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4,486 results for “exploration”
Collection of figures to explore intra-regime weather variability of North Atlantic-European year-round weather regimes as Supplementary Dataset for Gerighausen et al. (2024)
<p>This is a supplementary dataset accompanying the publication <strong>Gerighausen et al. (2024) </strong>submitted to Meteorological Applications. It contains a collection of browsable figures, complementing selected regimes, seasons, and countries in the paper. The figures are provided as a zipped archive. The ZIP-File (1.2 GB) contains 4 subfolders and 4 auxiliary files as described in <strong>readme.md </strong>in the main folder. Once downloaded and unpacked, the .html navigation panels can be used in any browser to navigate through the plots. </p> <p>Data and methods used to generate the figures are explained in Gerighausen et al. (2024). In brief the analysis is based on ERA5 reanalysis 1979-2021 at 1° grid spacing and 6h temporal resolution aggregated to daily data. Anomalies are computed with respect to a 31-day running mean climatology. The figures are explained in the table below and in the navigation panel.</p> <p><strong>Gerighausen</strong>, J., J. Dorrington, M. Osman, and C. M. Grams, <strong>2024</strong>: Quantifying intra-regime weather variability for energy applications, <em>submitted to Meteorological Applications.</em> <a href="https://doi.org/10.48550/arXiv.2408.04302">doi:10.48550/arXiv.2408.04302</a></p>
Data set for the journal article ''Nanoscale chemical reaction exploration with a quantum magnifying glass''
<div>This data set includes the raw data of the esterification and hydrogenation discussed in the journal article alongside with the Scine Puffin Singularity container, steering protocol files, Swoose parameters, (pre-)releases of the software, and Python scripts for individual steps without the graphical user interface to reproduce the data.</div>
Exploring the total cost of whole fresh, fresh-cut and pre-cooked vegetables
<p>Abstract. Purpose: The food industry should evolve towards new business models which take into account the damage cost in decision making, considering the impact that its products generate on the natural and human environment. Hence, the present study aims to calculate the damage cost caused by the production of whole fresh (as average of potatoes, aubergines, and broccoli), and processed vegetables (fresh-cut and pre-cooked). Methods: The environmental life cycle approach was carried out per kilogram of assessed products (from cradle to the entrance of the market). The foreground Life Cycle Inventory was obtained from engineering procurement and construction projects of the whole fresh and processed vegetables industries. The Ecoinvent 3.8 and Agribalyse 3.0.1 databases were used for the background inventory. The ReCiPe 2016 method was used with a hierarchical perspective, evaluating eighteen midpoint categories as well as the endpoint categories (human health, ecosystems, and resources). The monetisation of these environmental impacts was then calculated using the endpoint monetisation factors developed by Ponsioen et al. (Monetisa- tion of sustainability impacts of food production and consumption. Wageningen Economic Research, Wageningen, 2020) for each product. It should be noted that this study does not include a comparative assessment. This study does not intend to compare the results for the three vegetable groups. Results and discussion: The damage costs were 0.16 €/kg for whole fresh vegetables, 0.37 €/kg for fresh-cut vegetables and 0.41 €/kg for pre-cooked vegetables. The agricultural production stage contributed most to these total damage costs due to the impact produced on land use and global warming in midpoint categories and human health and ecosystems in endpoint categories. In addition, the damage cost due to fossil resource scarcity (midpoint) and resource scarcity (endpoint) was mainly caused by the plastic packaging of fresh-cut and pre-cooked vegetables. The total cost was 1.02 €/kg for whole fresh vegetables, 2.99 €/kg for fresh-cut vegetables, and 3.43 €/kg for pre-cooked vegetables. Conclusions: These results suggest that some efforts should be made to reduce both environmental impacts and damage costs. For instance, to improve agricultural production, special attention should be paid to fertilisation and water consumption. Additionally, new packaging options should be explored as well as the inclusion of renewable sources in the electricity grid, and finally, on transporting the finished products to the market, by using trucks that run on cleaner fuels.</p>
Exploring Vocatives in Folk Songs of the Podillia Region
<p>This dataset is based on the folklore collection <em>Pisni Podillia: zapysy Nasti Prysiazhniuk v seli Pohrebyshche. 1920-1970 rr.</em> (Myshanych 1976). The collection consists of 850 songs, encompassing 13,005 lines and 78,888 tokens. Vocatives were manually distinguished and recorded in a separate column in the corpus without the assistance of RStudio, due to the complexity of distinguishing vocatives in Ukrainian.</p> <p>Vocatives in Ukrainian folk songs were analysed using the R programming language along with RStudio. </p> <p>Code written for text analysis in Estonian Literary Museum. </p> <p> </p> <p>This dataset consists of the following files:</p> <p>1. <strong>vocatives_Podillia_folk_songs.R</strong>: R script used for analyzing the corpus, including vocative counting, song length analysis, POS-tag analysis, semantic group and structural types analysis. </p> <p>2. <strong>corpus_vocatives.csv</strong>: Contains the text data of Podillia folk songs with manually distinguished vocatives. </p> <p>3. <strong>corpus_POS_tokens.csv</strong>: Contains verified the POS-tagged tokens of the corpus.</p> <p> </p>
Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets
<p>The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms – AdaBoost and Random Forests – on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.</p>
High-frequency, hourly, and daily measurements from Explorers Cove Meteorological Station (EXEM), McMurdo Dry Valleys, Antarctica (1997-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Explorers Cove Meteorological Station (EXEM), which was established in 1997 near Coral Ridge in the Fryxell Basin of Taylor Valley, McMurdo Dry Valleys, Antarctica. Parameters include air temperature, relative humidity, photosynthetically active radiation, incoming and outgoing shortwave radiation, wind speed and direction, as well as soil bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
Haptic three-dimensional curved surface exploration fMRI dataset
Open the record for dataset details and reuse information.
Diffraction images used to solve the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-alpha-1,2-Mannanases."
<p>Raw diffraction images used for generating the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-a-1,2-Mannanases" (available <a href="https://doi.org/10.1002/chem.201800435">here</a>). Full single-crystal datasets are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Data From: Exploring Gelatin-A and Mouse Proline-Rich Protein 5 as Probes for Wine Polyphenols analysis by Quartz Crystal Microbalance with Dissipation Monitoring
<p>Polyphenols are essential in winemaking, affecting the wine's quality, color, astringency, bitterness, and chemical stability. Conventional methods for assessing polyphenolic content are both expensive and time-intensive, underscoring the need for new, efficient techniques.</p> <p>The Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) sensor is recognized for its speed and reliability as a label-free detection tool. This study applies QCM-D to evaluate Gelatin Type A (Gel-A) from porcine skin and Mouse Proline-Rich Protein 5 (MP5) for polyphenol analysis in red wines without pre-treatment. MP5 notably exhibited a linear dissipation signal response with both total polyphenol and hydroxybenzoic acid concentrations. These findings highlight the potential for creating a stand-alone sensor platform for real-time polyphenol monitoring in winemaking.</p>
Exploring Large-Scale Entanglement in Quantum Simulation
<p>Here we provide data for the manuscript " <a href="https://arxiv.org/abs/2306.00057">Exploring Large-Scale Entanglement in Quantum Simulation</a> " with arXiv id <a href="https://arxiv.org/abs/2306.00057">"arXiv:2306.00057</a>". The data set contains both raw and analyzed data saved as ".mat files" Please see the uploaded readme file to understand the data structure. The peer-reviewed article will appear in the future. Please check the published article for recent figures. </p>
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
<p>Open challenges in computational drug design include the understanding and accurate description of solvent effects as well as collective dispersion interactions for realistic drug-like molecules. Both interactions profoundly influence the conformational stability of drug molecules and, consequently, the determination of other important quantum-mechanical (QM) observables. In this context, we here introduce the Aquamarine (AQM) dataset -- an extensive QM dataset that contains the structural and electronic information -- of 59,786 low-and high-energy conformers of 1,653 molecules containing up to 54 non-hydrogen atoms (including C, N, O, F, P, S and Cl). To gain insights into the solvent effects, we have carried out QM calculations of structures and properties in gas phase and in an aqueous solution modeled with implicit solvent. AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per molecular structure computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD supplemented with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By treating both molecule-solvent and dispersion interactions, the AQM dataset can help understand the impact of both interactions in structure-property and property-property relationships of realistic drug-like molecules. Therefore, we propose the AQM dataset as a benchmark for current state-of-the-art machine learning methods for property prediction as well as for the <em>de novo</em> generation of large and flexible (solvated) molecules with pharmaceutical and biological relevance.</p>
Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration
<p>Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration</p> <p>Data contains the georeferenced map plate of our geologic map that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the Planetary Science Journal publication and the Zenodo dataset.</strong></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>The Planetary Science Journal</em>, <em>5</em>(6), 147. <a href="https://iopscience.iop.org/article/10.3847/PSJ/ad2c04">https://iopscience.iop.org/article/10.3847/PSJ/ad2c04</a></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Supplementary Data for Wueller et al. (2024): Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>Zenodo Dataset</em>. <a href="https://doi.org/10.5281/zenodo.10693820" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10693820</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Mapping Scale is 1:100,000</p> <p>Print Scale is 1:1,000,000</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact lwueller@uni-muenster.de</p> <p>Lukas Wueller, Institut für Planetologie, Universität Münster, Germany, June 2024</p>
Co-design – Part 1: Workshops to explore current imaginaries behind smart home technologies development and use
<h3>Description</h3> <p>This qualitative dataset is the <strong>first part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of independent <strong>in-person workshops</strong> with professionals developing smart technology, its early-adopters, and late/non-adopters. The data collected during the subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals</h3> <ul> <li><strong>P1_WSP-PRO-TRANSCR_R02.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-PRO-VIS_000 </strong>till _<strong>013.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with early-adopters</h3> <ul> <li><strong>P1_WSP-EA-TRANSCR_R01.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-EA-VIS_000 </strong>till _<strong>011.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P1_WSP-LN-TRANSCR_R00.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-LN-VIS_000 </strong>till _<strong>012.jpg</strong> (participant-generated visual data)</li> </ul> <h3> </h3> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Climate Solutions Explorer - hazard, impacts and exposure data
<p><a name="_GoBack"></a>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <a href="https://www.climate-solutions-explorer.eu"><strong>www.climate-solutions-explorer.eu</strong></a></p> <p>Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.</p> <p>Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.</p> <p>Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.<br><br></p> <p><strong>The dataset includes: </strong></p> <ul> <li>Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C<br><br>For each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.<br><br></li> <li>Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period <br><br>For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators. <br><br></li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted<br><br>The .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators. <br><br></li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)<br><br>The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.</li> </ul> <p> </p> <p>Further details are available on the Data Story page – <a href="http://www.climate-solutions-explorer.eu/story/data">www.climate-solutions-explorer.eu/story/data</a>. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in <a title="Global warming levels indicators of climate change and hotspots of exposure" href="https://doi.org/10.1088/2752-5295/ad8300" target="_blank" rel="noopener">Werning, M. et al. (2024).</a></p> <p> </p> <p><strong>Release notes (v1.1)</strong></p> <p>Changes in this version:</p> <ul> <li>Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0</li> <li>Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023</li> <li>Fixed issue with the region mask for the EU</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> </ul> <p> </p> <p><strong>Release notes (v1.0)</strong></p> <p>Changes in this version:</p> <ul> <li>Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)</li> <li>Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> <li>Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from <a>2023</a></li> <li>Added the indicator ‘Heatwave days’</li> <li>Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators<br><br></li> </ul> <p><strong>Release Notes (v0.4)</strong></p> <p>Changes in this version:</p> <ul> <li>Removed ssp and metric from variable name in netCDF files</li> <li>Removed obsolete coordinates in netCDF files for 'Drought intensity'</li> <li>Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators</li> </ul> <div> </div>
Model simulation data used in "Exploring the uncertainties in the aviation soot-cirrus effect" (Righi et al., Atmos. Chem. Phys., 2021)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2021). For details see the README.md file and Table 1 in the paper.</p>
Replication data for: 'A First-Order Statistical Exploration of the Mathematical Limits of Micromagnetic Tomography'
<p>This repository contains the random data generated for obtaining results described in "A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography". All tested parameters are systematically divided over different folders and subfolders. This dataset contains only .npy files, generated with python version 3.8.8 and numpy version 1.21.5.</p> <p>Each file can be opened with numpy.load(filename)</p> <p>The resulting figures are constructed with data of at least 15 iterations; each iteration is stored in a separate folder 'test_' followed by the iteration number.</p> <p>The README file inside provides a detailled overview of the files included.</p>
Marine plastics alter the organic matter composition of the air-sea boundary layer, with influences on CO2 exchange: a large-scale analysis method to explore future ocean scenarios
<p>Microplastics are substrates for microbial activity and can influence biomass production. This has potentially important implications in the sea-surface microlayer, the marine boundary layer that controls gas exchange with the atmosphere and where biologically produced organic compounds can accumulate. In the present study, we used six large scale mesocosms to simulate future ocean scenarios of high plastic concentration. Each mesocosm was filled with 3 m3 of seawater from the oligotrophic Sea of Crete, in the Eastern Mediterranean Sea. A known amount of standard polystyrene microbeads of 30 μm diameter was added to three replicate mesocosms, while maintaining the remaining three as plastic-free controls. Over the course of a 12-day experiment, we explored microbial organic matter dynamics in the sea-surface microlayer in the presence and absence of microplastic contamination of the underlying water. Our study shows that microplastics increased both biomass production and enrichment of carbohydrate-like and proteinaceous marine gel compounds in the sea-surface microlayer. Importantly, this resulted in a 3 % reduction in the concentration of dissolved CO2 in the underlying water. This reduction was associated to both direct and indirect impacts of microplastic pollution on the uptake of CO2 within the marine carbon cycle, by modifying the biogenic composition of the sea's boundary layer with the atmosphere.</p>
Dataset accompanying the article: Exploring the Effects of Additional Vibration on the Perceived Quality of an Electric Cello
<p>Dataset accompanying the article: Exploring the Effects of Additional Vibration on the Perceived Quality of an Electric Cello. </p>
Exploring Housing Affordability in Illinois: An In-Depth Study of the State's Real Estate Market
<p>“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.</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.