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327 results for “Time-Series”
Data from: Whiskers provide time-series of toxic and essential trace elements, Se:Hg molar ratios, and stable isotope values of an apex Antarctic predator, the leopard seal
<p>In an era of rapid environmental change and increasing human presence, researchers need efficient tools for tracking contaminants to monitor the health of Antarctic flora and fauna. Here, we examined the utility of leopard seal whiskers as a biomonitoring tool that reconstructs time-series of significant ecological and physiological biomarkers. Leopard seals (<em>Hydrurga leptonyx</em>) are a sentinel species in the Western Antarctic Peninsula due to their apex predator status and top-down effects on several Antarctic species. However, there are few data on their contaminant loads. We analyzed leopard seal whiskers (n = 18 individuals, n = 981 segments) collected during 2018–2019 field seasons to acquire longitudinal profiles of non-essential (Hg, Pb, and Cd) and essential (Se, Cu, and Zn) trace elements, stable isotope (ẟ<sub>15</sub>N and ẟ<sub>13</sub>C) values and to assess Hg risk with Se:Hg molar ratios. Whiskers provided between 46 and 286 cumulative days of growth with a mean ~125 days per whisker (n = 18). Adult whiskers showed variability in non-essential trace elements over time that could partly be explained by changes in diet. Whisker Hg levels were insufficient (<20 ppm) to consider most seals being at "high" risk for Hg toxicity. Nevertheless, maximum Hg concentrations observed in this study were greater than that of leopard seal hair measured two decades ago. However, variation in the Se:Hg molar ratios over time suggest that Se may detoxify Hg burden in leopard seals. Overall, we provide evidence that the analysis of leopard seal whiskers allows for the reconstruction of time-series ecological and physiological data and can be valuable for opportunistically monitoring the health of the leopard seal population and their Antarctic ecosystem during climate change.</p>
Dataset: Evaluation of post-hoc interpretability methods in time-series classification
<p>This repository contains the dataset, trained models as well as results for the article <em>Evaluation of post-hoc interpretability methods in time-series classification.</em></p> <p>The code to reproduce the results presented in the article is available on <a href="https://github.com/hturbe/InterpretTime">GitHub</a>. More details on the data and results can be found in the article.</p> <p><strong>Files:</strong></p> <ul> <li><strong>datasets.zip: </strong>Include the three datasets used in the article: <ul> <li><strong>ECG: </strong>Processed version of the CPSC dataset from <em>Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020.</em></li> <li><strong>fordA: </strong>Dataset from the <a href="https://www.cs.ucr.edu/~eamonn/time_series_data_2018/">UCR Time Series Classification Archive</a></li> <li><strong>synthetic: </strong>Synthetic dataset developed specifically for the purpose of the article</li> </ul> </li> <li><strong>trained_models.zip: </strong>Include CNN, transformer and bi-lstm trained on the three datasets</li> <li><strong>results_paper.zip: </strong>Computed relevance and evaluation metrics for the trained models <ul> <li><strong>model_interpretability: </strong>Include the relevance computed using the different interpretability methods as well as the computed metrics for each method </li> <li><strong>summary_results: </strong>Summary of the evaluation metrics across all interpretability methods for each dataset as well as an excel file summarising the metrics across all datasets.</li> </ul> </li> </ul>
STE CECs: Coastal Radon Time-series v1.0
<p><strong>Description: </strong>Radon in coastal water time-series data and accompanying water and meteorological parameters collected from 27 different locations globally. These data were used to train and validate two deep learning models.</p> <p>Data for each study site were provided by authors - please see the "References" sheet in the document for the original source and citation of the data.</p> <p>Associated code can be found here: 10.5281/zenodo.7581389</p> <p>Associated publication accepted (in press) in <em>Water Resources Research.</em></p> <p><strong>Column names in the "data" sheet are as follows:</strong></p> <p>datetime = date and time of measurement (local time, MM/DD/YY HH:MM)</p> <p>Location = name of location of measurement (data = string data)</p> <p>Aquifer_type = categorical aquifer type (data = string data, options: rocky, sandy, or muddy).</p> <p>depth_m = depth of water column (m). Measured with CTD probe or similar.</p> <p>ctdtemp_C = water temperature (degrees Celsius) at point of radon measurement. Measured with CTD probe or similar.</p> <p>ctdsal = water salinity (unitless) at point of radon in water measurement. Measured with CTD probe or similar.</p> <p>windsp_ms = wind speed, 10 m above sea level (units = m/s). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/)</p> <p>airtemp_C = air temperature (units = degrees Celsius). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/)</p> <p>Rn_Bqm3 = radon in water (units = Bq/m^3). Measured with Durridge RAD7 or similar radon-in-air detector or underwater gamma spectrometer (e.g., Dulai et al., 2016: https://doi.org/10.1007/s10967-015-4580-9).</p>
Data and code example for the article: "Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification"
<p>Data needed to reproduce the figures of <a href="https://arxiv.org/abs/2305.05881">https://arxiv.org/abs/2305.05881</a> and a simple code example of a quantum-convex-classical neural network used to train a sine versus cosine classification problem.</p>
Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series
<p>A set of datacubes where z-dimension is time, thus each (x,y,.) is a timeseries. The idea is to detect sudden changes in each series, assuming 1) the series can be quite short and noisy 2) the change occurs in spatial patches. The set has synthetic examples with known change-events, and a real-world dataset with unknown change-events. The data files are related to the pape</p> <p>T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, "Journal of Agricultural, Biological and Environmental Statistics", https://doi.org/10.1007/s13253-023-00546-1</p> <p>More of the NFI data is available from Natural Resources Institute Finland, https://kartta.luke.fi/index-en.html</p> <p>See `data/00data_readme.txt` for further details.</p> <p> </p>
Eccentricity signal in the nannofossil time-series across the Middle Pleistocene Transition in the Northwestern Pacific Ocean
<p>Supplementary data to the paper "Eccentricity signal in the nannofossil time-series across the Middle Pleistocene Transition in the Northwestern Pacific Ocean" of Bordiga et al 2023 in Quaternary Science Reviews.</p>
UK Electricity consumption time-series from Elexon data portal (Actual Total Load Per Bidding Zone)
<p>Data from 2015-01-01 to 2023-08-10. Downloaded using ElexonDataPortal for Python.</p> <p>Dataset B0610 – Actual Total Load per Bidding Zone: <a href="https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=0CDYQw7AJahcKEwjw1P2089yAAxUAAAAAHQAAAAAQAw&url=https%3A%2F%2Fwww.elexon.co.uk%2Fdocuments%2Fbmrs-api-and-data-push-guide-for-p408%2F&psig=AOvVaw3JTwF_pxNDLFZp3HSDJa_s&ust=1692128319855369&opi=89978449">https://www.elexon.co.uk/documents/bmrs-api-and-data-push-guide-for-p408/</a></p>
SOMLIT-Astan time-series (2009-2016) rDNA 18S V4 ASV table (dada2)
<p>This repository contains a rDNA 18S V4 ASV table (astan-18sv4_dada2_v1.0.filtered.table.with.taxo.lulu.tsv.gz) for SOMLIT-Astan time-series (2009-2016). Each ASV, one per row, is described by the following fields: <strong>amplicon</strong> = ASV identifier; <strong>taxonomy</strong> = taxonomic path assigned to the ASV using IDTAXA; <strong>confidence</strong> = IDTAXA confidence scores for each taxonomic rank; <strong>sequence</strong> = ASV nucleic acid sequence; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>RAXXXXXX-X</strong> = number of reads in each of the 375 SOMLIT-Astan time-series samples. Sample ids contain information about the sampling date and the size fraction. The six digits after RA indicate the date (year, month and day), and the value after - indicate the size fraction, 02 for 0.2 to 3 µm and 3 for superior to 3 µm.</p> <p>How this table has been generated:</p> <p>The procedures used for DNA extraction and amplification of the 18S V4 region of the ribosomal operon are described in <a href="https://doi.org/10.1111/mec.16539">https://doi.org/10.1111/mec.16539</a>. The eukaryote-specific primers used were TAReuk454FWD1 (5’-CCAGCASCYGCGGTAATTCC-3’, Saccharomyces cerevisiae position 565‐584) and TAReukREV3 (5’-ACTTTCGTTCTTGATYRA-3’, Saccharomyces cerevisiae position 964‐981) (Stoeck et al., 2010). Raw sequences are available at the European Nucleotide Archive (ENA) under the project id PRJEB48571.</p> <p>The paired-end fastq files obtained from sequencing were demultiplexed and primers were removed using Cutadapt v2.8, filtering out untrimmed reads. Then, forward and reverse reads were trimmed at position 210 and reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R package dada2 version 1.22 with R version 4.1.1 . For each run, error rates were defined using the function learnErrors(), reads were dereplicated using the function derepFastq() function and denoised using the dada() function with default options before being merged. Remaining chimaeras were removed using the function removeBimeraDenovo(). Only amplicon sequence variants (ASVs) with at least three reads in two samples were retained. ASVs were taxonomically assigned using IDTAXA with default parameters with the PR2 database version 4.14. Finally, the LULU curation approach was applied to the ASV table to remove remaining erroneous amplicons. For more details relative to the bioinformatic pipeline used to generate the ASV tables, see <a href="https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo">https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo</a>.</p>
Lake Vansjø-Vanemfjorden long time-series data for nutrients_colour and cyanobacteria
<p>Data from lake Vansjø-Vanemfjorden basin from 1996-2020 for total phosphorus, total nitrogen, water colour and maximum biovolume of cyanobacteria.</p>
Monthly aggregated GLASS FAPAR V6 (250 m): 50th percentile monthly time-series (2005)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Bermuda Atlantic Time-Series Study (BATS) Pigment Data
<p>This dataset is published on Zenodo by the Simons CMAP curators for long-term care. All credits go to the data producers at the Bermuda Atlantic Time-series Study (BATS): https://bats.bios.asu.edu/bats-data/ </p><p>The BATS (Bermuda Atlantic Time-series Study) Pigment dataset is time-series spanning from 1988 to 2022. The dataset contains the 21 separate in-situ pigment measurements along with sampling depth and the BATS Cruise ID.</p><p>This description has been reproduced using https://www.dropbox.com/s/8kk760972lpj5sa/bats_pigments.txt?dl=0</p>
Two-point time-series of dental wear scores for baboon mandibular second molars
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Data from: Girth increment changes in response to soil water availability in lowland dipterocarp forest in Borneo: an individualistic time-series analysis
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RNA-seq data for an embryonic chicken digit tissue time-series, treated in vivo with smoothened agonist to induce ectopic feathers
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Data from: Whiskers provide time-series of toxic and essential trace elements, Se:Hg molar ratios, and stable isotope values of an apex Antarctic predator, the leopard seal
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Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2002.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Data are 5 minute averages of 10 second measuremsents.
Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2005.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Theses are the 5 minute averages of 10 second measuremsents.
Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2004.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water.of water temperatures at several depths from a moored chain of thermistors. Theses are the 5 minute averages of 30 second measuremsents.
Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2006.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water.of water temperatures at several depths from a moored chain of thermistors. Theses are the 5 minute averages of 10 second measuremsents.
Time-series of 5 minute water temperatures averages from Lake E5 near Toolik Field Station, Alaska Summer 2003.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water.of water temperatures at several depths from a moored chain of thermistors. Theses are the 5 minute averages of 10 second measuremsents.
ScienceDex guides
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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.