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13,587 results for “specification”
Data set for "Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices"
<p>Data set for: Liu Y, Bech P, Tamura K, Délez LT, Crochet S, Petersen CCH (2024) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices. eLife 13: RP97602. https://doi.org/10.7554/eLife.97602</p> <p>There are 3 files in this upload:</p> <p>1. The file named "2024_Liu_eLife.pdf" is the Open Access pdf of the online publication in eLife.</p> <p>2. The file named "Liu_anatomy_data_code.zip" (~35 GB) is a zipped version of a folder "Liu_anatomy_data_code" (~111 GB), which contains the anatomical data analysed in the study along with the Python codes used to generate the published figures 1-7 and their associated figure supplements. </p> <p>3. The file named "Liu_function_data_code.zip" (~10 GB) is a zipped version of a folder "Liu_function_data_code" (~35 GB), which contains the functional data analysed in the study along with the Python codes used to generate the published figure 8 and its associated figure supplement. </p> <p>After unzipping, the Python codes should run as a Jupyter notebook (anatomy .ipynb code) or Python code (function .py code) in Anaconda.</p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p><span>Summary: </span></p> <p><span><span> </span>Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</span></p> <p><span><span> </span>The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</span></p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p>Summary:</p> <p> Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</p> <p> The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</p>
Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14070345">Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Meteocean conditions of Canary Islands</li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset: </strong></p> <p>zip that contains 243 csv files.</p> <p>Each file containing one-hour time series of load simulation (time step 0.5s). </p> <p><strong>Filenames</strong> specify details about the simulation:</p> <ul> <li>dlc - Design Load Case [12 : normal power production, 64 : idling]</li> <li>Vh [wind speed]</li> <li>Y [yaw angle]</li> <li>W [wave height _ period]</li> <li>D [wind direction]</li> <li>M [wave misalignment= wave direction with respect to wind direction]</li> </ul> <p>The <strong>columns </strong>of each csv file includes followind signals according to OpenFAST nomenclature and reference frames:</p> <ul> <li>Time (s)</li> <li>PtfmSurge (m)</li> <li>PtfmSway (m)</li> <li>PtfmHeave (m)</li> <li>PtfmRoll (deg)</li> <li>PtfmPitch (deg)</li> <li>PtfmYaw (deg)</li> <li>GenPwr (kW)</li> <li>RotThrust (kN)</li> <li>GenTq (kN-m)</li> <li>RotSpeed (rpm)</li> <li>BlPitch1 (deg)</li> <li>TipDxc1 (m)</li> <li>RootMxc1 (kN-m)</li> <li>RootMyc1 (kN-m)</li> <li>TwrBsMxt (kN-m)</li> <li>TwrBsMyt (kN-m)</li> <li>FAIRTEN1 (N)</li> <li>FAIRTEN2 (N)</li> <li>FAIRTEN3 (N)</li> </ul>
Quantifying the Tissue-Specific Regulatory Information within Enhancer DNA Sequences
<p>Tables of mouse embryonic differential enhancers and promoters.</p> <p>The tables list the genomic coordinates of differential regulatory elements, the tissues in which they are active (labels column), a differential score, and the DNA sequence of the element. Tissues are numbered from 0 to 7, which correspond to heart, kidney, liver, limb, lung, forebrain, midbrain, and hindbrain, respectively. The tool <a href="https://github.com/pbenner/gonetics/tree/master/tools/segmentationDifferential">segmentationDifferential</a> from the <em>gonetics library</em> was used to compute differential elements. The length of all regions was set to 1000 base pairs around the center. Specifics of the experimental data can be found in the referenced publication.</p>
S81 | THSTPS | Thirdhand Smoke Specific Metabolites
<p>This is the collection associated with list S81 THSTPS Thirdhand Smoke Specific Metabolites on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Data compiled by Carla Merino and colleagues, URV, Spain.</p> <p> </p> <p>v0.1.1: fixed formatting issues in CSV files (spacing around CIDs, name errors, char errors in refs)</p>
Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"
<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2021_Sippy_FUNCTION.pdf</strong>" is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named "<strong>Sippy_data_code.zip</strong>" (~5 GB) is a zipped version of a folder ‘<em>Sippy_data_code</em>’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘<em>Sippy_data_code</em>’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘<em>Data</em>’ contains the data structure ‘<em>Data.mat</em>’ to be analyzed, as well as a Matlab file called ‘<em>p_value_colormap.mat</em>’ used to plot the p value color bars in some figures.</p> <p>The subfolder ‘<em>Functions</em>’ contains functions called by the main codes.</p> <p>The subfolder ‘<em>Codes</em>’ contains the following codes:</p> <p><em>‘AnalyzeDataStructure.m’: </em>computes the results and saves them as a new data structure called ‘<em>Analyzed_Data</em>’, in the subfolder ‘<em>Results</em>’.</p> <p><em>‘Figure_1.m’: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>‘Figure_2.m’: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>‘Figure_3.m’: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>‘SuppFigure_2.m’: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>‘SuppFigure_3.m’: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>‘SuppFigure_4.m': </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>‘Mouse_Name’</em>: name of the mouse.</p> <p><em>‘Mouse_RecordingDate’</em>: date of recording (YMD).</p> <p><em>‘Mouse_DateOfBirth’</em>: date of birth of the mouse (YMD).</p> <p><em>‘Mouse_Sex’</em>: sex of the mouse (F or M).</p> <p><em>‘Mouse_Genotype’</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>‘Mouse_Level’</em>: Training level (NAÏVE or EXPERT).</p> <p><em>‘Cell_Counter’</em>: cell recorded in a given mouse.</p> <p><em>‘Cell_Type’</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>‘Cell_TargetedBrainArea’</em>: Brain area targeted (DLS).</p> <p><em>‘Cell_Recovered’</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>‘Cell_Coordinates’</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>‘Cell_Fluorescence’</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>‘Sweep_Counter’</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>‘Sweep_Type’</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>‘Sweep_MembranePotential’</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>‘Sweep_CurrentInjected’</em>: current injected into the cell (pA).</p> <p><em>‘Sweep_PiezoLick’</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>‘Sweep_Trial’</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>‘Sweep_WhiskerStim’</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_Valve’</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>‘Sweep_SamplingRate’</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>‘Sweep_TimeStamp’</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>‘Sweep_Reward’</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_APThresh’</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p> </p>
TF-Marker: A comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.
<p>Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/) which is committed to a comprehensive manual curation of TFs and related markers with experimental evidence in specific cell and tissue types in human. Currently, through reviewing <strong>2,091</strong> published literature, we have manually classified TFs and related markers into five types according to their functions: 1) <strong>TF</strong>: TFs, which regulate the expression of markers; 2) <strong>T Marker</strong>: markers, which are regulated by TFs (TF and T Marker pairs can identify cell types more specifically); 3) <strong>I Marker</strong>: markers, which influence the activity of TFs (I Markers can also influence the development of specific cells and tissues); 4) <strong>TFMarker</strong>: TFs, which play roles as markers (TFMarkers are cell/tissue-specific TFs used as cell markers in biology experiments); and 5) <strong>TF Pmarker</strong>: TFs, which play roles as potential markers. By curating thousands of published literature, <strong>5,905</strong> entries including <strong>1,316</strong> TFs, <strong>1,092</strong> T Markers, <strong>473</strong> I Markers, <strong>1,600</strong> TFMarkers and <strong>1,424</strong> TF Pmarkers, were annotated in <strong>383</strong> cell types and <strong>95</strong> tissue types in human. Moreover, TF-Marker divided markers into disease markers and tissue/cell-specific markers. TF-Marker is an elaborate database, which provides TFs and related markers supported by experimental evidence. We believe TF-Marker will provide strong support for research into cell/tissue-specific TFs and related markers.</p>
Transcriptomic analyses of normal-appearing CNS white matter from multiple sclerosis donors reveal subtype-specific molecular signatures of disease (REVISED)
<p>Datasets of bulk RNA-sequencing of NAWM from MS donors + supplementary images of RNA deconvolution of cell trajectories</p>
Sex-specific changes in autosomal methylation rate in ageing common terns - Data
<p>In the manuscript "<strong>Sex-specific changes in autosomal methylation rate in aging common terns</strong>" published in Frontiers Ecology And Evolution, we investigate sex-specific age effects in global DNA methylation patterns in aging common terns in a longitudinal study. We collected blood at 1-, 3- and/or 4-year intervals, extracted DNA from the erythrocytes and estimated autosomal DNA methylation by mapping Reduced Representative Bisulfite Sequencing reads to a de novo assembled reference genome. </p> <p>The raw RRBS reads can be found at: https://www.ebi.ac.uk/ena, PRJEB48910 and the genome at https://www.ncbi.nlm.nih.gov/, PRJNA560234.</p> <p>This document contains the final data frame used for the GLMM. We further share a markdown document summarising the various scripts and programs used for the Reduced Representative Bisulfite Sequencing data analyses and GLMMs (e.g. RefFreeDMA, picard, bismark, and R) under another DOI: 10.5281/zenodo.7493357. </p>
Artifacts for Keyword Extraction From Specification Documents for Planning Security Mechanisms
<p>This dataset contains the data used for evaluating VDocScan - a keyword extraction based security vulnerability prediction method. The repository includes an extensive list of Products and Vulnerability reports from CVE, a custom created dataset mapping vulnerability reports to product documentations, as well as, intermediate results from the study such as decision trees rendered for each vulnerability, correlation matrix of vulnerabilities etc. </p>
Raw MS data for "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor"
<p>These are the raw HDX/MS data for our paper: "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor."</p>
IMAGS II Requirements Specification
<p>This repository contains the IMAGS II requirements specificiation described in "Din, C. Y., & Rine, D. C. (2013). Requirements Statements Content Goodness and Complexity Measurement. International Journal of Next-Generation Computing, 4(1)."</p>
Data for patient-specific solution of the electrocorticography forward problem in deforming brain
<p>This dataset contains magnetic resonance (MR) and computed tomography (CT) images of a patient undergoing intracranial electrical monitoring using electrocorticography grid electrodes, together with patient-specific geometry and computational grids created from these images applied in the research reported in NeuroImage article “Patient-specific solution of the electrocorticography forward problem in deforming brain”. The images were acquired at Boston Children’s Hospital and provided to The University of Western Australia’s Intelligent Systems for Medicine Laboratory for analysis. The analysis was conducted using our open-source SlicerCBM software extension for the 3D Slicer medical imaging platform. The analysis steps include image processing to obtain the patient-specific brain geometry, construction of computational grids (tetrahedral grid for meshless solution of biomechanical model and regular hexahedral grid for finite element solution of the electrocorticography forward problem), biomechanics-based image warping to predict the postoperative images corresponding to the brain configuration deformed by placement of subdural electrodes, and patient-specific solution of the electrocorticography forward problem to compute the electric potential distribution within the patient’s head. We use well-established open-source data file formats including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry and Visualization Toolkit (VTK) files for computational grids. This facilitates the re-use of this dataset in a range of studies that rely on medical image analysis, and computational biomechanics and electrostatics to solve the electrocorticography forward problem for electrical source imaging.</p>
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
<p>Summary:</p> <p>There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.</p> <p>The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>How to cite this dataset:</p> <p>Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. <em>Earth System Science Data</em>, <em>14</em>(9), pp.4397-4418.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Update history:</p> <p>I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)</p> <p>Version 1.03.1: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.</p> <p>Version 1.02: New data summary, add Netcdf data format</p> <p>Version 1: Initial dataset for peer-review, CSV format only</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.</p> <p>SPAM2010: </p> <p>Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.</p> <p>GAEZv4:</p> <p>FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021</p> <p>The dataset of Ricciardi et al.'s:</p> <p>Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.</p> <p>The global dominant field size dataset:</p> <p>Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.</p> <p>GLC-Share:</p> <p>Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.</p> <p>CAAS-IFPRI cropland extent map:</p> <p>Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.</p>
Carbon-specific remineralization rates of small and large organic particles in the North Atlantic
<p>This repository provides the carbon-specific remineralization rates of small and large organic particles in the North Atlantic which were calculated by using BGC-Argo observations of backscatter (a proxy of particulate organic carbon, POC) and dissolved oxygen. Details are given in the following article:</p> <p>Wang, B. and Fennel, K. (2022), Biogeochemical-Argo data suggest significant contributions of small particles to the vertical carbon flux in the subpolar North Atlantic. Limnol Oceanogr, 67: 2405-2417. <a href="https://doi.org/10.1002/lno.12209">https://doi.org/10.1002/lno.12209</a></p>
Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences
<p>This repository consists of minisatellite VNTR genotypes for 2,800 samples (2,770 individuals). The raw VCF files were produced using <a href="https://github.com/yzhernand/VNTRseek">VNTRseek</a> on xxx data sources: <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/">30 high coverage WGS datasets</a> from the 1000 Genomes Project phase 3, <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">2,504 unrelated genomes</a> from New York Genome Center (NYGC), <a href="https://www.internationalgenome.org/data-portal/data-collection/sgdp">253 genomes from Simons Diversity Genome Project</a> (SGDP), <a href="https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/tumor-normal.html">two tumor-normal breast cancer samples</a> from Illumina Basespace, haploid genomes <a href="https://www.ncbi.nlm.nih.gov/sra/SRX652547">CHM1 </a>and <a href="https://www.ncbi.nlm.nih.gov/sra/SRX1009644">CHM13</a>, and seven genomes from the Personal Genome Project from the Genome In A Bottle Consortium (GIAB). Raw VCF files are provided for each data source separately.</p> <p>The raw VCF files were preprocessed (preprocess.sh) to extract genotypes and provided in VNTRseek_preprocessed_data.tar.gz (uncompressed size 10G). The R Markdown code to analyze the preprocessed data and produce figures and tables is also provided (tables_and_figures.Rmd). For more information see the ReadMe file.</p> <p>This work was supported in part by NSF grants IIS-1423022 and DBI-1559829.</p>
Dataset 2 for "Host-specificity and repeatability of haemosporidian infection parameters and potential consequences when testing host species-level hypotheses"
<p>Dataset with 154 host species (min 45 sampled individuals sampled at 1+ sites) for the second part of the analysis in "Host-specificity and repeatability of haemosporidian infection parameters and potential consequences when testing host species-level hypotheses". One file contains the data table. One file contains a table with descriptions of the columns in the data table.</p>
Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures
<p>Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a></p> <p>Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a></p> <p> </p>
Data from: Inter-laboratory comparison of plant volatile analyses in the light of intra-specific chemodiversity
<p>Data for: Inter-laboratory comparison of plant volatile analyses in the light of intra-specific chemodiversity</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.