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9,882 results for “Scale”
Datasets Energy Citizenship Scale Development and Validation (Task 2.4, D2.2, D2.3)
<p>The datasets are part of the Reports on the development (D2.2) and validation (D2.3) of the energy citizenship scale. They are also part of the publication "Energy citizenship as people's perceived (collective) rights and responsibilities in a just and sustainable energy transition - scale development and validation" (https://doi.org/10.1016/j.jenvp.2024.102310). </p>
Supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale
<p>This file contains the supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale (<em>submitted</em>). </p> <p>For more details see the corresponding manuscript (once it is published) and the github repository (https://github.com/SophievF/Global_Mox_analysis/tree/main). </p>
Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Sensing Reflector Height Variation on Seconds-Scale Using Low Earth Orbit Interferometric Reflectometry (LEO-IR)
<p><span>The simulated data (SNR, elevation and azimuth angles etc.) used in this study.</span></p> <p><span>File name format: arc number for GNSS (or LEO) <span>constellation</span> '_' PRN '_' 'seg'+arc number for a GNSS (or LEO) satellite</span></p> <p><span>File content format: 1[year] 2[month] 3[day] 4[hour] 5[minute] 6[seconds] 7[elevation angle] 8[azimuth angle] 9[chang rate of elevation angle] 10[SNR] 11[rh_ref] 12[rhdot_ref] 13[t_seconds]</span></p> <p> </p>
Studying the scale selection of mixed Rossby-gravity waves: Idealized simulations with the TIGAR model
<p>Mixed Rossby-gravity waves peak in two atmospheric regions in reanalysis: the upper troposphere and the upper stratosphere. The scales of MRG waves are different in these two regions, which can be seen e.g. on real-time MRG wave vertical profiles (https://modes.cen.uni-hamburg.de/products#MRG). In order to understand the MRG wave scale selection in these regions, we run idealized simulations with the TIGAR model (Vasylkevych and Zagar, 2021) with a symmetric initial height perturbation with respect to the equator and zonal wind profiles derived from ERA5 reanalysis (Hersbach et al, 2020). In addition, we also run TIGAR simulations with symmetric initial height perturbation and idealized zonal jets centered at various latitudes.</p>
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
<p>Datasets provided in this storage are introduced in the paper: <em>Towards Neural Scaling Laws for Foundation Models on Temporal Graphs</em></p> <ul> <li>Each .csv file represents all transactions of the token network that has the same name as the file name (<em><tokenname.csv>)</em></li> <li>Each transaction corresponds to a row in each file.</li> <li>Each transaction has: <ul> <li> blockNumber : is the block ID of Ethereum that includes this transaction</li> <li>timestamp: time that the transaction is made in UNIX timestamp format</li> <li>tokenAddress : the address that specifies a unique ERC20 token</li> <li>from: address of sender</li> <li>to: address of receiver</li> <li>value: the amount the transaction</li> <li>fileBlock: we split the whole number of blocks count to 35 buckets and assigned the bucket ID to the transaction to trace the blocks </li> </ul> </li> <li>To use the same setting as described in the papers, we include edge list and label that contain node interactions and labels for each snapshot in each token network. <ul> <li>Each transaction in the edge list also has "from","to" and "amount" fields, but with an additional "snapshot" field to indicate the index of the snapshot that the transaction below to</li> <li>Each row in label file indicates the ground truth label of the snapshot having an index corresponding to the index of the row (e.g first row indicates the label of the first snapshot)</li> <li>We provided the way to generate edge lists and label files in the following Github repository: https://github.com/benjaminnNgo/ScalingTGNs/blob/main/script/utils/TGS.py</li> </ul> </li> <li>However, we also provide raw <em>.csv </em> to divide into generate <em>edgeslist </em>and <em>label with a different setting.</em></li> </ul> <p><br><br></p>
Validating Instructional Practice Scale (IPS) for University Instructors: Confirmatory Factor Analysis
<p>.csv dataset for the study "validating instructional practice scale for instructors in some selected Ethiopian public universities: confirmatory factor analysis"</p>
Data for the Article "Maturity Assessment of Grid-Scale Flexibility and Energy Storage Services Towards a Decarbonized Europe"
<p>This dataset includes the data collected form various stakeholders regarding grid-scale flexibility and energy services as part of the SINNOGENES Project.</p>
Sea ice core temperature and salinity data collected during the 2022 SCALE Winter Cruise
Open the record for dataset details and reuse information.
Novel Activation Modulating Sites in a GPCR Identified with a Large-Scale Alchemical Mutation Scan
<p>This dataset contains the starting configurations and mdp files for the alchemical free energy calculations using pmx.<br>The MD parameter files (*.mdp) for the forward reaction (lambda 0 to 1, wt to mutant) and reverse reaction (lambda 1 to 0, mutant to wt) start with f (forward) and r (reverse). Mdp files are available for the energy minimization (em), npt (npt), equilibrium run (equilibrium) and non equilibrium alchemical transition (nonequilibrium). <br>The starting configurations for mutants of the DRD2 receptor can be found in WT7jvr and WT6cm4 for the active and inactive state respectively. Mutant folders are labeled with the residue number and the amino acid it is mutated to.</p>
Data from: Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data
<p>Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms random forests, support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while random forests and support vector machines were biased towards dominant classes. Random forests using 5-second windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events.</p>
Data from: Bristol stool scale: Patient versus expert score data
<p>The Bristol Stool Scale (BSS) is one of the most commonly used tools for evaluation of stool consistency.<strong> </strong>BSS ranges from 1-7 and each score is assigned to a given consistency of the feces. Self-reported characterizations can differ from an expert evaluation, and the reliability of BSS is unclear. The dataset consists of BSS scores by patients with inflammatory bowel disease collected throughout a 3-year follow-up, matched with scores assessed by experienced bioengineers (experts). The purpose of the study where data was collected was to compare patient scores to expert scores and hence determine the reliability of the BSS.</p>
Fig. 1 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 1: Map of the study area.
Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California
<p>We combined two climate-based distribution models with three finer-scale suitability models to identify habitat for pronghorn recovery in California now and into the future.</p> <p>Location: California, United States </p> <p>Methods: We used a consensus approach to identify areas of suitable climate now (1980-2010) and future (2031-2060) for pronghorn in California. We compared the results of models from two separate hypotheses about their historical ecology in the state, specifically the migration hypothesis and the niche reduction hypothesis. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat models: (1) an ensemble model using Random Forests, Maxent, Classification and Regression Trees, and a Generalized Linear Model; (2) a step selection function; and (3) an expert-driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. </p> <p>Results: Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be on suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat also depended on the methods used, but areas of consensus among all three exist in large patches throughout the state.</p> <p>Main Conclusions: Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multi-method, multi-hypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest.</p>
Figure 2. Rooting filaments, drawn from paratype. Scale bar 5 in Corymorpha rubicincta, a new Hydroid (Hydrozoa, Anthoathecata, Corymorphidae) from Port Phillip, Australia
Figure 2. Rooting filaments, drawn from paratype. Scale bar 5 mm.
Data from: Positive spatial and temporal density-dependence drive early reproductive economy-of-scale effects of masting in a European old-growth forest community
<p>Masting, the spatial synchronization of interannual variation in seed production, can enhance reproductive efficiency through positive density-dependent processes (DD) that result in economies of scale (EOS), such as decreased pollen limitation and predator satiation in years of high reproduction. While the general occurrence of such EOS effects has been documented for masting species, few studies simultaneously investigated how spatial and temporal variation in reproduction affects pollination and predation. Furthermore, it is unclear whether the same mechanisms apply to co-occurring species with different levels of conspecific density, pollen limitation, and seed defenses. Here, we use a long-term data set with high spatial resolution of seed production of European beech (<em>Fagus sylvatica</em>), Norway spruce (<em>Picea abies</em>), and silver fir (<em>Abies alba</em>) in a primeval montane forest to investigate the relationship between reproductive effort, pollination efficiency, and predispersal predation by insects. We found that, along the temporal axis, the proportion of sound (fertilized and unpredated) seeds correlated positively with annual seed production over the 14-year study period in all three species, most strongly in beech and only weakly in silver fir. Moreover, the results show that in beech, spatial seed density interacts with plot-wide annual seed rain to enhance DD effects on seed predation, suggesting additive effects of synchronous reproduction on fitness benefits.</p> <p>Synthesis: For both pollination and predispersal predation in beech and spruce, the strongest DD effects occur at low levels of reproduction and quickly reach asymptotes at higher levels, suggesting the presence of thresholds in different EOS mechanisms. As variability and synchrony in mast-seeding are expected to decline with climate change, EOS effects driven by DD may remain stable until the threshold is reached, at which sudden declines would result in devastating effects on the availability of viable seeds for germination and recruitment.</p>
Raw numerical data and cleavage assay blot for Figures found in the article: "The ion channel Anoctamin 10/TMEM16K coordinates organ morphogenesis across scales in the urochordate notochord"
<p><span>In relation to our publication: "The ion channel Anoctamin 10/TMEM16K coordinates organ morphogenesis across scales in the urochordate notochord" we provide all the individual quantitative observations that underlie the data summarized in the figures and results of our paper. More specifically we provide the data for <span><span>Fig. 1G, H, I, P, Q; Fig. 2D-G, R-U; Fig. 3E-H; Fig. 4D-F, Q-S; Fig. 5D-G, K-N; R-U; Fig. 6D-F, S-U; Fig. 7G, H, J-T; Fig. 8G-I, M, N, S, X, Y; Fig. 9D-G, R, S; Fig. 10D-Q, K-R; Fig. 11F, G; Fig. S2T-X; Fig. S3G; Fig. S4K, O, P and Fig. S6E-J</span></span></span>.</p> <p>In addition we provide a non-annotated and an annotated version of the original, uncropped gel picture shown in Fig. S4A</p>
Datasets for evaluating SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data
<p>This resource contains pre-processed A. thaliana root , the H. sapiens aortic valve datasets, PBMC Covid atlas and public 10x datasetse used in the paper, SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data. The raw datasets provided in the links below are pre-processed for quality control with respect to both cells and genes. </p> <p>A. thaliana datasets are sourced from the following locations at <a href="https://www.ebi.ac.uk/gxa/sc/home">Single-cell Gene expression Atlas </a>and <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus (GEO)</a>:</p> <ol> <li>E-GEOD-121619 : <a title="E-GEOD-121619" href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results</a></li> <li>E-GEOD-152766 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results</a></li> <li>E-GEOD-158761 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results</a></li> <li>E-GEOD-123013 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results</a></li> </ol> <p>H. sapiens datasets are obtained from the NCBI database : <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/ </a></p> <ol> <li>GSE152766: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766</a></li> <li>GSE158761: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761</a></li> </ol> <p>All COVID atlas datasets are from: <a href="http://covid19.cancer-pku.cn">http://covid19.cancer-pku.cn</a> . covid_atlas_data1.zip contains the h5ad files and covid_atlas_data2.zip contains the Seurat rds files.</p> <p>PBMC datasets are from the following public sources:</p> <table> <tbody> <tr> <td>Dataset Name</td> <td>Chemistry Version</td> <td>Web Link</td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high</a></td> </tr> <tr> <td>20k Human PBMCs, 3' HT v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0">https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0</a></td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium Controller</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 1)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 5)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0</a></td> </tr> <tr> <td>10k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v2 chemistry)</td> <td>v2.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>Fresh 68k PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor B)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor C)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0</a></td> </tr> <tr> <td>PBMCs from a Healthy Donor: Whole Transcriptome Analysis</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0">https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0</a></td> </tr> <tr> <td>PBMC 600K</td> <td>v1</td> <td><a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads">https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads</a></td> </tr> <tr> <td>GSM4560071</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071</a></td> </tr> <tr> <td>GSM4560074</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074</a></td> </tr> <tr> <td>GSM4560070</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070</a></td> </tr> </tbody> </table> <p>References for the Datasets :</p> <ol> <li>H. sapiens dataset: Kang Xu, Shangbo Xie,Yuming Huang,Tingwen Zhou, Ming Liu, Peng Zhu, Chunli Wang, Jiawei Shi, Fei Li,Frank W. Sellke and Nianguo Dong (2020) Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.</li> <li>E-GEOD-152766: Shahan R, Hsu C, Nolan TM, Cole BJ, Taylor IW et al. (2020) A single cell Arabidopsisroot atlas reveals developmental trajectories in wild type and cell identity mutants.</li> <li>E-GEOD-121619: Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. (2019) Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana.</li> <li>E-GEOD-123013: Ryu KH, Huang L, Kang HM, Schiefelbein J. (2019) Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells.</li> <li>E-GEOD-158761: Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. (2020) A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.</li> <li>COVID Atlas Reference: Xianwen Ren, Wen Wen, Xiaoying Fan et.al. (2021) COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas</li> <li>PBMC data are downloaded from respective links</li> </ol>
Fig. 8 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 8: Biomass of gillnet landings.
Fig. 17 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 17: Total value and selling price per unit weight of gillnet landings.
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.