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2,322 results for “precipitations”
Fig. 2 in Gastrointestinal parasite infestation in the alpine mountain hare (Lepus timidus varronis): Are abiotic environmental factors such as elevation, temperature and precipitation affecting prevalence of parasite species?
Fig. 2. Number of parasite types per faeces and severity of parasitic infestation. Correlation between number of parasite types per Alpine mountain hare faeces and severity of parasitic infestation (n = 28) found in Vorarlberg (Austria) during the years 2014 and 2015. Count visualises the number of faecal samples. The severity of infestation is indicated by scattered ((+)), low (+), intermediate (++), and high (+++) infestation. See text for details on statistics.
Fig. 1 in Larval pheromone disrupts pre-excavation aggregation of Cactoblastis cactorum (Lepidoptera: Pyralidae) neonates precipitating colony collapse
Fig. 1. Percent survival of caterpillars in cohorts of Cactoblastis cactorum on plants sprayed with caterpillar extract (gray bar), solvent-only (white bar), or unsprayed (black bar) for 4 separate experiments. Experiment 1 = laboratory study; experiment 2 = greenhouse study; experiment 3 = field study 1; experiment 4 = field study 2.
Fig. 2 in Plague transforms positive effects of precipitation on prairie dogs to negative effects
Fig. 2. Relationship between visual count changes in prairie dogs (Cynomys spp.) and annual precipitation (cm) on plots without plague management and with plague management by treating burrows with deltamethrin dust for flea control. Population change (λ) was indexed by visual counts conducted in mid-summer of adults plus juveniles, and annual precipitation was cumulative during the 12-month period prior to the typical date of the second count (interval of 1 July-30 June). Visual counts are presented as treated in the analysis (re-scaled λ, natural log transformed), although the repeated measures analysis retained the pairings of treatments that cannot be illustrated here. Points above the dashed line indicate population increases; points below the dashed line indicate population declines with points on the zero-line indicating population collapse to 0 animals.
Fig. 1 in Plague transforms positive effects of precipitation on prairie dogs to negative effects
Fig. 1. Study sites in the western United States where the influence of precipitation on prairie dog population change was evaluated on paired plots with and without deltamethrin treatment to control the flea vectors of plague. Prairie dog sketch by D. Crawford.
Precipitation hydrogen isoscape for East China from 1969 to 2017 generated based on data fusion of iGCMs simulations
<p>The dataset includes the stable hydrogen isotope of precipitation for East China over the 1969-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. This dataset was built based on the Convolutional Neural Network (CNN) method, fusing observations and isotope-equipped general circulation models (iGCMs) simulations of hydrogen isotope composition. Some physical-based ancillary data are also introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion.</p>
Data for "On the atomic structure of the β′′ precipitate by density functional theory"
<p>The dataset contains the DFT results which is the basis for the results and discussions in the related article, "On the atomic structure of the β′′ precipitate by density functional theory". The details of the DFT calculations are written in the article.</p> <p>The names of the OUTCAR files in enthalpy_study_OUTCARS.tar.gz are more or less self-explanatory, at least within the context of the journal article. The KPOINT tests have the following format for the KPOINTS "XYZ" where X is always a single digit, Y is first to get a double-digit, while Z gets a double-digit second. The max distance in reciprocal space is thus not a constant as the OUTCAR files would suggest.</p> <p> </p> <p>The LET_DATA is the linear-elastic theory displacement-field as explained in the article for different aspect ratios of the precipitate eye structure.</p>
Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons
<p>Figures, data, and code used in my paper describing "Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons"</p>
Relativistic Electron Precipitation Events (driven by waves or field line scattering) from POES 2-second data
<p>This repository contains the list of relativistic electron precipitation from POES data since 2012.</p> <p>Please refer to the read_me file for further details.</p> <p> </p> <p><strong>You are free to use this for your research. However, before doing so, please contact Luisa Capannolo at luisacap@bu.edu.</strong></p> <p> </p> <p>This dataset is associated with the paper under review titled "Properties of Relativistic Electron Precipitation: A Comparative Analysis of Wave-Induced and Field Line Curvature Scattering Processes" by Capannolo, Staff, Li, Duderstadt, Sivadas, Petitt, Elliot, Qin, Shen, and Ma.</p>
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Dataset for "GPTCast: a weather language model for precipitation nowcasting"
<p>Dataset for "<em><strong>GPTCast: a weather language model for precipitation nowcasting</strong></em>"</p> <ul> <li>Preprint: <a href="https://arxiv.org/abs/2407.02089">https://arxiv.org/abs/2407.02089</a></li> <li>Code: <a href="https://github.com/DSIP-FBK/GPTCast">https://github.com/DSIP-FBK/GPTCast</a></li> <li>Pretrained models: <a href="https://doi.org/10.5281/zenodo.13594332">https://doi.org/10.5281/zenodo.13594332</a></li> </ul> <p>Version 2 of this dataset contains also the "Forecaster Test Set" (fts.tar) which includes all generated forecasts for GPTCast8x8, GPTCast16x16, and Linda, to ensure reproducibility of the results.</p>
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </p>
Precipitation and Temperature data from the on-site weather station at the Rüdersdorf limestone quarry near Berlin, Germany, from January 2023 to February 2024
<p>The weather data from the on site station is provided as Temperature [°C] every 5 minutes and the daily sums of Precipitation [mm]. </p>
Asynchronous changes in precipitation and soil water content decelerate alpine vegetation greening
<p>data for "Asynchronous changes in precipitation and soil water content decelerate alpine vegetation greening".</p>
Monthly precipitation intensity maxima for 14 aggregation times at 132 stations in Germany
<p>This dataset contains monthly precipitation intensity maxima for 14 aggregation times at 132 stations in Germany that serve as the basis of the study<em> Modeling seasonal variations of extreme rainfall on different time scales in Germany.</em></p> <p> </p> <p><strong>Generation of the dataset:</strong><br> We use precipitation measurements at 132 stations in Germany that provide a temporal resolution of one minute. The majority (129) of these stations are operated by the German Meteorological Service (DWD) and were obtained via ftp://ftp-cdc.dwd.de/climate_environment/CDC/observations_germany/climate. The available time series at these stations range from 19 to 28 years. Additionally we use three stations operated by the Wupperverband (https://www.wupperverband.de) with time series of more than 43 years.</p> <p>The observations were accumulated to the following durations: <span class="math-tex">\(d \in 2^{\left\lbrace 0,1,2,..,13 \right\rbrace}\,\text{min} = \left\lbrace 1,2,4,...,8192 \right\rbrace\,\text{min}\)</span>. Thus, resulting in 14 time series per station.</p> <p> </p> <p><strong>Files and variables</strong></p> <p>meta_data_seasonal_variations_IDF_germany.csv</p> <ul> <li>Meta information about the stations</li> <li>Variables: station name, station id (as provided by DWD), position (longitude, latitude), length of timeseries available</li> <li>Variable names: "StationName", "StationID", "Longitude", "Latitude", "NumberYears"</li> </ul> <p>monthly_maxima_seasonal_variations_IDF_germany.csv</p> <ul> <li>Monthly maxima for different durations (aggregation times)</li> <li>Variables: station id (as in meta file), year and month of observation, duration [h], observed monthly intensity maximum [mm/h]</li> <li>Variable names: "StationID", "Year", "Month", "Duration [h]", "MonthlyIntensityMaximum [mm/h]"</li> </ul> <p> </p> <p><strong>Abstract of the study</strong></p> <p>We model monthly precipitation maxima at 132 stations in Germany for a wide range of durations from one minute to about six days using a duration-dependent generalized extreme value (d-GEV) distribution with monthly varying parameters. This allows for the estimation of both monthly and annual intensity--duration--frequency (IDF) curves:<br> (1) The monthly IDF curves are steeper in summer and exhibit higher intensities for short durations than in the rest of the year. Thus, everywhere in Germany short convective extreme events occur very likely in summer. In contrast, extreme events with a duration of several hours up to about one day are more likely to occur within a longer period or even spread throughout the whole year, depending on the station. There are major differences within Germany with respect to the months in which long-lasting stratiform extreme events are more likely to occur. At some stations the IDF curves (for a given quantile) for different months intersect. The meteorological interpretation of this intersection is that the season at which a certain extreme event is most likely to occur shifts from summer towards autumn or winter for longer durations.<br> (2) We compare the annual IDF curves resulting from the monthly model with those estimated conventionally, that is, based on modeling annual maxima. We find that adding information in the form of smooth variations during the year leads to a considerable reduction of uncertainties. We additionally observe that at some stations, the annual IDF curves obtained by modeling monthly maxima deviate from the assumption of scale invariance, resulting in a flattening in the slope of the IDF curves for long durations.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors would like to thank the Wupperverband, and in particular Marc Scheibel, as well as the Climate Data Center of the DWD, for providing and maintaining the precipitation time series.</p>
Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards
<p>The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">here</a>.</p> <p>The two following variables are extracted from the product:</p> <ul> <li> <p>Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell.</p> </li> <li> <p>Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell.</p> </li> </ul> <p>Importation of the raw data</p> <p>Input data is divided into 4 files for each variables representing 4 periods:</p> <ol> <li>1979-1986</li> <li>1987-1997</li> <li>1998-2008</li> <li>2009-2019</li> </ol> <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> <p>Intermediary data</p> <p>Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> <ul> <li> <p>allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster.</p> </li> <li> <p>99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell)</p> </li> <li> <p>interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”.</p> </li> <li> <p>metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”.</p> </li> <li> <p>rainPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing precipitation data for the period X.</p> </li> <li> <p>rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”.</p> </li> <li> <p>timePX: [vector] contain vectors of time for the 4 periods.</p> </li> <li> <p>windPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing wind gust data for the period X.</p> </li> </ul> <p>Output data</p> <p>Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> <ul> <li> <p>CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified</p> </li> <li> <p>_hdat: [data.frame] hourly data of precipitation and wind gust clusters.</p> </li> <li> <p>_ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX.</p> </li> <li> <p>_meta:[data.frame] metadata for wind gust and precipitation clusters</p> </li> <li> <p>stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters</p> </li> <li> <p>sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information</p> </li> </ul> <p>Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH</p>
Daily precipitation and temperature for 2021–2050 over China: multiple RCMs and emission scenarios corrected by a trend-preserving method
<p>The datasets with spatial resolution of 0.5˚×0.5˚ were corrected from CORDEX-EA regional climate models based on a trend–preserving bias correction method, including daily daily maximum and minimum temperature, and precipitation (Tmax, Tmin and Pre). The datasets cover the main land area of China and two periods, the historical period (from 1980 to 2005 ) and future period (from 2021 to 2050). The observations used in the correction were obtained from China Meteorological Administration (http://cdc.cma.gov.cn), and were derived from 2472 weather stations over China. The evaluation indicated that the corrected datasets are reliable for the investigations related to climate change across China.</p>
OH-IIUNAM network precipitation data
<p>NetCDF files for all 51 working stations of the OH-IIUNAM (Observatorio Hidrológico del Instituto de Ingeniería de la Universidad Nacional Autónoma de México), a network of 39 laser disdrometers and 12 weighting rain gauges deployed across Mexico City.</p> <p>The OH-IIUNAM network relies on the OTT Parsivel<sup>2</sup> disdrometer and Pluvio<sup>2</sup> L weighing gauge<sup> </sup>instruments, which use a laser diode to produce a horizontal sheet of light to detect the number and size of hydrometeors and, a weight-based sensor to measure liquid and solid precipitation, respectively.</p>
Fig. 1. Precipitation during 2009 and 2010 in Nesting ecology and nest site selection of green-legged partridge
Fig. 1. Precipitation during 2009 and 2010 and the nesting period for the same two years of green-legged partridge at Khao Yai National Park.
Shacham radar data for 41 heavy precipitation events in the eastern Mediterranean
<p>This dataset includes two Matlab files:</p> <p>(a) "shachamCoordinates.mat"</p> <p>Which is a 527X527 matrix of x and y coordinates (<a href="https://en.wikipedia.org/wiki/Israeli_Transverse_Mercator">Israeli Transverse Mercator</a>) for the radar data.</p> <p>(b) "radarRainV2.zip"</p> <p>Which contains 41 *.mat files of the 41 heavy precipitation events analyzed (detailed in Armon et al., 2020).</p> <p>Each of the files consists of four fields:</p> <p>(1) "time" - 1D vector of Matlab time stamps.</p> <p>(2) "r" - 2D matrix of rain rate data [mm/h]</p> <p>(3) "rain" - 2D matrix of total rainfall for the event.</p> <p>(4) "pix2reallyUse" - pixels of acceptable data quality.</p> <p> </p> <p>Shacham radar data were provided by the EMS-Mekorot projects (<a href="http://www.emsmekorotprojects.com">http://www.emsmekorotprojects.com</a>).</p> <p> </p> <p>Data were corrected and calibrated by Dr. Francesco Marra, as detailed in Marra and Morin (2015).</p> <p> </p> <p>References:</p> <p>Armon, M., Marra, F., Enzel, Y., Rostkier-Edelstein, D., & Morin, E. (2020). Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model. Hydrology and Earth System Sciences, 24(3), 1227–1249. https://doi.org/10.5194/hess-24-1227-2020</p> <p>Marra, F., & Morin, E. (2015). Use of radar QPE for the derivation of Intensity–Duration–Frequency curves in a range of climatic regimes. Journal of Hydrology, 531, 427–440. https://doi.org/10.1016/j.jhydrol.2015.08.064</p>
Average daily air temperature, precipitation and relative sunshine duration for Vallon de Nant catchment, extracted from gridded MeteoSwiss data (1961-2020)
<p>This excel file contains time series of daily temperature, precipitation and relative sunshine duration obtained as the spatial average of gridded data sets. The underlying original gridded data sets produced by MeteoSwiss are known as RhiresD, TabsD and SrelD. All meta data are included in the excel file.</p> <p>The data can e.g. be used for hydrological modelling. Comparison to local station data is not included.</p> <p><strong>This data set as well as the original data set should be cited</strong>.</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.