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858 results for “Energy Data”
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
Measuring frontier orbital energy levels of OLED materials using cyclic voltammetry in solution - accompanying data
<p>This dataset contains the data and the Matlab scripts to create the main figures in the published manuscript.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for nuclear power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>nuclear power generation</span></span><span> <span>from</span><span> the open literature</span><span>. </span></span><span><span>Nuclear energy is the second-largest source of low-carbon generation, supplying 9% of global electricity</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>604</span></span><span><span> datapoints from </span></span><span><span>19</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span> Technoeconomic data on nuclear power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </p>
Data for: Alchemical free-energy calculations at quantum-chemical precision
<p><span><span>In the last decade, machine-learned potentials (MLP) have </span><span>demonstrated</span><span> the capability to predict vario</span><span>us</span><span> QM properties learned from </span><span>a set of reference</span><span> QM calculations. </span><span>Accordingly</span><span>,</span><span> hybrid QM/MM simulation</span><span>s </span><span>can be accelerated</span><span> by replacement of </span><span>expensive</span><span> QM calculation</span><span>s</span><span> with </span><span>efficient </span><span>MLP </span><span>energy prediction</span><span>s</span><span>.</span> <span>At the same time</span><span>, alchemical free energy </span><span>perturbation</span><span>s</span><span> (FEP) </span><span>remain</span> <span>un</span><span>ach</span><span>ie</span><span>vable</span><span> at the QM level of theory.</span> <span>In this work</span><span>,</span><span> we extend the capabilities of the Buffer Region Neural Network </span><span>(</span><span>BuRNN</span><span>) </span><span>QM</span><span>/MM</span><span> scheme towards </span><span>FEP</span><span>.</span> <span>BuRNN</span> <span>introduces a buffer region that experiences full electronic polarization by the QM region to minimize artifacts</span> <span>at </span><span>the </span><span>QM/MM interface</span><span>. </span><span>A </span><span>MLP</span> <span>is </span><span>used to </span><span>predict the energies for the QM </span><span>region</span><span> and its interactions with the buffer region</span><span>. Furthermore, </span><span>BuRNN</span> <span>allow</span><span>s</span><span> us to implement </span><span>FEP </span><span>directly into</span> <span>the </span><span>MLP </span><span>H</span><span>amiltonian</span><span>. </span><span>Here</span><span>, </span><span>we describe the alchemical change </span><span>from methanol to methane in water </span><span>at</span><span> the </span><span>MLP</span><span>/MM level as a proof of concept.</span></span><span> </span></p>
Automated identification of small molecules in cryo-electron microscopy data with density- and energy-guided evaluation
<p>Data and turorials for ligand identification with EMERALD-ID. Each Zip file contains ligand models and data used to generate their respective figures in the EMERALD-ID manuscript. For specific details, consult the README file in each directory.</p>
Impact of declining renewable energy costs on electrification in low emission scenarios - Scenario Data
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Impact of declining renewable energy costs on electrification in low emission scenarios</strong></p> <p>by<br> <em>Gunnar Luderer, Silvia Madeddu, Leon Merfort, Falko Ueckerdt, Michaja Pehl, Robert Pietzcker, Marianna Rottoli, Felix Schreyer, Nico Bauer, Lavinia Baumstark, Christoph Bertram, Alois Dirnaichner, Florian Humpenöder, Antoine Levesque, Alexander Popp, Renato Rodrigues, Jessica Strefler, Elmar Kriegler</em></p> <p>forthcoming in <em>Nature Energy (2021).</em></p> <p> </p> <p><em><strong>ModelRuns </strong></em>(directory) contains all model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains all RMarkDown-Notebooks that were used for the data analysis and the generation of the figures of the paper.</p> <p><em><strong>ScenarioNames.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the directory ModelRuns).<br> <br> <em><strong>ScenarioData_IAMC_Format.xlsx </strong></em>contains the scenario output date in the generic IAMC-format (https://data.ene.iiasa.ac.at/database/) as submitted to the IPCC-AR6-database (https://iiasa.ac.at/web/home/research/researchPrograms/Energy/200513_IPCCwebinar.html)</p>
Demagnetization energy and internal stress in magnetite from temperature dependent hysteresis measurements - temperature hysteresis data
<p><strong>Data set of hysteresis data files for the <em>Geophysical Research Letters</em> article <em>"Demagnetization energy and internal stress in magnetite from temperature dependent hysteresis measurements"</em></strong></p> <p>MicroMag 2900/3900 Data Files (Series 0016.002) - The file name and description in the file provide the specimen information. Each file contains information on the instrument, settings, measurement, script, and the measured data: field, moment, and temperature.</p>
Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal
<p>Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal</p> <p>This database contains the measurement using a model wind turbine with 0.6m diameter(D) in a wind tunnel. A short-range Lidar WindScanner facilitated mapping the wake with a high spatial and temporal resolution in vertical, cross-stream planes at different downstream locations and in a horizontal plane at hub height.</p> <p>The measurement campaign was conducted in the large wind tunnel at ForWind-University of Oldenburg. The wind tunnel has a test section cross-section with the dimensions of 3m x3m. For this study three movable test section elements of 6m length were attached for a total enclosed length of 18m. The roof of the test section was adjusted to compensate for boundary layer growth to achieve a zero pressure gradient for the target wind speed of the experiments, nominally 7.5m/s, with an empty tunnel with no grid or turbine installed. The three-bladed MoWiTO 0.6 wind turbine model(Schottler et al.(2016)), with a hub height (h) of 0.77m and a diameter of 0.58m was placed at a distance of 2.4D downstream of the test section inlet, where the distance was measured to the centre of the rotor. In addition, the distance between the rotor center and the tower center is 110mm.<br> The flow blockage, based on rotor swept area and tower flow-facing area, was 2.7%. The wind turbine controller is based on the torque of the generator (Petrovi ́c et al. (2018)) leading to a tip speed ratio of 5.7 at the operational point during non-misaligned cases with no grid. More information can be found in the paper.</p> <p>The folder contains 12 unique .mat files each containing a matlab structure. The matlab structure conatins the vertical and horizontal scan for each inflow and operational condition:<br> With the upstream turbine installed:<br> - Yaw0_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yaw30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yawneg30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal</p> <p> - Yaw0_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p> - Yaw0_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p>Without the upstream turbine installed:<br> - NoTurbine_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D<br> - NoTurbine_Uniform_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D<br> - NoTurbine_BoundaryLayer_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D </p> <p>Within each substructure the following parameters are provided:<br> - v_los [m/s] ----------> Line of sight velocity<br> - sigma [m/s] ----------> Spectrum width<br> - x_Global_frame [m] ---> x-coordinate referenced at the lower grid midpoint<br> - y_Global_frame [m] ---> y-coordinate referenced at the lower grid midpoint<br> - z_Global_frame [m] ---> z-coordinate referenced at the lower grid midpoint<br> - xx [m] ---------------> Grid of the x-coordinate referenced at the lower grid midpoint<br> - yy [m] ---------------> Grid of the y-coordinate referenced at the lower grid midpoint<br> - zz [m] ---------------> Grid of the z-coordinate referenced at the lower grid midpoint<br> - uu [m/s] -------------> Horizontal wind speed at the position of the gridded coordinates, these data have been interpolated onto the grid<br> </p> <p>When using this database please reference to the journal paper.</p> <p>All data has been included without warranty, express or implied.</p> <p>For further questions, please contact the corresponding author.<br> </p>
Data and data analysis codes for Palacios et al. "Single-domain Bose condensate magnetometer achieves energy resolution per bandwidth below ℏ"
<p>Data and data analysis codes for the article "Single-domain Bose condensate magnetometer achieves energy resolution per bandwidth below ℏ" by S. Palacios, et al. https://arxiv.org/abs/2108.11716</p>
Global Socio-Economic and Environmental 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> data files </strong>contain various open data for improving energy system modelling decisions. A thorough description with license restrictions will follow soon.</p>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p>
Data accompanying the paper "Stable Numerical Implementation of a Turbulence Scheme with Two Prognostic Turbulence Energies".
<p>Data accompanying the paper <strong>"Stable Numerical Implementation of a Turbulence Scheme with Two Prognostic Turbulence Energies",</strong> submitted to Monthly Weather Review.</p> <p>This repository contains results from the idealized simulations of GABLS1 case,<br> obtained by the OpenIFS single column model version with the TOUCANS two-energy<br> turbulence scheme.</p> <p>Each simulation has its own folder, containing single NetCDF file 'progvar.nc'.<br> Experimental settings, explained in the paper, are following:</p> <pre><code>----------------------------------------------------------------- folder timestep delta beta beta_tau [s] [1] [1] [1] ----------------------------------------------------------------- GABLS1_2TE-R_dt001s_delta00_bt15/ 1 0.00 1.0 1.5 GABLS1_2TE-R_dt045s_delta00_bt15/ 45 0.00 1.0 1.5 GABLS1_2TE-R_dt090s_delta00_bt15/ 90 0.00 1.0 1.5 ----------------------------------------------------------------- GABLS1_2TE-R_dt090s_delta25_bt15/ 90 0.25 1.0 1.5 GABLS1_2TE-R_dt090s_delta25_bt10/ 90 0.25 1.0 1.0 GABLS1_2TE-R_dt180s_delta25_bt10/ 180 0.25 1.0 1.0 -----------------------------------------------------------------</code></pre> <p>Names of the NetCDF fields examined in the paper are:</p> <pre><code>tke - turbulence kinetic energy [J/kg] tte - turbulence total energy [J/kg] efb3 - turbulent heat flux [W/m^2] t - thermodynamic temperature [K] </code></pre> <p>They are all 2D fields, indexed by the timestep number and the model level.<br> Turbulent heat flux is given on the model half levels, remaining quantities<br> on the model full levels. Heights of the model levels are stored in the<br> NetCDF fields:</p> <pre><code>height_f - full level heights [m] height_h - half level heights [m] </code></pre> <p>Simulation time in seconds is stored in the NetCDF field 'time'.</p> <p><strong>CAUTION:</strong><br> Since the simulations were run with the prescribed forcings and only with<br> the turbulence scheme activated, some fields in NetCDF files are meaningless<br> (e.g. skin temperature).</p>
Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy
<p>This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.</p> <p> </p> <p>The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.</p>
Dance Your Ph.D. 2018 Physics FINALIST: Dark energy and dark matter via probabilistic data analysis
<p>Dark matter (dancer in black mask; brings dancers together) and dark energy (dancer in black mask; pulls dancers apart) are the mysterious constituents of the universe that determine the evolution of the largest structures, clusters of galaxies (dancers in tie dye), and I (dancer in gold mask) want to learn about them them by analyzing a huge set of 3D galaxy positions. The telescope (dancer in silver mask) gives me 2D images of galaxies with no distances, so I have to estimate galaxy locations with "redshift," which is correlated with distance. The exciting telescopes of the next decades will build large samples of galaxies by taking lots of superficial color data that's not informative enough to determine the redshift. One thing that tells us about dark matter and dark energy is the redshift distribution (dancer in red stripes) -- I can still learn about dark energy and dark matter if I can figure out how many galaxies have each redshift. Since I don't actually know the redshifts of any galaxies, I consider the probability of redshift (dancers in white shirts) for each galaxy based on its limited color data. However, the redshift probabilities are biased by prior beliefs (dancer in gray scarf) about physics that are transmitted by the way we derive the probabilities from the galaxy colors. If unaccounted for, the prior beliefs could cause us to draw the wrong conclusions about dark matter and dark energy, meaning we won't learn anything from all this amazing telescope data we're going to have. For my thesis, I developed a method that puts the prior beliefs in their proper place and lets the data guide the inference of the redshift distribution, using an approach called Bayesian statistics. I audition many possible redshift distributions and check how likely each is to have caused the observed galaxy colors, with the redshift probabilities as the judges; this procedure is called Markov chain Monte Carlo (MCMC) sampling, and it leads to an ensemble of possible redshift distributions each with an associated probability of having produced the observed galaxy colors. By doing the math carefully and correctly, we will learn more about dark matter and dark energy even with the coarsest data!</p>
Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"
<p>Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019). </p> <p> </p>
Data from: Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus
<p>Household Survey Data for journal article 'Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus', without protected data.</p>
Data release for the paper "Measurements of protons and charged pions emitted from the $\nu_{\mu}$ charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector"
<p>This data release is associated with the paper "Measurements of protons and charged pions emitted from the <span class="math-tex">\(\nu_{\mu}\)</span> charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector". It is currently available on <a href="http://arxiv.org/abs/2203.08367">arXiv:2203.08367</a> and to be submitted to Phys. Rev. D.</p> <p><strong>When citing this data release, please cite as well the paper.</strong></p> <p>The provided zip file contains the data as below.</p> <ol> <li>event.root: Event by event information of 183 iron-target interactions.</li> <li>plot.root: Plot information as shown in the paper.</li> <li>detector_efficiency.root: Detectrion efficiencies for muons, charged pions, and protons.</li> <li>momentum_resolution.root: Relation between true and reconstructed momentum for muons, charged pions, and protons.</li> <li>misPID.root: Mis-PID rates of protons and pions.</li> <li>syscov.root: Covariance matrices of systematic uncertainties.</li> <li>flux.root: The neutrino flux and the covariance matrix of the flux error.</li> </ol> <p>The zip file also contains a README.pdf file with detailed information on the included files. Please read it.</p>
Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions
<p>Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:</p> <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> <p>`ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p>[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)</p>
Replication data for: "Ultrafast energy exchange between two single Rydberg atoms on the nanosecond timescale"
<p>Replication data for Figure 3 and 4 of "Ultrafast energy exchange between two single Rydberg atoms on the nanosecond timescale"</p> <p>Preprint at: https://arxiv.org/abs/2111.12314</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.