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4,230 results for “Energie”
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
<p>Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments we gather electrophysiological data with sensor nodes. We expose the plant <em>Zamioculcas zamiifolia</em> to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.</p> <p> </p> <p>Data repository for our paper "PhytoNodes for Environmental Monitoring: Stimulus Classification based on<br> Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System", submitted to the GoodIT conference. Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>raw_data: </em>The datasets from our plant experiments for the stimuli wind, temperature, red light, blue light, and no stimulus.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. Find the training and testing datasets in the archives folder as well as the trained classifiers in the results folder.</li> <li><em>classification_results.ods: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time).</li> <li><em>TFLite_Models: </em>The trained classifiers in TensorFlow Lite Format.</li> <li><em>00_AI_BLE_MeasuringOnlyWind: </em>Source code for classification on STM-based PhytoNodes (using MCDCNN two-class classifier) and Bluetooth communication. The code is written for the STM32WB55 Nucleo board and can be transferred to the dongle.</li> <li><em>zavrsniProjekt_iOS: </em>Source code of the iOS app used to receive data from the STM-based PhytoNodes.</li> <li><em>Watchplant_application_documentation.pdf: </em>Instructions to build and use the iOS app.</li> </ul>
Data and code repository for Science Advances submission: Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy
<p>Data and codes related to the findings reported in the manuscript, "Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy", are deposited. Please refer to the notes located within each folder for further descriptions.</p>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file. </p> <p>This supplementary data package includes the following files: </p> <p> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
Climate Change and 2030 Cooling Demand in Ahmedabad, India: Opportunities for Expansion of Renewable Energy and Cool Roofs (Supplemental Information)
<p>Supplemental information and analysis files for article, "Climate change and 2030 cooling demand in Ahmedabad, India: opportunities for expansion of renewable energy and cool roofs" (Original article available at: https://doi.org/10.1007/s11027-022-10019-4)</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models" to be published in the journal Animal - Open Space.</p>
dataset for paper "Activation energy for pore opening in lipid membranes under an electric field"
<p>Dataset for the paper "Electropermeabilization of hydroperoxidized lipid membranes".</p> <p>Data was generated from Orbit Mini miniaturized bilayer workstation (Nanion Technologies, Munich, Germany), with an inserted microelectrode cavity array (MECA 4) recording chip (Ionera Technologies, Freiburg, Germany).</p> <p>The data files have format .abf, a standard format for electrophysiological data. <br> It can be read by applications such as for instance</p> <p>- Clampex and ClampFit, from the patch-clamp software suite pCLAMP, <br> - Elements Data Analyzer, associated with the elements data reader software from Elements-IC, </p> <p><br> or imported into Python through the package pyABF 2.3.5.</p> <p>import pyabf // abf=pyabf.ABF(path+"/"+f+"/"+abffile) // data = np.vstack((abf.sweepX, abf.data)) </p> <p>Data is organized in five folders named according to target hydroperoxidation degrees:<br> POPC<br> POPC-OOH 25%<br> POPC-OOH 50%<br> POPC-OOH 75%<br> POPC-OOH 100%</p> <p>Inside each of the five above files data is organized by date, and informed with the actual measured hydroperoxidation degree for a given sample. </p>
Bulk and critical material demand for selected 'Starter Kit' energy system models - dataset
<p>This repository contains the data related to the Data in Brief article titled: <strong>Bulk and critical material demand for selected ‘Starter Kit’ energy system models.</strong></p> <p>The data include the modeled mass of materials and their embodied emissions. A metadata file is also included to clarify the units, materials and scenario names.</p>
Two-dimensional vanadium sulfide flexible graphite/polymer films for near-infrared photoelectrocatalysis and electrochemical energy storage
<p>Raw data of published article "Two-dimensional vanadium sulfide flexible graphite/polymer films for near-infrared photoelectrocatalysis and electrochemical energy storage", DOI: 10.1016/j.cej.2022.135131</p>
2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage
<p>Raw data of published journal article "2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage", DOI: 10.1016/j.apmt.2021.101301</p>
Flexible Graphite−Poly(Lactic Acid) Composite Films as Large-Area Conductive Electrodes for Energy Applications
<p>Raw data of energy storage part -author Kalyan Ghosh </p>
Exceptions to the rule: Relative roles of time, diversification rates and regional energy in shaping the inverse latitudinal diversity gradient
<p><strong>Aim</strong>: Inverse latitudinal diversity gradients (i-LDG), whereby regional richness peaks outside the tropics, have rarely been investigated and their causes remain unclear. Here, we investigate three prominent explanations, postulating that species-rich regions have had (1) longer time to accumulate species, (2) faster diversification, and (3) more energy to support more diverse communities. These mechanisms have been shown to explain the tropical megadiversity, and we examine whether they can also explain i-LDG.</p> <p><strong>Location</strong>: Global</p> <p><strong>Time period</strong>: Contemporary</p> <p><strong>Major taxa studied</strong>: Amphibians, birds, mammals </p> <p><strong>Methods</strong>: We estimated the time for species accumulation, regional diversification rates, and regional energy for six tetrapod taxa (≈ 800 species). Then, we quantified the relative effects and interactions among these three classes of variables, using variance partitioning, and confirmed the results across alternative metrics for time (community phylometrics and BioGeoBEARS), diversification rates (BAMM and DR), and regional energy (past and current temperature, productivity).</p> <p><strong>Results</strong>: While regional richness across each of the six taxa peaked in the temperate region, it varied markedly across hemispheres and continents. The effects of time, diversification rates, and regional energy varied greatly from one taxon to another, but high diversification rates generally emerged as the best predictor of high regional richness. The effects of time and regional energy were limited, with the exception of salamanders and cetaceans. </p> <p><strong>Main conclusions</strong>: Together, our results indicate that the causes of i-LDG are highly taxon-specific. Consequently, large-scale richness gradients might not have a universal explanation and different causal pathways might converge on similar gradients. Moreover, regional diversification rates might vary dramatically between similar environments and, depending on the taxon, regional richness might or might not depend on the time for species accumulation. Together, these results underscore the complexity behind the formation of richness gradients, which might involve a symphony of variations on the interplay of time, diversification rates, and regional energy.</p>
Excitation energy transfer and vibronic coherence in intact phycobilisomes — multidimensional electronic spectroscopy data set and MATLAB and Julia analysis code
<p>Data sets used in the article "Excitation energy transfer and vibronic coherence in intact phycobilisomes" by Sil et al. The phycobilisomes were isolated from the short-filament mutant (SF33) of <em>Fremyella diplosiphon</em> UTEX 481 (also known as <em>Tolypothrix</em> sp. PCC 7601). Multidimensional electronic spectroscopy was performed with 6.7 fs mid-visible pulses (520–700 nm) using a pump–probe optical configuration using adaptive pulse shaping techniques. In addition to the full set of two-dimensional spectra and analysis files generated using global and target modeling and analysis of coherences (3DES oscillation maps), we provide here a linear absorption spectrum with phycobiliprotein component analysis as well as a set of 2D excitation–emission fluorescence spectra of intact and broken phycobilisome preparations. </p> <p>Sil, S.; Tilluck, R. W.; Mohan TM, N.; Leslie, C. H.; Rose, J. B.; Domínguez-Martín, M. A.; Lou, W.; Kerfeld, C. A.; Beck, W. F. Excitation energy transfer and vibronic coherence in intact phycobilisomes. Nat. Chem. (2022), DOI: 10.1038/s41557-022-01026-8.</p> <p><a href="https://urldefense.com/v3/__https://www.nature.com/articles/s41557-022-01026-8__;!!HXCxUKc!yaVwTZFk8T-j3ROhygpOGW5Xy_E2wQvf-QgNGr9FZZbp4oNpfp_ZmhkdWYLdg2mKSDP8yYrNAZs$">https://www.nature.com/articles/s41557-022-01026-8</a></p> <p> </p> <p> </p>
National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits
<p>Supplementary materials for publication Gebara et al. 2022, "National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits". </p>
Data for FEgrow: An Open-Source Molecular Builder and Free Energy Preparation Workflow
<p>Data illustrating the use of de novo design in building and scoring protein-ligand complexes.</p> <p>This is relationship to the FEgrow publication with the intiial preprint here: <br> https://chemrxiv.org/engage/chemrxiv/article-details/6287bb98a42e9c78d34769f6<br> </p> <p>The FEgrow software snapshot used can be found here: https://zenodo.org/record/7105647#.YzFwINLMIUE</p>
Inflation Reduction Act Energy Communities
<p>The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:</p> <ol> <li>a brownfield site,</li> <li>a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment <em>or</em> (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; <em>and</em> an unemployment rate at or above the national average for the previous year, or</li> <li>a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.</li> </ol> <p>These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:</p> <ol> <li>Brownfields – Source: <a href="https://www.epa.gov/frs/geospatial-data-download-service">EPA</a>. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).</li> <li>Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census <a href="https://www.census.gov/programs-surveys/cbp.html">County Business Patterns</a>. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).</li> </ol> <p>--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.</p> <p>--Relevant columns include:</p> <p> a) SUM_nhgis0: Total employment in 2020.</p> <p> b) SUM_nhgis1: Total unemployment in 2020.</p> <p> c) P_Unemp: Percent unemployment in 2020.</p> <p> d) Q_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.</p> <p> e) FF_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.</p> <p> f) final_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.</p> <ol> <li>Retired Power Plants – Source: EIA via <a href="https://hifld-geoplatform.opendata.arcgis.com/maps/ee0263bd105d41599be22d46107341c3/about">HFLID</a>. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.</li> </ol> <p>--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.</p> <ol> <li>Abandoned Coal Mines – Source: <a href="https://www.msha.gov/mine-data-retrieval-system">MSHA</a>. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.</li> </ol> <p>--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.</p> <p>5) US State Borders– Source: <a href="https://data2.nhgis.org/main">IPUMS NHGIS</a>.</p> <p> </p> <p>Also included here are polygon shapefiles for Onshore <a href="https://zenodo.org/record/5021146#.Y0XbRnbMK39">Wind and Solar Candidate Project Areas</a> from <a href="https://repeatproject.org/">Princeton REPEAT</a>. These files have been updated to include columns related to the energy communities.</p> <p>New columns include:</p> <ol> <li>CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.</li> <li>CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.</li> <li>FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.</li> <li>UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.</li> <li>MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.</li> <li>P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.</li> </ol>
Research data for "Exploring the configurational space of amorphous graphene with machine-learned atomic energies"
<p>This dataset supports the paper: "Exploring the configurational space of amorphous graphene with machine-learned atomic energies" (<a href="https://doi.org/10.1039/D2SC04326B">https://doi.org/10.1039/D2SC04326B</a>).</p> <p>Trajectory data for the 200-atom structures (Fig. 3) and the final configurations for the 612-atom structures as well as the GAP-17-optimised 610-atom structure from Toh et al are provided (Fig. 4). Additionally, the structures used for data analysis in Fig. 5 are given.</p> <p>The files are in extended xyz (.xyz) format and contain the raw data for coordinates, forces, and atomic energies (labelled 'c_1'). The files also contain the atomic energies relative to pristine graphene, labelled "Energy_per_atom", and the locally averaged energy relative to pristine graphene, labelled "NN_Energy_per_atom". Topological information is included at the end of the .xyz file for the 612-atom structures ('fig_4'/) and for the structures in 'fig_5/'.</p> <p>All raw atomic energies were computed using LAMMPS default settings and were output with six significant figures, with the exception of the Toh et al. structure (for which ASE was used, outputting a higher number of significant figures). </p> <p>The data can be read using, for example, the Atomic Simulation Environment (ASE), or visualised using Ovito.</p> <p> </p>
Thermodynamic database and calculator of free energies and potentials for redox reactions involving iron minerals in aqueous media (IMTD)
<p>Database of free energies of formation for iron minerals and associated aqueous species, which are used in a tableu style spreadsheet to calculate free energies of redox reactions involving iron minerals, which in turn are used to calculate free energies and formal potentials for these reactions, under specified environmental conditions.</p> <p>The database and calculators were assembled by students and postdocs (Jeff Hudson, Ania Pavitt, Ying Lan, and Miranda Bradley) working under direction of Professor Paul G. Tratnyek at the Oregon Health & Science University, Portland, Oregon, USA. Drew Latta, Thomas Robinson, and Michelle Scherer contributed to the database and extended the calculations.</p> <p>Early versions of this tool were used in several publications, including (i) Fan, D., Y. Lan, P. G. Tratnyek, R. L. Johnson, J. Filip, D. M. O'Carroll, A. N. Garcia, and A. Agrawal. 2017. <em>Environ. Sci. Technol.</em> 51(22): 13070–13085. [DOI: 10.1021/acs.est.7b04177] and (ii) Bradley, M. J., and P. G. Tratnyek. 2019. <em>ACS Earth & Space Chemistry</em> 3(3): 688-699. [DOI: 10.1021/acsearthspacechem.8b00200].</p> <p>This tool is provided as a spreadsheet in .xlsx format. The file includes six sheets. The first contains background, constants, and calculations that apply throughout the remaining tabs. The second contains free energies of formation from various authoritative sources, and a mechanism for designating “recommend values”. The third contains a tableu that calculates free energies of redox reactions using the recommended free energy of formation and user-specified stoichiometries. The fourth calculates free energies and formal potentials of the redox reactions using the standard potentials, and specific solution conditions. The last tab summarizes previous published formal potentials from a variety of sources. </p> <p>While the database was checked thoroughly, it still is unlikely to be completely accurate. For critical applications, we recommend that you track-down the primary sources (listed on the first tab of the spreadsheet) and use them for data, conditions, and other caveats. Obviously, we do not accept any responsibility for what anyone does with information obtained from this document.</p> <p>In the future, if significantly corrections or additions are made to this document, we may publish it here as new versions. If the contributions of others result in major improvements, we are open to adding new authors to those versions. Feel free to contact us with corrections, suggests, or offers to help.</p> <p>The development of this version of the tool was funded through grants from the Strategic Environmental Research and Development Program (SERDP) and the U.S. Department of Energy.</p>
Triggering Sustainable Biogas Energy Communities through Social Innovation- ISABEL ---- Social Innovation and Community energy best preactices, methods and tools across Europe ----Semi-structured interviews from communities
<p>Having identified through the literature review various success and failure factors for social innovation applied to community energy projects, ISABEL has further conducted 18 semi-structured interviews of a range of stakeholders. The interviewee sample was a convenience sample of participants in existing projects and thus, inevitably, they are able to speak more to successful than unsuccessful projects and they likely have had less exposure to obstacles to the success of their projects. T The interviews have focused on identifying answers to the questions: <em>What were the key success factors? What obstacles were overcome? How? Participants were also asked to specify the type of renewable energy and community energy model. </em></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.