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852 results for “grain”
Snow grain data for Niwot Ridge and Green Lakes Valley, 1995 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
North Carolina Outer Banks, USA Coastal Foredune Sediment Cores - Grain Size Data & Core Log Descriptions
<p>This repository includes sediment core data collected at seven sites along the northern Outer Banks, North Carolina, USA. From north to south, the sites include Pine Island, Corolla Reserve, Duck, the US Army Corps of Engineers Field Research Facility (FRF) North, FRF South, Southern Shores (i.e., Hillcrest Beach Access), and Nags Head (Bonnett St. Beach Access).</p><p>At each site, internal dune sedimentology and stratigraphy were characterized using sediment vibracores, each 1.5–2.2 m long, collected along a cross-shore transect from the dune toe to the dune heel. Coring locations were selected based on dune morphology to capture the stratigraphy of the dune toe, stoss slope, primary dune crest, lee slope, swale, and secondary dune crest, as applicable. Sediment core locations were documented using RTK-GPS and are included in the .kmz file.</p><p>All sediment cores were split, photographed, described for sedimentary structures, texture (as compared to standards), mineralogy, and color (Munsell, 2012). Sediment cores were described using the Modified Burmister System in 10-cm intervals, with additional intervals added as needed to capture stratigraphic units with thicknesses less than 10 cm but greater than 1 cm. Sediment core log descriptions are included in the NOAA_NCDunes_Vibracore_CoreLogs.xlsx data file.</p><p>Sediment size and shape were analyzed from oven-dried samples using a CAMSIZERX2Ⓡ. These data are included in the Dune_Grain_Size_camsizer_outputs.csv data file. Metrics reported for each sample include the following: Site, Core ID, Sample Number, Depth (cm below ground surface), Elevation (m, NAVD88), D2 (mm), D5 (mm), D10 (mm), D16 (mm), D25 (mm), D50 (mm), D75 (mm), D84 (mm), D90 (mm), D95 (mm), D98 (mm), average grain symmetry, average grain sphericity, average grain aspect ratio, percent pebble, percent granule, percent very coarse sand, percent coarse sand, percent medium sand, percent fine sand, percent very fine sand, and percent silt.</p><p><strong>More details regarding these measurements can be found in the following manuscript:</strong></p><p>Davis, E.H., Hein, C.J., Cohn, N., White, A.E., Zinnert, J.C. Differences in internal sedimentologic and biotic structure between natural, managed, and constructed coastal foredunes (in review).</p>
Bulk, biomarker and mineralogy data of grain size fractions along a land-sea transect offshore the Atchafalaya river, northern Gulf of Mexico
<p>This dataset comprises the bulk, biomarker and mineralogy data of partitioned surface sediments along a land-sea transect offshore the Atchafalaya River, northern Gulf of Mexico. It includes the total concentrations of the biomarkers and proxies as presented in the accompanied publication, as well as concentrations of single isomers. Supplement to: Yedema et al., (2024); Influence of Organo-mineral Associations on Terrestrial Particulate Organic Matter Dispersal in the northern Gulf of Mexico (doi.)</p> <p> </p> <p><strong>This research has been supported by the Netherlands Earth System Science Centre (grant no. 024.002.001)</strong></p> <p> </p>
Sediment grain size in the Virginia coastal bays, 2022
This dataset contains sediment grain size distributions from benthic sediment cores collected from shallow sites across coastal bays of Virginia, USA. The samples were collected in July 2022 at 50 long-term sampling sites. Most sites were sampled in seagrass meadows (eelgrass Zostera marina), but some sites are unvegetated (bare substrate).
3D Tomography Images of wheat grains for several development stages
<p>Images of wheat grains acquired by 3D tomography at various stages of the early development of the grain. This data set serves as companion for the article "Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development" submitted to the "Plant Methods" journal.</p> <p><strong>Grain samples</strong></p> <p>A total of 41grains obtained at height different stages was imaged. The files correspond to the collections of grains at each stage:</p> <ul> <li>060 degree-days: 5 grains</li> <li>080 degree-days: 5 grains</li> <li>100 degree-days: 5 grains</li> <li>120 degree-days: 5 grains</li> <li>180 degree-days: 6 grains</li> <li>210 degree-days: 5 grains</li> <li>270 degree-days: 5 grains</li> <li>310 degree-days: 5 grains</li> </ul> <p>A more detailed description is provided in the file "<a href="https://zenodo.org/api/files/923a7508-b325-4264-8553-6a62092bb84d/wheatGrainTomoDataset.pdf">wheatGrainTomoDataset.pdf</a>".</p> <p><strong>Image format</strong></p> <p>All images are in TIFF format.</p> <p>Two kinds of images are provided: the volumes of the whole grains after conversion tu 256 gray levels, and the results of the segmentation of the grains as described in the manuscript. </p>
Evolution of software code at the level of fine-grained elements: data files
<p>The data files available here (68GB uncompressed) have been used for studying the evolution of code at the level of fine-grained elements. The data are associated with the processing of the 89 open source software repositories hosted on GitHub. Details regarding each individual GitHub project are stored in the repos folder under directories matching the owner and project name used on GitHub. For example, the files under repos/KDE/kdevelop correspond to the project hosted on https://github.com/KDE/kdevelop. Data associated with the statistical analysis of the processed repositories are stored in the statistical-analysis folder. The file project_details.txt contains the data used for selecting the processed projects.</p>
Atomistic Structures discussed in "Segregation-enhanced grain boundary embrittlement of recrystallised tungsten evidenced by site-specific microcantilever fracture"
<p>The tar file Sigma7_GB.tar contains all data to reproduce the results shown and discussed in the Publication "Segregation-enhanced grain boundary embrittlement of recrystallised tungsten evidenced by site-specific microcantilever fracture", DOI: <a href="https://doi.org/10.1016/j.actamat.2023.119256">10.1016/j.actamat.2023.119256</a></p><p>It contains three folders for the grain boundary creation, decoration with P atoms, and fracture simulations.<br>The naming conventions and additional information are provided in README.txt files in the directories.</p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
HEroBM: a deep equivariant graph neural network for high-fidelity backmapping from coarse-grained to all-atom structures
<p><span>Molecular simulations play a pivotal role in chemistry, biology, and material sciences, enabling the</span><br><span>study of complex dynamic properties within systems. Coarse-grained (CG) techniques have emerged</span><br><span>as indispensable tools in this domain, facilitating the sampling of large-scale systems and extending</span><br><span>simulation timescales by simplifying system representation. However, CG approaches involve a trade-</span><br><span>off: they sacrifice atomistic details that may be crucial for understanding the underlying processes.</span><br><span>To address this challenge, a recommended strategy is to identify key CG conformations and employ</span><br><span>backmapping methods to retrieve atomistic coordinates. Currently, rule-based methods often yield</span><br><span>suboptimal geometries and rely on energy relaxation, resulting in less-than-optimal outcomes. In</span><br><span>contrast, machine learning techniques offer higher accuracy but may lack transferability between</span><br><span>systems or be tied to specific CG mappings. In this study, we present HEroBM, a dynamic and scalable</span><br><span>method that utilizes deep equivariant graph neural networks and a hierarchical approach to achieve</span><br><span>high-resolution backmapping. HEroBM is capable of handling any type of CG mapping, providing a</span><br><span>versatile and efficient protocol for reconstructing atomistic structures with high accuracy. Grounded</span><br><span>in local principles, HEroBM spans the entire chemical space and can be applied across systems of</span><br><span>varying composition and sizes. We demonstrate the versatility of our framework through a range of</span><br><span>biological systems, including a complex real-case scenario. Here, our end-to-end backmapping approach</span><br><span>accurately generates atomistic coordinates for a G protein-coupled receptor bound to an organic small</span><br><span>molecule within a cholesterol/phospholipid bilayer. The high-fidelity HEroBM backmapping enables</span><br><span>researchers to effortlessly transition between CG and all-atom simulations, opening unprecedented</span><br><span>avenues for molecular investigations.</span></p>
FTIR-ATR spectra of culinary grain legumes (pulse) flours
<p>FTIR-ATR spectra of 5 culinary grains: </p> <p><br> 1. chickpea (Cicer arietinum n=87)<br> 2. lentil (Lens culinaris n=93))<br> 3. grass pea (Lathyrus sativus n=116)<br> 4. pea (Pisum sativus n=119)<br> 5. faba bean (Vicia faba n=93)</p> <p>Grains were dried at 40 °C and milled using a miller Retsch cyclone mill with a particle size under 0.8 mm. The different flours were stored at -20 °C. For FTIR-ATR analysis there was no need for further sample preparation.</p> <p> </p> <p>Files: </p> <p><a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_Pulse.mat">FTIRATR_Pulse.mat</a>: Matlab structure with, as fields:<br> <Data> : a 491x1734 matrix, each line corresponds to a spectra<br> <Wavelength>: a 1730 length vector, Wavelength of the incident light<br> <Tag> : a 491 length vector, labelling of the samples<br> <Label4Rag> : Names of the label.</p> <p><a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_pulse_data.csv">FTIRATR_pulse_data.csv</a>: csv file with the data, wavelength and tag fields</p> <p> <a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_pulse_labels.csv">FTIRATR_pulse_labels.csv</a>: Names of the label</p> <p> </p> <p><br> </p> <p> </p> <p> </p>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"
<p>This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication:</p> <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>
Grain-size data from the loess profiles Ostrau and Gleina in Saxony (Germany)
<p><strong>Grain-size data from the loess profiles Ostrau and Gleina in Saxony (Germany)</strong></p> <p>The samples were taken between 2009 and 2010 in the framework of the DFG project <a href="https://gepris.dfg.de/gepris/projekt/46526743"><em>"Rekonstruktion der Umweltbedingungen des Spätpleistozäns in Mittelsachsen anhand von Löss-Paläobodensequenze</em>n" (DFG FU 417/7-1 and FA 239/13-1</a>) from the loess records Ostrau and Gleina. Both located in the Saxonian-Loess-Region in Germany. For further details on the project profiles (with further references therein), we refer to Meszner et al. (2011,2013), Kreutzer et al. (2012), Meszner (2015) and Zech et al. (2017). </p> <p>Samples for the data reported here were selected in 2015. 212 samples were taken from the loess profile Ostrau and 269 samples from the loess profile Gleina. Full details on sampling and sample preparation can be found in the Grassl (2016) (unpublished master thesis in Germany, available upon request). The most relevant details are extracted below. </p> <p><strong>Preparation and measurements</strong></p> <p>Sample preparation and measurements were carried out at the GFZ in Potsdam (Germany). Thirty-nine samples from the profile Ostrau were separated into eight equal parts to obtain representative samples. The samples were labeled with "G" for Gleina and "O" for Ostrau. All other samples were sampled without applying this separation method. <br> For samples from the profile Ostrau, the suffix "mT" (with separation) and "oT" (without separation) indicates whether this <br> separation method was used. </p> <p>The samples were treated with HCl (10 %, 12 h to 20 h) and rinsed in the demineralized water. To suspend the samples, NO<sub>3</sub>P0<sub>4</sub> was used on twelve pars of H<sub>2</sub>O<sub>2</sub>. </p> <p>A <em>Retch Laser Scattering Particle Size Distribution Analyzer (HORIBA LA- 950)</em> was used for the grain-size measurements. Details <br> on the settings are reported separately in each file.</p> <p><br> <strong>The data in the repository </strong></p> <p>Grainsize_data.zip This folder contains 4,853 ASCII TXT-files with the raw granulometric data. Filenames are unique timestamps (measurement date and time in the format <em>YYYYMMDDHHMMSS</em> CET). Each file comes with a header with relevant metadata and the measurement data. The metadata also contains the sample name, e.g., <em>O_55_oT</em> reads "O" for Ostrau, "55" sampling depth in cm, and "oT" for "ohne Teiler" (without separator, while "mT", "mit Teiler" would stand for with separator). For files for the profile Gleina, a "G" is used followed by the sampling depth range (two numbers, e.g., <em>G_380_382</em>) in cm. </p> <p>The files <em>Gleina_depth.txt</em>, <em>Ostrau02_depth.txt</em>, and <em>Ostrau03_depth.txt</em> allow a correlation with the profiles graphs published in Meszner (2015). </p> <p><br> <strong>References</strong></p> <p>Grassl, W., 2016. End-Member-Modellierungsanalyse an hochauflösenden Korngrößen der Lössprofile Ostrau und Gleina, Lommatzscher Pflege, Sachsen. unpublished Master thesis, TU Dresden.</p> <p>Kreutzer, S., Fuchs, M., Meszner, S., Faust, D., 2012. OSL chronostratigraphy of a loess-palaeosol sequence in Saxony/Germany using quartz of different grain sizes. Quaternary Geochronology 10, 102–109. doi:10.1016/j.quageo.2012.01.004</p> <p>Meszner, S., Fuchs, M., Faust, D., 2011. Loess-Paleosol-Sequences from the loess area of Saxony (Germany). E & G, Quaternary Science Journal 60, 47–65.</p> <p>Meszner, S., 2015. Loess from Saxony. A reconstruction of the Late Pleistocene landscape evolution and palaeoenvironment based on loess-palaeosol sequences from Saxony (Germany). Dresden. PhD thesis. TU Dresden. </p> <p>Meszner, S., Kreutzer, S., Fuchs, M., Faust, D., 2013. Late Pleistocene landscape dynamics in Saxony, Germany: Paleoenvironmental reconstruction using loess-paleosol sequences. Quaternary International 296, 95–107. doi:10.1016/j.quaint.2012.12.040</p> <p>Zech, M., Kreutzer, S., Zech, R., Goslar, T., Meszner, S., McIntyre, C., Häggi, C., Eglinton, T., Faust, D., Fuchs, M., 2017. Comparative 14C and OSL dating of loess-paleosol sequences to evaluate post-depositional contamination of n-alkane biomarkers. Quaternary Research 87, 180–189. doi:10.1017/qua.2016.7</p>
Data from "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region"
<p>Table 1 and Table 2 from Tazzari et al., 2021, "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region", Monthly Notices of the Royal Astronomical Society, arXiv:2010.02248</p> <p>Both tables are available in IPAC format, which is in human- and machine-readable:</p> <pre><code class="language-python">from astropy.io import ascii tb = ascii.read('Table1.txt', format='ipac')</code></pre> <p>Table comments (stored at the beginning of the ASCII file as lines starting with "/") can be read as:</p> <pre><code class="language-python">tb.meta['comments'] </code></pre> <p> </p>
Data for "Formation of very large 'blocky alpha' grains in Zircaloy-4" by V. Tong and T.B. Britton published in Acta Materialia (2017)
<p>Data for "Formation of very large ‘blocky alpha’ grains in Zircaloy-4"</p> <p>Vivian S Tong, T Ben Britton<br> Department of Materials, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK</p> <p>For more information please contact: b.britton@imperial.ac.uk (Ben Britton)</p> <p>---</p> <p>Figures_data.xlsx contains the data for line graphs in the following figures on separate labelled sheets:<br> Figure 2(a)<br> Figure 2(b)<br> Figure 4(c)<br> Figure 6.</p> <p>Figures_data.xlsx also contains the HR-EBSD GND density data in Figures 3(b) and 3(d), which have been plotted on a log10 colour scale in the published figure.</p> <p>The EBSD orientation data have been exported as text files (.ctf) directly from Bruker Esprit 2.1 software.</p> <p>Orientations are described using Bruker EBSD software conventions, described in the paper "Tutorial: Crystal orientations and EBSD — Or which way is up?" by Britton et al.(http://dx.doi.org/10.1016/j.matchar.2016.04.008).</p> <p><br> EBSD data is provided for the following figures:<br> Figure 3(a)<br> Figure 3(c)<br> Figure 4(b), Figure 5(c), Figure 7(c) -- these are all the same dataset<br> Figure 5(b)<br> Figure 6 - EBSD maps of these two datsets were not shown, but this is the raw data from which twin fractions were calculated.<br> Figure 7(a)<br> Figure 7(b)</p>
Tables of results for lower mantle grain size and viscosity estimates
<p>Data on diffusivity, grain size, and viscosity calculations are shown in Figs. 7-10 and Figs. S4-S5 in Okamoto and Hiraga's "A Common Diffusional Mechanism for Creep and Grain Growth in Polycrystalline Rocks: Application to Lower Mantle Viscosity Estimates".</p>
Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM). Original dataset.
<p>The data repository contains data obtained with the microscope Nikon Eclipse LV100ND that was stitched with <a href="https://imagej.net/plugins/trakem2/">TrakEM2 software</a>. The files allow reproducing the results obtained and plot in <a href="https://doi.org/10.1111/jmi.13284">Acevedo et al. (2024)</a> <strong>"Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM)."</strong> by Acevedo Zamora, M. A., Schrank, C. E., & Kamber, B. S.</p> <p>The prototype uses MatLab scripts (<a href="https://github.com/marcoaaz/AcevedoEtAl._2024a_POAM">AcevedoEtAl._2024a_POAM</a>) that were documented in the paper Supplementary Material 1. The metadata can be found in Supplementary Material 3 and follows the structure of this data repository. The user needs downloading and changing the paths to run the same scripts and reproduce the results.</p> <p>Note: After download, unzip and merge (copy-paste) the folders (parts 1, 2 and 3). Before merging, the containing folder should be re-named to 'paper 2_datasets' to match exactly the MatLab scripts and reproduce our work.</p> <p>The remaining questions should be addressed to Marco Acevedo (maaz.geologia@gmail.com ; marco.acevedozamora@qut.edu.au)</p> <p>Thanks.</p>
Datasets for "Stable nanofacets in [111] tilt grain boundaries of face-centered cubic metals"
<p>This repository contains raw data for the paper “Stable nanofacets in [111] tilt grain boundaries of face-centered cubic metals”. It contains the input files, scripts, and raw data of the simulations, as well as raw data from scanning transmission electron microscopy.</p>
Dataset for "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion"
<p>This repository contains the data of the simulations and theoretical<br>calculations of the paper "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion".</p>
Presolar Grain Database - Silicon Carbide
<p>The Presolar Grain Database (PGD) contains the vast majority of isotope data (published and unpublished) on presolar grains and was first released as a collection of spreadsheets in 2009. It has been a helpful tool used by many researchers in cosmochemistry and astrophysics. However, over the years, accumulated errors compromised major parts of the PGD. Here, we provide a fresh start, with the PGD for silicon carbide (SiC) grains rebuilt from the ground up.</p> <p>The PGD is provided here in two formats: (1) as Microsoft Excel (.xlsx) file, containing the main database as one large spreadsheet and additional information on extra spreadsheets, (2) as comma-separated ASCII (.csv) file containing the main database.</p> <p>The PGD is also available for graphite grains at the DOI <a href="../doi/10.5281/zenodo.11188115">10.5281/zenodo.11188115</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.