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.
1,394
datasets available to search
ShareScore release 0.9.0
Dataset results
1,394 results for “duration”
Update of: The Global Fire Atlas of individual fire size, duration, speed and direction
<p>This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the original version featured in Andela et al. (2019) was based on collection 6.0 burned area and collection 5.1 land cover products, respectively). The timeseries is extended to cover the period 2002 to August 2024.</p> <h2><strong>Methodological Notes:</strong></h2> <p>The method employed to create the dataset precisely follows the approach described by Andela et al. (2019).</p> <p>The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. </p> <p>The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. </p> <p>Note that while the methods have remained the same compared to Andela et al. (2019), we do observe small differences between the Global Fire Atlas products originating from differences between the MCD64A1 collection 6.1 burned area data used here and the collection 6 data used in the original product. In addition, we observe more substantial differences in the dominant land cover class associated with each fire due to the differences between the MCD12Q1 collection 6.1 data used here and collection 5.1 data used in the original product. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. For each MODIS tile, the fire season is defined as the twelve months centred on the month with peak burned area (see Andela et al., 2019). For example, for a MODIS tile with peak burned area in December, the 2023 fire season would be defined as the period from July 2023 to June 2024, with the current record ending in August 2024. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019).</p> <p>Here we extended the time-series to include the fire season of 2002, and extended the time-series until February 2025.</p> <h2> </h2> <h2><strong>Usage Notes:</strong></h2> <h3><strong>Incomplete Observations for the Latest Fire Seasons:</strong></h3> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. As such, the time-series can be incomplete for the latest fire season (e.g. the "2024 fire season") and also for the penultimate fire season (e.g. the "2023 fire season") due to the way that fire seasons are defined (see above). For example, if the month with peak burned area for a tile is December, then full data covering the 2023 fire season in that tile are not available until midway through the 2024 calendar year. This contrasts with the original dataset from Andela et al. (2019), which only included the data for entire fire seasons between 2003 and 2016. </p> <h3><strong>Observational Outages:</strong></h3> <p>For the purpose of time-series analysis, we note that the 2002 product may have been affected by outages of Terra-MODIS (most notably, June 15 2001 - July 3 2001 and March 19 2002 - March 28 2002), which affects the burn date estimates and Global Fire Atlas product. Following the launch of Aqua-MODIS in May 2002 burn date estimates are more reliable as estimated from both MODIS sensors onboard Terra and Aqua. </p> <h3><strong>File Naming Convention:</strong></h3> <p>GFA_v<em>{time-stamp}</em>_<em>{data-type}</em>_<em>{fire_season}</em>.<em>{file_type}</em></p> <p><em>{time-stamp}</em><strong> </strong>= Date that code was run.</p> <p><em>{data-type}</em><strong> </strong>= “ignitions” or “perimeters” for vector files; “day_of_burn”, “direction”, “fire_line”, or “speed” for raster files.</p> <p><em>{fire_season} </em>= the locally-defined fire season in which the fire was ignited (see more below).</p> <p><em>{file_type} </em>= ".shp" for vector files; ".tif" for raster files. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. </p> <h3>Coordinate systems (Map Projections):</h3> <p>Vector data are provided on the WGS84 projection.</p> <p>Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. The WKT string defining this projection is:</p> <pre><code>'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not_specified_based_on_custom_spheroid",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</code></pre> <p> </p> <h2><strong>Data Layers:</strong></h2> <p><em><strong>Table 1: Overview of the Global Fire Atlas data layers. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses.</p> <table> <tbody> <tr> <td>File name</td> <td>Content</td> </tr> <tr> <td>SHP_ignitions.zip</td> <td>Shapefiles of ignition locations with attribute tables (see Table 2)</td> </tr> <tr> <td>SHP_perimeters.zip</td> <td>Shapefiles of final fire perimeters with attribute tables (see Table 2)</td> </tr> <tr> <td>GeoTIFF_direction.zip</td> <td>500 m resolution daily gridded data on direction of spread (8 classes)</td> </tr> <tr> <td>GeoTIFF_day_of_burn.zip</td> <td>500 m resolution daily gridded data on day of burn (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_speed.zip</td> <td>500 m resolution daily gridded data on speed (km/day)</td> </tr> <tr> <td>GeoTIFF_fire_line.zip</td> <td>500 m resolution daily gridded data on the fire line (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_monthly_summaries.zip</td> <td>Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). </td> </tr> </tbody> </table> <p> </p> <p><em><strong>Table 2: Overview of the Global Fire Atlas shapefile attribute tables. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire.</p> <table> <tbody> <tr> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>lat, lon</td> <td>Coordinates of ignition location (°)</td> </tr> <tr> <td>size</td> <td>Fire size (km2)</td> </tr> <tr> <td>perimeter</td> <td>Fire perimeter (km)</td> </tr> <tr> <td>start_date, start_DOY</td> <td>Start date (yyyy-mm-dd), start day of year (1-366)</td> </tr> <tr> <td>end_date, end_DOY</td> <td>End date (yyyy-mm-dd), end day of year (1-366)</td> </tr> <tr> <td>duration</td> <td>Duration (days)</td> </tr> <tr> <td>fire_line</td> <td>Average length of daily fire line (km)</td> </tr> <tr> <td>spread</td> <td>Average daily fire growth (km2/day)</td> </tr> <tr> <td>speed</td> <td>Average speed (km/day)</td> </tr> <tr> <td>direction, direc_frac</td> <td>Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction</td> </tr> <tr> <td>MODIS_tile</td> <td>MODIS tile id</td> </tr> <tr> <td>landcover, landc_frac</td> <td>MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023</td> </tr> <tr> <td>GFED_regio</td> <td>GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/)</td> </tr> </tbody> </table> <p> </p> <p> </p>
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.
EURO-CORDEX Precipitation Intensity-Duration-Frequency Curves
<p>This dataset provides preliminary precipitation intensity-duration-frequency (IDF) values calculated from the <a href="https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels">CMIP5 based EURO-CORDEX regional climate model simulations</a>. Values for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece are provided for a historical and future period for the RCP 2.6, 4.5 and RCP 8.5 atmospheric greenhouse gas concentration scenarios for a total of 123 simulations.</p> <p>Technical Info</p> <p>The return period values are computed for exceedance periods of 2, 5, 10, 25, 50, 100, 200, and 500-years and durations of 3, 6, 12, and 24-hours using the generalized extreme value (GEV) distribution with the <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.genextreme.html">scipy.stats.genextreme </a>function from the Python SciPy library. Computations are performed from the output of a combination of 123 regional climate models (RCMs), global climate models (GCMs), climate change scenarios, and ensemble members. The historical data cover the period 1976 through 2005 and the future 2041 through 2070 for the RCP 2.6, 4.5 and 8.5 atmospheric greenhouse gas concentration scenarios. Included in the analysis, there are 39 unique RCM-GCM combinations with outputs from 48 historical, 15 RCP 2.6, 13 RCP 4.5, and 47 RCP 8.5 simulations.</p> <p>Data description: This dataset contains EURO CORDEX return period estimates computed for exceedance periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-years and 3-, 6-, 12-, and 24-hour durations using the generalized extreme value (GEV) distribution. The data includes values based on outputs from a total of 123 simulation combinations, including 39 RCM–GCM configurations, three climate change scenario combinations for a historical period (1976-2005) and future period (2041-2070), and three ensemble members.</p> <p>Format:</p> <p>The format of this dataset is organized in one ZIP files: IDF_{Cityname}.zip. The zip file contains 48 csv files for each RCM-GCM combination and ensemble member that include IDFs for each available RCP scenario.</p>
North Temperate Lakes LTER: Ice Duration - Trout Lake Area 1981 - current
Data includes day of freeze-up and thaw dates of seven northern primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, Crystal Bog, and Trout Bog). Observations are made approximately every other day during times of freeze and thaw. A lake is considered ice covered when the sampling station (the deepest part of the lake) is ice covered. The lake is considered thawed when it is possible to drive a boat from the boat landing to the sampling station without encountering ice. The ice duration is the number of days during the winter season that the lake surface was frozen. This is calculated using the average date of ‘lastopen’ and ‘ice_on’ for freeze-up, and the average date of ‘lastice’ and ‘ice_off’ for the thaw. The average is rounded forward. A few early years do not have all four dates so duration in those cases use whatever dates are available. The ‘comments’ column notes the dates when multiple freeze/thaws occurred during the winter. Sampling Frequency: annually. Number of sites: 7.
North Temperate Lakes LTER: Ice Duration - Madison Lakes Area 1853 - current
Data include dates of freeze-up, thaw, and ice duration of three primary lakes in the Madison area (Lake Mendota, Lake Monona, and Lake Wingra). Data are obtained from the State Climatologist. For Monona and Wingra, the freeze date (ice on) is defined as the first date on which the water body is observed to be completely ice covered, and the breakup date (ice off) is the date of the last breakup observed before the open water phase. Ice duration is the number of days that a water body is completely covered with ice and excludes any period during which the lake thawed in mid-winter before freezing again. For Mendota, duration is calculated in the same manner, but ice on and ice off dates are assigned as follows: the lake is deemed frozen if it has solid ice from Picnic Point to Maple Bluff, and total ice cover is greater than 50 %. The lake is deemed open if it is ice-free from Picnic Point to Maple Bluff and total ice cover is less than 50%. Sampling Frequency: annually. Number of sites: 3.
Standing Balance Experiment with Long Duration Random Pulses Perturbation
<p>Standing balance experiment and the measured data-set are fundamental for identifying postural feedback controllers. As the generalized feedback controllers can only be identified from long duration balance data (under random external perturbations), a standing balance experiment is conducted and the long duration motion data was recorded. The data-set includes the perturbation reaction data from eight subjects. Each subject performed four experiment trials, including two quiet standing and two perturbed trials. Each trial lasted five minutes. A total of 80 minutes quiet standing and 80 minutes perturbed standing data are included in this data-set. Recorded information including three dimensional trajectories of thirty-two markers (27 on subjects' trunk and legs and 5 on the treadmill frame), six dimensional ground reaction forces, and nine Electromyography signals (EMGs, on subjects' right leg). In addition, joint angles and torques were calculated using a human body model and inverse dynamics. Basic statistical analysis of the data is also included.</p> <p>Measured raw data for each subject in each experimental trial includes three files:</p> <ol> <li>Mocapxxxx.txt: contains motion capture marker data, ground reaction force, and 76 analog channels. Data was recorded at 100 Hz sampling rate.</li> <li>Mocapxxxx_Motion Analysis_analog.txt: contains 76 high sampling rate (1000Hz) analog channels' data. Analog data is consisted of the analog singal from the froce sensor on the treadmill, EMG signals in the Delsys EMG sensors, and 3 axises acceeleration signals of the Delsys EMG sensors.</li> <li>Recordxxxx.txt: contains the sway motion data of treadmill and the three-axis acceleration data of two Xsens MTi-10 series sensors.</li> </ol> <p>Measured raw data also includes two files of the unloaded trial, which is used for the inertia compensation.</p> <ol> <li>Mocap0000.txt: contains motion capture marker data (5 markers on the treadmill frame) and ground reaction forces.</li> <li>Record0000.txt: contains the treadmill sway motion data and the acceleration data (three-axis) of two Xsens MTi-10 series sensors.</li> </ol> <p>Processed data of each subject in each experimental trial contains four files:</p> <ol> <li>Mocapxxxx.txt: contains the gap filled motion capture marker data and the inertia compensated ground reaction force data.</li> <li>Motionxxxx.txt: contains the calculated the trajectories of three joints' (hip, knee, and ankle) angles, angular velocities, moments, and joint contact forces.</li> <li>Data_infoxxxx.txt: contains the quality of recorded raw marker data (percentage and biggest duration of missing marker data), and the percentage of removed inertia artifacts in ground reaction forces</li> <li>MotionAnalysis.fig: shows the mean and standard deviation of three joints' trajectories in four experimental trials.</li> </ol> <p>There are two more plots in the processed data folder which shows the joint motion/moment and the raw/compensated ground reaction forces of one example experimental trial (subject 07 trial 03).</p> <p>The processed data was generated using the code in the 'Processing_Code' folder. The code was wrote using Matlab and the main function is "Data_Processing_Main.m"</p> <p>More details of the standing balance experiment can be found in the document 'Standing_Balance_Experiment_with_Long_Duration_Random_Pulses_Perturbation.pdf'</p>
Trained convolutional neural network for the identification of long-duration mixed precipitation in Montréal (Canada)
<p>In this dataset the trained convolutional neural network is published that accompanies the paper "A deep learning approach for the identification of long-duration mixed precipitation in Montréal (Canada)" submitted to the special issue on "Machine-Learning Applications in the Atmospheric and Oceanic Sciences" by the journal Atmosphere&Ocean.</p> <p>The files were created using tensorflow in python. The trained network is available in .h5-format the history as numpy-file (npy).</p>
Disco Time: The Relationship Between Perceived Duration and Tempo in Music
<p>Data set from a study published in Music & Science.</p>
ring fiber cavity for generation of solitons of short duration
<p>This dataset includes all the theoretical and experimental results obtained during the implementation of a fiber cavity, in ring configuration and including an active fiber, to generate solitons of duration shorter than the ones generated so far. The scope of this work has been acquiring familiarity with short solitons, which are the solitons that should be generated in nonlinear cavities of tents of cm of length (CAFR). The dataset includes also the metadata for each file uploaded. </p>
Peak Flow Event Durations in the Mississippi River Basin and Implications for Temporal Sampling of Rivers
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This repository corresponds to all the input and output files that were used in the study reported in:</p> <ul> <li>Cerbelaud, A., David, C. H., Biancamaria, S., Wade, J., Tom, M., Prata de Moraes Frasson, R., & Blumstein, D. (2024). Peak flow event durations in the Mississippi River basin and implications for temporal sampling of rivers. Geophysical Research Letters, 51, e2024GL109220. <a href="https://doi.org/10.1029/2024GL109220" target="_blank" rel="noopener">https://doi.org/10.1029/2024GL109220</a>.</li> </ul> <p>When making use of any of the output files of this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Main goals of the publication</strong></p> <p>The corresponding work aims to quantify peak flow event durations at an hourly time scale and their impact on high-frequency river sampling requirements using sampling ratios. The analysis is performed over the Mississippi basin using hourly USGS gages over 2010-2022.</p> <p>The findings derived from these output files have direct implications for future satellite missions concerned with capturing high-frequency dynamics in rivers, including flood events.</p>
Snow cover duration maps for Gran Paradiso
<p>Number of days for which a pixel is covered by snow. The snow cover duration (SCD) is generated from the 250 meter resolution snow cover maps realized with the algorithm described in Notarnicola, Claudia, et al. "Snow cover maps from MODIS images at 250 m resolution, Part 1: Algorithm description." Remote Sensing 5.1 (2013): 110-126.</p>
Snow cover duration maps for Bayerischer Wald
<p>Number of days for which a pixel is covered by snow. The snow cover duration (SCD) is generated from the 250 meter resolution snow cover maps realized with the algorithm described in Notarnicola, Claudia, et al. "Snow cover maps from MODIS images at 250 m resolution, Part 1: Algorithm description." Remote Sensing 5.1 (2013): 110-126.</p>
Long-duration Superluminous Supernovae at Late Times
<p>Single-composition models in three flavours (pure O, OMg, C burn). Zone mass, power level, and clumping free parameters. Distance 10 Mpc assumed.</p>
Language Recognition for SSB modulated HF Radio Signals of Short Duration - Example Files
<p>These files are example files of the dataset used in our work "Language Recognition for SSB modulated HF Radio Signals of Short Duration". Two files are 10 second HF radio segments from Russian amateur radio communications. The other six files are a single recording from CommonLanguage (https://huggingface.co/datasets/common_language) and five modified versions of this file, created by applying the proposed HF radio simulation approach. Additional details can be found in the Paper.</p>
Two Large-Scale Meteorological Patterns Are Associated with Short-Duration Dry Spells in the Northeastern United States
<p><strong>Description</strong></p> <p>This dataset contains processed data from the ERA5 dataset for some of the atmospheric fields considered in this study. Original (pre-processed) ERA5 data (Hersbach et al. 2020) is available at <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>. For each processed data file (netCDF format), the time steps correspond with the events and numerical order as listed in Table 1 of the main manuscript text. Processed data files are given for some of the 12-day averaged dry periods. Other processed data files are available from the authors upon reasonable request. </p> <p> </p> <p><strong>Abstract</strong></p> <p>Large-scale meteorological pattern (LSMP) – based analysis is used novelly to understand antecedent conditions and characteristics of short-duration dry spell events over the northeastern United States. Dry spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting twelve days or longer, which correspond to ~10% of dry spell events, are examined. The 500-hPa stream function anomaly fields for the first twelve days of each event are time-averaged and k-means clustering is applied to isolate the dry spell-related LSMPs. The first cluster has a strong, low-pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong, high-pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm track density across the Northeast is dramatically reduced during these dry spell events. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but quite different remote, properties. More (56%) short-duration dry spells occurred during the numerous non-drought months than drought months, however the frequency of dry spells is more than three times greater during drought than non-drought months.</p> <p> </p>
Variant calls for 'Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration'
<p>A VCF of SNP calls used for input to GWAS in https://elifesciences.org/articles/26255</p>
MCR LTER: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality
Data are from an 18-month field experiment on the fore reef of Moorea, testing how different forms of nitrogen (nitrate vs. urea) impact coral bleaching and mortality during two mild thermal stress events in the Austral summers of 2016 and 2017. These data are associated with a manuscript currently in review at Ecosystems. Tentative mansucript title and author list are: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality Deron E. Burkepile, Andrew A. Shantz, Thomas C. Adam, Katrina S. Munsterman, Kelly E. Speare, Mark C. Ladd, Mallory M. Rice, Shelby McIlroy, Andrew J. Brooks, Russell J. Schmitt, and Sally J. Holbrook These data are part of the NSF project: RAPID: How does nutrient availability alter coral bleaching, mortality, and recovery on Moorea coral reefs? (funded wholly or part by NSF Awards OCE-1619697).
The medial temporal lobe supports mnemonic discrimination for event duration
Open the record for dataset details and reuse information.
Fluorescent Microglia Images for Analyzing Morphological Changes due to Injury Duration in the Ischemic Rat Brain
<p>The image data included in this dataset are the confocal microscope images (converted from original .nd2 file to .tiff form) used for the publication: Joseph, A., Liao, R., Zhang, M., Helmbrecht, H., McKenna, M., Filteau, J. R., & Nance, E. (2020). Nanoparticle-microglial interaction in the ischemic brain is modulated by injury duration and treatment. <em>Bioengineering & translational medicine</em>, <em>5</em>(3), e10175. https://doi.org/10.1002/btm2.10175</p> <p>The data is organized by brain slice number, region, and image number. There is also an included excel file 'datadescriptions.xlsx' that provides more information about the metadata of the dataset. </p> <p> </p> <p>The data was procured and processed by the Disease Directed Engineering Lab, PI: Elizabeth Nance, at the University of Washington.</p>
Prediction of ice duration along large rivers
<p>Contains two files:</p> <p>shp files: contain river centerline points and its associated downstream distance:</p> <p>* "name": name of the river</p> <p>* "dsDistKm": downstream distance in km</p> <p>* "dsPointId": unique id for each centerline point for a river, together with river name uniquely identify a point in the dataset</p> <p>"river_ice_duration_winter_temperature" (csv file)—contains river ice duration and winter mean temperature prediction:</p> <p>* "name" : name of the river </p> <p>* "dsPointId": unique id for each centerline point for a river, together with river name uniquely identify a point in the dataset</p> <p>* "year": year (from 2019–2099) </p> <p>* "tasmean": winter (Dec, Jan, and Feb) mean surface air temperature (from ERA5, in degree Celsius) </p> <p>* "iceDuration": ice duration in days</p> <p>Temperature and ice duration were based on CESM1-BGC under RCP8.5.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.