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4,612 results for “labelled”
Design of facilitated dissociation enables control over cytokine signaling duration - SMT raw data - unstimulated - calibration beads - long term tracking - labelled ligand
<p>This dataset contains the raw data for the Single-molecule tracking data of the manuscript: "Design of facilitated dissociation enables control over cytokine signaling duration". Presicely, it contains the calibration beads for all imaging experiments, the long term tracking experiments, the experiments with labelled ligand and the unstimulated probes</p>
TABLE 1 in High-quality herbarium-label transcription by citizen scientists improves taxonomic and spatial representation of the tropical plant family Annonaceae
<p>TABLE 1. — Species per dataset and continent.</p><table><tbody><tr><th><b>Region</b></th><th><b>GBIF</b></th><th><b>Herbonautes</b></th><th><b>Herbonautes and not</b> <b>found in GBIF</b></th></tr></tbody><tbody><tr><th>Americas</th><td>700</td><td>248</td><td>7</td></tr><tr><th>Africa</th><td>268</td><td>189</td><td>17</td></tr><tr><th>Madagascar</th><td>79</td><td>79</td><td>10</td></tr><tr><th>Asia and Oceania</th><td>621</td><td>492</td><td>125</td></tr></tbody></table>
Bach Chorales Multiple Chord Labels (BCMCL) dataset
<p>The Bach Chorales Multiple Chord Labels dataset was proposed in our DLfM (Digital Library for Musicology) 2020 conference paper: "Automatic Chord Labelling: A Figured Bass Approach". There are four parallel tracks of chord labels, produced by each of the four rule-based algorithms (Algorithms A, B, C, and D). The chord labels of each algorithm can be found in each folder, where the music (Bach chorales) and annotations are presented in the MusicXML format. Overall, there are 120 Bach chorales (or 123 since there are three chorales with two versions) annotated this way.</p> <p>If you make use of BCMCL, please cite the following <a href="https://dl.acm.org/doi/abs/10.1145/3424911.3425513">publication</a>:</p> <pre><code>@inproceedings{ju_automatic_2020, title = {Automatic {Chord} {Labelling}: {A} {Figured} {Bass} {Approach}}, shorttitle = {Automatic {Chord} {Labelling}}, booktitle = {Proceedings of the 7th {International} {Conference} on {Digital} {Libraries} for {Musicology}}, author = {Ju, Yaolong and Margot, Sylvain and McKay, Cory and Fujinaga, Ichiro}, year = {2020}, pages = {27--31}, } </code></pre> <p>Please email to <strong>yaolong.ju@mail.mcgill.ca</strong> if you have any questions.</p>
Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), and single cell segmentation. Output of this process was a dataframe of nearly 130,000 cells with fluorescence values quantified from each marker. We used this dataframe as input to 1 of the 5 normalization techniques of which we compared z, double-log(z), min/max, and arcsinh normalizations to the original unmodified dataset. We used these normalized dataframes as inputs for 4 unsupervised clustering algorithms: k-means, leiden, X-shift euclidian, and X-shift angular.</p> <p>From the clustering outputs, we then labeled the clusters that resulted for cells observed in the data producing 20 unique cell type labels. We also labeled cell types by hiearchical hand-gating data within cellengine (cellengine.com). We also created another gold standard for comparison by overclustering unormalized data with X-shift angular clustering. Finally, we created one last label as the major cell type call from each cell from all 21 cell type labels in the dataset. </p> <p>Consequently the dataset has individual cells segmented out in each row. Then there are columns for the X, Y position in pixels in the overall montage image of the dataset. There are also columns to indicate which region the data came from (4 total). The rest are labels generated by all the clustering and normalization techniques used in the manuscript and what were compared to each other. These also were the data that were used for neighborhood analysis for the last figure of the manuscript. These are provided at all four levels of cell type level granularity (from 7 cell types to 35 cell types). </p>
Codes and source data files for: Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans
<p>The germ line produces gametes that transmit genetic and epigenetic information to the next generation. Maintenance of germ cells and development of gametes require germ granules—well-conserved membraneless and RNA-rich organelles. The composition of germ granules is elusive owing to their dynamic nature and their exclusive expression in the germ line. Using <i>C. elegans</i> germ granule, called P granule, as a model system, we employed a proximity-based labeling method in combination with mass spectrometry to comprehensively define its protein components. This set of experiments identified over 200 proteins, many of which contain intrinsically disordered regions. An RNAi-based screen identified factors that are essential for P granule assembly, notably EGGD-1 and EGGD-2, two putative LOTUS-domain proteins. Loss of <i>eggd-1</i> and <i>eggd-2</i> results in separation of P granules from the nuclear envelope, germline atrophy and reduced fertility. We show that intrinsically disordered regions of EGGD-1 are required to anchor EGGD-1 to the nuclear periphery while its LOTUS domains are required to promote perinuclear localization of P granules. Together, our work expands the repertoire of P granule constituents and provides new insights into the role of LOTUS-domain proteins in germ granule organization.</p>
Tracking label images with TrackMate
<p>Dataset used in a tutorial that shows how to use TrackMate to track label images.</p> <p>For more details see here: https://imagej.net/plugins/trackmate/trackmate-label-image-detector</p> <p> </p>
IsoAnalyst: A System-Wide Stable Isotopic Labeling Aproach for Connecting Natural Products to Their Cognate Biosynthetic Gene Clusters
<p>Processed mass spectrometry data input used to develop and validate the IsoAnalyst program and the output from these analyses. The S_erythraea folder contains all MS input and output data for <em>Saccharopolyspora erythraea</em>. The Micromonospora_RL09050HVFA folder contains MS input and output data for <em>Micromonopora sp.</em> RL09050HVFA, as well as the full antiSMASH output for the <em>Micromonopora sp.</em> RL09050HVFA genome.</p> <p>Version 2 contains updated IsoAnalyst results for both organisms. </p>
Supplementary datasets for the paper of "Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization"
<p>There are two eCLIP datasets (cell lines of K562 and HepG2). The eCLIP datasets were then divided into five categories for each cell line: low, medium 1, medium 2, high 1 and high 2 with peaks of >1,000 but <2,000, >2,000 but <4,000, >4,000 but <7,000, >7,000 but <10,000 and >10,000, respectively. </p>
Wikidata Dump EN labels+desc+aliases+links
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/1706">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Generating a Labeled Dataset to Train Machine Learning Algorithms for Lithological Classification of Drill Cuttings
<p>This dataset contains 16,700 fully labeled SEM images of rock chips isolated from 14 thin sections of drill cutting samples. These samples come from a low-permeability reservoir in western Canada.</p>
Labeled motor neurons following single and double nerve reinnervation of the long head of the biceps
<p>We developed a novel rat model to investigate the neurophysiological effects of peripheral double nerve transfers to a single target muscle. In 62 male Sprague-Dawley rats the ulnar nerve of the antebrachium alone (n=30) or together with the anterior interosseus nerve (n=32) was transferred to reinnervate the long head of the biceps brachii. To further assess reinnervation, retrograde labeling was performed. Twelve weeks following surgery retrograde tracer dyes were injected in the long head of the biceps muscle to stain the reinnervating motoneurons. In 7 control animals, the number of innervating motorneurons was assessed by injecting retrograde tracer in the untreated long head of the biceps.</p> <p>The aim of this dataset was to compare the effects of single nerve transfer and double nerve transfer on motor neuron count.</p>
Labelled and unlabelled hand acceleration data captured unobtrusively from PD patients and Healthy Controls
<p>The dataset contains acceleration signals captured in-the-wild via the IMU sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). It contains two different groups of subjects:</p> <ul> <li>tremor_sdata.pickle --> A group of 45 subjects that have been subjected to neurological examination (the same dataset as https://zenodo.org/record/3519213)</li> <li>tremor_gdata.pickle --> A group of 454 subjects who just self-reported their PD status</li> </ul> <p>All subjects contributed accelerometer data using their personal smartphones, for a period spanning many months. Tri-axial acceleration values were recorded automatically whenever a phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a python dictionary, indexed by the subject ids. Each element of the dictionary is a list with the following significance:</p> <table> <thead> <tr> <th scope="col">Index</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>List of np.arrays, Each array contains the power spectral density for an acceleration segment of 5s duration</td> </tr> <tr> <td>1</td> <td>Dictionary, Denotes subject updrs</td> </tr> <tr> <td>2</td> <td>List of str containing a unique identifier of the acceleration session that each segment in the other lists belongs to</td> </tr> <tr> <td>3</td> <td>List of np.arrays, Each array contains the pre-processed acceleration values for a 5s segment</td> </tr> </tbody> </table> <p> </p><p>The subject updrs is represented as dictionary containing the following tremor-related annotation values (FOR THE FIRST GROUP ONLY):<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p></p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.</p>
Multi-label Datasets used in "Adapting Transformers for Multi-Label Text Classification"
<p>The three Multi-Label datasets used in the article "Adapting Transformers for Multi-Label Text Classification".</p> <p>- AAPD Dataset (ArXiv Academic Paper Dataset) [Yang et al. 2018]<sup>1</sup></p> <p>- Reuters-21578 Dataset: https://archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection</p> <p>- MFHAD (Multilabel French HAL Abstracts Dataset)</p> <p> </p> <p><sup>1</sup>Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma, Wei Wu, and Houfeng Wang. 2018.<br> SGM: Sequence Generation Model for Multi-label Classification. In Proceedings<br> of the 27th International Conference on Computational Linguistics. Association for<br> Computational Linguistics, Santa Fe, New Mexico, USA, 3915–3926.</p>
Brain labels computed with FreeSurfer 6 for all ABIDE I subjects
<p># ABIDE I FreeSurfer 6 'label' data</p> <p><br> This archive contains the following ABIDE I FreeSurfer 6 labels:<br> cortex, aparc, aparc.a2009s.</p> <p><br> ## Credits</p> <p>This data is derived from the MRI scans of the ABIDE I dataset:</p> <p>* ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/</p> <p>Quoting from that website:</p> <p> "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first<br> ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17<br> international sites, sharing previously collected resting state functional<br> magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets<br> made available for data sharing with the broader scientific community.<br> This effort yielded 1112 dataset, including 539 from individuals with<br> ASD and 573 from typical controls (ages 7-64 years, median 14.7 years<br> across groups). This aggregate was released in August 2012. Its<br> establishment demonstrated the feasibility of aggregating resting<br> state fMRI and structural MRI data across sites; the rate of these<br> data use and resulting publications (see Manuscripts) have shown its<br> utility for capturing whole brain and regional properties of the brain<br> connectome in Autism Spectrum Disorder (ASD). In accordance with<br> HIPAA guidelines and 1000 Functional Connectomes Project / INDI<br> protocols, all datasets have been anonymized, with no protected<br> health information included."</p> <p>Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).<br> The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.</p> <p>## How this data was produced</p> <p>The following steps were used to create the data:</p> <p>* We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).<br> * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.<br> - We did not run any quality metrics on the scans or exclude any subjects.</p> <p><br> ## Contained files</p> <p>* In order to reduce the size of this dataset, for each subject, we only included the following files:<br> - <subject>/label/lh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for left hemisphere.<br> - <subject>/label/rh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for right hemisphere.<br> - <subject>/label/lh.aparc.annot : Desikan atlas surface parcellation for left hemisphere.<br> - <subject>/label/rh.aparc.annot : Desikan atlas surface parcellation for right hemisphere.<br> - <subject>/label/lh.aparc.a2009s.annot : Destrieux atlas surface parcellation for left hemisphere.<br> - <subject>/label/rh.aparc.a2900s.annot : Destrieux atlas surface parcellation for right hemisphere.</p> <p><br> All files are in ASCII FreeSurfer label format.<br> For the annot files, see FreeSurferColorLut.txt that comes with FreeSurfer for meaning of integers specifying a region.</p> <p>## What is NOT contained</p> <p>* The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.</p> <p>## Author and License</p> <p>Note: For the authors of the original ABIDE I dataset, see the Credits section above.</p> <p>This label data was created by:</p> <p> Dr. Tim Schäfer<br> Postdoc Computational Neuroimaging<br> Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy<br> University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany<br> http://rcmd.org/ts</p> <p>The data is published under the following license:</p> <p>Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)</p> <p>See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.</p> <p>See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.</p> <p> </p>
RGB-LWIR Labeled Dataset for Air-based platforms
<p>Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms. </p>
Super Resolved and Multi-Label Segmented PEMFC
<p>Super Resolved and Multi-Label Segmented PEMFC with Configuration Files for; Large-scale Physically Accurate Modelling of Real Proton Exchange Membrane Fuel Cell with Deep Learning, Nature Communications.</p> <table> <tbody> <tr> <td><a href="https://zenodo.org/api/files/19f99ad1-b88b-45aa-8731-a88d2753fe3c/ffov_crop_origsize.tiff">ffov_crop_origsize.tiff </a>- LOW RESOLUTION REGISTERED TRAINING BLOCK<br> md5:0b8293b1bf6ded48df1cdd6a36b22e61</td> <td>8.8 MB</td> <td> </td> <td> </td> </tr> <tr> <td><a href="https://zenodo.org/api/files/19f99ad1-b88b-45aa-8731-a88d2753fe3c/inputFile.db">inputFile.db </a>- LBPM INPUT FILE<br> md5:542d244a9552d6b4b1b7ecfa8fd1c153</td> <td>1 kB</td> <td> </td> <td> </td> </tr> <tr> <td><a href="https://zenodo.org/api/files/19f99ad1-b88b-45aa-8731-a88d2753fe3c/LRTest.tif">LRTest.tif </a>- LOW RESOLUTION FULL FOV DOMAIN<br> md5:85f2ccaff7b96188062efd6ab241630b</td> <td>1.3 GB</td> <td> </td> <td> </td> </tr> <tr> <td><a href="https://zenodo.org/api/files/19f99ad1-b88b-45aa-8731-a88d2753fe3c/PEFC_hres_0p7um.tiff">PEFC_hres_0p7um.tiff </a>- HIGH RESOLUTION REGISTERED TRAINING BLOCK<br> md5:ec918d150a6bb6ee5af576cb0bc55c03</td> <td>1.7 GB</td> <td> </td> <td> </td> </tr> <tr> <td><a href="https://zenodo.org/api/files/19f99ad1-b88b-45aa-8731-a88d2753fe3c/SRSegFinal_Processed_oneWeave_Opened.tif">SRSegFinal_Processed_oneWeave_Opened.tif </a>- SUPER RESOLVED AND MULTI-LABEL SEGMENTED FULL FOV DOMAIN<br> md5:20c7aea8c3b832b400811958b8ea243a</td> <td>2.2 GB</td> <td> </td> <td> </td> </tr> <tr> <td><a href="https://zenodo.org/api/files/19f99ad1-b88b-45aa-8731-a88d2753fe3c/SRSegWithGC.tif">SRSegWithGC.tif </a>- SUPER RESOLVED AND MULTI-LABEL SEGMENTED FULL FOV DOMAIN WITH GAS CHANNELS<br> md5:8a6cdd51d7b1b55f1ce9e0beaaa0aaed</td> <td>2.5 GB</td> <td> </td> <td> </td> </tr> <tr> <td><a href="https://zenodo.org/api/files/19f99ad1-b88b-45aa-8731-a88d2753fe3c/SRVis_1100_4000_8000.raw">SRVis_1100_4000_8000.raw </a>- SUPER RESOLVED FULL FOV DOMAIN<br> md5:3ba78b624e60845c09f1d34820eb68e5</td> <td>35.2 GB</td> <td> </td> <td> </td> </tr> </tbody> </table> <table> <tbody> <tr> <th> </th> <th> </th> <th> </th> <th> </th> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> </tr> </tbody> </table>
LABELLED MEDITERRANEAN FISH DATASET
<p>This dataset contains 1548 underwater images containing over 17965 labelled Mediterranean fish of 20 species. It includes some general categories such as"stain" (lens stain, occupying > approx 20% of the image), "small" (very small fish, <100 square pixels) and "fish poly", corresponding to unidentified fish. Labels are linked to bounding boxes, in YOLOv5 format.</p>
Cell-free Synthesis of Proteins with Selectively 13C-Labelled Methyl Groups from Inexpensive Precursors- Dataset
<p>NMR Spectra relating to the publication:<br> <br> Cell-free Synthesis of Proteins with Selectively 13C-Labelled Methyl Groups from Inexpensive Precursors.</p> <p>Spectra are labelled as such in the folder name and in the description.</p>
Multi-Label Datasets with Missing Values
<p>Consisting of six multi-label datasets from the <a href="https://archive.ics.uci.edu">UCI Machine Learning repository.</a></p> <p>Each dataset contains missing values which have been artificially added at the following rates: 5, 10, 15, 20, 25, and<br> 30%. The “amputation” was performed using the “Missing Completely at Random” mechanism.</p> <p>File names are represented as follows:</p> <p> amp_<em>DB</em>_<em>MR</em>.arff</p> <p>where:</p> <p> <em>DB </em>= original dataset;</p> <p> <em>MR </em>= missing rate.</p> <p>For more details, please read:</p> <p>IEEE Access article (in review process)</p> <p> </p>
A streamlined approach for fluorescence labelling of low copy-number plasmids for determination of conjugation frequency by flow cytometry
<p><span>Bacterial conjugation plays a major role in the dissemination of antibiotic resistance and virulence traits through horizontal transfer of plasmids. </span>Robust <span>measurement</span> of<span> the conjugation frequency of plasmids between bacterial strains and species </span>is therefore important <span>to understand the transfer dynamics </span>and epidemiology <span>of conjugative plasmids. In this study, we present a streamlined experimental approach for fluorescence labelling of low copy-number conjugative plasmids that allows plasmid transfer frequency during filter mating to be measured by flow cytometry. A blue fluorescence gene is inserted into a conjugative plasmid of interest using a simple homologous recombineering procedure. </span><span>A small non-conjugative plasmid, which carries a red fluorescence gene with a toxin-antitoxin system that functions as a plasmid stability module, is used to label the recipient bacterial strain. This offers the dual advantage of circumventing chromosomal modifications of recipient strains and ensuring that the red fluorescence gene-bearing plasmid can be stably maintained in recipient cells in an antibiotic-free environment during conjugation. A strong constitutive promoter allows the two fluorescence genes to be strongly and constitutively expressed from the plasmids, thus allowing flow cytometers to clearly distinguish between donor, recipient and transconjugant populations in a conjugation mix for monitoring conjugation frequencies more precisely over time. </span></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.