regards to the household survey, the sample size was derived using Cochran’s formula as described by Bartlett et. al. in Organizational Research: Determining Appropriate Sample Size in Survey Research. A minimum sample size of 119 households was derived by applying a margin of error of 0.03 and an alpha of 0.5. A breakdown of the focal group and specific sites where the surveys were delivered in Rwanda and Nepal is shown in tables 2.1 and 2.2 respectively. In Rwanda, a total of 814 surveys including 622 households, 155 enterprises and 37 community facilities from across three sites were conducted. The sample distribution across camp shows 211 for Gihembe, 202 for Kigeme and 209 for Nyabiheke. In Gihembe more than half of the respondents (118, 55.9%) sampled were females with the remaining 93 (44.1%) being males. This is in contrast with Kigeme where almost equal numbers of both male (100, 49.5%) and females (102, 50.5%) were sampled. In Nyabiheke the sample covered more females (123, 58.9%) than males (86, 41.1%). In Nepal, the sample covered 181 households, 18 enterprises and 3 community facilities (see table 2.2). The household sample in Nepal covered more males (126, 69.6%) than females (55, 30.4%). </p> <p><strong>Folder Structure:</strong><br> <strong>Surveys:</strong></p> <p>Gihembe Community Facility Survey – Gihembe_CF.csv<br> Gihembe Enterprise Survey – Gihembe_EN.csv<br> Gihembe Household Survey – Gihembe_HH.csv</p> <p>Kigeme Community Facility Survey – Kigeme_CF.csv<br> Kigeme Enterprise Survey – Kigeme_EN.csv<br> Kigeme Household Survey – Kigeme_HH.csv</p> <p>Nepal Community Facility Survey – Nepal_CF.csv<br> Nepal Enterprise Survey – Nepal_EN.csv<br> Nepal Household Survey – Nepal_HH.csv</p> <p>Nyabiheke Community Facility Survey - Nyabiheke_CF.csv<br> Nyabiheke Enterprise Survey – Nyabiheke_EN.csv<br> Nyabiheke Household Survey – Nyabiheke_HH.csv</p> <p><strong>Location Maps</strong>:</p> <p>Gihembe Community Facility Survey Map – CF_GIS_gihembe.csv<br> Gihembe Enterprise Survey Map – EN_GIS_gihembe.csv<br> Gihembe Household Survey Map – HH_GIS_gihembe.csv</p> <p>Kigeme Community Facility Survey Map – CF_GIS_kigeme.csv<br> Kigeme Enterprise Survey Map – EN_GIS_kigeme.csv<br> Kigeme Household Survey Map – HH_GIS_kigeme.csv</p> <p>Nepal Community Facility Survey Map – CF_GIS_nepal.csv<br> Nepal Enterprise Survey Map – EN_GIS_nepal.csv<br> Nepal Household Survey Map – HH_GIS_nepal.csv</p> <p>Nyabiheke Community Facility Survey Map - CF_GIS_nyabiheke.csv<br> Nyabiheke Enterprise Survey Map – EN_GIS_nyabiheke.csv<br> Nyabiheke Household Survey Map – HH_GIS_nyabiheke.csv</p> <p>The following information was gathered from each of the surveys:</p> <ul> <li>Households: The datasets contain information about household demographics, access to and use of electricity and lighting technologies, access to and use of cooking technologies and fuels, self-reported needs and priorities by the household, and ownership of energy products. Several key areas, such as solar lighting products and issues around fuel usage, are covered in more detail. </li> <li>Enterprises: The datasets contain information about the enterprise, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. </li> <li>Community facility: The datasets contain information about the community facility or institution, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. Community facilities offered healthcare services were presented additional questions about specific medical devices. </li> </ul> <p>The survey results together with other methodological tools including field visits, workshops - ‘Design for Displacement (D4D)’ and ‘Energy for End-Users’ (E4E) workshops have provided relevant data and contextual knowledge to inform the design of the various interventions associated with the HEED. The data sets and results have been compiled, organised and uploaded in the data portal for use by researchers, students and all both within and outside of the project consortium, during and beyond the project lifetime.</p>
Data from "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region"
<p>Table 1 and Table 2 from Tazzari et al., 2021, "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region", Monthly Notices of the Royal Astronomical Society, arXiv:2010.02248</p> <p>Both tables are available in IPAC format, which is in human- and machine-readable:</p> <pre><code class="language-python">from astropy.io import ascii tb = ascii.read('Table1.txt', format='ipac')</code></pre> <p>Table comments (stored at the beginning of the ASCII file as lines starting with "/") can be read as:</p> <pre><code class="language-python">tb.meta['comments'] </code></pre> <p> </p>
Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects
<p>This dataset and its associated report contain the results of an online survey on using a model-based requirements engineering approach in three European projects. </p>
Survey of open data and information seeking in Kenya's Urban Slums and Rural Settlements
<p>This dataset provides survey responses from 240 people surveyed as part of the "Investigating the Impact of Kenya’s Open Data Initiative on Marginalized Communities: Case Study of Urban Slums and Rural Settlements" project.</p> <p>The data, collected in mid-2013 looks at issues of where citizens look for data, and how successful they have been in getting government information from different sources, as well as their awareness of the Kenya open data portal, and their interest in getting information through different digital channels in future.</p> <p>Descriptive statistics have been analysed in the publication "Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating The Kenya's Open Data Initiative in Urban Slums and Rural Settlements", but no further analysis has yet been carried out.</p> <p><strong>Data descriptions</strong></p> <p>The Codebook.csv file lists variable names and the questions asked to elicit each response.</p> <p>JHC-Data.csv contains the results from the questionnaires collected through structured in-person interview in the three locations. The questionnaires were administered at chiefs centres, community resource centres, constituency development fund office and religious centres). The questionnaires were filled in by every 2nd these centres.</p> <p><strong>More information</strong></p> <p>More information on the research project can be found at http://opendataresearch.org/project/2013/jhc</p>
AfBo: A world-wide survey of affix borrowing
<p>Seifart, Frank. 2013. AfBo: A world-wide survey of affix borrowing. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://afbo.info)</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.