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24 results for “UNITE database”
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.).</p> <p>Author Information</p> <p> A. Author<br> Name: Carlos Andrés Araiza Iturria<br> Email: caraizai@uwaterloo.ca<br> <br> B. Co-author<br> Name: Mary Hardy<br> Email: mary.hardy@uwaterloo.ca</p> <p> C. Co-author<br> Name: Paul Marriott<br> Email: pmarriott@uwaterloo.ca<br> <br> Institution: University of Waterloo<br> Address: 200 University Ave W, Waterloo, ON N2L 3G1</p> <p><br> Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015.</p> <p>The Python scripts to create the database can be directly accessed through related identifiers in this page.</p> <p>Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.</p> <p> </p>
United States Flood Database
<p>This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.</p> <p>Description of filenames:</p> <p>1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.</p> <p>Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code</p> <p>2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity</p> <p>3. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx?versionId=b7c2ba8d-1aad-4268-9f0b-cdadd95b38c3">emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx</a> – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.</p> <p>Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI</p> <p>4. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/extracted_events_NOAA.csv?versionId=3c47db6f-f908-4c04-93c4-62afb5f8a68f">extracted_events_NOAA.csv</a> – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE<strong> </strong></p> <p>5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.</p> <p>Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry</p> <p>6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.</p> <p>Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection</p> <p>7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.</p> <p>Data attributes: id, obtime, category, description, description_id, lon, lat</p> <p>8. USFD_v1.1.csv – A merged United States Flood Database from 1900 to the present (UPDATED)</p> <p>Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.</p> <p>Details of attributes:</p> <p>DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS</p> <p>DATE_END: end datetime of an event. yyyymmddHHMMSS</p> <p>DURATION: duration of an event in hours</p> <p>LON: longitude in degrees</p> <p>LAT: latitude in degrees</p> <p>COUNTRY: United States of America</p> <p>STATE: US state name</p> <p>AREA: affected areas in km^2</p> <p>FATALITY: number of fatalities</p> <p>DAMAGE: economic damages in US dollars</p> <p>SEVERITY: event severity, (1/1.5/2) according to DFO.</p> <p>SOURCE: flood information source.</p> <p>CAUSE: flood cause.</p> <p>SOURCE_DB: source database from item 1-7.</p> <p>SOURCE_ID: original ID in the source database.</p> <p>DESCRIPTION: event description</p> <p>SLOPE: calculated slope based on SRTM DEM 90m</p> <p>DEM: Digital Elevation Model</p> <p>LULC: Land Use Land Cover</p> <p>DISTANCE_RIVER: distance to major river network in km,</p> <p>CONT_AREA: contributing area (km^2), from MERIT Hydro</p> <p>DEPTH: 500-yr flood depth</p> <p>YEAR: year of the event.</p> <p>9. attribution_table.xlsx – description of each database, and URLs are provided to retrieve these databases.</p> <p>The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.</p> <p>If you intend to use this dataset, please cite our description paper:</p> <p>Li, Z., Chen, M., Gao, S., Gourley, J. J., Yang, T., Shen, X., Kolar, R., and Hong, Y.: A multi-source 120-year US flood database with a unified common format and public access, Earth Syst. Sci. Data, 13, 3755–3766, https://doi.org/10.5194/essd-13-3755-2021, 2021.</p> <p> </p>
Fire history database of the western United States, 1994
To create a database of existing published and unpublished tree-ring reconstructions of fire regimes in forested areas, before circa 1900, west of 100 W longitude in the continental United States, exclusive of Alaska. The studies included in the database are restricted to tree-ring reconstructions of fire history and the information extracted includes citations to the data sources, site information, estimated fire regimes, and information on individual fire events (when readily available). Fire regimes vary greatly across short distances in the western United States, so that a reconstruction of fire history over a small area may not represent the history of a larger area. Therefore, we extracted information on the size of the study area and the amount of fire evidence (number of trees scarred and/or number of tree origin dates) used in computing the fire regimes to allow the user to gauge the applicability of each reconstruction to larger areas.
The conservation burden of Intact Forest Landscapes (IFLs): A global database of management units and IFLs
<p><strong>Introduction</strong></p> <p>This dataset includes a global overview of publicly available forest Management Units (MUs), Intact Forest Landscapes (IFLs) and their overlap. This includes both the boreal forests of Canada and Russia, and the tropical forests in the Amazon, the Congo basin, South-East Asia. The dataset was developed for the paper "Feasibility and effectiveness of global Intact Forest Landscape protection through forest certification: The conservation burden of Intact Forest Landscapes" by Zwerts et al. (2024). A comprehensive list of MUs with % and absolute overlap with IFLs is presented in Table S1 of Zwerts et al. (2024).</p> <p><strong>Data collection</strong></p> <p>We collected and collated all publicly available MU and IFL data of Central Africa, Southeast Asia, the Amazon, and of the boreal forests in Canada and Russia. As such, we included MU data from Cameroon, Canada, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, the Republic of Congo and Russia. Together, these forests comprise the majority of all IFLs (Potapov et al., 2017). We utilized the 2020 intact forest landscape (IFL) dataset generated by Potapov et al. (2017). Both FSC-certified and non-FSC MUs were considered and FSC-certification status data was collected using the FSC public dashboard (FSC, 2023). All data was collected in March 2023. Our dataset is not exhaustive. To our knowledge, not all MU data is publicly available. For Southeast Asia no public MU data is available for Papua New Guinea and Peninsular Malaysia. For the Amazon, insufficient public MU data was available to create an accurate representation of the situation. This area was excluded from the main analysis in Zwerts et al., 2024. We included a distinction between FSC-certified and non-FSC MUs in Russia, even though the FSC has withdrawn all certificates in Russia in April 2023 following the invasion of Ukraine. We chose to retain the distinction between FSC and non-FSC MUs for the Russian data because of the uncertainty of the current situation and the significant influence of FSC-certification in the Russian management of IFLs.</p> <p><strong>Overlap analysis</strong></p> <p>All area was transformed to geodesic distance. Furthermore, several MU names were altered because of duplicate names. The number of hectares of MUs that overlap with IFLs was calculated in ArcGIS Pro 3.0.0, using the WGS_1984_Web_Mercator_Auxiliary_Sphere coordinate system. Using the intersect and multipart to singlepart tools every overlap fragment was isolated. For the results in Zwerts et al. (2024) the total overlap and the percentage of overlap was calculated in R. </p> <p><strong>Abstract of the related article</strong></p> <p>Intact Forest Landscapes (IFLs) are defined as forested areas of at least 500 km2 that show no signs of remotely sensed human activity. They are considered to be of high conservation value due to their role in maintaining biodiversity and mitigating climate change. In 2014, the members of the Forest Stewardship Council (FSC), one of the major global certification schemes for responsible forest management, took a conservation stand by restricting logging in FSC-certified IFLs. However, this move raised concerns about the economic viability of FSC-certified logging in these areas. To address these challenges, in 2022, FSC proposed an integrated landscape approach, considering local conditions and stakeholders' needs to balance IFL protection, economic sustainability, and community interests. Here, we leverage publicly available management unit (MU) data, to provide a global quantitative overview of IFLs designated for timber production. We use the concept of 'conservation burden' for the extent that MUs overlap with IFLs, representing the impact that IFL protection has on forest management operations if logging is disallowed. Our data indicates that currently FSC-certified MUs affect 0.6% of global IFLs. Too restrictive policies for logging in IFLs may discourage FSC-certification in global IFLs. Considering the environmental and social benefits of FSC certification, it warrants careful examination whether the benefits of protecting a limited subset of FSC-certified IFLs outweighs the cost of potentially reduced growth of the total FSC-certified area. Our data can provide a basis to facilitate stakeholder engagement for landscape-level IFL management.</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>The parameter estimates along with their 90% confidence intervals obtained for the 20 multinomial logistic regressions are shown here in two presentations. In 'Covariate trends' we show the annual trends for the 20 years of data by type of covariate. In 'Covariates magnitude for each year', we show for each year the magnitude that each covariate has in contrast with the other 23 covariates (the intercept is not included due to scaling issues).</p> <p>All parameter estimates and their confidence intervals can be found in a table format in 'allparameters.csv'.</p>
Text-fig. 3. MN 12 localities in the NOW database (The NOW Community 2019). Note that the unit is not represented in major parts of Europe. in Generically Speaking, A Survey On Neogene Rodent Diversity At The Genus Level In The Now Database
Text-fig. 3. MN 12 localities in the NOW database (The NOW Community 2019). Note that the unit is not represented in major parts of Europe.
Porous single crystal unit-cell simulation database for ductile fracture by void growth and coalescence
<p>Ductile fracture through void growth to coalescence occurs at the grain scale in numerous metallic alloys encountered in engineering applications. In order to perform mechanical homogenization of porous single crystals, a database of porous single crystal unit-cell simulation results has been gathered through Finite Element Modeling and Fast-Fourrier Transform simulations, respectively performed on Z-set and Amitex_FFTP. In these simulations, a cubic unit-cell with a unique central spherical void undergo axisymmetric mechanical loading. Mechanical simulations are performed within finite strain theory. Input parameters of interest are stress triaxiality, crystallographic orientation, initial porosity and strain hardening law type; results include macroscopic stress, macroscopic deformation gradient, porosity, void aspect ratio, ligament size and cell aspect ratio.</p>
The Western United States MTBS-Interagency Database of Large Wildfires, 1984–2024 (WUMI2024a)
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A database of Defra statutory biodiversity metric unit values for terrestrial habitat samples across England, with plant, butterfly and bird species data
<p>Policies requiring biodiversity no net loss or net gain as an outcome of environmental planning have become more prominent worldwide, catalysing interest in biodiversity offsetting as a mechanism to compensate for development impacts on nature. Offsets rely on credible and evidence-based methods to quantify biodiversity losses and gains. Following the introduction<span> of the United Kingdom's Environment Act in November 2021, all new developments requiring planning permission in England are expected to demonstrate a 10% biodiversity net gain from 2024, calculated using the statutory biodiversity metric framework (Defra, 2023). </span><span>The metric is used to calculate both baseline and proposed post-development biodiversity units, and is </span>set to play an increasingly prominent role in nature conservation nationwide.<span> </span><span>The metric has so far </span>received limited scientific scrutiny.</p> <p><span>This dataset comprises a database of statutory biodiversity metric unit values for terrestrial habitat samples across England. For each habitat sample, we present </span><span>biodiversity units alongside five long-established single-attribute proxies for biodiversity (</span><span>species richness, individual abundance, number of threatened species, mean species range or population, mean species range or population change)</span><span>. </span><span>Data were compiled </span><span>for species from three taxa (vascular plants, butterflies, birds), from sites across England. The dataset includes 24 sites within </span>grassland, wetland, woodland and forest, sparsely vegetated land, cropland, heathland and shrub, i.e. <span>all terrestrial broad habitats except urban and individual trees. Species data were reused from long-term ecological change monitoring datasets</span> (mostly in the public domain), whilst biodiversity units were calculated following field visits. Fieldwork was carried out in April-October 2022 to calculate biodiversity units for the samples. <span>Sites were initially assessed using metric version 3.1, which was current at the time of survey, and were subsequently updated to the statutory metric for analysis using field notes and species data. </span>Species data <span>were derived from </span>24 <span>long-term ecological change monitoring</span> sites across the Environmental Change Network (ECN), Long Term Monitoring Network (LTMN) and Ecological Continuity Trust (ECT), collected between 2010 and 2020.</p>
Database on 16 reinforced concrete walls with lap splices and 8 reference units with continuous reinforcement (V2 models included)
<p>Recent post-earthquake missions have shown that both old and code-compliant reinforced concrete wall buildings can experience critical damage due to lap splices, which led to a recent surge in experimental tests of walls with such constructional detail. Most of the 16 walls with lap splices described in the literature thus far were carried out in the last five years. A database with these wall tests, plus 8 reference unit walls with continuous reinforcement, is herein provided alongside with the shell element models developed in Vector2 to simulate their inelastic F-D response</p> <p><strong>If the experimental data or the models are used, please cite:</strong></p> <p>- J.P. Almeida, O. Prodan, D. Tarquini, K. Beyer, 2017. "<em>Influence of lap-splices on the cyclic inelastic response of<br> reinforced concrete walls. I: Database assembly, recent experimental data, and findings for model development</em>", ASCE<br> Journal of Structural Engineering 143 (12), DOI: 10.1061/(ASCE)ST.1943-541X.0001853.</p> <p>- D. Tarquini, J.P. Almeida, O. K. Beyer, 2017. <em>"Influence of lap-splices on the cyclic inelastic response of reinforced<br> concrete walls. II: Shell element simulation and equivalent uniaxial model</em>", ASCE Journal of Structural Engineering 143<br> (12), DOI: 10.1061/(ASCE)ST.1943-541X.0001859.</p>
FIG. 3 in Thermal Traits of Anurans Database for the Southeastern United States (TRAD): A Database of Thermal Trait Values for 40 Anuran Species
FIG. 3. Adult trait completeness, or number of traits with at least one trait value in the literature, varies among and within anuran genera (A) and families (B). Each y-axis is ordered by the group with the highest trait completion to group with the lowest trait completion. Points represent individual species. Boxplots indicate standard delineations of median, 25th, and 75th percentiles, and lines indicate the lesser of largest or smallest values or 1.5 times the interquartile range.
FIG. 2 in Thermal Traits of Anurans Database for the Southeastern United States (TRAD): A Database of Thermal Trait Values for 40 Anuran Species
FIG. 2. Species' thermal trait data, measured as sources with unique species and trait combinations (points), have increased since 1945.
FIG. 1 in Thermal Traits of Anurans Database for the Southeastern United States (TRAD): A Database of Thermal Trait Values for 40 Anuran Species
FIG. 1. Counts of adult thermal trait values found within the literature for 37 species of frogs and toads (anurans) within the southeastern United States. Species Pseudacris brimleyi, Pseudacris nigrita, and Pseudacris ocularis are not shown due to no trait values reported. States indicated in gray in the inset map of the conterminous United States are considered the southeastern United States for this database. Trait Name is ordered based on type of trait: warm colored traits are mass and physiological traits, and cool colored traits are behavioral traits. Physiological traits include critical thermal maximum (CTmax), critical thermal minimum (CTmin), and Tpref (preferred temperature). Behavioral traits include basking temperature (Tbask), foraging temperature limits (Tforage_min and Tforage_max), emergence temperature (Tmerge), and activity. An * indicates a species of conservation concern. Conservation status, as defined by the International Union for the Conservation of Nature Red List, was determined on 15 August 2020 (International Union for Conservation of Nature, 2017).
FIG. 4 in Thermal Traits of Anurans Database for the Southeastern United States (TRAD): A Database of Thermal Trait Values for 40 Anuran Species
FIG. 4. The number of traits with at least one trait value in the literature (trait completeness) increases with range size for adults (A) and for all life stages (B) for 37 anurans native to the southeastern United States. Maximum trait completeness is 9 for adults and 22 for all life stages. Each point represents a species that has at least one trait value in the TRAD database, with the symbol and shade in (B) representing the total number of life stages (egg/embryo, tadpole, metamorph, juvenile, and adults) with trait data.
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database
<p><b>Objective:</b> The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized.We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.</p> <p><b>Design: </b>A retrospective cohort study.</p> <p><b>Setting and Participants: </b>Data were extracted from the MIMIC-III database. Data on 1,177 heart failure patients were analysed.</p> <p><strong>Methods</strong>: Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation and validation groups. Independent risk factors for in-hospital mortality were screened using XGBoost and LASSO regression models in the derivation sample. Multivariable logistic regression analysis was used to build prediction models. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.</p> <p><b>Results:</b> Among the 1,177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (P<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.</p> <p><b>Conclusions</b><b>:</b> Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.</p>
Database of Building Units Generated by mBUD
<p>The building unit (BU) <a href="https://cnislab.com/mbud/database">database</a> consists of all the unique BU extracted from the subset Computation-Ready, Experimental (CoRE) MOF 2019-ASR database. The subset of CoRE database consists of 9,268 MOF structures. We extracted 2,580 BUs ( including metal nodes, and organic linkers) from this subset. The provided database comprises both the experimental and computational BUs. Experimental BUs are essential for the visualization of the MOF chemistry. On the other hand, the computational BUs can be readily employed to construct MOF crystals computationally.</p>
Intensive Care Unit Activity in France From the National Database Between 2013 and 2019
ClinicalTrials.gov study NCT05353023. IPD Sharing: NO. Countries: 1. Publications: 1.
Development of an Early Warning Model for Intensive Care Unit-Acquired Weakness in Mechanically Ventilated Children: A Disease-Specific Cohort and Database Study
ClinicalTrials.gov study NCT07150637. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database
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A database of Defra statutory biodiversity metric unit values for terrestrial habitat samples across England, with plant, butterfly and bird species data
Open the record for dataset details and reuse information.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.