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.
167
datasets available to search
ShareScore release 0.9.0
Dataset results
167 results for “Disaster”
Open Access on GNSS Permanent Networks Data in Case of Disaster
<p>Earthquakes, as a natural phenomenon causing large physical and social destruction, are the subject of intensive research throughout the world. Spurred by the fact that in year 2020, two catastrophic earthquakes hit Croatia, in March with epicenter near Zagreb and December with epicenter near Petrinja, at the Faculty of Geodesy, University of Zagreb activities were initiated with the aim of strengthening the ability to react in these situations. Focus of those activities is on providing fast, adequate, and complete information on the disaster in the field of geodesy and geoinformatics. The research was focused on interpretation of kinematics of surface motion during the earthquake itself for what high rate permanent GNSS (Global Navigation Satellite System) network stations registrations are necessary. The Croatian earthquakes experience as well as the Mexico (June 2020) and Samosa earthquake (October 2020), pointed out, related to the use of high-rate registration GNSS data, that the primary problem in the use of this data is open access to the data itself. That is why this study has been launched - to gain a global picture of the availability of data from permanent GNSS networks around the world. The research included the collection and processing of information on open access policies for permanent GNSS networks data in the event of natural disasters with an emphasis on earthquakes. A global survey of institutions around the world responsible for managing GNSS permanent networks has been conducted. The survey contains three groups of questions that include general information on the type of permanent networks, models of access to network data and the readiness of countries to reach an international agreement on the opening data of the GNSS network in the event of a disaster. The results indicated that a high percentage of countries participating in the survey were ready to agree to open the data and introduce a common international portal through which scientists and researchers would be able to download GNSS permanent network data free of charge in the event of natural disasters.</p>
A car burnt by the disaster scans with iPhone12
Materials damaged by the Great Kantō Earthquake. https://tokyoireikyoukai.com/data/661 The Great Kantō Earthquake struck the Kantō Plain on the main Japanese island of Honshū at 11:58:44 JST (02:58:44 UTC) on Saturday, September 1, 1923. Varied accounts indicate the duration of the earthquake was between four and ten minutes. Extensive firestorms and even a fire tornado added to the death toll. Ethnically-charged civil unrest after the disaster (i.e. the Kantō Massacre) has been documented. The body of the car was lost, and only the chassis remained. This car has a long history as the first car number, and was used at Meidi-ya store in Ginza until just before the earthquake. Yokoamicho Park 2-3-25, Yokoami, Sumida-ku, Tokyo 130-0015, Japan   Source: Objaverse 1.0 / Sketchfab
Experimental Data for Natural Disaster Mobility Model and Typhoon Haiyan Scenario
<p>The experimental data set for running the <em>Typhoon Haiyan</em> scenario with the <em>Natural Disaster Mobility Model</em> presented in the paper:</p> <p>Milan Stute, Max Maass, Tom Schons, and Matthias Hollick, “<strong>Reverse Engineering Human Mobility in Large-scale Natural Disasters</strong>,” to appear in <em>ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)</em>, November 2017, Miami Beach, USA.</p>
A dataset of community perspectives on living conditions and disaster risk management in informal settlements: A case study in KwaZulu-Natal Province, South Africa
<p>This article describes a dataset of community perspectives on living conditions and disaster risk management in Khan Road, a non-serviced informal settlement, located in Pietermaritzburg, the capital of KwaZulu-Natal province in South Africa. The data were collected by local community researchers via a structured questionnaire of 159 participants conducted between August and September 2022, using mobile phones via KoboToolbox. The dataset was analysed using exploratory data analysis (EDA) techniques. This household survey is part of a research project aiming to develop an evidence base of opportunities, risks and vulnerabilities related to housing construction and resource management in incremental upgrading of informal settlements in South Africa. This dataset can be used by local practitioners and policymakers involved in decision-making for informal settlement upgrading and help them prioritise resources and upgrading interventions based on what informal dwellers need. Furthermore, this cleaned dataset could support the analysis of further South African data guiding the development of digital platforms as a real-time resource management tool or guide the enhancement of existing theoretical frameworks in the field of participatory design and co-production used by academic scholars. </p>
Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems
<p>Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6's states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6's State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters.</p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
Supplementary material of article Mining disaster in Brumadinho (Brazil): social vulnerability from the perspective of the fisherman community
Open the record for dataset details and reuse information.
Tweets informing about resource needs and availabilities in post-disaster situation
<p>This is the dataset for the paper:</p> <p>Moumita Basu, Anurag Shandilya, Prannay Khosla, Kripabandhu Ghosh, Saptarshi Ghosh. Extracting Resource Needs and Availabilities from Microblogs for Aiding Post-Disaster Relief Operations. IEEE Transactions on Computational Social Systems, 2019.</p> <p>The data contains tweetids of tweets (from Twitter) posted during (1) the 2015 Nepal earthquake, and (2) 2016 Italy earthquake. The tweets that inform about need and availability of various types of resources are identified.</p> <p>The dataset can be used for developing algorithms for microblog retrieval / classification, and for understanding social media activity in the aftermath of a disaster event.</p>
FIRE 2018 IRMiDis track dataset: Fact-checkable tweets posted during disasters
<p>This is the dataset used for the <a href="http://fire.irsi.res.in/fire/2018/home">FIRE 2018</a> track on Information Retrieval from Microblogs during Disasters (IRMiDis). </p> <p>The dataset contains ~50K tweets (microblogs) and ~6.8K news articles posted after the 2015 Nepal earthquake. The dataset can be used for tasks such as: (i) Identifying fact-checkable tweets from among tweets posted during a disaster -- the dataset includes a gold standard set of fact-checkable tweets, (2) Identifying news articles supporting / opposing a fact-checkable tweet, etc. </p>
Retrieval and summarization of microblogs posted after a disaster event: SMERP 2017 dataset
<p>This is the dataset used in the Data Challenge track of the ECIR 2017 Workshop on Exploitation of Social Media for Emergency Relief and Preparedness (<a href="https://www.computing.dcu.ie/~dganguly/smerp2017/">SMERP 2017</a>).</p> <p>The Data Challenge track was about extracting and summarizing information relevant to a set of practical information needs (topics) that are critical for post-disaster relief operations, such as need and availability of resources, infrastructure damage and restoration, etc. The track used a dataset of tweets / microblogs posted during the August 2016 earthquake in central Italy. Specifically, the data challenge consisted of two tasks:<br> (1) Retrieve the microblogs that are relevant to the given set of topics, and<br> (2) Summarizing the microblogs that are relevant to the given set of topics. </p> <p><br> This dataset can be used to develop algorithms for retrieval and summarization of microblogs that are useful for post-disaster relief operations, in the aftermath of a disaster.</p> <p>For more details, refer to the <a href="https://dl.acm.org/citation.cfm?id=3130338">workshop report.</a></p>
Constructing a Comprehensive Disaster Resilience Index: The case of Italy
<p>The file contains raw data used to construct disaster resilience index for Italian municipalities.</p>
Disaster Resilient and Self-Assessing Multifunctional Transportation Structures
<p>Corresponding data set for Tran-SET Project No. 18STTAM02. Abstract of the final report is stated below for reference:</p> <p>"This research designs and characterizes multifunctional materials, in particular inexpensive shape memory alloys, for transportation structures that possess excellent mechanical properties and self-sensing capabilities for strengthening and health monitoring. The properties are the Fe-SMAs are sensitive to part size in that the grain size of the material, which can be grown to several inches, should exceed the smallest dimension of the part. In the current work, maximum part size of the large dimension Fe-SMA rods were determined through detailed microstructural investigations. Samples were subjected to abnormal grain growth heat treatments and found out that part size be increased up to 4.6 mm. Work to correlate computational and experimental work concerning the magnetic sensing of Fe-SMA transformation was conducted using via tensile loading of Fe-SMA wire. A model was developed to simulate a grain by grain transformation of a wire with large (4mm) grains along the wire, modeled as partitioned segments."</p>
Estimating Disaster Resilience of Hurricane Helene on Florida Counties
<p><strong>The Disaster Resilience Index (DRI) was calculated for each county along the path of Hurricane Helene in Florida.</strong></p>
Introgression dynamics from invasive pigs into wild boar following the March 2011 natural and anthropogenic disasters at Fukushima
<p>Natural and anthropogenic disasters have the capability to cause sudden extrinsic environmental changes and long-lasting perturbations including invasive species, species expansion, and influence evolution as selective pressures force adaption. Such disasters occurred on March 11th 2011, in Fukushima, Japan when an earthquake, tsunami, and meltdown of a nuclear power plant all drastically reformed anthropogenic land use. Here, we demonstrate, using genetic data, how wild boar (<em>Sus scrofa leucomystax</em>) have persevered against these environmental changes, including an invasion of escaped domestic pigs (<em>Sus scrofa domesticus</em>). Concurrently, we show evidence of successful hybridization between pigs and native wild boar in this area, however in future offspring, the pig legacy has been diluted through time. We speculate that the range expansion dynamics inhibit long-term introgression and introgressed alleles will continue to decrease at each generation while only maternally inherited organelles will persist. Using the gene flow data among wild boar, we assume that offspring from hybrid lineages will continue dispersal north at low frequencies as climates warm. We conclude that future risks for wild boar in this area include intraspecies competition, revitalization of human related disruptions, and disease outbreaks.</p>
Natural Disaster Trends
<p><strong>Data fields</strong></p> <ul> <li> <p><code>Disaster_Group</code>: EM-DAT stores different types of disasters: natural, technological and complex. The dataset has been filtered to contain only natural disasters so this column is "Natural" for all rows but kept for compatibility reasons.</p> </li> <li> <p><code>Disaster_Subgroup</code>: Every natural disaster is assigned to one of the following six subgroups: <em>Biological, Geophysical, Climatological, Hydrological, Meteorological and Extra-terrestrial</em> to describe the type of natural disaster. No missing values are present for this attribute.</p> </li> <li> <p><code>Disaster_type</code>: For every natural disaster event one main disaster type is identified. If two or more disasters are related because they are consequences of each other, then this information is encoded in the attributes <code>Associated_Dis</code> and <code>Associated_Dis2</code>. No missing values are present for this attribute.</p> </li> <li> <p><code>Disaster Sub-Type</code>: Subdivision related to the attribute <code>Disaster_type</code> so that a the disaster type Storm can be further classified as tropical, extra-tropical or convective storm.</p> </li> <li> <p><code>Disaster Sub-Sub Type</code>: Any appropriate sub-division of the disaster sub-type (not applicable for all disaster sub-types). Types of natural disasters could be further broken down using two more categories which would be available in the database. For example, the Disaster type <em>Storm</em> could be further subdivided into <em>Tropical storm</em>, <em>Extra-tropical storm</em> or <em>Convective storm</em>. Even a further subdivision of the category <em>Convective storm</em> would be possible. Since the analysis is aimed at detecting trends on a high level, the classification of each event based on the attributes <code>Disaster_Subgroup</code> and <code>Disaster _Type</code> was considered sufficient and the further subdivisions into <code>Disaster sub-type</code> and <code>Disaster Subsubtype</code> is only intended to be considered for detailed analysis. The full table is shown in the Appendix.</p> </li> <li> <p><code>Associated_Dis</code>: Secondary event triggered by a natural disaster (i.e. Landslide for a flood, explosion after an earthquake, ...)</p> </li> <li> <p><code>Associated_Dis2</code>: Another secondary event triggered by a natural disaster. (i.e. Landslide for a flood, explosion after an earthquake, ...)</p> <p>Example: If a tsunami is triggered by an earthquake, then the attribute <code>Disaster_Type</code> would be <em>Earthquake</em>, the attribute <code>Disaster_Subtype</code> would be <em>Ground movement</em> and the attribute <code>Associated_Dis</code> would be <em>Tsunami/Tidal wave</em>.</p> </li> <li> <p><code>Country</code>: The country in which the disaster has occurred or had an impact. If a disaster has affected more than one country, a seperate entry is created in the database for each country affected. No missing values are present for this attribute.</p> </li> <li> <p><code>ISO</code>: Unique 3-letter code for each country defined by ISO 3166. No missing values are present for this attribute.</p> </li> <li> <p><code>Region</code>: The region to which the country belongs, based on the UN regional division. No missing values are present for this attribute.</p> </li> <li> <p><code>Continent</code>: The continent to which the country belongs. No missing values are present for this attribute.</p> </li> <li> <p><code>Start_Year</code>: The year when the disaster occurred. No missing values are present for this attribute.</p> </li> <li> <p><code>End Year</code>: The year when the disaster ended. No missing values are present for this attribute. For sudden-impact disasters also the month and the day are well defined and available. For disaster situations developing gradually over a longer time period (i.e. drought) with no specific start date the day attribute is empty. For our questions the exact date plays a subordinate role and therefore the year of the beginning of the disaster is completely sufficient for our analysis.</p> </li> <li> <p><code>Total_Deaths</code>: Number of people who lost their life because the event happened plus the number of people whose whereabouts since the disaster are unknown, and presumed dead based on official figures. Missing values present for approx. 25% of all events.</p> </li> <li> <p><code>No_Affected</code>: Number of people which requiring immediate assistance during an emergency situation. The indicator affected is often reported and is widely used by different actors to convey the extent, impact, or severity of a disaster in non-spatial terms. In case that no values for the attribute <code>Total_Deaths</code> are available this attribute could be used as a proxy.</p> </li> </ul> <p><strong>Appendix</strong></p> <table> <tbody> <tr> <td> <p><strong>Disaster </strong></p> <p><strong>Group</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Sub-Group</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Type</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Sub-Type</strong></p> </td> <td> <p><strong>Disaster </strong></p> <p><strong>Sub-Sub Type</strong></p> </td> </tr> <tr> <td> <p><strong>Natural</strong></p> </td> <td> <p>Geophysical</p> </td> <td> <p>Earthquake</p> </td> <td> <p>Ground movement</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Tsunami</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Volcanic activity</p> </td> <td> <p>Ash fall</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Lahar</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Pyroclastic flow</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Lava flow</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Mass Movement</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Meteorological</p> </td> <td> <p>Storm</p> </td> <td> <p>Tropical storm</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Extra-tropical storm</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Convective storm</p> </td> <td> <p>Derecho</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Hail</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Lightning/thunderstorm</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Rain</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Tornado</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Sand/dust storm</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Winter storm/blizzard</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Storm/surge</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Wind</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Severe Storm</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Extreme Temperature</p> </td> <td> <p>Cold wave</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Heat Wave</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Severe winter conditions</p> </td> <td> <p>Snow/ice</p> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <p>Frost/freeze</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Fog</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Hydrological</p> </td> <td> <p>Flood</p> </td> <td> <p>Coastal flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Riverine flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Flash flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Ice jam flood</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Landslide</p> </td> <td> <p>Avalanche (snow, debris, mudflow, rock fall)</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Wave action</p> </td> <td> <p>Rogue wave</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Seiche</p> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Climatological</p> </td> <td> <p>Drought</p> </td> <td> <p>Drought</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Glacial Lake outburst</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Wildfire</p> </td> <td> <p>Forest fires</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Land fire: Brush, bush, pasture</p> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Biological</p> </td> <td> <p>Epidemic</p> </td> <td> <p>Viral diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Bacterial diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Parasitic diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Fungal diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Prion diseases</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Insect Infestation</p> </td> <td> <p>Locust</p> <p>Grasshopper</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Animal accident</p> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Extra-terrestrial</p> </td> <td> <p>Impact</p> </td> <td> <p>Airburst</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>Space weather</p> </td> <td> <p>Energic particles</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Geomagnetic storm</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> <p>Shockwave</p> </td> <td> </td> </tr> </tbody> </table> <p>Sourced from https://public.emdat.be/about.</p>
Gridded and aggregated tropical cyclone disaster datasets in China (1990-2015)
<p><strong>Gridded_Datasets:</strong> The geotransformation from image coordinate space to georeferenced coordinate space for each tiff file is (97.0, 0.1, 0.0, 54.0, 0.0, -0.1).</p> <p><strong>Aggregated_Datasets: </strong></p> <p>Including variables aggregated to TC-event scale and province scale:</p> <ul> <li>year (year of the tropical cyclone)</li> <li>cnid (Chinese tropical cyclone ID)</li> <li>DEL (Direct Economic Loss, CNY)</li> <li>W (maximum of gridded sustained wind speed, m/s)</li> <li>P (maximum of gridded daily precipitation, mm)</li> <li>K (asset value exposure, CNY)</li> <li>I (GDP per capita, CNY)</li> <li>H (proportion of nonsteel-concrete residential buildings)</li> </ul> <p> </p>
AIS and START Grade With Films Transferring in Disaster Management
ClinicalTrials.gov study NCT05358418. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Seasonal dynamics in terrestrial insect communities after the impact of the Brumadinho Tailings Dam Disaster
Open the record for dataset details and reuse information.
Data for: Spatial-temporal analysis of natural hazards and disasters in the Greater Horn of Africa between 2010 and 2024 to inform disaster risk reduction, and surveillance and control strategies for climate and environmentally sensitive diseases
Open the record for dataset details and reuse information.
Introgression dynamics from invasive pigs into wild boar following the March 2011 natural and anthropogenic disasters at Fukushima
Open the record for dataset details and reuse information.
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.