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3,481 results for “data set”
GEODAR data of snow avalanches at Vallée de la Sionne: Seasons 2010/11, 2011/12, 2012/13 & 2014/15 [Data set]
<p>This data repository contains radar data from 77 snow avalanches recorded using the GEODAR (GEOphysical flow dynamics using pulsed Doppler radAR) system at the Swiss full-scale avalanche testsite Vallée de la Sionne. GEODAR is a purpose built, advanced phased-array FMCW system.</p> <p>The data contain range-time plots of intensities gained from moving target identification (MTI) processing (-MTI.h5), an PDF preview image (-MTI.pdf), the trajectory of the front in range and time (-TRAJ-001.h5), the corresponding Thalweg as steepest descent from release area (-Thal-001.h5) and a processing info file in Matlab format (-info.mat).</p> <p>This document covers details about the different versions of the radar setup and raw data processing steps as well as a description of the repository content (see file geodar_repository.pdf).</p> <p> </p>
Data set for anomaly detection on a HPC system
<p>This data set contains the data collected on the DAVIDE HPC system (CINECA & E4 & University of Bologna, Bologna, Italy) in the period March-May 2018.</p> <p>The data set has been used to train a autoencoder-based model to automatically detect anomalies in a semi-supervised fashion, on a real HPC system.</p> <p>This work is described in:</p> <p>1) "Anomaly Detection using Autoencoders in High Performance Computing Systems", <a href="https://arxiv.org/search/cs?searchtype=author&query=Borghesi%2C+A">Andrea Borghesi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartolini%2C+A">Andrea Bartolini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lombardi%2C+M">Michele Lombardi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Milano%2C+M">Michela Milano</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benini%2C+L">Luca Benini,</a> IAAI19 (proceedings in process) -- https://arxiv.org/abs/1902.08447</p> <p>2) "Online Anomaly Detection in HPC Systems", <a href="https://arxiv.org/search/cs?searchtype=author&query=Borghesi%2C+A">Andrea Borghesi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Libri%2C+A">Antonio Libri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benini%2C+L">Luca Benini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartolini%2C+A">Andrea Bartolini, </a>AICAS19 (proceedings in process) -- https://arxiv.org/abs/1811.05269</p> <p>See the git repository for usage examples & details --> https://github.com/AndreaBorghesi/anomaly_detection_HPC</p>
Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip: Experimental and simulation data sets
<p>This data set collates the experimental and simulation data for the research paper "Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip".</p>
Data sets for "Magnetic helicity dissipation and production in an ideal MHD code"
<pre>The tar archive Helicity_in_IdealMHDCode.tar contains an index.html file with links to a directory with "Add-ons" to the FLASH code and the flash.par file. We also list the IDL directory with secondary data and plot routines for each figure used in the paper "Magnetic helicity dissipation and production in an ideal MHD code" by Axel Brandenburg (Nordita) and Evan Scannapiecoo (Arizona State University) with the URL https://arxiv.org/abs/1910.06074.</pre>
Data set: Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes
<h1>Repository for "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes"</h1> <p>---</p> <p>These data scripts were used to perform analyses included in the research paper "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes" </p> <p>Main questions for the study:</p> <p>1. What are the main axes of morphological variation?<br>2. Does variation in morphology among species correlate with their current environments? <br>3. Are lineages that occupy ecologically similar habitats morphologically convergent? <br>4. Is speciation predominantly allopatric or sympatric? <br>5. Do sister species have greater morphological and ecological niche overlap than expected relative to non-sister species pairs?</p> <h2>## Data structure</h2> <p>Contents in the data folder is archived as a zip and can be downloaded from Zenodo (for all versions see https://zenodo.org/doi/10.5281/zenodo.10397830). Once you unzip the zipped files, you will see three folders and some files that are no in any folders. </p> <p>/data/ - files that were manually created and the phylogeny</p> <p>/data/script_generated_data/ - A combination of processed data needed to run the analyses </p> <p>/data/dorsal/ - photographs of the head from the dorsal view. These photos were used for digitising landmarks and semilandmarks. </p> <p>/data/worldclim2_30s/ - cropped and merged annual temperature from WorldClim2 (Fick and Hijmans 2017), soil bulk density from <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData.html">Soil and Landscape Grid of Australia</a>, and Global Aridity Index from Zomer et al. (2022). <br><br>/DREaD/ - contains some files required to replicate DREaD analysis</p> <h2>## Code/Software</h2> <p>All scripts can be run using open source software. Scripts should be run in order to create necessary files that will be saved in /data/script_generated_data/ for further scripts. R is required to run R scripts (.R).</p> <h3>### /Code</h3> <p> - utility/*.R - scripts for custom functions. These are sourced in other scripts.<br> - DREaD/*.R - scripts associated with DREaD analyses<br> - 00_linear_measurement_shaperatio.R - script used to account for sexual dimorphism and calculate conventional PCA. Addresses Q1.<br> - 01_model_fitting.R - script used to address Q2 and plot visualisations.<br> - 02_convergence.R - this script calculates Ct1-4 and C5 scores. Addresses Q3.<br> - 02_convergence_model_fitting.R - this script evaluates fit of different evolutionary models to traits. Addresses Q3.<br> - 02_convergence_test_simulations.R - simulation studies to show that our phylogeny has sufficient power to detect convergence.<br> - 03_niche_enmtools_bias_account.R - calculates ecological niche models (ENMs) for each species using MAXENT. Runs Age-Overlap Correlation tests for geography and ENMs. Partially addresses Q4.<br> - 03_DREaD_Blindsnakes_AS.R - script to run DREaD analysis. <br> - 03_morpho_niche_overlap_plots.R - Runs Age-Overlap Correlation tests for body shape and snout shape. Plots AOCs. Partially addresses Q4. <br> - 04_pairwise_distance_test.R - Binomial tests between sister and non-sister pairs for ENMs and Geographic Range. Partially addresses Q5<br> - 04_morpho_pairwise.R - Binomial tests between sister and non-sister pairs for body shape and snout shape. Partially addresses Q5</p> <h2>## Contact</h2> <p>Should you have questions about these scripts or would like to request raw data, please do not hesitate to contact Sarin Tiatragul (contact information can be found in the paper) or on Github (https://github.com/stiatragul/blindsnakemorphoevo)</p> <h2>## References</h2> <p><a name="ref-fickWorldClim2017"></a>Fick, S. E., and R. J. Hijmans. 2017. <a href="https://doi.org/10.1002/joc.5086">WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas</a>. International Journal of Climatology 37:4302–4315.</p> <p><a name="ref-zomerVersion2022"></a>Zomer, R. J., J. Xu, and A. Trabucco. 2022. <a href="https://doi.org/10.1038/s41597-022-01493-1">Version 3 of the global aridity index and potential evapotranspiration database</a>. Scientific Data 9:409.</p>
Modulation of bioelectric cues in the evolution of flying fishes [Data set]
<p>Assembled reference contigs for protein-coding exons and conserved non-coding regions from targeted sequence enrichment of beloniform fishes. </p> <p>Current citation: Daane JM, Blum N, Lanni J, Boldt H, Iovine MK, Johnson SL, Lovejoy NR, and MP Harris. (2021). Novel regulators of growth identified in the evolution of fin proportion in flying fish. <em>bioRxiv. </em>doi: 10.1101/2021.03.05.434157</p> <p>-contigs.tar.gz contains the assembled contigs for each species. Each contig represents a targeted region with the addition of flanking DNA sequence</p> <p>-cnes.tar.gz contains the targeted conserved non-coding regions isolated from the larger contigs in contigs.tar.gz</p> <p>-exons.tar.gz contains the targeted protein coding exons isolated from the larger contigs in contigs.tar.gz</p> <p>-translated_exons.tar.gz contains the translated protein coding exons from exons.tar.gz</p> <p>-Beloniformes.tre is the species tree </p> <p>-medaka_cne_great.txt contains the associations between the assembled CNEs and neighboring protein-coding genes based on the GREAT approach </p>
The VAROS Synthetic Underwater Data Set: Towards realistic multi-sensor underwater data with ground truth
<p>Underwater visual perception requires being able to deal with bad and rapidly varying illumination and with reduced visibility due to water turbidity. The verification of such algorithms is crucial for safe and efficient underwater exploration and intervention operations. Ground truth data play an important role in evaluating vision algorithms. However, obtaining ground truth from real underwater environments is in general very hard, if possible at all. In a synthetic underwater 3D environment, however, (nearly) all parameters are known and controllable, and ground truth data can be absolutely accurate in terms of geometry. In this paper, we present the VAROS environment, our approach to generating highly realistic underwater video and auxiliary sensor data with precise ground truth, built around the Blender modeling and rendering environment. VAROS allows for physically realistic motion of the simulated underwater (UW) vehicle including moving illumination. Pose sequences are created by first defining way-points for the simulated underwater vehicle which are expanded into a smooth vehicle course sampled at IMU data rate (200Hz). This expansion uses a vehicle dynamics model and a discrete-time controller algorithm that simulates the sequential following of the way-points. The scenes are rendered using the raytracing method, which generates realistic images, integrating direct light, and indirect volumetric scattering. The VAROS dataset version 1 provides images, inertial measurement unit (IMU) and depth gauge data, as well as ground truth poses, depth images and surface normal images.</p>
The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population: data set
<p>This archive contains pulsar data presented as part of the MNRAS paper: <em>"The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population"</em>.</p> <p>Folded, time-averaged pulse profiles (4 Stokes parameters, 8 frequency channels, 1024 time bins across the period) of the 1271 pulsars listed in Table 1 of the MNRAS paper are included in the ar_files.zip. Ephemerides of these pulsars (as used in the MNRAS paper) are included in the eph_files.zip. The pulsar data are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p> <p>Tables 1, 5, and 6 from the MNRAS paper are included in tables_files.zip as .csv files. The file column_descriptions.txt describes the quantities in columns of these tables.<br> </p>
hvulgaris scRNA data set objects
<p>Converted scRNA data from (Cazet et al. 2022), see a detailed description of the study here: https://doi.org/10.1101/2022.06.21.496857</p> <p>Data were downloaded from https://research.nhgri.nih.gov/HydraAEP/download/scriptsdata/aepAtlasNonDub.rds and converted into AnnData (h5ad) files only keeping the RNA assay (removed integrated and SCT assay) to be able to analyse with e.g. python scanpy package.</p> <p>Note: In the original rds file, the rownames(aepAtlasNonDub@meta.data) are sorted alpha-numerical, whereas the cell order in colnames(aepAtlasNonDub) are not. I have re-sorted the rownames(aepAtlasNonDub@meta.data) according to cell order prior AnnData conversion, so that h5ad data have correct observations.</p> <p>If you use this data, please cite Cazet et al. 2022.</p>
Chandra HRC-I Jupiter X-ray data set
<p>Data set to supplement code scripts contained in the repository: https://github.com/SeanMcEntee/cxo_goes_disk_study</p> <p> </p>
BGQNAPv1.0: A summer macronutrients binned data set for the Northern Antarctic Peninsula, Southern Ocean
<p>We compiled a time series spanning the period from 1996 to 2019 of the seawater hydrographic variables conservative temperature (<sup>o</sup>C), absolute salinity (g kg<sup>­–1</sup>) and dissolved oxygen (μmol kg<sup>­–1</sup>), and the macronutrients DIN (nitrate + nitrite + ammonium), phosphate, and silicic acid (μmol kg<sup>­–1</sup>). The study area covered the northern Antarctic Peninsula regions including the Gerlache Strait and the western, central, and eastern basins of Bransfield Strait. Most data (~90%) were obtained from the Brazilian High Latitude Oceanography Group (GOAL; http://goal.furg.br/) from austral summer field campaigns (January-March). In some years (1996, 2005, 2006, 2010, 2011) we used hydrographic and macronutrient data from GLODAP 2020 (Olsen et al., 2020) along the NAP and exceptionally for 1996 we used data available from December 1995 to February 1996 (the FRUELA cruises, García et al., 2022; Álvarez et al., 2002). Details on the sampling and analysis of macronutrient data obtained from GLODAP dataset can be accessed on the OCADS platform (<a href="https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system-portal/">https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system-portal/</a>).</p> <p>About 97% of the DIN data were composed of nitrate, followed by ammonium (2%) and nitrite (1%). Therefore, in some cases (11% of all data), we considered DIN as the nitrate concentration, when no nitrite and/or ammonium data were available. Discrete seawater samples were collected at irregular depth intervals from surface (5 m) to deep waters (at approximately 15 m from the bottom). We averaged the parameters for each region at regular depth intervals from the surface to the bottom (i.e., 0, 25, 50, 75, 100, 250, 500, 750, 1000, 1250, 1500, 1750, 2000, 2500 m) to obtain an averaged summer profile for each year.</p> <p>All sampling and analyses information of the hydrographic and macronutrients are detailed in Kerr et al. (2018), Mata et al. (2018), Dotto et al. (2021) and Costa et al. (2020), and references therein.</p>
Example data set for the R package riversCentralAsia
<p>This data set contains example data for demonstrating the functionality of the R package riversCentralAsia. riversCentralAsia (https://github.com/hydrosolutions/riversCentralAsia) includes several functions for pre-processing hydrological data to facilitate hydrological modelling with RS MINERVE (https://crealp.github.io/rsminerve-releases/). The package is used extensively in the open-source teaching course Modeling of Hydrological Systems in Semi-Arid Central Asia (https://hydrosolutions.github.io/caham_book/). </p>
Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</p>
Data set for (binary) text classification, involving spoken utterances and written text
<p>This data set contains sentences belonging to either of two classes: Transcripts of spoken<br> (informal) text (Class 0), and written, formal text (Class 1). Sentences in Class 0 were<br> obtained from publicly available transcripts of radio shows (e.g. NPR),<br> whereas Sentences in Class 1 were obtained from Wikipedia.</p> <p>The data set is divided into three subsets: Training, validation, and test (specified by the file names).<br> Each set contains a large number of sentences, belonging to either of the two classes:</p> <p>In total, there are 13,640,458 sentences, of which 6,374,487 in Class 0 and 7,265,971.<br> The training set contains 9.743,188 sentences (of which 4,553,205 in Class 0 and 5,189,983 in Class 1), <br> the validation set contains 1,948,639 sentences (of which 910,641 in Class 0 and 1,037,998 in Class 1), and the <br> test set contains 1,948,631 sentences (of which 910,641 in Class0 and 1,037,990 in Class1). </p> <p>The data sets are in plain text format. Every row contains (i) the class label (0 or 1) and<br> (ii) the text of the sentence, separated from the class label by a tab character.</p> <p>Note that the sentences contain 5 tokens or more (including punctuation marks). </p>
Global mangrove soil carbon data set at 30 m resolution for year 2020 (0-100 cm)
<p>Global soil organic carbon stocks in mangrove forests at 30 m resolution, and predicted for 2020 using spatiotemporal ensemble machine learning. Soil organic carbon stock (t/ha) was derived using predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks.</p> <p>The "mangroves_tiles_SOC_predictions_2020.zip" file contains predictions of SOC content, Bulk Density (BD) and aggregated SOC stocks (t/ha) for 0—100 cm depth interval. Example of a tile:</p> <ul> <li>089E_21N (89E to 90E, 21N to 22N): <ul> <li>sol_db.od_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted BD aggregated to 0—100 cm;</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..0cm_2020_global_v1.1.tif = predicted SOC content (%) at 0 cm depth (surface soil);</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..100cm_2020_global_v1.1.tif = predicted SOC content (%) for 0—100 cm;</li> <li>sol_soc.tha_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha (mean value);</li> <li>sol_soc.tha_mangroves.typology_l.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha lower 95% probability prediction interval;</li> <li>sol_soc.tha_mangroves.typology_u.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha upper 95% probability prediction interval;</li> </ul> </li> </ul> <p>Example of a tile:</p> <ul> <li>class : RasterLayer</li> <li>dimensions : 4004, 4004, 16032016 (nrow, ncol, ncell)</li> <li>resolution : 0.00025, 0.00025 (x, y)</li> <li>extent : 88.9995, 90.0005, 20.9995, 22.0005 (xmin, xmax, ymin, ymax)</li> <li>crs : +proj=longlat +datum=WGS84 +no_defs</li> <li>source : sol_db.od_mangroves.typology_m_30m_s0..0cm_2002_global_v0.1.tif</li> </ul> <p>To load global mosaics <strong><strong>Soil Carbon t/ha Maps (0—100cm)</strong></strong> as COGs directly into QGIS or similar, best use:</p> <ul> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> </ul>
Data set for the journal article: Colloidal-ALD Grown Metal Oxide Shells Enable the Synthesis of Photoactive Ligand/ Nanocrystal Composite Materials
<p>The data for each figure of the main manuscript is included in this folder.</p> <p>Figure 1 is not included as it contains no data.</p> <p>The folder for Figure 2 contains a sub-folder for the EDX and NMR data of 9-ACA/PbS@AlOx. The NMR data was processed by Mestrenova.</p> <p>The folder for Figure 3 contains optical absorption spectrum data of 9-ACA/PbS@AlOx.</p> <p>The folder for Figure 4 contains NMR data which was processed by Mestrenova. It contains the data for 9-ACA/CuInS2@AlOx, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx.</p> <p>The folder for Figure 5 is made of three sub-folders for figure 5A, 5B and 5C. 5A and 5B contain optical absorption for the CuInS2 and CsPbBr3 datasets while 5C contain time resolved data for CsPbBr3.</p> <p>The folder for Figure 6 contains time resolved PL for the as synthesized CsPbBr3, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx. The 9-PTA/CsPbBr3@AlOx data contain two decays that span 200 ns (short) or 13.5 us (long).</p> <p>The folder for Figure 7 contains time resolved PL for the as synthesized 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx. For both samples the data contain two decays that span 200 ns (short) or 13.5 us (long). Also an NMR folder is present with the 1H spectrum for 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx.</p> <p> </p> <p> </p>
Bots in Software Development: A Systematic Literature Review [Data Set]
<p>This repository contains al the artifacts of the research: Bots in Software Development: A Systematic Literature Review </p>
Data set of two dual-task paradigms to measure listening effort in cochlear implant users
<p>This data set presents the data from the paper by Hendrikse, Dingemanse, & Goedegebure (2022). This study aimed to investigate the feasibility of using listening effort to measure relatively small differences in SNR, as would arise from different hearing-device settings. Listening effort was chosen, because there are indications in literature that listening effort may be more sensitive to differences between hearing-device settings than established speech intelligibility measures. Two behavioral listening effort tests were performed at two signal-to-noise ratios (SNRs) where the intelligibility was high. A sentence final word identification and recall test (SWIRT), and a sentence verification test (SVT) were compared with a group of 18 Dutch CI users. SWIRT measured the ability to recall the final words of sentences after a list of five or seven sentences was presented. The SVT measured the ability and reaction time to determine whether a sentence was true or false. Both tests were conducted in background noise at SNRs +4 dB and +8 dB above the 50% speech perception threshold. The structure of the data files is explained in the README file.</p>
Data set for the journal article: Site-Specific Protein Ubiquitylation Using an Engineered, Chimeric E1 Activating Enzyme and E2 SUMO Conjugating Enzyme Ubc9
<p>Mutations observed in evolved chimeric E1 variants. Top row (1.X to 4.X) describes rounds of evolutions with respective variants in the round. </p> <p>Residues that appear to be enriched are highlighted with gray fill. Star (★) marks residues subjected to saturation mutagenesis in the round 4.</p>
CA-discharge data set, scripts and raw data
<p>Gauge locations of 295 gauges in Central Asia, including long-term norm discharge, basin outlines and basin characteristics compiled from 3rd-party data. Discharge time series for 135 gauge locations collected from the hydrological yearbooks of Hydrometeorological Organisations in Central Asia. </p> <p>Instructions of how to use the data can be found in the readme document in the folder CA-data-paper-scripts. </p> <p>! Important note: Please do not use the glacier thinning rates extracted from Hugonnet et al., 2021 (https://doi.org/10.1038/s41586-021-03436-z), i.e. features gl_dmdt_km3a and gl_dmdtda_mma. The ice density is not accounted for in our averages. </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.