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2,103 results for “Components”
PnP module: multi-material components manufacturing by Automated Tape Laying process
<p><strong>Introduction</strong></p> <p>The Automated Tape Laying (ATL) process is an automated technique used for composites manufacturing based on fiber placement processes. This module is part of AIMEN Technology Centre Open Pilot Line focusing on manufacturing of multi-material components. This module is composed by a movement system (robot) and heating system (ATL head), which can be composed by IR system or laser source. </p> <p><strong>Asset Administration Shell</strong></p> <p>The Asset Administration Shell (AAS) modelling follows the <em>Product</em>, <em>Process</em> and <em>Resources</em> (PPR) model. The relation between the assets allows the traceability of the Product by demonstrating a Digital Thread based on AAS and how the active AAS modelling allows the Plug and Produce capabilities in a modular production scheme.</p> <p>In this repository some examples of AAS modeling (.aasx files) for a subset of assets in the shop floor (Resources), Product and Process can be found, as well as the architecture of the whole module.</p> <p><strong>Architecture</strong></p> <p>The information gathered by the central unit/industrial PC (Operational Technology) will be available in DIMOFAC platform (Information Technology) as well as the Product information related to the design and/or simulation (Engineering Technology). In the central unit the software in charge of taking the decision and allowing Plung and Produce capabilities is named “Orchestrator”, and in the product side, the software in charge of register all the information related with a specific software “Digital Thread”.</p> <p> </p> <p><strong>AAS Demonstration</strong></p> <p>A demonstration video is available: <a href="https://www.youtube.com/watch?v=aOP6QWiF5FE&t=7s">PnP module: multi-material components manufacturing by Automated Tape Laying process - YouTube</a></p>
Organic components of decomposing hardwood boles at the Hubbard Brook Experimental Forest, 1990-2016
In 1990-1991 segments of boles from felled sugar maple (Acer saccharum), yellow birch (Betula alleghaniensis) and American beech (Fagus grandifolia) trees were placed in the field to study the rate of decomposition and nutrient loss (or gain) over time. The segments incubated in the field, ranging from 0.5-1.3 meters in length, were paired with fresh segments from the same trees. The fresh segments were taken to the lab shortly after felling, dried, weighed and subsampled. Fresh samples of wood and bark were collected separately. Incubated bole segments were collected in 1993 (T1), 1997 (T2), 2001 (T3), 2007 (T4) and 2015/2016 (T5). The whole bole segments were transported to the lab, measured, dried and weighed to determine mass loss. Subsamples of the bole wood and bark were collected for chemical analysis, including cross-polarization with magic-angle spinning (CPMAS) 13C NMR. Chemical analyses were conducted concurrently on the fresh (T0) and incubated samples. This data package includes the unprocessed NMR data, phased spectra, and integrated (spectral area) data in chemical shift regions that correspond to key structural groups. This data set includes data for T1, T3, T4, and T5 samples and their paired T0 fresh samples. Samples from T2 were measured for mass, but inadvertently discarded prior to chemical analysis. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
MCR LTER: Coral Reef: Estimates of component primary production and respiration, 2006-2015
Estimates of primary production and respiration of three representative components of the Moorea coral reef ecosystem were made yearly in a laboratory flume from 2006 through 2015. The components are: algal turf communities, the macroalga Sargassum pacificum, and the common branching coral Pocillopora verrucosa. Metabolism estimates were made using changes in dissolved oxygen over time in a flume in unidirectional flow at saturating irradiances and dark. Rates were normalized to projected (planar) surface area (all components) and biomass (algal turfs, Sargassum). This timeseries completed in 2015.
BAMBI ITS - Analysis of the fungal component (via ITS amplicon sequencing) of stool samples from preterm babies
<p>Amplicon analysis of ITS amplicons from preterm babies.</p> <p>Associated GitHub repository: <a href="https://github.com/quadram-institute-bioscience/bambi-its">https://github.com/quadram-institute-bioscience/bambi-its</a></p>
Genome-wide association summary statistics of chronic musculoskeletal pain at four anatomic sites and their genetically independent components
<p>The dataset contains results of a genome-wide association study of distinct chronic musculoskeletal pain conditions: back pain, knee pain, neck pain, and hip pain. Additionally, there are genome-wide association summary statistics for four genetically independent components of pain conditions, listed above. For more details, please, read the paper XXX.</p> <p>All files contain association summary statistics for genome-wide association meta-analysis of the 265,000 white British individuals from the UK Biobank and additional 191,580 individuals of European Ancestry from the UK biobank (total N = 456,580). Cases and controls were defined based on questionnaire responses. First, participants responded to “Pain type(s) experienced in the last months” followed by questions inquiring if the specific pain had been present for more than 3 months. Those who reported back, neck or shoulder, hip, or knee pain lasting more than 3 months were considered chronic back, neck/shoulder, hip, and knee pain cases, respectively. Participants reporting no such pain lasting longer than 3 months were considered controls (regardless of whether they had another regional chronic pain, such as abdominal pain, or not). Individuals who preferred not to answer were excluded from the study. Besides this, we excluded individuals who reported more than 3 months of pain all over the body.</p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilization of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite the corresponding paper and this repository:</strong></p> <ol> <li>Tsepilov et al 2020</li> </ol> <p><strong>Funding:</strong></p> <p>The work of YSA and SZS was supported by the Russian Ministry of Education and Science under the 5-100 Excellence Programme and by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project 0324-2019-0040). The work of YAT, ASSh, and EEE was supported by the Russian Foundation for Basic Research (project 19-015-00151). The contribution of LСK was funded by PolyOmica. Dr. Suri was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development (RR&D) Service. Dr. Suri is a Staff Physician at the VA Puget Sound Health Care System. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.</p> <p><strong>List of files:</strong></p> <ol> <li>Back_output_done.csv: GWAS summary statistics for the chronic back pain</li> <li>gpc1_output_done.csv: GWAS summary statistics for the GIP1</li> <li>gpc2_output_done.csv: GWAS summary statistics for the GIP2</li> <li>gpc3_output_done.csv: GWAS summary statistics for the GIP3</li> <li>gpc4_output_done.csv: GWAS summary statistics for the GIP4</li> <li>Hip_output_done.csv: GWAS summary statistics for the chronic hip pain</li> <li>Knee_output_done.csv: GWAS summary statistics for the chronic knee pain</li> <li>Neck_output_done.csv: GWAS summary statistics for the chronic neck pain</li> </ol> <p><strong>Column headers:</strong></p> <ol> <li>gwas_id: uninformative field</li> <li>rs_id: dbSNP rsID (GRCh37 build) </li> <li>snp_num: uninformative field</li> <li>chr: chromosome (GRCh37 build) </li> <li>bp: position (GRCh37 build) </li> <li>ea: effect allele (coded as "1")</li> <li>ra: reference allele (coded as "0")</li> <li>eaf: effect allele frequency</li> <li>af_ref: uninformative field</li> <li>beta: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>p: P-value of association (without GC correction)</li> <li>n:Total sample size</li> <li>z: Z-statistic of association</li> <li>info: uninformative field</li> <li>af_outlier: uninformative field</li> <li>pz_outlier: uninformative field</li> </ol>
Beak Shape in Birds and Squid: Principal Components Analysis of 2D Landmarks
<p>R code to analyze observations of beak traces from specimens of birds and squid.</p> <p>Notes are in the code. Watch for updates.</p> <p>Where the csv files include data published by different authors, the doi references to the original publications are included in the R code. I took care to correctly download/process/transcribe where applicable, but please do notify me if there are errors.</p>
Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction"
<p>The tooling, data and results for the racetrack case study in the paper <em> Components in Probabilistic Systems: Suitable by Construction, ISoLA 2020, <a href="https://doi.org/10.1007/978-3-030-61362-4_13">DOI</a></em></p>
Contour method dataset for as-deposited and rolled wire+arc additive manufacturing Ti–6Al–4V components
<p>This is an archive of the raw metrology of the EDM cut surface data files used for the contour method analysis of Wire+Arc Additive Manufacture (WAAM) Ti6Al4V components appearing in: "Residual stress of as-deposited and rolled wire+arc additive manufacturing Ti–6Al–4V components" by F. Martina, M. J. Roy, B. A. Szost, S. Terzi, P. A. Colegrove, S. W. Williams, P. J. Withers, J. Meyer and M. Hofmann.</p> <p>The files are described by their filenames and side of each EDM cut. For example, 'Control_1.dat' refers to one side of the cut performed on the as-deposited specimen, while '50kN_1.dat' refers to one side of a specimen rolled at 50 kN load, etc.</p> <p>Data is in the form of a point cloud with one point per line, whitespace delimited in microns. Data was captured with a Nanofocus CF-4 laser profilometer sensor with point spacing 30 µm apart. Data with z coordinates below or above 500 µm are considered outside of the surface detection limits.</p>
SDSS-IV MaNGA DR17 Principcal Component Analysis spaxel classifcations
<p>The zip files contains 10120 fits.gz files and two Python .p files.</p><p>The files spaxel_properties_master_DR17.p and elliptical_radii_params_DR17.p contain all of the PCA values of spaxels from all galaxies and all the fraction of spaxels of a particular type in an ellipse (so looping over all of the maps to get this information is not necessary). </p><p>There is one map per MaNGA galaxy. The data structure of each fits.gz file is:</p><p>HDU 0: [image] primary header from the DAP MAPS file. </p><p>HDU 1: [image] 'PC1' - PC1 amplitude. </p><p>HDU 2: [image] 'PC2' - PC2 amplitude. </p><p>HDU 3: [image] 'PC3' - PC3 amplitude. </p><p>HDU 4: [image] 'PC1ERR' - PC1 error. </p><p>HDU 5: [image] 'PC2ERR' - PC2 error. </p><p>HDU 6: [image] 'PC3ERR' - PC3 error. </p><p>HDU 7: [image] qualmask – Mask applied to PC1 map, has mask=(snr_4000A.T < 4.) | (pc1_map_reshaped.T < -10.) | (nocov) | (lowcov) | (donotuse) | (deadfiber) | (forestar). i.e. it excludes low S/N spaxels, weird PCA values and bad spaxels. </p><p>HDU 8: [image] 'snr4000A' - Median signal-to-noise in the 4000A break region. </p><p>HDU 9: [image] 'norm' - normalisation of the spectrum in the PCA. Used for reconstruction of the spectrum. </p><p>HDU 10: [image] 'class_map' - map of PCA classifications. 1=quiescent, 2=star-forming, 3=starburst, 4=green valley, 5-post-starburst, 0=unclassified (do not use)</p><p>HDU 11: [image] 'spx_bin_mask' - Mask accounting for identical values in a bin (see below for more details). </p><p>The PCA code (see https://github.com/KateRowlands/MaNGA-PCA, Rowlands et al. 2018, based on Wild et al. 2007) is run on the HYB10-MILESHC-MASTARSSP cubes, where the stellar continuum is binned but the emission line measurements are done on the unbinned spectra (see SDSS DR17 DAP documentation for more details). In these maps the spectra in each stellar continuum bin are identical, so the PC amplitudes are identical. The analysis is done in this way to preserve the shape of the maps for comparing to other quantities. The identical nature of spaxels in the same bin needs to be accounted for in some analysis e.g. those which count spaxels of a certain PCA class. The spx_bin_mask accounts for this double counting by providing a mask which has the central spaxel in the Voronoi bin set to 1. For spaxels with unique PCA values, set spx_bin_mask==1.</p><p>If plotting 2D maps of the PCA classes then spx_bin_mask should not be applied otherwise there will be gaps in the maps.</p><p>To flag out poor quality spaxels, reject anything with snr4000A < 4, although different S/N cuts may be applied depending on your science case. Furthermore, the PCA parameters are affected by dust. PCA classifications of PSBs in regions with visible dust lanes e.g. in edge-on and or/ dusty galaxies should be closely examine by hand to ensure robustness. Values of -99 and 99 indicate no data or bad data and should be excluded.</p>
Choice of the right supporting electrolyte in electrochemical reductions: a principal component analysis
<h2>Introduction</h2> <p>This dataset contains the raw data as well as an HTML-based visualization of our dataset using Python Bokeh. We have also added a feature to highlight commercially available supporting electrolytes. The data is taken from the PubChem database. For each of the 6650 cations, the known neutral compounds in the PubChem dataset were identified with their corresponding anions. For each of these compounds, the vendor information stored in PubChem was queried.</p> <h2>Directory structure</h2> <ul> <li>Raw Data <ul> <li>[<a href="../records/10813969/files/raw_data.tar.xz?download=1" target="_blank" rel="noopener">raw_data.tar.xz</a>] Compressed directory with the output from the automated feature calculation.</li> <li>[<a href="../records/10813969/files/raw_data.csv?download=1" target="_blank" rel="noopener">raw_data.csv</a>] CSV file with the values of the calculated properties of all cations.</li> </ul> </li> <li>Visualization <ul> <li>[<a href="../records/10813969/files/pca_qac_tool_QC.html?download=1" target="_blank" rel="noopener">pca_qac_tool_QC.html</a>] HTML page with Javascript to display PC1 and PC2 for the quantum chemical PCA model.</li> <li>[<a href="../records/10813969/files/pca_qac_tool_RDKit.html?download=1" target="_blank" rel="noopener">pca_qac_tool_RDKit.html</a>] HTML page with Javascript to display PC1 and PC2 for the PCA model based on non empirical RDKit descriptors.</li> </ul> </li> <li>Tools <ul> <li>[<a href="../records/10813969/files/pca_qac_tool.py?download=1" target="_blank" rel="noopener">pca_qac_tool.py</a>] Python script to generate the HTML output using Bokeh. Depends on the data_pca_qac.csv and the data_commercial.json file.</li> <li>[<a href="../records/10813969/files/PubChem_get_Vendor_information.py?download=1" target="_blank" rel="noopener">PubChem_get_Vendor_information.py</a>] Crawler that checks a list of PubChem CIDs for net-neutral compounds and whether they are commercially available.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-2D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-2D.py</a>] Python script to calculate all available 2D RDkit descriptors based on a list of SMILES strings.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-3D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-3D.py</a>] Python script to calculate the RDKit 3D descriptors based on the CREST and ORCA GeoOpt geometries.</li> </ul> </li> </ul>
Data from: Evolutionary potential and constraints in an aposematic species: Genetic correlations between warning coloration and fitness components in wood tiger moths
<p>Phenotypic data and pedigrees of two laboratory populations of wood tiger moths (<em>Arctia plantaginis</em>) of Finnish (=FIN) and Estonian (=EST) ancestry.</p> <p><strong>Pedigree: </strong><br>ID: individual identifier<br>sire = Father<br>dam=mother</p> <p><strong>Pheno.data: </strong><br>ID: individual identifier<br>Sex: 1=male; 2=female<br>hatchingdate: date when larva hatched<br>pupadate: date of pupation<br>adultdate: date of exclusion<br>Pupa.Weight: weight of pupa [mg]<br>Female.Colour = hindwing colour of females. In this species hindwing colour in females varies continuously from yellow to red. It was quantified by visual matching of hinwdings against a colour scale ranging from 1 = yellow to 6 = red. <br>Signal.Size = larva signal size. Larvae show an orange patch of variable size on the back of their black body. The size is given as number of segments<br>Egg.N = egg number produced by the individual<br>Off.N = offspring number. Larvae were counted 2-3 weeks after egg laying</p> <p> </p>
Radiocarbon in the land and ocean components of the Community Earth System Model: data to prepare figures
<p>The files contain the data to plot the graphics displayed in the publication by Frischknecht, T., Ekici, A., Joos, F. Radiocarbon in the land and ocean components of the Community Earth System Model, Global Biogeochemical Cycles, 2022, in press.</p>
Supporting information - A value creation model from science-society interconnections: Components and archetypes
<p>Data protocol and datasets used for the study entitled 'A value creation model from science-society interconnections: Components and archetypes'. </p> <p><strong>Abstract of the paper:</strong></p> <p>The interplay between science and society takes place through a wide range of intertwined relationships and mutual influences that shape each other and facilitate continuous knowledge flows. Stylised consequentialist perspectives on valuable knowledge moving from public science to society in linear and recursive pathways, whilst informative, cannot fully capture the broad spectrum of value creation possibilities. As an alternative we experiment with an approach that gathers together diverse science-society interconnections and reciprocal research-related knowledge processes that can generate valorisation. Our approach to value creation attempts to incorporate multiple facets, directions and dynamics in which constellations of scientific and societal actors generate value from research. The paper develops a conceptual model based on a set of nine value components derived from four key research-related knowledge processes: production, translation, communication, and utilization. The paper conducts an exploratory empirical study to investigate whether a set of archetypes can be discerned among these components that structure science-society interconnections. We explore how such archetypes vary between major scientific fields. Each archetype is overlaid on a research topic map, with our results showing that different archetypes correspond to distinctive topic areas. The paper finishes by discussing the significance and limitations of our results and the potential of both our model and our empirical approach for further research.</p>
Components of VGE Cycle
<p>Project: Hybrid-BioVGE</p> <p>The Hybrid – BioVGE project is proposed with the primary objective to develop, design and demonstrate a highly integrated solar/biomass hybrid air conditioning system for space cooling and heating of residential and commercial buildings that is affordable, operating with improved efficiency and with a strong market potential.</p> <p>Project details at https://hybrid-biovge.inegi.up.pt/index.asp</p> <p>File Description: Components of VGE Cycle</p>
Ancillary data for article "The general formulation for runoff components estimation and attribution at mean annual time scale"
<p>The mean annual (1960-1990) precipitation and estimated model parameters wetting potential (Wp), vaporization potential (Vp) and upper limit of <span>the portion remaining after precipitation (</span>Up) of 312 catchments over China.</p>
Components of spore capture device in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>These STL files enable the 3D printing of the referenced spore capture device. The complete device can be assembled following printing of the: (1) petri dish holder base; (2) lid; and (3) flange. The STL file extension stands for stereolithography, colloquially referred to as Standard Triangle Language or Standard Tessellation Language, and is a popular file format for 3D printing. The 3D models were created, and can be viewed, with CAD software.</p>
G-quadruplexes as pivotal components of cis-regulatory elements in the human genome
<p>This repository stores the scripts for analyzing the relationship between G-quadruplexes (G4s) and <em>cis</em>-regulatory elements (CREs), as well as the data generated directly from the manuscript.</p> <p>Manuscript: <a href="https://doi.org/10.1186/s12915-024-01971-5" target="_blank" rel="noopener">G-quadruplexes as pivotal components of <em>cis</em>-regulatory elements in the human genome</a></p> <p>G4Hunter_w25_s1.5_hg38.txt: All potential G-quadruplexes in the human genome predicted by the G4Hunter software. </p> <ul> <li>Genome assembly: hg38.</li> <li>G4Hunter software parameters were set as follows: score threshold 1.5, window size 25.</li> </ul> <p>G4_cCRE_annotation.txt: Annotation file indicating the presence of G4s in cCREs (candidate CREs; from <a title="SCREEN database" href="https://screen.encodeproject.org/" target="_blank" rel="noopener">SCREEN database</a>).</p> <p>scripts.zip: Source code used for data analysis in this project, based on the R language.</p>
RAGE - Data Final Evaluation RAGE Components
<p><strong>General description: </strong>Data from the final (summative) evaluation of RAGE game components (assets)</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology<br> Geographical information: Europe<br> Time information: October-November 2018</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - industry participants</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: RAGE software components for applied games development<br> Evaluation variables: usability, usefulness, relevance, game engineering, benefits, cost effectiveness, quality of support material</p> <p><strong>Instruments:</strong> Usability Metric for User Experience - UMUX (Finstad, 2010); items adapted from perceived usefulness scale (Davis, 1989; Davis and Venkatesh, 2004); questionnaire items defined for the purpose of the evaluation<br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209200">10.5281/zenodo.1209200</a>, <a href="https://doi.org/10.5281/zenodo.1209204">10.5281/zenodo.1209204</a></p>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</p>
Rutford Ice Stream, Antarctica M_sf tidal velocity components derived from COSMO-SkyMED SAR data
<p>This repository provides rasters for velocity components of a tidal (periodic) model for Rutford Ice Stream (RIS), Antarctica. The tidal model consists of a secular (constant) term and a single sinusoidal component corresponding to the M_sf tidal cycle (14.76529 days). The tidal model is fit to time-dependent velocity fields over RIS derived from speckle tracking of COSMO-SkyMed SAR data, collected over 9 months beginning in August 2013. The original methodology and source dataset are described in the publication:</p> <p>Minchew, B. M., Simons, M., Riel, B., & Milillo, P. (2017). Tidally induced variations in vertical and horizontal motion on Rutford Ice Stream, West Antarctica, inferred from remotely sensed observations. <em>Journal of Geophysical Research: Earth Surface</em>, <em>122</em>(1), 167-190. doi: <a href="https://doi.org/10.1002/2016JF003971">10.1002/2016JF003971</a></p> <p>The rasters are provided in GeoTIFF format in the Polar Stereographic South (EPSG: 3031) coordinate system. The velocity components are also referenced to Polar Stereographic South coordinates. The pixel spacing is 400 meters (in both the X- and Y-directions). The individual files are:</p> <ol> <li>vx_secular.tif: secular velocity in X-direction in meters/day.</li> <li>vy_secular.tif: secular velocity in Y-direction in meters/day.</li> <li>vx_amp.tif: M_sf velocity amplitude in X-direction in meters/day.</li> <li>vy_amp.tif: M_sf velocity amplitude in Y-direction in meters/day.</li> <li>vx_phase.tif: M_sf velocity phase delay in X-direction in days.</li> <li>vy_phase.tif: M_sf velocity phase delay in Y-direction in days.</li> </ol>
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.