Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
659
datasets available to search
ShareScore release 0.9.0
Dataset results
659 results for “E. coli”
PFAS, Coliform, and E. coli Data from Freshwater Canals, Brackish Water in Biscayne Bay, and Ocean Salt Water from Miami Beaches, Florida, USA, July 2023
This package contains data on levels of Per- and polyfluoroalkyl substances (PFAS), Coliform, and E. coli from samples collected at 15 water sampling sites in and around Biscayne Bay, Florida on 2023-07-27 as part of the Coastal Ecosystem-Research Experience for Teachers (CE-BIORETS) program. The sites included waterways leading to Biscayne Bay (freshwater canals), Biscayne Bay directly (marine brackish water), and ocean water from Miami beaches (marine salt water). A total of 500 mL of surface water was collected (at approximately 30 cm depth) from each site. Samples were run through a Solid Phase Extraction process (SPE) and then dried using a Nitrogen Evaporator. Extracts were then analyzed by Liquid Chromatography-Triple quadrupole Mass Spectrometry (LC-MS/MS) to determine the quantity of each of the different PFAS (ng/L) present in the sample. The same sites were selected for Coliform and E. coli colony forming units’ detection, and samples were collected at the same time as the PFAS samples. Samples were analyzed by pouring 100 mL of collected water into the wells on a ColiPlate. The 15 plates were incubated for 1 day at 37°C to determine if Coliforms and E. coli were detected. Data collection for this project is complete.
Alphafold and ColabFold models of E. coli and consensus Bcs complexes
<p>ColabFold and AlphaFold 3 models used for structure modeling and interpretation in Anso et al. 'Structural basis for synthase activation and cellulose modification in the <em>E. coli</em> Type II Bcs secretion system'. </p>
BBS phase 1 & phase 2 high quality E. coli bin assembled genomes
<p>1,402 <em>Escherichia coli</em> bin assembled genomes derived from the metagenome data collected as part of the <a href="https://www.ucl.ac.uk/global-health/research/a-z/baby-biome-study">BabyBiome study (BBS)</a> phase 1 & phase 2.</p> <p>The data in this upload was first published as part of "<em>Group 2 and 3 ABC-transporter dependant K-antigen loci contribute significantly to variation in the invasive potential of Escherichia coli"</em> (Gladstone et al. 2024, to be released).</p> <h2>Files</h2> <p>Assembly data:</p> <ul> <li>BBS_E_coli_BAGs.tar: Archive containing sequences of the 1,402 bin assembled genomes.</li> <li>BBS_E_coli_metadata.tsv: Table linking the sequence assemblies to the subject data.</li> </ul> <p>Capsule predictions:</p> <ul> <li>BBS_E_coli_Kaptive_output.csv: Capsule predictions for all sequence data.</li> <li>BBS_E_coli_deduplicated_sequences_IDs.txt: Filenames for assemblies that constitute the 873 deduplicated sequences analysed in Gladstone et al. 2024.</li> </ul> <p>Quality control data:</p> <ul> <li>BBS_E_coli_demix_check_scores.tsv: Output from demix_check for the sequence assemblies.</li> <li>BBS_E_coli_checkm_results.tsv: Output from checkm.</li> <li>BBS_E_coli_gunc_results.tsv: Output from gunc.</li> </ul> <h2>Methods</h2> <h3>Bin assembled genomes</h3> <p>Source data:</p> <ul> <li>BBS phase 1: <a href="https://doi.org/10.1038/s41586-019-1560-1">Shao et al. 2019</a></li> <li>BBS phase 2: <a href="https://doi.org/10.1038/s41564-024-01804-9">Shao et al. 2024</a></li> </ul> <p>The data was produced using the mSWEEP and mGEMS pipeline (<a href="https://doi.org/10.12688/wellcomeopenres.15639.2">Mäklin et al. 2020</a> & <a href="https://doi.org/10.1099/mgen.0.000691">Mäklin et al. 2021</a>) following the steps described in <a href="https://doi.org/10.1038/s41467-024-49591-5">Khawaja, Mäklin, Kallonen, et al. 2024</a>.</p> <h3>Quality control</h3> <p>The BAGs in this upload were filtered with demix_check (<a href="https://github.com/harry-thorpe/demix_check">https://github.com/harry-thorpe/demix_check</a>) and only those with a quality score 1 or 2 are included. For the capsule type annotations, contigs shorter than 5,000bp were removed but the short contigs are still present in the uploaded files). Further QC data is available from checkm (<a href="https://genome.cshlp.org/content/25/7/1043.short">Parks et al. 2015</a>) and gunc (<a href="https://link.springer.com/article/10.1186/s13059-021-02393-0">Orakov et al. 2022</a>) results.</p> <h3>Multilocus sequence typing</h3> <p>Sequence type (ST) was determined using fastmlst (<a href="https://journals.sagepub.com/doi/10.1177/11779322211059238">Guerrero-Araya et al. 2021</a>) with the `ecoli#1` database.</p> <h3>PopPUNK clustering</h3> <p>Sequence clusters (SC) correspond to the database available from <a href="https://zenodo.org/records/12528310">https://zenodo.org/records/12528310</a> and were created using PopPUNK (<a href="https://genome.cshlp.org/content/29/2/304.short">Lees et al. 2019</a>). Construction is described in <a href="https://doi.org/10.1038/s41467-024-49591-5">Khawaja, Mäklin, Kallonen, et al. 2024</a>.</p> <h3>Capsule type annotations</h3> <p>The capsule type annotations were created using Kaptive (<a href="https://doi.org/10.1099/mgen.0.000800">Lam et al. 2022</a>) with an <em>E. coli</em> specific database available from <a href="https://github.com/rgladstone/EC-K-typing">https://github.com/rgladstone/EC-K-typing</a> and described in Gladstone et al. 2024.</p>
Alternative architecture of the E. coli chemosensory array.
<p>We present molecular models for extended patches of the <em>E. coli</em> chemosensory array. The models display either the canonical p6-symmetric architecture or an alternative p2-symmetric architecture recently observed in <em>E. coli</em> minicells. For more information, please see the associated BioRxiv preprint. Questions regarding the coordinates may be directed to Keith Cassidy (keith.cassidy@bioch.ox.ac.uk).</p> <p> </p>
Supplementary Data - Impacts of selective digestive decontamination on the pangenome composition of ESBL-E. coli
<p>Supplementary Data of:</p> <p><span>Impacts of selective digestive decontamination on the pangenome composition of ESBL<em>-E. coli</em></span></p>
Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. coli
<p>Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. col</p> <p>This dataset pertains to our work developing TESEC Mtb ALR, a genetically engineered strain of E. coli expressing the enzyme ALR derived from Mtb. We used the TESEC Mtb ALR strain in a high-throughput drug screen and identified benazepril as targeted inhibitor of the ALR enzyme. We then performed additional experiments to characterize the activity of benazepril against E. coli, Mtb and purified enzymes. Finally, we tested the extensibility of the platform by constructing and screening against similar strains for additional targets: Asd, CysH, DapB, and TrpD.</p> <p>These files include growth measurements, biochemical assays and other forms of biological data. They are packaged together with scripts used to analyze the data and present them in figures. Our goal in creating this archive was to present our complete analysis pipeline in the spirit of open science. It is not intended to be a stand-alone resource. Consult the associated manuscript for protocols, units of measurement and other essential technical context.</p> <p>Our scripts were written for Python 3.8. The raw data is presented as human-readable .csv files intended to be imported as Pandas DataFrames. Some data is also packaged as Python dictionaries saved with the Pickle package.</p>
Input features of E. coli proteome for predicting and modeling protein-protein interactions with AF2Complex
<p>Input features to be used with AF2Complex for predicting protein-protein interactions among ~4400 E. coli proteins. A pickled feature file was generated by the feature data pipeline of AF2Complex for each E. coli protein. To reduce storage size, we limited up to 10,000 MSA sequences and up to 10 structural templates from the Protein Data Bank. The cutoff date for sequence libraries and the Protein Data Bank releases used for feature generation is no later than 11-30-2021.</p> <ul> <li>ecoli_af2c_fea.txt -- A list of all E coli protein with pre-generated input features</li> <li>af2c_fea_ecoli_220331_msa10ktem10.tar -- Input features named after the UniProt ID of each proteins. Note that after untar the tarball, you may use the gzipped feature pickle files directly with AF2Complex w/o gunzip.</li> </ul>
Original X-ray diffraction images for 5-aminolevulinic acid dehydratase (ALAD) from E. coli complexed with porphobilinogen.
<p>The diffraction images which allowed the original 2.1 Angstrom resolution structure determination of <em>Escherichia coli</em> ALAD co-crystallised with a non-covalently bound moiety of the product, porphobilinogen (PBG), are presented. </p>
X-ray diffraction images for 5-aminolevulinic acid dehydratase (ALAD) from E. coli.
<p>X-ray diffraction images for <em>Escherichia coli</em> 5-aminolevulinic acid dehydratase (ALAD) which was crystallised in the presence of the inhibitor levulinic acid (15 mM) and bismuth nitrate (1 mM). The data were collected at beamline 9.6 at the SRS Daresbury Laboratory (UK) on 10th March 1994 using a 30 cm Marresearch image plate detector, a crystal temperature of 100 K, a wavelength of 0.88 Å and a crystal-to-detector distance was 300 mm. The oscillation angle was 2.5 degrees and 21 images were collected at constant dose in the time available. A wax image for determining the direct beam position was taken with the detector at a distance of 400 mm.</p>
Antimicrobial resistance - Salmonella, E. Coli, prevalence ESBL data
<p>The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E. Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.</p> <p>Format XLSX; Contact zoonoses_support@efsa.europa.eu (EFSA); FWD@ecdc.europa.eu (ECDC)</p> <p> </p>
Antimicrobial resistance - Salmonella, E. Coli, prevalence ESBL data
<p>The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E.Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.</p> <p> </p> <p><strong>Format XLSX; Contact zoonoses_support@efsa.europa.eu (EFSA); FWD@ecdc.europa.eu (ECDC)</strong></p>
Şekil 11. Saf kitin ve gümüş nanoparÇacıkları dekore edilmiş kitinin E. coli ATCC 25922 ve S. aureus ATCC 29213'e karşı antibakteriyel aktivitelerinin kuyu difüzyon görüntüleri. K (Kontrol), A (Kitin), C (Gümüş nanoparÇacıklı kitin. in Eustigmaeus absens (Acari: Stigmaeidae) türünden elde edilen kitin ve Ag-dekore edilmiş kitin nanokompozit: İzolasyonu, karakterizasyonu ve antibakteriyel aktivitesi
Şekil 11. Saf kitin ve gümüş nanoparÇacıkları dekore edilmiş kitinin E. coli ATCC 25922 ve S. aureus ATCC 29213'e karşı antibakteriyel aktivitelerinin kuyu difüzyon görüntüleri. K (Kontrol), A (Kitin), C (Gümüş nanoparÇacıklı kitin.
Dataset for: Mechanisms of uropathogenic E. coli mucosal association in the gastrointestinal tract (Science Advances)
<p>This dataset contains the plotted data for Figures 1B, 1B, 1F, Figure 4C, Figures 5C, 5E, 5G, 5H, 5J, 5K, Figure S5B, Figure S6A, S6B, S6C, Figure S7 A, S7C, S7D, and Figure S8C.</p>
DeepBacs – E. coli SIM prediction dataset and CARE model
<p>Training and test images of live, membrane-labeled <em>E. coli </em>cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example image shows a widefield fluorescence image and SIM reconstruction of FM5-95 labelled, live <em>E. coli </em>cells.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (FM5-95)</p> <p><strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)</p> <p><strong>Cell type</strong>: <em>E. coli </em>DH5α grown under agarose pads</p> <p><strong>File format</strong>: .tif (16-bit for widefield images and 32-bit for SIM reconstructions)</p> <p><strong>Image size</strong>: 1024 x 1024 px² (40 nm/px)<br> <strong>Image preprocessing</strong>: <em>E. coli</em> widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size. </p> <p> </p> <p><strong>CARE model</strong></p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 5500 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation and flipping.</p> <p>Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.</p> <p> </p> <p><strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup></p> <p><strong>Contact email</strong>: <a href="mailto:pmatos@itqb.unl.pt">pmatos@itqb.unl.pt</a> and <a href="mailto:mgpinho@itqb.unl.pt">mgpinho@itqb.unl.pt</a></p> <p> </p> <p><strong>Affiliation</strong>: </p> <p>1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal</p> <p>2) ORCID: https://orcid.org/0000-0002-1426-9540</p> <p>3) ORCID: https://orcid.org/0000-0002-7132-8842</p>
DeepBacs – Artificial labeling of E. coli membranes dataset and fnet/CARE models
<p>Training and test images of <em>E. coli </em>cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>Example image shows an <em>E. coli</em> bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm).</p> <p> </p> <p><strong>Training and testing dataset</strong></p> <p><strong>Data type</strong>: Paired bright field and super-resolution images</p> <p><strong>Microscopy data type</strong>: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images)</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli </em>K12 strain derivatives</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512x512 px<sup>2</sup> with different pixel sizes:</p> <p>1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images)</p> <p>1.5x tube lens: 106 nm (raw) (widefield fluorescence only)</p> <p> </p> <p><strong>fnet model (PAINT membrane images)</strong></p> <p>The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook.</p> <p> </p> <p><strong>CARE model (PAINT membrane images):</strong></p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Analytical kinetic model of native tandem promoters in E. coli
<p><span>Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in <i>E. coli</i>. We also studied the sequence (distance between transcription start sites '<i>d<sub>TSS</sub>'</i>,<i> </i>pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and <i>d<sub>TSS</sub> </i>are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when <i>d<sub>TSS</sub> </i>≤ 35 bp) and RNAP occupancy of the downstream promoter (when <i>d<sub>TSS</sub> </i>> 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of <i>d<sub>TSS</sub> i</i>s implemented by a continuous step function, fit to <i>in vivo</i> data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of <i>d<sub>TSS</sub></i>. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.</span></p>
Genome assembly of E. coli C
<p>This record contains the following files:</p> <ol> <li><code>Ecoli_C_assembly.fna</code> - finished assembly in FASTA format. Contains two sequences: nuclear genome of <em>E. coli </em>C and genomes of bacteriophage phiX174, which was used as a spike-in</li> <li> <p><code>Ecoli_C_ONT.pass.fast5.tar.gz</code> - fast5 files from Oxford Nanopore Run</p> </li> <li> <p><code>ont.fastqsanger.gz</code> - oxford nanopore reads in fastqsanger format</p> </li> <li> <p><code>forward.fastqsange</code> - forward Illumina reads in fastqsanger format</p> </li> <li> <p><code>reverse.fastqsanger</code> - reverse Illumina reads in fastqsanger format</p> </li> </ol>
Combining epidemiological and ecological methods to quantify social effects on E. coli transmission
<p>Enteric microparasites like <i>Escherichia coli</i> utilize multiple transmission pathways to propagate within and between host populations. Characterizing the relative transmission risk attributable to host social relationships, and direct physical contact between individuals is paramount for understanding how microparasites like <i>E. coli </i>spread within affected communities and estimating colonization rates. To measure these effects, we carried out commensal <i>E. coli</i> transmission experiments in two cattle (<i>Bos taurus</i>) herds, wherein all individuals were equipped with real-time location tracking devices. Following transmission experiments in this model system, we derived temporally dynamic social and contact networks from location data. Estimated social affiliations, and dyadic contact frequencies during transmission experiments informed pairwise accelerated failure time models that we used to quantify effects of these sociobehavioral variables on weekly <i>E. coli</i> colonization risk in these populations. We found that sociobehavioral variables alone were ultimately poor predictors of <i>E. coli</i> colonization in feedlot cattle, but can have significant effects on colonization hazard rates (<i>p </i>≤ 0.05). We show, however, that observed effects were not consistent between similar populations. This work demonstrates that transmission experiments can be combined with real-time location data collection and processing procedures to create an effective framework for quantifying sociobehavioral effects on microparasite transmission.</p>
Database of Ca2+ transients from single cell E. coli measurements
<p><strong>Database configuration</strong><br> To efficiently compare conditions from over 7 Tb of image data, we constructed an HDF5 database with the extracted fluorescent traces for each experiment. The database was configured such that the fluorophore specifies the first group (<em>e.g.,</em> '/gcamp'). The second group is the unique experimental identifier generated at the time of the experiment (<em>e.g.,</em> '/20210322_5 Hz_gcamp_90pts'). The third group specifies the variable name (See Table below for description). The database is also available from https://bitbucket.org/meyerct6/soc_bacteria_repo/src/master/ in (Database/soc_bacteria_database.h5) and can be used to recreate most figures in the manuscript.</p> <table> <caption>Description of HDF5 database variables for each experiment</caption> <tbody> <tr> <td>/meanintMatG</td> <td>Matrix of mean intensity within each segmented cell (rows) at all time points (columns).</td> </tr> <tr> <td>/corrIntMatG</td> <td>Matrix of mean intensity corrected for photobleaching.</td> </tr> <tr> <td>/fitRsqr</td> <td>Vector of R-squared fit values for photobleach equation.</td> </tr> <tr> <td> /timeMat</td> <td>Matrix of acquisition times for each cell (rows) at every timepoint (columns).</td> </tr> <tr> <td>/heat_kill</td> <td>Boolean vector denoting whether the cell was heat killed</td> </tr> </tbody> </table> <p> </p>
Beta-lactam polyspecificity in E. coli
<p>This image dataset supports the findings in our manuscript entitled <a href="https://doi.org/10.1021/acschembio.9b00141">Morphological deconvolution of beta-lactam polyspecificity in <em>E. coli. </em></a></p> <p>The images display fluorescently-labeled <em>E. coli</em> strains subjected to compound treatment at multiple concentrations. Cells were imaged in 96-well plates at 100X magnification. Our novel method for preparing <em>E. coli </em>cells for imaging enabled us to use commercially-available plate-based, automated HCI microscopes. We used the images to build our <a href="https://doi.org/10.1093/bioinformatics/btx069">deep neural network classifier </a>to identify bacterial morphologies as well as our Hidden Markov models for target deconvolution.</p> <p> </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.