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48 results for “flood risk”
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
First Street Foundation Property Level Flood Risk Statistics V1.3
<p>The property level flood risk statistics generated by the First Street Foundation Flood Model Version 1.3 come in CSV format. The data that is included in the CSV includes:</p> <ul> <li> <p>An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.</p> </li> <li> <p>The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.</p> </li> <li> <p>The property’s Flood Factor as well as data on economic loss.</p> </li> <li> <p>The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms in 2021, 2036, and 2051.</p> </li> <li> <p>Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for years 2021, 2036, and 2051.</p> </li> <li> <p>Information on historical events and flood adaptation, such as ID and name.</p> </li> </ul> <p>You can download a sample of the property level flood risk statistics generated by First Street's Flood Model on this page. You can purchase the property level data for areas within the contiguous United States on the First Street website <a href="https://firststreet.org/data-access/paid-access/?utm_source=Property_Statistics&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component">here</a>. You can find the data dictionary which breaks down the data that is available with each property-level data purchase <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Property_Statistics&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>. If you are also interested in the hazard layers, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Property_Statistics&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo">here</a>.</p>
First Street Foundation Property Level Flood Risk Statistics V2.0
<p>The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format. </p> <p>The data that is included in the CSV includes:</p> <ul> <li> <p>An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.</p> </li> <li> <p>The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.</p> </li> <li> <p>The property’s Flood Factor as well as data on economic loss.</p> </li> <li> <p>The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.</p> </li> <li> <p>Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.</p> </li> <li> <p>Information on historical events and flood adaptation, such as ID and name.</p> </li> </ul> <p> </p> <p>This dataset includes <a href="https://firststreet.org/">First Street</a>'s aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various <a href="https://floodfactor.com/">Flood Factor</a> risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.</p> <p>If you are interested in acquiring First Street flood data, you can request to access the data <a href="https://firststreet.org/data-access/paid-access/?utm_source=Summary_Statistics_v1.3&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component">here</a>. More information on First Street's flood risk statistics can be found <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-data-dictionaryv2/">here</a> and information on First Street's hazards can be found <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Summary_Statistics_v1.3&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo">here</a>.</p> <p>The data dictionary for the parcel-level data is below.</p> <table> <tbody> <tr> <td> <p><strong>Field Name</strong></p> </td> <td> <p><strong>Type</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>fsid</p> </td> <td> <p>int</p> </td> <td> <p>First Street ID (FSID) is a unique identifier assigned to each location</p> </td> </tr> <tr> <td> <p>long</p> </td> <td> <p>float</p> </td> <td> <p>Longitude</p> </td> </tr> <tr> <td> <p>lat</p> </td> <td> <p>float</p> </td> <td> <p>Latitude</p> </td> </tr> <tr> <td> <p>zcta</p> </td> <td> <p>int</p> </td> <td> <p>ZIP code tabulation area as provided by the US Census Bureau</p> </td> </tr> <tr> <td> <p>blkgrp_fips</p> </td> <td> <p>int</p> </td> <td> <p>US Census Block Group FIPS Code</p> </td> </tr> <tr> <td> <p>tract_fips</p> </td> <td> <p>int</p> </td> <td> <p>US Census Tract FIPS Code</p> </td> </tr> <tr> <td> <p>county_fips</p> </td> <td> <p>int</p> </td> <td> <p>County FIPS Code</p> </td> </tr> <tr> <td> <p>cd_fips</p> </td> <td> <p>int</p> </td> <td> <p>Congressional District FIPS Code for the 116th Congress</p> </td> </tr> <tr> <td> <p>state_fips</p> </td> <td> <p>int</p> </td> <td> <p>State FIPS Code</p> </td> </tr> <tr> <td> <p>floodfactor</p> </td> <td> <p>int</p> </td> <td> <p>The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)</p> </td> </tr> <tr> <td> <p>CS_depth_RP_YY</p> </td> <td> <p>int</p> </td> <td> <p>Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00</p> </td> </tr> <tr> <td> <p>CS_chance_flood_YY</p> </td> <td> <p>float</p> </td> <td> <p>Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00</p> </td> </tr> <tr> <td> <p>aal_YY_CS</p> </td> <td> <p>int</p> </td> <td> <p>The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low</p> </td> </tr> <tr> <td> <p>hist1_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to a historic storm event modeled by First Street</p> </td> </tr> <tr> <td> <p>hist1_event</p> </td> <td> <p>string</p> </td> <td> <p>Short name of the modeled historic event</p> </td> </tr> <tr> <td> <p>hist1_year</p> </td> <td> <p>int</p> </td> <td> <p>Year the modeled historic event occurred</p> </td> </tr> <tr> <td> <p>hist1_depth</p> </td> <td> <p>int</p> </td> <td> <p>Depth (in cm) of flooding to the building from this historic event</p> </td> </tr> <tr> <td> <p>hist2_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to a historic storm event modeled by First Street</p> </td> </tr> <tr> <td> <p>hist2_event</p> </td> <td> <p>string</p> </td> <td> <p>Short name of the modeled historic event</p> </td> </tr> <tr> <td> <p>hist2_year</p> </td> <td> <p>int</p> </td> <td> <p>Year the modeled historic event occurred</p> </td> </tr> <tr> <td> <p>hist2_depth</p> </td> <td> <p>int</p> </td> <td> <p>Depth (in cm) of flooding to the building from this historic event</p> </td> </tr> <tr> <td> <p>adapt_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to each adaptation project</p> </td> </tr> <tr> <td> <p>adapt_name</p> </td> <td> <p>string</p> </td> <td> <p>Name of adaptation project</p> </td> </tr> <tr> <td> <p>adapt_rp</p> </td> <td> <p>int</p> </td> <td> <p>Return period of flood event structure provides protection for when applicable</p> </td> </tr> <tr> <td> <p>adapt_type</p> </td> <td> <p>string</p> </td> <td> <p>Specific flood adaptation structure type (can be one of many structures associated with a project)</p> </td> </tr> <tr> <td> <p>fema_zone</p> </td> <td> <p>string</p> </td> <td> <p>Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders</p> </td> </tr> <tr> <td> <p>footprint_flag</p> </td> <td> <p>int</p> </td> <td> <p>Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)</p> </td> </tr> </tbody> </table> <p> </p>
Longitudinal data to explore changes in flood risk awareness and preparedness
<p>This upload includes two different longitudinal datasets. The Panel datasets includes two rounds of surveys where the same individuals were interviewed. The Repeated Cross-Sectional includes two rounds of surveys where different individuals were interviewed in each round. The first survey round is the same in the two datasets. Data were collected in the municipality of Negrar (VR), in North-eastern Italy in February 2019 and in February 2020, following a flash flood which occurred in September 2018. </p>
Data to support the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia"
<p>This dataset supports the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia". This dataset contains the delineated boundaries for 24 refugee camps in Ethiopia. Also included are refugee camp building footprint data (where available). All datasets are in shapefile format.</p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
Analyzing the sensitivity of a flood risk assessment model towards its input data, twelve damage scenarios
<p>This dataset contains the output shapefiles of twelve different risk assessment scenarios for the case study of Annotto Bay, Jamaica. These assessments were performed in the context of the research 'Analyzing the sensitivity of a flood risk assessment model towards its input data', published in the journal Natural Hazards and Earth System Sciences. More information on the input data and methodology can be found in this paper.</p>
Flash Flood Risk Map Hallstatt / Gosau / Bad Goisern: Risk Map
Damage Risk Map of FFRM catchment Hallstatt / Gosau / Bad Goisern based on max. water depth in all timesteps, zoning plan, building density and specific damage function
Compound Flood Risk Guidelines test data
<p>Time series data of flood drivers (discharge, rainfall, coastal water levels) for various case studies in support of compound fllood risk guidelines.</p> <p>version 1: data for Charleston, NC, USA & BrisBane, AUS.</p> <p>version 2; added data for Toamasina, MDG</p> <p>version 3: consistent file structure</p> <p>version 4: reduce coastal time series to 30min temporal resolution</p>
Repo for Compound Continental Risk of Multiple Extreme Floods in the United States
Open the record for dataset details and reuse information.
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
Data from the stochastic flood study of the Area of Special Flood Risk (ARPSI in Spanish) of Zamora, Spain.
<p>The published data are part of the stochastic flood study of the city of Zamora, where the different uncertainties affecting the hydraulic model are considered in order to obtain a series of stochastic maps with important implications for flood risk management.</p> <p>HEC-RAS 2D has been used for the flood study and Python has been used to automate the stochastic analyses and modeling.</p> <p>The data corresponds to:<br> - Model inputs: the basic files to generate the model, the hydraulic model of HEC-RAS 2D and all the data related to the stochastic sampling of the procedure are considered as inputs.<br> - Model Outputs: Outputs are considered to be the results obtained in the different phases of the stochastic analysis (convergence maps, sensitivity maps and stochastic maps).</p>
Estimating household preferences for coastal flood risk mitigation policies under ambiguity
<p>Risk mitigation policies (like dike rising) are essential to address increasing coastal flood risks due to global warming. Furthermore, the optimal level of risk mitigation policy should be determined by public preferences for risk reduction. However, it is difficult to reveal public preferences for coastal flood risk reduction because projections of coastal flood risks inevitably involve uncertainty. This study aims to estimate household preference for coastal flood reduction under ambiguity and multiple projections of coastal flood risks. By coupling storm surge inundation simulations and stated preference experiments with decision models, we estimate the expected loss reduction, risk premium, and ambiguity premium for coastal flood risk mitigation policies. Results of the study show that ignoring the ambiguity premium causes significant undervaluation of coastal flood risk mitigation, and the ambiguity premium stems from households' over-concern about the worst projection, which may lead to an over-allocation of resources to prevent inundation damage caused from the worst-case flood before a disaster. The study concludes that a risk mitigation policy combining public insurance for the worst projection and pre-disaster prevention measures can be effective and efficient.</p>
Supporting data for "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV"
<p>Supporting data for the paper "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV".</p> <p><a href="https://doi.org/10.1016/j.cliser.2023.100395">https://doi.org/10.1016/j.cliser.2023.100395</a></p>
philip928lin/Flood-Risks-of-Cyber-physical-Attacks-in-a-Smart-Storm-Water-System: Flood Risks of Cyber-physical Attacks in a Smart Storm Water System
<p>This is the code archive for the publication "Flood Risks of Cyber-physical Attacks in a Smart Storm Water System" in Water Resources Research.</p>
Estimating household preferences for coastal flood risk mitigation policies under ambiguity
Open the record for dataset details and reuse information.
Data from: "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in non-stationary climate with changing risk of flooding"
<p>In the paper "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in a non-stationary climate with changing risk of flooding", life cycle assessment is used to compare two ways to maintain the state of a coastal urban area in a changing climate with increasing flood risk. On one side, the construction of a dike, a hard and proactive scenario, is modeled using a bottom-up approach. On the other, the systematic repair of houses flooded by sea surges, a post-disaster measure, is assessed using a Monte Carlo simulation allowing for aleatory uncertainties in predicting future sea level rise and occurrences of extreme events. Two metrics are identified, normalized mean impacts and probability of dike being most efficient. The methodology is applied to three case studies in Denmark representing three contrasting areas, Copenhagen, Frederiksværk, and Esbjerg. For all case studies the distribution of the calculated impact of repairing houses is highly right skewed, which in some cases has implications for the comparative LCA. </p><p>This dataset contains the underlying data to support the findings of the paper. In particular, two sets of characterized environmental impacts are reported: (1) the impacts of flood-related repairs summed over a century, for each Monte Carlo simulation and (2) the impacts of building a dam. Both sets of results are reported for each of the three cities studied.</p>
Current and Future Flood maps for Flood risk assessment under the Shared Socioeconomic Pathways in the Greater Accra region, Ghana
<p>The study used 15 flood conditioning factors in simulating current and future flood conditions under the SSP scenarios using the Frequency Ratio (FR) model </p>
Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta: supporting data - high water levels
<p>This dataset contains modelled high water levels in the Guayas delta supporting the paper 'Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta' published in HESS 2024.</p> <p>The dataset is organised through two folders:</p> <ul> <li>mangroves: all data from the scenarios with mangroves included in the domain</li> <li>no_mangroves: all data from the scenarios without mangroves included in the domain</li> </ul> <p>Each folder is further divided along 6 subfolders:</p> <ul> <li>I: El Niño Ocean & river</li> <li>II: El Niño Ocean</li> <li>III: El Niño river</li> <li>IV: Neutral</li> <li>I_50per: 50 % increase in seaward El Niño anomalies</li> <li>I_150per: 150 % increase in seaward El Niño anomalies</li> </ul> <p>Each folder contains two files:</p> <ul> <li>vars.csv - each row represents a mesh node and there are three columns: <ul> <li>X: x value of the model mesh node [m]</li> <li>Y: y value of the model mesh node [m]</li> <li>HIGH_WATER: high water level [m]</li> </ul> </li> </ul>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
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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.