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305 results for “Data deposit”
List of the structures of S-protein in complex with ligands deposited in the Protein Data Bank until the 1st January 2021.
<p>All 131 structures of SARS-CoV-2 S-protein in complex with a ligand released on the PDB until the 1<sup>st</sup> January 2021 were categorised by ligand type: hACE2, antibody Fab fragments, VHH antibody fragments or <em>de novo</em> designed peptide scaffolds. The ligands’ amino acid sequences, the method by which the structures were determined and their resolution were retrieved from the PDB. Information regarding the ligands' production method, dissociation constants (K<sub>D</sub>), S-protein segment against which the K<sub>D</sub> were measured and the determination methods were retrieved from the respective references. The categorisation of ligands by S-protein binding site and listing of S-protein conformation in each structure were achieved by visual analysis of all the structures using molecular visualisation software PyMOL.</p>
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico
Humans are creating significant global environmental change, including shifts in climate, increased nitrogen (N) deposition, and the facilitation of species invasions. A multi-factorial field experiment is being performed in an arid grassland within the Sevilleta National Wildlife Refuge (NWR) to simulate increased nighttime temperature, higher N deposition, and heightened El Niño frequency (which increases winter precipitation by an average of 50%). The purpose of the experiment is to better understand the potential effects of environmental change on grassland community composition and the growth of introduced creosote seeds and seedlings. The focus is on the response of three dominant species, all of which are near their range margins and thus may be particularly susceptible to environmental change. It is hypothesized that warmer summer temperatures and increased evaporation will favor growth of black grama (Bouteloua eriopoda), a desert grass, but that increased winter precipitation and/or available nitrogen will favor the growth of blue grama (Bouteloua gracilis), a shortgrass prairie species. Furthermore, it is thought that the growth and survival of introduced creosote (Larrea tridentata) seeds and seedlings will be promoted by heightened winter precipitation, N addition, and warmer nighttime temperatures. Treatment effects on limiting resources (soil moisture, nitrogen mineralization), species growth (photosynthetic rates, creosote shoot elongation), species abundance, and net primary production (NPP) are all being measured to determine the interactive effects of key global change drivers on arid grassland plant community dynamics. To measure above-ground NPP (i.e., the change in plant biomass, represented by stems, flowers, fruit and foliage, over time), the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots. The data from these pl
Data for the extraction of the critical temperature of niobium films deposited on copper produced at CERN for SRF applications
<p>Each data set contains the voltage signal amplitude induced in a pickup coil by an alternating magnetic field, crossing the niobium-film-on-copper sample during its state transition from normal conducting to superconducting, the corresponding sample temperature and the estimated errors for the two measured quantities. File columns: #1 sample temperature in Kelvin, #2 estimated temperature error, #3 voltage amplitude in pickup coil in Volt, #4 estimated amplitude error.</p> <p>One data set corresponds to one sample. The critical temperature can be extracted as fit parameter by fitting the amplitude versus temperature data with a logistic function, where it corresponds to the half height of the state transition curve.</p>
Ferromagnetic resonance of Co thin films grown by atomic layer deposition on the Sb2Te3 topological insulator (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Ferromagnetic resonance of Co thin films grown by atomic layer deposition on the Sb<sub>2</sub>Te<sub>3</sub> topological insulator</em>" by E. Longo et al., JMMM 209, 166885 (2020): <a href="https://linkinghub.elsevier.com/retrieve/pii/S0304885319336029">https://linkinghub.elsevier.com/retrieve/pii/S0304885319336029</a></p>
Field data collected from pyroclastic and lahar deposits of the 472 AD (Pollena) and 1631 Vesuvius eruptions
<p><strong><span>Field data collected from pyroclastic and lahar deposits of the 472 AD (Pollena) and 1631 Vesuvius eruptions</span></strong></p> <p><span>Mauro A. Di Vito<sup>1</sup>, Ilaria Rucco<sup>2</sup>, Sandro de Vita<sup>1</sup>, Domenico M. Doronzo<sup>1</sup>, Marina Bisson<sup>3</sup>, Elena Zanella<sup>4</sup></span></p> <p><sup><span>1</span></sup><span> Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Napoli, Italy</span></p> <p><sup><span>2</span></sup><span> Heriot-Watt University, School of Engineering and Physical Sciences, Edinburgh, United Kingdom</span></p> <p><sup><span>3</span></sup><span> Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy</span></p> <p><sup><span>4</span></sup><span> Università di Torino, Dipartimento di Scienze della Terra, Torino, Italy</span></p> <p><span> </span></p> <p><span>This dataset is organized in an Excel file, and it includes all the data collected and reviewed during the last 20 years from drill cores, outcrops, archaeological excavations, stratigraphic trenches, and the existing literature. It focuses on the primary (pyroclastic) and secondary (lahar) deposits of the 472 AD (Pollena) and 1631 eruptions from the Somma-Vesuvius volcano. The aim is to collect stratigraphic, stratimetric, sedimentological, lithological and chronological data to generate distribution maps and to validate the numerical simulations and models for the risk assessment. In particular, this dataset is complementary to – and in support of – the full work by Di Vito et al. (2024), in which the distribution of those deposits all around the Somma-Vesuvius complex and further is presented and discussed. Such dataset was used to inform the shallow-water model of lahars by de’ Michieli Vitturi et al. (2024), which in turns was used by Sandri et al. (2024) to elaborate probabilistic maps of lahar invasion in the Somma-Vesuvius and Apennine areas.</span></p> <p><span>All the data are organized in columns: the first four aim to identify the sites, and so there is a numeric identification code (ID), the name of the site (NAME), and the metric coordinates (East-North) in the UTM WGS 84 – Zone 33 reference projection (X, Y). The last two columns are “MUNICIPALITY” and “PROVINCE” and give a spatial location to the points.</span></p> <p><span>For the two eruptions, several columns have been created:</span></p> <p><span><span>·<span> </span></span></span><span>“472_PRIM”, “1631_PRIM” and “472_ASH” and “1631_ASH” indicate, respectively, the fallout primary deposits of the eruptions and the primary ash, particularly the ash related to the last phases of the eruptions (generally phreatomagmatic).</span></p> <p><span><span>·<span> </span></span></span><span>“472_SYN” and “1631_SYN” indicate the syn-eruptive lahars related to the two eruptions, recognized from the similar composition between the primary deposit and the lahar and from the evidence of a short-term exposure between the two.</span></p> <p><span><span>·<span> </span></span></span><span>“472_POST” AND “1631_POST” indicate the post-eruptive lahars related to the two eruptions. They are considered “post” when in the deposit there are pumices belonging to older eruptions, indicating their involvement in the progressive erosion of the slopes and valleys, and when there is evidence of long periods without deposition, such as the presence of</span><span> </span><span>slightly humified surfaces or traces of human artifacts (excavations, ploughing).</span></p> <p><span><span>·<span> </span></span></span><span>“EROSION_fallout_472” refers to the sites where it was possible to find erosional unconformities between the pyroclastic deposit of the 472 AD eruption and the lahar, as well as between the lower and upper lahar flow units. The erosional features are for example the lack of one or more primary eruptive layers (eroded by the overlying deposit), a change in the granulometry, or lateral discontinuity of the deposit. </span></p> <p><span><span>·<span> </span></span></span><span>“Pdyn (kPa)”, “v (m/s)”, “C (%)” and “T” are all the parameters quantified to validate the numerical models and to assess the hazard from lahars. Pdyn is the flow dynamic pressure, which represents the capability of the flow to entrain a clast, and it depends on the velocity (v) and the flow density, which in turn results from a combination of the density of the particles and the water through the “C (%)”, that is the particle volume concentration. To calculate the flow dynamic pressure and the velocity, the parameters taken into account are the dimensions of the biggest clasts and the nature of the clasts (limestone, ceramic, brick, tephra, lava, sandstone, iron) found in the lahar deposits. The concentration is estimated considering some sites in which the flow expands in correspondence with some obstacles (for example a Roman wall). This can be assumed to be the initial height of the flow before the emplacement. Finally, “T” refers to the estimated deposition temperature of the deposit quantified by the magnetic analysis, in particular in some sites where the lahar interacted with anthropogenic structures.</span></p> <p><span><span>·<span> </span></span></span><span>“DEPOSIT (472)” indicates the type of lahar deposit (syn- or post-eruptive) of the 472 AD eruption in which the fragments were found.</span></p> <p><span><span>·<span> </span></span></span><span>“MULTIPLE LAHAR UNITS” indicates the sites in which multiple flow units are vertically identified in the lahar deposits. They are generally a result of rapid and progressive aggradation of multiple flow pulses, each one resulting from single-pulse “en masse” emplacement</span><span>.</span></p>
Effect of Substrates and Thermal Treatments on Metalorganic Chemical Vapor Deposition-Grown Sb2Te3 Thin Films (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Effect of Substrates and Thermal Treatments on Metalorganic Chemical Vapor Deposition-Grown Sb<sub>2</sub>Te<sub>3</sub> Thin Films</em>" by <a href="https://pubs.acs.org/doi/pdf/10.1021/acs.cgd.1c00508">M. Rimoldi et al., <em>Cryst. Growth Des.</em> 2021, 21, 9, 5135–5144</a></p> <p>Note for users: </p> <p>The data in Figure 10 can be retrieved from <a href="https://zenodo.org/record/5725028#.Ybcxb73MI2w">Table 2 in the main text</a>.</p>
Dreissenid mussel shell deposition, and benthic community data in the Rouge and Huron Rivers, Southeastern, MI., USA.
This data package was assembled and accompanies a project entitled "Investigating the effects of Dreissenid mussel shells in streams post-invasion," carried out in the Rouge and Huron Rivers in Southeastern, MI., USA in 2017. We assessed the impacts of Dreissenid shells on macroinvertebrates and fish communities. This package includes dreissenid shell density data, water quality data during macroinvertebrate sampling, macroinvertebrate data, water quality data during fish sampling in spring, fish data from spring, water quality data during fall sampling, and fish data from fall. All data tables feature rivers, identifiers, GPS coordinates, and sample dates.
Plant aboveground biomass data: Long-Term Nitrogen Deposition During Grassland Succession
The purpose of this experiment is to measure how initially disturbing an area and adding nitrogen over a long time will affect the number of species, the type of species present, the amount of annual growth, and the change from year to year in the growth of each species in a plant community which is also relieved of grazing by large and small mammals. This experiment is conducted within fields (A, B, and C) which were initially low in soil nutrients. The ground was disturbed by thoroughly disking the area prior to establishment of the experiment. There are 8 different levels of nitrogen addition with other nutrients added to ensure that nitrogen remains the limiting nutrient, and a control which receives no nutrients. Nitrogen fertilizer (NH4NO3) is applied twice per year, once in early May and once in late June. There are six replicates of each treatment per field. The treatments were randomly assigned to plots of size 4 by 4 meters. The plots are in 6 plot by 9 plot grids with 1 meter aisles between plots. The plot grids are enclosed by a fence to keep out mammalian herbivores. Gophers are trapped and removed as they appear. In the spring of 1992, subexperiments E097 and E098 were established. E097 is in fields A and C where randomly selected plots within each treatment no longer receive fertilizer. E098 is in field B where randomly selected plots within each treatment are burned. Note that the design of E002 is similar to E001 except E002 was thoroughly disked prior to establishment.
Atmospheric deposition chemistry data from dryfall at the Jornada Basin LTER: 1983-ongoing
This data package contains concentrations of water soluble components from dryfall (dust) atmospheric deposition collected at the Jornada Basin LTER weather station north of Las Cruces, NM in Dona Ana County, New Mexico, USA. Atmospheric deposition as found in dryfall (dust) and wetfall precipitation has been collected at this location since 1983 using an Aerochem Metrics wetfall/dryfall collector. Dryfall occurring as atmospheric fallout is collected monthly. Each sample is analyzed for Br, Ca, Cl, F, HPO4, K, Mg, Na, NH4, NO3/NO2, SO4, Total N, and Total P. Analysis of Sr and Dissolved Organic Nitrogen was discontinued in 2003. Wetfall precipitation chemistry is available in data package knb-lter-jrn.210128002.
Atmospheric deposition chemistry data from wetfall at the Jornada Basin LTER: 1983-ongoing
This ongoing data package contains concentrations of water soluble components in wetfall (precipitation) atmospheric deposition collected at the Jornada Basin LTER weather station north of Las Cruces, NM in Dona Ana County, New Mexico, USA. Atmospheric deposition as found in dryfall (dust) and wetfall precipitation has been collected at this location since 1983 using an Aerochem Metrics wetfall/dryfall collector. Wetfall occurring as precipitation is collected after each event with a sample size large enough to analyze. Each sample is analyzed for Br, Ca, Cl, F, HPO4, K, Mg, Na, NH4, NO3/NO2, SO4, Total N, and Total P. Analysis of Sr and Dissolved Organic Nitrogen was discontinued in 2003. Dryfall atmospheric deposition chemistry data is available in data package knb-lter-jrn.210128001.
Hyperspectral X-ray CT data set of mineralised ore sample with Au and Pb deposits
<p><strong>General data description:</strong></p> <p>This is a hyperspectral (energy-resolved) X-ray CT projection data set of a mineralised ore sample with small gold and galena deposits. It was acquired in a laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The data included contains all the relevant files required for reconstruction, following a hyperspectral scan of a mineralised ore sample. The sample contains a number of mineral phases, of varying concentration, distributed throughout. Some phases (including gold, and lead-based Galena) produce unique absorption edges, which act as spectral identifiers that can be measured by an energy-sensitive detector.</p> <p><strong>File descriptions:</strong></p> <p>The data set consists of one .txt file and three .mat (MATLAB) data files.</p> <p>Au_rock_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections. The number of horizontal detector pixels accounts for the fact that a set of 5 tiled scans of the sample were collected and later stitched together.</p> <p>Au_rock_sinogram_full.mat contains the full 4D sinogram constructed following flat-field normalisation of the raw projection data. The data matrix contains the total number of energy channels acquired during scanning, as well as the conventional elements of vertical/horizontal detector pixel number and total projection angles.</p> <p>commonX.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p> <p>FF.mat contains the 4D flatfield data acquired when no sample was present. This data was used to normalise the projection datasets, as the sinogram was constructed.</p>
Image Repository Decision Tree - Where do I deposit my imaging data
<p>Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories. </p> <p>To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories. </p> <p>Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. These versions with three trees can be used for web-view. Update: also the editable versions in powerpoint format (.pptx) are now provided. Please be aware that opening the versions with another program might lead to shifted formatting.</p> <p>Update: we now also provide ready-to-print versions designed to be printed on A3 format. One page shows the open bioimaging data tree and one page combines the preclinical and human imaging data trees. Also the editable versions of these are provided.</p>
Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)
<p>This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.</p> <p>The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.</p> <p>The deposited data includes:</p> <ul> <li>krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.</li> <li>krkCMd_images.zip: archive containing source image files in folders by test series: <br>- stacked images of cracks in subsequent stages of self-healing (.tif files),<br>- zip archives assigned to image stacks and containing sets of ImageJ data files .roi,<br>- ImageJ .roi files specifying the locations of grid lines in the images.</li> <li>krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.</li> </ul> <p><span>For details please see the <a href="https://doi.org/10.1038/s41597-025-04485-z">data descriptor [1]</a>. When referring to the data in publications please cite [1].</span></p> <p>[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. <em>Sci Data</em> <strong>12</strong>, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z</p> <p>[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. <em>Constr. Build. Mater.</em> <strong>422</strong>, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768</p>
FAIR raw data and heat maps of ARAP deposition modeling
<p>FAIR Supplementary Information and Raw Data for <a href="https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofer S. et al., 2021, SARS-CoV-2-Laden Respiratory Aerosol Deposition in the Lung Alveolar-Interstitial Region Is a Potential Risk Factor for Severe Disease: A Modeling Study, Journal of Personalized Medicine 11(5):431</a>, DOI: <a href="https://doi.org/10.3390/jpm11050431">https://doi.org/10.3390/jpm11050431</a></p> <p>1. pdf/A of deposition heat maps (incl probability values) for 5 different ARAP modes</p> <p>2. xls-formatted file of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes</p> <p>3.-7. rpt-formatted MPPD v3.04 files of deposition raw data sets for 5 different ARAP modes</p> <p>8.-12. csv-formatted files of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes</p> <p>13. pdf/A of deposition heat maps (incl probability values) for 5 different ERAP modes (upon rehydration of ARAPs)</p> <p>14. txt-formatted README file for Hofer et al 2021</p>
Testing a biological mechanism of the insurancehypothesis in experimental aquatic communities - Data Deposit
<p>1.The insurance hypothesis predicts a stabilizing effect of increasing species richness on commu-nity and ecosystem properties. Difference among species’ responses to environmental fluctuationsprovides a general mechanism for the hypothesis. Previous experimental investigations of theinsurance hypothesis have not examined this mechanism directly.</p> <p>2.First, responses to temperature of four protist species were measured in laboratory microcosms.For each species, we measured the response of intrinsic rate of increase (r) and carrying capacity(K) to temperature.</p> <p>3.Next, communities containing pairs of species were exposed to temperature fluctuations. Com-munity biomass varied less when correlation inKbetween species (but notr) was more negative,and this resulted from more negative covariances in population sizes, as predicted. Results werecontingent on species identity, with findings differing between analyses including or not includingcommunities containing one particular species.</p> <p>4.These findings provide the clearest support to date for this mechanism of the insurance hypo-thesis. Biodiversity, in terms of differences in species’ responses to environmental fluctuations (i.e.functional response diversity) stabilizes community dynamics.</p>
Raw data for the plot in the article entitled "On the electrophoretic deposition of Bi2Te3 nanoparticles through electrolyte optimization and substrate design"
<p>raw data of transport presented in Fig1a of the open access article with the following details:</p> <p>On the electrophoretic deposition of Bi2Te3nanoparticles through electrolyte optimization and substrate design</p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects">Colloids and Surfaces A: Physicochemical and Engineering Aspects</a></p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects/vol/649/suppl/C">Volume 649</a>, 20 September 2022, 129537</p> <p><a href="https://doi.org/10.1016/j.colsurfa.2022.129537">https://doi.org/10.1016/j.colsurfa.2022.129537</a></p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Estimating heavy metal deposition in Germany using model calculations and biomonitoring data, link to research data and scientific software
<p>Research data and scientific software related to an investigation dealing with modelled data on Cd and Pb deposition (LOTOS-EUROS, EMEP/MSC-East) and monitoring data from the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation Moss Survey) and the German Environmental Specimen Bank (ESB) providing corresponding parameters on HM concentration in various biota. The study aimed at examining, whether an integrated use of model calculations and monitoring data can extend established methods for estimating and evaluating spatial patterns of atmospheric Pb and Cd deposition across Germany.</p>
DATA: As-deposited and dewetted Cu layers on plasma treated glass: adhesion study and its effect on biological response
<p>Dataset contains data related to improving the adhesion of nanosized copper films to a glass substrate. </p>
Raw data and heatmaps of VLP deposition modeling
<p>Supplementary Information and Raw Data for <a href="https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofstätter N., Hofer S., Duschl A., and Himly M.<br> Children’s privilege in COVID-19: The protective role of the juvenile lung morphometry and ventilatory pattern on airborne SARS-CoV-2 transmission and severe pulmonary disease (2021). <em>Biomedicines </em>9(10):1414.</a> DOI: <a href="https://doi.org/10.3390/biomedicines9101414">https://doi.org/10.3390/biomedicines9101414</a></p> <p>1. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon nose breathing</p> <p>2. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon mouth breathing</p> <p>3. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon nose breathing</p> <p>4. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon mouth breathing</p> <p>5. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon nose breathing</p> <p>6. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon mouth breathing</p> <p>7. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon nose breathing</p> <p>8. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon mouth breathing</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.