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.
1,003
datasets available to search
ShareScore release 0.9.0
Dataset results
1,003 results for “darkness”
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Half-hourly growing season, chamber-based, CO2 flux data, with dark daytime measurements, 2014
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. This data contains CO2 fluxes measured using an automated chamber system that measures net CO2 exchange (NEE). Measurements are made every ~1.5 hours and modeled half-hourly. Half hour ecoystem respiration is modeled using an exponential Q10 relationship when light conditions are low (PAR<5umol/m2/s) and using a hyperbolic light relationship when PAR>5umol/m2/s. Addditional dark daytime Reco measurements were used to define the Q10 model. GPP is calculated as the difference between NEE and Reco.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Weekly dark CO2 fluxes, 2014
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. How does warming impact the phenology of dominant plant species? This data contains manual CO2 flux measurements collected using a dark, portable CO2 flux chamber. These measurements are intended to estimate daytime ecosystem respiration to provide some validation for ecosystem respiration modeled with an exponential temperature response from clear, automated CO2 flux chambers at this same site. Dark flux measurements were taken weekly from 25 April 2014 to 23 Sep 2014.
Plant-mediated root methane emissions and oxidation in a thermokarst bog complex in the Bonanza Creek LTER Experimental Forest III - Non-standard Dark Methane Fluxes and Associated Standard Oxic Fluxes 2015
Vascular plants are important in the wetland methane cycle but their effect on production, oxidation, and transport has high uncertainty, limiting our ability to predict emissions. Vegetation operated on top of baseline methane emissions, which varied with proximity to the thawing permafrost margin. Emissions from vegetated plots increased over the season, resulting in cumulative seasonal methane emissions that were 4.1-5.2 g m-2 season-1 greater than unvegetated plots. Mass balance calculations signify these greater emissions were due to increased methane production (3.0-3.5 g m-2 season-1) and decreased methane oxidation (1.1-1.6 g m-2 season-1). Minimal oxidation occurred along the plant-transport pathway and oxidation was suppressed outside the plant pathway. Our data indicate suppression of methane oxidation was stimulated by root exudates fueling competition among microbes for electron acceptors. Root exudates are known to fuel methane production and our work provides evidence they also decrease methane oxidation. This dataset contains the 2015 weekly anoxic control methane flux from treatment plots within a bog complex in the Bonanza Creek LTER. Anoxic control measurements used to assess the effect of the anoxic fluxes on results. Treatments include natural-vegetation, simulated-aerenchyma, and sphagnum-only. No updates are planned.
QCD Uncertainties in Particle Spectra from Dark Matter Annihilation (updated data can be found in GitHub: https://github.com/ajueid/qcd-dm.github.io.git)
<p>************************************************************************************************</p> <p>QCD Uncertainties on Particle Spectra from Dark Matter Annihilation</p> <p><strong>Please check the updated data at GitHub: https://github.com/ajueid/qcd-dm.github.io.git</strong></p> <p>Authors: Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, and Peter Skands</p> <p>If you use these tables, please cite:</p> <p>S. Amoroso et al. arXiv: 1812.07424 [hep-ph], JCAP05(2019)007</p> <p>************************************************************************************************</p> <p>We provide the spectra of stable particles in dark matter annihilation, in the galactic region or beyond, in a tabulated form using PYTHIA8 version 8235. In addition to the central prediction, we estimate for the first time the QCD uncertainties both due to hadronization as well as to showering. The uncertainties on the spectra are provided in separate tables. A wide range of dark matter masses from 10 GeV to 100 TeV is covered. We consider 11 primary annihilation channels:</p> <p>DM DM -> e+e-, mu+ mu-, tau tau, qq (q=u,d,s), cc, bb, tt, WW, ZZ, gg, and hh.</p> <p>Each file contains 13 columns: the dark matter mass, the fraction x -- defined as the kinetic energy of the particle divided by the DM mass -- in the logarithmic scale, and dN/dLog_10(x) for 11 primary channels. The provided tables correspond to the dN/dLog_10(x) of Standard Model stable particles, i.e. of photons, positrons, electron anti-neutrinos, muon anti-neutrinos and tau anti-neutrinos.</p> <p>The work on the spectra of anti-protons is ongoing (please come back soon). </p> <p>For each particle species, we provide twelve tables which can be found in zip format. The notation of the different tables is given below:</p> <p> 1) The table corresponding to the central prediction for the spectra is denoted by 'AtProduction-Hadronization1-$TYPE.dat' with $TYPE=Nuel, Numu, Nuta, Ga which refers to the three flavours of neutrinos, and photons respectively.</p> <p> 2) There are nine tables corresponding to the different variations of the light quark fragmentation function's parameters. These tables are denoted by 'AtProduction-Hadronization$h-$TYPE.dat' with h=2,..,10.</p> <p> 3) The particle spectra corresponding to the variations of the shower evolution scale (mu_R) are denoted by 'AtProduction-Shower-Var$s-$TYPE.dat' with s=1,2 corresponds to 1/2 mu_R and 2 mu_R. </p> <p><em><strong>IMPORTANT:</strong></em></p> <p> i) Uncertainty on the spectra, from hadronization, is obtained from the envelope of all the variations (including the central prediction).</p> <p> ii) In the variations of the parton shower evolution scale, the parameters of the hadronization function are fixed to their central value.</p> <p> iii) In principle, showering uncertainties are uncorrelated to hadronization uncertainties. To obtain the full uncertainty, one might combine those uncertainties in quadrature.</p> <p><em><strong>If you use the data on the site, please cite:</strong></em></p> <p>Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, Peter Skands, "Estimating QCD uncertainties in Monte Carlo event generators for gamma-ray dark matter searches," <strong>JCAP 05 (2019) 007</strong>, arXiv: 1812.07424.</p> <p><em><strong>In addition, if you use the data corresponding to shower uncertainties, please cite:</strong></em></p> <p>S. Mrenna and P. Skands, "Automated Parton-Shower Variations in Pythia 8,'' <strong>Phys. Rev. D 94 (2016) no.7</strong>, 074005, arXiv:1605.08352 [hep-ph].</p> <p><em><strong>Finally, please cite the paper of M. Cirelli et al. if you use their data for comparison or other tasks:</strong></em></p> <p>M.Cirelli, G.Corcella, A.Hektor, G.Hütsi, M.Kadastik, P.Panci, M.Raidal, F.Sala, A.Strumia, "PPPC 4 DM ID: A Poor Particle Physicist Cookbook for Dark Matter Indirect Detection'', <strong>JCAP 1103 (2011) 051</strong>, arXiv 1012.4515, Erratum: <strong>JCAP 1210 (2012) E01</strong>.</p> <p>Contact: <em>Adil Jueid</em> <adil.hep@gmail.com></p>
Dataset for paper "Decaying dark matter signal across the Milky Way?"
<p>Summary of observations after cleaning.</p> <p>File naming: mw-xmm-obs-<region>.csv, where <region> identifies corresponding region bounds in arcmin.</p> <p>List of fields:<br> ObsId - XMM-Newton observation id<br> l - galactic longitude, degrees<br> b - galactic latitude, degrees<br> off-center - an angular distance of the instrument pointing direction from the Galactic center, arcmin<br> Exposure - cleaned exposure of the MOS1/MOS2/PN cameras, ksec<br> FoV - spectra extraction regions of the MOS1/MOS2/PN spectra (field-of-view), arcmin^2</p>
Data from: Dark ophiuroid biodiversity in a prospective abyssal mine field
The seafloor contains valuable mineral resources, including polymetallic (or manganese) nodules that form on offshore abyssal plains. The largest and most commercially attractive deposits are located in the Clarion Clipperton Fracture Zone (CCZ), in the eastern Pacific Ocean (EP) between Hawaii and Mexico, where testing of a mineral collection system is set to start soon [1]. The requirement to establish pre-mining environmental management plans has prompted numerous recent biodiversity and DNA barcoding surveys across these remote regions. Here we map DNA sequences from sampled ophiuroids (brittle-stars, including post-larvae) of the CCZ and Peru Basin onto a substantial tree-of-life to show unprecedented levels of abyssal ophiuroid phylogenetic diversity including at least three ancient (>70 my), previously unknown clades. While substantial dark (unobserved) biodiversity has been reported from various microbial meta-barcoding projects [2, 3], our data shows that we have considerably under-estimated the biodiversity of even the most conspicuous mega-faunal invertebrates [4] of the EP abyssal plain.
Probing Dark Matter with Disappearing Tracks at the LHC
<p>This data set includes:</p> <p>1. Tables with efficiencies and cross section limits from disappearing charged tracks in ( life time - DM mass) plane for </p> <p>i2HDM (Inert Doublet Model), MFDM (Minimal Fermion DM model) and VTDM (Vector Triplet DM model).</p> <p>These are *_efficiency_and_xs_limit.dat files</p> <p>2. Tree-level cross sections VS DM mass for Pair DM production: D+D- and D+/- D</p> <p>These are * _prod_xs.dat files.</p> <p> </p> <p>See details in https://arxiv.org/pdf/2008.08581.pdf</p>
Haberlea darkness 2018 gene expression
<p>The desiccation-tolerant plant Haberlea rhodopensis can withstand months of darkness without any visible senescence. Here, we investigated the molecular mechanisms of this adaptation to prolonged (30 d) darkness and subsequent return to light. H. rhodopensis plants remained green and viable throughout the dark treatment. Transcriptomic analysis revealed that darkness regulated several transcription factor (TF) genes. Stress- and autophagy-related TFs such as ERF8, HSFA2b, RD26, TGA1, and WRKY33 were up-regulated, while chloroplast- and flowering-related TFs such as ATH1, COL2, COL4, RL1, and PTAC7 were repressed. PHYTOCHROME INTERACTING FACTOR4, a negative regulator of photomorphogenesis and promoter of senes-cence, also was down-regulated. In response to darkness, most of the photosynthesis- and photorespiratory-related genes were strongly down-regulated, while genes related to autophagy were up-regulated. This occurred concomitant with the induction of SUCROSE NON-FERMENTING1-RELATED PROTEIN KINASES (SnRK1) signaling pathway genes, which regulate responses to stress-induced starvation and autophagy. Most of the genes associated with chlorophyll catabolism, which are induced by darkness in dark-senescing species, were either unregulated (PHEOPHORBIDE A OXYGENASE, PAO; RED CHLOROPHYLL CATABOLITE REDUCTASE, RCCR) or repressed (STAY GREEN-LIKE, PHEOPHYTINASE, and NON-YELLOW COLORING1). Metabolite profiling revealed increases in the levels of many amino acids in darkness, suggesting increased protein degrada-tion. In darkness, levels of the chloroplastic lipids digalactosyldiacylglycerol, monogalactosyldiacylglycerol, phosphatidylglyc-erol, and sulfoquinovosyldiacylglycerol decreased, while those of storage triacylglycerols increased, suggesting degradation of chloroplast membrane lipids and their conversion to triacylglycerols for use as energy and carbon sources. Collectively, these data show a coordinated response to darkness, including repression of photosynthetic, photorespiratory, flowering, and chloro-phyll catabolic genes, induction of autophagy and SnRK1 pathways, and metabolic reconfigurations that enable survival under prolonged darkness.</p>
Data from: Quantifying the dark data in museum fossil collections as palaeontology undergoes a second digital revolution
Large-scale analysis of the fossil record requires aggregation of palaeontological data from individual fossil localities. Prior to computers these synoptic datasets were compiled by hand, a laborious undertaking that took years of effort and forced palaeontologists to make difficult choices about what types of data to tabulate. The advent of desktop computers ushered in palaeontology's first digital revolution – online literature-based databases, such as the Paleobiology Database (PBDB). However, the published literature represents only a small proportion of the palaeontological data housed in museum collections. Although this issue has long been appreciated, the magnitude, and thus potential significance, of these so-called "dark data" has been difficult to determine. Here, in the early phases of a second digital revolution in palaeontology the digitization of museum collections – we provide an estimate of the magnitude of palaeontology's dark data. Digitization of our nine institutions' holdings of Cenozoic marine invertebrate collections from California, Oregon, and Washington in the United States reveals that they represent 23 times the number of unique localities than are currently available in the Paleobiology Database. These data, and the vast quantity of similarly untapped dark data in other museum collections, will when digitally mobilized enhance palaeontologists' ability to make inferences about the patterns and processes of past evolutionary and ecological changes.
Data from: Breeding season length and nest mortality drive cryptic life history variation in Dark-eyed Juncos (Junco hyemalis) breeding across a montane elevation gradient
The manner in which individual life history traits respond to the environment and to each other, and how these traits combine to form overall patterns of life history variation, remains poorly characterized in wild populations. We monitored breeding Dark-eyed Juncos (Junco hyemalis) across a 700-m elevational range. We compared breeding season length, temporal patterns of breeding activity, adult body size, clutch size, brood size, nestling quality, and nest mortality among elevations. We also compared environmental measures across the studied elevations to determine whether abiotic factors explained life history trait variation. We used 12 microsatellite loci to test for genetic differentiation in populations at different elevations. Finally, we constructed a computer simulation to evaluate the combined effects of observed variation in life history traits. We found differences among elevations in breeding season length and in patterns of reproductive timing, which did not match each other and which were not explained solely by abiotic factors. We found no differences among elevations in adult body size, clutch size, brood size, or nestling quality. Nest mortality increased significantly with elevation. Genetic differentiation was too low to define distinct subpopulations. The simulation suggested that differences in mortality, in combination with differences in breeding season length, contributed to substantial differences in reproductive success among elevations. Thus, although individual life history traits showed little evidence of variation in response to the environment or to each other and little genetic differentiation, variation in breeding season length and in nest mortality were potential drivers of substantial elevational variation in overall life history in this system. These results demonstrate that individual life history traits may vary substantially in their patterns of variation, and that some life history traits may have disproportionate effects on overall life history.
Data from: Concealed by darkness: interactions between predatory bats and nocturnally migrating songbirds illuminated by DNA sequencing
Recently, several species of aerial-hawking bats have been found to prey on migrating songbirds, but details on this behaviour and its relevance for bird migration are still unclear. We sequenced avian DNA in feather-containing scats of the bird-feeding bat Nyctalus lasiopterus from Spain collected during bird migration seasons. We found very high prey diversity, with 31 bird species from eight families of Passeriformes, almost all of which were nocturnally flying sub-Saharan migrants. Moreover, species using tree hollows or nest boxes in the study area during migration periods were not present in the bats' diet, indicating that birds are solely captured on the wing during night-time passage. Additional to a generalist feeding strategy, we found that bats selected medium-sized bird species, thereby assumingly optimizing their energetic cost-benefit balance and injury risk. Surprisingly, bats preyed upon birds half their own body mass. This shows that the 5% prey to predator body mass ratio traditionally assumed for aerial hunting bats does not apply to this hunting strategy or even underestimates these animals' behavioural and mechanical abilities. Considering the bats' generalist feeding strategy and their large prey size range, we suggest that nocturnal bat predation may have influenced the evolution of bird migration strategies and behaviour.
Data presented in "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states"
<p>These are the data presented in our paper "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states" which has been accepted for publication in the New Journal of Physics (as of 07 November 2016).</p>
Crater 2: An Extremely Cold Dark Matter Halo
<p>supplementary data products, including all sky-subtracted spectra from individual targets, as well as random draws from posterior PDFs for model parameters (see enclosed README file)</p> <p> </p>
Database Darkly files
<p><a href="https://shu251.github.io/Database-Darkly/" target="_blank" rel="noopener">Database Darkly website</a></p> <p><strong>File description</strong></p> <ul> <li>deepsea-18s-merged-asv-table-05022025.qza - QIIME2 output of merged ASV table</li> <li> <p>deepsea-18s-merged-asv-table-05022025.tsv - tab separated version of merged ASV table</p> </li> <li> <p>ref-seqs-deepsea-DB_052025.fasta - reference sequences for all ASVs - matches Feature ID</p> </li> <li> <p>deepsea-18s-merged-seqs-05022025.qza - QIIME2 output of merged reference sequences</p> </li> <li> <p>taxonomy-05022025.tsv - tab seaprated PR2 (version 5.0.0) assigned taxonomy for ASV table above (matcheds Feature ID). Qiime2 code used 0.9 identity with 0.8 consensus</p> </li> <li>axial-2023-metadata.csv - metadata for Axial 2023 and 2022 data<br><br></li> <li>metadata-deepsea_GR_MCR_Axial-18S.csv - metadata for Gorda Ridge, Mid-Cayman Rise, and archived Axial data. This metadata is also associated with this publication: S. K. Hu, A. R. Smith, R. E. Anderson, S. P. Sylva, M. Setzer, M. Steadmon, K. L. Frank, E. W. Chan, D. S. S. Lim, C. R. German, J. A. Breier, S. Q. Lang, D. A. Butterfield, C. S. Fortunato, J. S. Seewald, J. A. Huber, Globally-distributed microbial eukaryotes exhibit endemism at deep-sea hydrothermal vents. <em>Mol. Ecol.</em> <strong>32</strong>, 6580–6598 (2023).</li> </ul> <p><strong>Project goals:</strong></p> <p>Curate and annotate what we know about deep-sea marine microbial eukaryotes. Much of what we know about deep-sea microorganisms comes from the biogeography and inferred ecology of environmental species or strains. This database originates from deep-sea marine habitats, specifically hydrothermal vents. Species and strains capture are assumed to be present in these environments; however what we know about their ecological roles remains understudied. This database project involves extensive literature searches, comparative analyses, and inventories of these species, so we can get closer to having a more complete understanding of the roles protists play in the deep ocean.</p> <p>There are three primary goals of this project: (1) Complement our comprehensive sequence database of deep-sea microbial eukaryotic diversity with metadata on taxonomic nomenclature (Costello et al. 2013; Horton et al. 2017), tree of life placement (e.g., iTOL and Open Tree of Life Letunic and Bork 2021; Michonneau et al. 2016; OpenTreeOfLife et al. 2019), a functional trait database (e.g., cell morphology, motility, ploidy, etc.; Ramond et al. 2019), and accounts of planktonic protist interactions (Bjorbækmo et al. 2020), (2) to highlight the number of taxonomic lineages that are severely lacking representation in the sequence reference database (Guillou et al. 2012), and (3) to look for patterns in the assignment of the phylogenetically elusive protists that may be related to fluid geochemistry or biogeography.</p> <p> </p> <p><strong>Contributors to curation effort:</strong></p> <ul> <li>Sarah Hu (PI, TAMU)</li> <li>Abby Day (Undergrad, TAMU)</li> <li>Madeleine Lerma (Undergrad, TAMU)</li> <li>Meagan Sonsel (Undergrad, TAMU)</li> <li>Siddarth Seshampally (Undergrad, TAMU)</li> <li>Alexis Adams (PhD student, TAMU)</li> <li>Kayla Nedd (PhD student, TAMU)</li> </ul> <p> </p> <p><strong>Project updates:</strong></p> <p><strong>Version 2 updates - May 2025</strong></p> <p>- New dataset added from Axial Seamount. All sequences re-merged and re-assigned taxonomy with updated PR2 version 5.0.0</p> <p> </p> <p><strong>Version 1 files: (published June 2024)</strong></p> <ol> <li>DatabaseDarkly_taxa_06052024.csv - version 1 of a curated database. Taxonomic assignments from all deep-sea hydrothermal vent ASVs were compiled with exisiting knowledge</li> <li>reference-seqs-databasedarkly.fasta - reference sequences for all ASVs and taxonomic assignments</li> <li>reference-taxIDs-databasedarkly.tsv - Table with taxonomic assignments and sequence IDs (Feature ID)</li> <li>QIIME2 files - Use the below .qza files in your qiime2 pipeline to compare ASVs. <ul> <li>microeuk-merged-asv-table.qza</li> <li>microeuk-merged-ref-seqs.qza</li> <li>microeuk-merged-taxa.qza</li> </ul> </li> </ol>
Dark Matter Annihilation and Pair Instability Supernovae
<p>Reproduction package for the paper "Dark Matter Annihilation and Pair Instability Supernovae".</p>
Data for figures and tables in: "Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument"
<p>Supplementary material to DESI's publication 'Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument' to comply with the data management plan.</p>
- Wings with dark patterns, yellowish to black (a, b); tropical Africa ………………………………4 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Wings with dark patterns, yellowish to black (a, b); tropical Africa ………………………………4
A large-scale longitudinal structured dataset of the dark web cryptomarket Evolution (2014–2015)
<p>Data includes a structured dataset of the forum and marketplace of the dark web cryptomarket Evolution as well as a longitudinal network snapshot dataset of a temporal weighted communication network extracted from the forum data. The datasets were extracted from Dark Net Market archive data while resolving many of the data quality issues inherent to raw data scraped from online sources.</p><p>Dark web cryptomarkets facilitate the online trade of illicit goods. Evolution was active from January 2014 until March 2015; and formed a combination of a carding forum and an underground drug market. This dataset includes the vast majority of data that could be obtained from the webpages of the forum and marketplace of Evolution. This includes, for example, information on the number of sales each individual vendor has completed, forum post contents, and product categories.</p><p>The extracted communication network models users' co-posting in the same forum topics, in essence modelling communication between them.</p><p>Additionally included is the code used to produce the dataset from the Dark Net Market archive data.</p>
Establishing Fully-Automated Fundus-Controlled Dark Adaptometry: A Validation and Retest-Reliability Study
<p>This is the data presented in our manuscript <i>'Establishing Fully-Automated Fundus-Controlled Dark Adaptometry: A Validation and Retest-Reliability Study'</i> published in Translational Vision Science & Technology.</p>
Supplementary animations for "Dynamical friction in self-interacting ultralight dark matter"
<p>Animations to supplement "Dynamical friction in self-interacting ultralight axion dark matter", <em>Phys.Rev.D</em> 109 (2024) 6, 063501.</p> <p>These animations are also available from <a href="https://www.youtube.com/playlist?list=PLHrf0iQS5SY6COihRVYJkz31smUyDvFwW">https://www.youtube.com/playlist?list=PLHrf0iQS5SY6COihRVYJkz31smUyDvFwW</a>.</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.