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1,118 results for “Time series”
Paired time series of daily discharge and storm surge
<p>This dataset presents daily time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014. </p> <p>Daily river discharge is the product of routing the mean daily runoff of the JULES model from the eartH2Observe WRR2 reanalysis data at 0.5° resolution (Best et al., 2011; Clark et al., 2011; Schellekens et al., 2017) with CaMa-Flood at a 0.25° resolution (Yamazaki et al., 2011). The maximum daily storm surge is obtained from the Global Tide and Surge Model (GTSM) (Muis et al., 2016; Verlaan et al., 2015). Each discharge location at the river mouth of coastal catchments larger than 1,000 km<sup>2</sup> is paired with the nearest (≤ 75 km) GTSM output location (Eilander et al., 2019). </p> <p> </p>
Impact of the COVID-19 pandemic on antidepressant use in eleven European regions: a comparative time series analysis 2018–2022
<p>Data and code supporting the article:</p> <p>Impact of the COVID-19 pandemic on antidepressant use in eleven European regions: a comparative time series analysis 2018–2022</p> <p>Prescription, prevalence and incidence data from January 2018 to December 2022 for Croatia, the Czech Republic, Finland, Germany, Slovenia, Sweden, and the United Kingdom (England, Northern Ireland, Scotland, and Wales).<br>Data include the numbers of dispensed defined daily doses (DDDs) and packs, aggregated by country and month, and prevalence and incidence of antidepressant dispensing.</p> <p>For more information, see the accompanying document ReadMe.md.</p>
Time series of electrical conductivity, temperature and relative stream stage recorded in surface water and streambed sediments of River Erpe and River Gruendlach, Germany
<p><span><a href="../api/records/13336325/draft/files/temp_EC_timeseries.csv/content" target="_blank" rel="noopener noreferrer">temp_EC_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC) and relative stream stage (cm) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Gruendlach, Germany.</p> <p> </p> <p><span><a href="../api/records/13336325/draft/files/porewater_ec_timeseries.csv/content" target="_blank" rel="noopener noreferrer">porewater_ec_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
Pressure time series for waves on shelf
<div>Time: in UTC</div> <div>location: N 28° 52.077’ W 90° 29.466’</div> <div>Units: Pressure (dbar); Sea Pressure (dbar); depth (m)</div> <div>Instrument: RBRvirtuoso pressure sensor</div> <div>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</div> <div>PI: Chunyan Li</div> <div>Funded by NSF (1736713)</div> <div> </div>
Pressure time series for waves on shelf south of Timbalier Island
<div>Time: in UTC</div> <div>Position 90º32.00’W 29º3.20’N</div> <div>Platform CSI 5</div> <div>Station ioos:station:WAVCIS:CSI05</div> <div>Description S. of Timbalier Island, LA </div> <div>Units: Pressure (dbar); Sea Pressure (dbar); depth (m)</div> <div>Instrument: RBRvirtuoso pressure sensor</div> <div>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</div> <div>Time offset: 578073773077 millisecond + 5 hours (due to setup error, this time offset must be added) which gives time in UTC.</div> <div> </div> <p> </p>
LoRa signal quality and GPS positioning time series dataset
<p>This time series dataset contains measurements taken by moving devices equipped with LoRa communication and GPS positioning capabilities. The measurements have been captured at the Sálvora Archipelago in Galicia (Spain), which belong to the Atlantic Islands of Galicia National Park, in which we have deployed three LoRa gateways at the following locations (latitude, longitude, altitute):</p> <ul> <li>Gateway 1 (42.46972, -9.01345, 73)</li> <li>Gateway 2 (42.49955, -9.00654, 5)</li> <li>Gateway 3 (42.50893, -9.04902, 31)</li> </ul> <p>The dataset is provided as a single comma-separated values (CSV) file, with the following data in each line:</p> <ul> <li>Device identification</li> <li>Received signal strength indicator (RSSI) from LoRa Gateway 1</li> <li>Signal-to-noise ratio (SNR) from Gateway 1</li> <li>RSSI from LoRa Gateway 2</li> <li>SNR from Gateway 2</li> <li>RSSI from LoRa Gateway 3</li> <li>SNR from Gateway 3</li> <li>LoRa spreading factor</li> <li>Timestamp</li> <li>Device latitude received from GPS</li> <li>Device longitude received from GPS</li> <li>Device altitude received from GPS</li> </ul>
NYC Bike Sharing Network: Time-Series Enhanced Nodes and Edges Dataset
<p>This dataset presents a comprehensive graph representation of the New York City Bike Sharing system, structured with nodes representing stations and edges delineating trips between these stations. The dataset is distinctive in integrating dynamic properties as time series data, which are meticulously updated using historical records (csv files) and live data feeds (gbfs files) provided by<a href="https://citibikenyc.com/system-data" target="_blank" rel="noopener"> NYC Bike sharing system</a>. </p> <ul> <li> <p><strong>Nodes</strong>:</p> <ul> <li><strong>Source</strong>: Data is collected from the New York City Bike Station Information API.</li> <li><strong>Attributes</strong>: <ul> <li><strong>ID</strong>: Unique identifier for each station.</li> <li><strong>Name</strong>: Name of the station.</li> <li><strong>Capacity</strong>: Number of bikes the station can accommodate.</li> <li><strong>Short ID</strong>: A condensed identifier used internally.</li> </ul> </li> <li><strong>Time Series Data</strong>: <ul> <li>Updated every 5 minutes from the Station Status API.</li> <li>Captures changes in bike availability, recording values only when they differ from previous data points.</li> </ul> </li> </ul> </li> <li> <p><strong>Edges</strong>:</p> <ul> <li><strong>Source</strong>: Compiled from trip data provided in CSV format specific to NYC Bike Sharing.</li> <li><strong>Attributes</strong>: <ul> <li><strong>Trip Counter</strong>: Total number of trips recorded.</li> <li><strong>Bike Type Counter</strong>: Counts trips made with electric versus classic bikes.</li> <li><strong>Trip Type Counter</strong>: Separates trips made by members versus casual riders.</li> <li><strong>Active Trips Tracker</strong>: Tracks the number of active trips at any given moment.</li> </ul> </li> <li><strong>Aggregation</strong>: Trip data between identical start and end points, in the same direction, are aggregated into a single edge, with time-series tracking the frequency of these trips.</li> </ul> </li> </ul>
PECD consistent heat demand and COP time series from when2heat
<p>This dataset uses the when2heat package to compute heat demand and COP time series. The data covers the years 1979 to 2019 and the sub-national level down to a resolution grouping Europe into 96 clusters based on ERA5 reanalysis data. Thanks to this temporal scope and spatial resolution, the data is consistent with the Pan-European Climatic Database (PECD) and enables multi-year analysis of multi-sector energy systems that include the heating sector.</p> <p>The "main data" folder provides the time series with COPs for various heat pump technologies and heat demand. The heat demand data deviates from the immediate when2heat outputs in two ways. First, it aggregates the demand for space heating and warm water in commercial and residential buildings into a single time series. The compositionHeatDemand.xlsx in the "background info" folder provides the weights assumed for this aggregation. Since the composition of heat demand changes in the future, the data includes a different time series for every fifth year from 2015 to 2050. Second, we normalize each profile over the historical years from 1979 to 2019 to its maximum value. Accordingly, multiplying the time series with maximum heat demand will give an absolute demand profile.</p> <p>A subfolder in "background_info" provides shape files describing the different spatial resolutions used. The "mappingRegions.csv" specifies which region names in this dataset correspond to region names in the PECD data. The "heating_thresholds.csv" in the "background info" folder provides the assumed heating thresholds to compute the heating demand.</p>
Linked collectors and determiners for: Time series of zooplankton abundance in the Florida Keys, collected by the South Florida Program (NOAA/AOML) and the Marine Biodiversity Observation Network (MBON).
Natural history specimen data linked to collectors and determiners held within, "Time series of zooplankton abundance in the Florida Keys, collected by the South Florida Program (NOAA/AOML) and the Marine Biodiversity Observation Network (MBON)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912">https://bionomia.net/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912">https://gbif.org/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912</a>. Formatted as a Frictionless Data package.
Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario
<p><strong>Paper Abstract:</strong></p> <p>Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales. </p> <p> </p> <p><strong>Dataset details:</strong></p> <p>See paper. </p> <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul> <p> </p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a></p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
Pre-Processed Power Grid Frequency Time Series
<p><strong>Overview</strong><br> This repository contains ready-to-use frequency time series as well as the corresponding pre-processing scripts in python. The data covers three synchronous areas of the European power grid:</p> <ul> <li>Continental Europe</li> <li>Great Britain</li> <li>Nordic</li> </ul> <p>This work is part of the paper "Predictability of Power Grid Frequency"[1]. Please cite this paper, when using the data and the code. For a detailed documentation of the pre-processing procedure we refer to the supplementary material of the paper.</p> <p><strong>Data sources</strong><br> We downloaded the frequency recordings from publically available repositories of three different Transmission System Operators (TSOs).</p> <ul> <li><strong>Continental Europe </strong>[2]: We downloaded the data from the German TSO <em>TransnetBW GmbH,</em> which retains the Copyright on the data, but allows to re-publish it upon request [3].</li> <li><strong>Great Britain </strong>[4]: The download was supported by National Grid ESO Open Data, which belongs to the British TSO <em>National Grid</em>. They publish the frequency recordings under the NGESO Open License [5].</li> <li><strong>Nordic</strong> [6]: We obtained the data from the Finish TSO <em>Fingrid</em>, which provides the data under the open license CC-BY 4.0 [7].</li> </ul> <p><strong>Content of the repository</strong></p> <p><strong>A) Scripts</strong></p> <ol> <li>In the "Download_scripts" folder you will find three scripts to automatically download frequency data from the TSO's websites.</li> <li>In "convert_data_format.py" we save the data with corrected timestamp formats. Missing data is marked as NaN (processing step (1) in the supplementary material of [1]).</li> <li>In "clean_corrupted_data.py" we load the converted data and identify corrupted recordings. We mark them as NaN and clean some of the resulting data holes (processing step (2) in the supplementary material of [1]).</li> </ol> <p>The python scripts run with Python 3.7 and with the packages found in "requirements.txt".</p> <p><strong>B) Yearly converted and cleansed data</strong><br> The folders "<year>_converted" contain the output of "convert_data_format.py" and "<year>_cleansed" contain the output of "clean_corrupted_data.py".</p> <ul> <li><strong>File type</strong>: The files are zipped csv-files, where each file comprises one year.</li> <li><strong>Data format</strong>: The files contain two columns. The second column contains the frequency values in Hz. The first one represents the time stamps in the format <em>Year-Month-Day Hour-Minute-Second</em>, which is given as naive local time. The local time refers to the following time zones and includes Daylight Saving Times (python time zone in brackets): <ul> <li>TransnetBW: Continental European Time (<em>CE)</em></li> <li>Nationalgrid: Great Britain (<em>GB</em>)</li> <li>Fingrid: Finland (<em>Europe/Helsinki</em>)</li> </ul> </li> <li><strong>NaN representation</strong>: We mark corrupted and missing data as "NaN" in the csv-files.</li> </ul> <p><strong>Use cases</strong><br> We point out that this repository can be used in two different was:</p> <ul> <li><strong>Use pre-processed data</strong>: You can directly use the converted or the cleansed data. Note however, that both data sets include segments of NaN-values due to missing and corrupted recordings. Only a very small part of the NaN-values were eliminated in the cleansed data to not manipulate the data too much.</li> </ul> <ul> <li><strong>Produce your own cleansed data</strong>: Depending on your application, you might want to cleanse the data in a custom way. You can easily add your custom cleansing procedure in "clean_corrupted_data.py" and then produce cleansed data from the raw data in "<year>_converted".</li> </ul> <p><strong>License</strong></p> <p>This work is licensed under multiple licenses, which are located in the "LICENSES" folder.</p> <ul> <li>We release the code in the folder "Scripts" under the MIT license .</li> <li>The pre-processed data in the subfolders "**/Fingrid" and "**/Nationalgrid" are licensed under CC-BY 4.0.</li> <li>TransnetBW originally did not publish their data under an open license. We have explicitly received the permission to publish the pre-processed version from TransnetBW. However, we cannot publish our pre-processed version under an open license due to the missing license of the original TransnetBW data.</li> </ul> <p><strong>Changelog</strong><br> Version 2:</p> <ul> <li>Add time zone information to description</li> <li>Include new frequency data</li> <li>Update references</li> <li>Change folder structure to yearly folders</li> </ul> <p>Version 3:</p> <ul> <li> <p>Correct TransnetBW files for missing data in May 2016</p> </li> </ul>
Daily streamflow time series in the Tirgua River Basin (Paso Viboral), Venezuela
<p>Daily streamflow time series derived from the raw data provided by the National Institute of Meteorology and Hydrology (INAMEH) for the period from January 1963 to December 1996 (34 years). Site: Paso Viboral, San Carlos, Cojedes [-68,605964° W; 9,719648° N]. Data frame with year, month, day and flow in m<sup>3</sup>/s. </p>
The dataset for the submitted paper " Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".
<p>These files are the data of the result in the submitted paper, titled "Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".</p> <ul> <li>File Description</li> </ul> <p>pv13.data : Exp. 1<br> pv17.data : Exp. 2</p> <p>The raw potential vorticity (PV) data for the Exp.1 and Exp.2, respectively, used in drawing the Fig.1, 2, and the supplemental movie 1 and 2.<br> These are the grid point value files, 72 levels for the zonal direction, 30 levels for meridional direction. More details are described in the next ctl files.</p> <p> </p> <p>pv13.ctl<br> pv17.ctl</p> <p>Description files for pv13.data and pv17.data. This will be called from grads_pv13.gs and grads_pv17.data, respectively.</p> <p>grads_pv13.gs<br> grads_pv17.gs</p> <p>GrADS script for mapping the PV.</p> <p> </p> <p>energy17.txt : Exp.2</p> <p>The time series table of energy values for exp.2.<br> One raw is identified by combination of the TIME in the experiment and zonal wave number N.</p>
Sentinel-2 Time Series for Pheno-VAE
<p>This repository contains a SQLite file with Sentinel-2 B4 (red) and B8 (near infra-red) bands time series, used to compute NDVI time series for pheno-VAE : https://gitlab.cesbio.omp.eu/zerahy/pheno-VAE</p> <p> </p>
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
Code and data used for the study: 'BioDeepTime: a database of biodiversity time series for modern and fossil assemblages'
<p>The repository includes code and data to reproduce the results in the manuscript ‘BioDeepTime: a database of biodiversity time series for modern and fossil assemblages' by Smith et al. (<code>analysis_biodeeptime.zip</code>).</p>
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
PB preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed PB data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=325 representing 325 traffic detectors ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 114.23MB<br> *** Key: 'data_TR_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'position_TR_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</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.