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2,212 results for “space”
Landslides from Space - Glacier Bay Landslide, Alaska USA (28th June 2016)
<p>On 28th June 2016 seismometer recorded an event with a magnitude of 5.2. Later it was visually confirmed that this was caused by a landslide and not an earthquake.</p> <p>The pre-event acquisition is from 5th February 2016 (Sentinel-2) and the post-event acquisition is from 29th September 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Landslides from Space - Mocoa Debris Flow, Columbia (1st April 2017)
<p>More than 330 people died in a rainfall-triggered landslide which occured on 1st April 2017 in Mocoa, Columbia.<br> <br> The pre-event acquisition is from 13th February 2017 (Sentinel-2) and the post-event acquisition is from 4th April 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Landslide between Aickens and Jacksons, New Zealand (18th January 2017)
<p>Heavy rainfall triggered on 18th January 2017 a landslide on the West Coast of New Zealand. The landslide blocked a street and disconnected the villages Aickens and Jacksons.<br> <br> The pre-event acquisition is from 4th January 2017 (Sentinel-2) and the post-event acquisition is from 24th April 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Kakanj Mine Waste Landslide, Bosnia (24th February 2017)
<p>On February 24<sup>th</sup> 2017 a massive mine waste landslide from an open pit coal mine occurred. It had approximately the dimension of 600 metres in width and 800 metres in length. Through this slide the stream Ribnica was dammed up and created a small lake. Because of potential dam breach 150 people of two villages had to be evacuated downstream.<br> <br> The pre-event acquisition is from 14th February 2017 (Sentinel-2) and the post-event acquisition is from 16th March 2017 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Koshe Garbage Landslide, Ethiopia (11th March 2017)
<p>On 11th March 2017 a garbage dump collapsed and caused a landslide in Koshe. Nearby buildings were buried and 115 people died.<br> <br> The pre-event acquisition is from 10th March 2017 (Sentinel-2) and the post-event acquisition is from 9th April 2017 (Sentinel-2).</p> <p><em>Contains modified Copernicus Sentinel data (2017)</em></p>
Phlorest phylogeny derived from Lee & Hasegawa 2013 'Evolution of the Ainu Language in Space and Time'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S, Hasegawa T (2013) Evolution of the Ainu Language in Space and Time. PLoS ONE 8(4): e62243. doi: 10.1371/journal.pone.0062243</p> </blockquote>
Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV-Vis satellite products - Dataset
<p>This data set represents a long-term (1996-2017) harmonised record of lower tropospheric ozone (surface - 450 hPa or surface - approximately 6 km) from satellite instruments. These instruments include the Global Ozone Monitoring Experiment (GOME-1, 1996–2002), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2004) and the Ozone Monitoring Instrument (OMI, 2005–2017). These original products were produced by the Rutherford Appleton Laboratory (RAL) Space using the retrieval scheme described by Miles et al., (2015 - doi:10.5194/amt-8-385-2015). Pre-print of accepted manuscript can be found at https://doi.org/10.5194/egusphere-2023-1172.</p>
Data for "Demographic inequalities in digital spaces in China: The case of Weibo"
<p>These data underlie the results and figures used in the article "Demographic inequalities in digital spaces in China: The case of Weibo" (https://doi.org/10.36190/2023.01). This research was presented at the ICWSM workshop "Data for the wellbeing of the most vulnerable" on June 5, 2023.</p><p>The corresponding workflow can be found in the linked repository.</p><p>`README.md` provides more details.</p>
Data from: Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space
<p>This data accompanies the paper entitled <strong>Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space.</strong></p> <p> </p> <p>The zip archive contains the results of Lattice Boltzmann Molecular Dynamics simulations of the systems investigated and presented in the manuscript. Computational Fluid Dynamics data are in VTK format. Molecular Dynamics trajectories are in XYZ format.</p>
Additional Phase Space Files of a Liac HWL linac
<p>Additional phase-space files (PSFs) calculated with <em>penEasy 2020</em> for the Liac HWL mobile accelerator (S.I.T.). This record complements the original record <a href="https://doi.org/10.5281/zenodo.14029134">https://doi.org/10.5281/zenodo.14029134</a> with the applicator diameters of 7, 9 and 12 cm.</p> <p><em>C7B0_12MeV</em> stands for applicator with a diameter of 7 cm, a 0º bevel ending and simulated with a beam energy of 12 MeV.</p> <p>NOTE: To recreate a realistic setup, the simulation should include a PMMA cylindrical applicator with the selected inner diameter and a wall thickness of 0.5 cm. This PSFs can be rotated to simulate beveled applicators.</p> <p>All files are provided in the IAEA PHSP format, including the corresponding header files, making them compatible with most Monte Carlo codes for radiation transport simulation.</p>
CLDF dataset derived from Lee and Hasegawa's "Evolution of the Ainu Language in Space and Time" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee Sean, Hasegawa Toshikazu (2013). Evolution of the Ainu Language in Space and Time. PLOS ONE 8(4): e62243. https://doi.org/10.1371/journal.pone.0062243</p> </blockquote>
R code for archaeological examples of calculating isotopic niche space and overlap using the rKIN package
<p>This R code was written to apply the tools of the rKIN package to calculate isotopic niche space and overlap for the three archaeological case studies for the manuscript Investigating Isotopic Niche Space: Using rKIN for Stable Isotope Studies in Archaeology published in the Journal of Archaeological Method and Theory. Raw data for the case studies are available in the supplemental Excel file.</p>
Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.
<p>The publications and research associated to cite is in:</p><p><a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a></p><p>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a></p><p>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:</p><p>Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements </p><p>Dune1</p><p> </p><p>Multi-grid solver</p><p> </p><p>11</p><p> </p><p>3</p><p> </p><p>2,304</p><p> </p><p>Complex..</p><p> </p><p>2,304</p><p> </p><p>HSMGP1</p><p> </p><p>Stencil-grid solver</p><p> </p><p>14</p><p> </p><p>3</p><p> </p><p>3,456</p><p> </p><p>..equation..</p><p> </p><p>3,456</p><p> </p><p>HiPAcc1</p><p> </p><p>Image processing framework</p><p> </p><p>33</p><p> </p><p>2</p><p> </p><p>13,485</p><p> </p><p>..solving..</p><p> </p><p>13,485</p><p> </p><p>Trimesh2</p><p> </p><p>Triangle mesh library</p><p> </p><p>13</p><p> </p><p>4</p><p> </p><p>239,360</p><p> </p><p>..time</p><p> </p><p>239,360</p><p> </p><p>GEC</p><p> </p><p>Generic edge computing</p><p> </p><p>552</p><p> </p><p>2</p><p> </p><p>~5.3*108</p><p> </p><p>Energy Consumption</p><p> </p><p>132500</p><p> </p><p>The dataset zip file contains:</p><ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul><p>References:</p><p>[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.</p><p>[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).</p>
Online survey of needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector
<p>The present dataset was generated as part of the "Needs and challenges of innovation ecosystems and intermediaries for taking up activity in the EU space sector" of the H2020 <a href="http://innorbit.eu">InnORBIT project</a>.</p> <p>The aim of this study was to identify and explore the available and missing skills of innovation intermediaries to provide business support services to innovators within their local ecosystems to develop commercial activity in space. The assessment of skills was based on a baseline framework encompassing a wide array of skills and competencies innovation intermediaries are supposed to possess in order to provide effective business support services to space innovators. The skills of the baseline framework are grouped into five broad categories: (i) space industry knowledge, (ii) business assessment knowledge, (iii) business support skills, (iv) organisational and digital skills and (v) soft skills. The baseline framework was originally developed by the InnORBIT consortium through research in related works of EU funded projects and publications and validated through a series of 15 interviews with top-level executives of organisations across the CEE and SEE area, belonging to the two target groups of the study (i.e., innovation intermediaries and innovators). An online survey was deployed from May 26th to June 18th using the EU Survey tool, to innovation intermediaries and innovators across the EU and CEE/SEE countries in particular. Two online questionnaires were developed building on the baseline skills framework - the first intended for innovation intermediaries asking them to perform a self-assessment of their skills in terms of providing business support services and the second targeting innovators, asking them to state their perception on how innovation intermediaries they have worked with, perform in each of the skills.</p> <p>The dataset contains four files:</p> <p>1. Zip file including the transcripts from 6 interviews with space innovators in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>2. Zip file including the transcripts from 9 interviews with innovation intermediaries in Eastern Europe for the evaluation of the baseline framework of skills.</p> <p>3. a pdf file of the digital questionnaires developed in EU Survey deployed to innovation intermediaries and innovators in the region</p> <p>4. An excel file with 104 valid responses collected from the online survey (56 innovation intermediaries and 48 innovators) across 16 EU countries / 21 countries total.</p> <p>The dataset contains only non-sensitive anonymised information and is in full compliance with the GDPR provisions. Any information leading to the identification of participants in activities (interviews, survey) is either modified or omitted and deonted with brackets.</p>
A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation
<p>Thermocatalytic CO<sub>2</sub> hydrogenation to methanol is an attractive decarbonization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In<sub>2</sub>O<sub>3</sub>-, and ZnO-ZrO<sub>2</sub>-based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models (<em>R</em><sup>2</sup> > 0.85) are developed to predict the methanol space-time yield (<em>STY</em>) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 g<sub>MeOH</sub> h<sup>−1</sup> g<sub>cat</sub><sup>−1 </sup>between the actual and predicted methanol<em> STY</em>. The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization.</p>
Inference products for "Finite inflation in curved space"
<p>These are the MCMC and nested sampling inference products and input files that were used to compute results for the paper <strong>"Finite inflation in cuved space"</strong> by <strong>L. T. Hergt</strong>, <strong>F. J. Agocs</strong>, <strong>W. J. Handley</strong>, <strong>M. P. Hobson</strong>, and <strong>A. N. Lasenby</strong> from 2022.</p> <p>Example plotting scripts (as <span>\(\texttt{.ipynb}\)</span> or as <span>\(\texttt{.html}\)</span> files) and figures from the paper are included to demonstrate usage.</p> <p> </p> <p>We used the following python packages for the genertion of MCMC and nested sampling chains:</p> <table> <tbody><tr> <th>Package</th> <th>Version</th> </tr> </tbody><tbody> <tr> <td>anesthetic</td> <td>2.0.0b12</td> </tr> <tr> <td>classy</td> <td>2.9.4</td> </tr> <tr> <td>cobaya</td> <td>3.0.4</td> </tr> <tr> <td>GetDist</td> <td>1.3.3</td> </tr> <tr> <td>primpy</td> <td>2.3.6</td> </tr> <tr> <td>pyoscode</td> <td>1.0.4</td> </tr> <tr> <td>pypolychord</td> <td>1.20.0</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Filename conventions:</p> <ul> <li><span>\(\texttt{mcmc}\)</span>: MCMC run</li> <li><span>\(\texttt{pcs#d####}\)</span>: PolyChord run (in synchronous mode) with <span>\(\texttt{#d}\)</span> repeats per parameter block (where <span>\(\texttt{d}\)</span> is the number of parameters in that block) and with <span>\(\texttt{####}\)</span> live points.</li> <li><span>\(\texttt{_cl_hf}\)</span>: Using Boltzmann theory code CLASS with nonlinearities code halofit.</li> <li><span>\(\texttt{_p18}\)</span>: Using Planck 2018 CMB data.</li> <li><span>\(\texttt{_TTTEEE}\)</span>: Using the high-l TTTEEE likelihood.</li> <li><span>\(\texttt{_TTTEEElite}\)</span>: Using the lite version of the high-l TTTEEE likelihood.</li> <li><span>\(\texttt{_lowl_lowE}\)</span>: Using the low-l likelihoods for temperature and E-modes.</li> <li><span>\(\texttt{_BK15}\)</span>: Using data from the 2015 observing season of Bicep2 and the Keck Array.</li> <li><span>\(\texttt{lcdm}\)</span>: Concordance cosmological model called LCDM (standard 6 cosmological sampling parameters, no tensor perturbations, zero spatial curvature)</li> <li><span>\(\texttt{_r}\)</span>: Extension with a variable tensor-to-scalar ratio <span>\(r\)</span>.</li> <li><span>\(\texttt{_omegak}\)</span>: Extension with a variable curvature density parameter <span>\(\Omega_K \)</span>.</li> <li><span>\(\texttt{_H0}\)</span>: Sampling over <span>\(H_0\)</span> instead of <span>\(\theta_\mathrm{s}\)</span>.</li> <li><span>\(\texttt{_omegakh2}\)</span>: Extension with a variable curvature density parameter, but sampling over <span>\(H_0\)</span> instead of <span>\(\theta_\mathrm{s}\)</span> and over <span>\(\omega_K\equiv\Omega_Kh^2\)</span> instead of <span>\(\Omega_K \)</span>.</li> <li><span>\(\texttt{_mn2}\)</span>: Using a quadratic monomial potential for the computation of the primordial universe.</li> <li><span>\(\texttt{_nat}\)</span>: Using the natural inflation potential for the computation of the primordial universe.</li> <li><span>\(\texttt{_stb}\)</span>: Using the Starobinsky potential for the computation of the primordial universe.</li> <li><span>\(\texttt{_AsfoH}\)</span>: Using the primordial sampling parameters {`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`}.</li> <li><span>\(\texttt{_perm}\)</span>: Assuming a permissive reheating scenario.</li> </ul> <p> </p> <p> </p>
Climatology of deep O+ dropouts in the night-time F-region in solar minimum measured by a Langmuir Probe onboard the International Space Station
<p>Dataset contains data pertaining to an accepted JGR Space Physics article of the same name as the dataset. The link to the article is the following: <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>. The dataset contains the high level data that were used to generate Figs 2-5 in the aforementioned paper. </p> <p>The observations recorded by ISS FPMU will be uploaded to NASA SPDF as well. A previous dataset already exists in CDAweb under ISS/FPMU. The O+ information will be added with the new upload.</p> <p>For any questions about the data or the tools used to derive the figures from the data, please take a look at the paper <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>, or contact Shantanab Debchoudhury at debchous@erau.edu. </p> <p> </p>
Benchmark for deterministic traffic simulator - parameter space exploration (Prague, Jun 6 2021)
<p>The benchmark is meant for deterministic traffic simulator for optimising traffic flow within a city. The simulator is one part of a traffic modeling framework for intelligent transportation in smart cities. In contrast to standard navigation systems where the navigation is optimised for drivers, we aim to optimise a distribution of the global traffic flow. We utilise HPC resources for the simulator’s parameters exploration for which EVEREST SDK is used.</p> <p>The traffic simulator is available at: <a href="https://github.com/It4innovations/ruth">github.com/It4innovations/ruth</a><strong>.</strong></p> <p><br> The benchmark contains input data, routing map, and skript to run it with HyperQueue. Simulator v1.0 was used.</p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020
<p>Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".</p> <p> </p> <p>Please cite the original paper:</p> <p>Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” <em>Applied Geography </em> 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.</p> <p><br>WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).</p> <p>All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.</p> <p>Three WUI maps were generated using structures built on of before the years indicated below:</p> <p>2000 - "CA_WUI_2000.tif"</p> <p>2010 - "CA_WUI_2010.tif"</p> <p>2020 - "CA_WUI_2020.tif" </p> <p> </p> <p>Acknowledgments -</p> <p>We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.</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.