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48 results for “Earth Science”
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
Data and code associated with "The Observed Availability of Data and Code in Earth Science and Artificial Intelligence"
<p>Data and code associated with "The Observed Availability of Data and Code in Earth Science <br>and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern.</p> <p>Instructions: To reproduce figures, download all associated Python and CSV files and place<br> in a single directory.<br> Run BAMS_plot.py as you would run Python code on your system.</p> <p>Code:<br>BAMS_plot.py: Python code for categorizing data availability statements based on given data<br> documented below and creating figures 1-3. </p> <p> Code was originally developed for Python 3.11.7 and run in the Spyder <br> (version 5.4.3) IDE.<br> <br> Libraries utilized:<br> numpy (version 1.26.4) <br> pandas (version 2.1.4)<br> matplotlib (version 3.8.0)<br> <br> For additional documentation, please see code file.</p> <p>Data:<br>ASDC_AIES.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence for the Earth Systems (AIES)<br>ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence in Geosciences (AI in Geo.)<br>ASDC_AIJ.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence (AIJ)<br>ASDC_MWR.csv: CSV file containing relevant availability statement data for Monthly <br> Weather Review (MWR)<br><br></p> <p><br>Data documentation:<br>All CSV files contain the same format of information for each journal. The CSV files above are <br>needed for the BAMS_plot.py code attached.</p> <p>Records were analyzed based on the criteria below.</p> <p> Records:<br> 1) Title of paper<br> The title of the examined journal article.<br> 2) Article DOI (or URL)<br> A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is <br> generally given. For AIJ, the URL is given.<br> 3) Journal name<br> The name of the journal where the examined article is published. Either a full<br> journal name (e.g., Monthly Weather Review), or the acronym used in the <br> associated paper (e.g., AIES) is used.<br> 4) Year of publication<br> The year the article was posted online/in print.<br> 5) Is there an ASDC?<br> If the article contains an availability statement in any form, "yes" is <br> recorded. Otherwise, "no" is recorded.<br> 6) Justification for non-open data?<br> If an availability statement contains some justification for why data is not <br> openly available, the justification is summarized and recorded as one of the <br> following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be <br> obtained from other entities, 4) Sensitive information, 5) Available at later <br> date. If the statement indicates any data is not openly available and no <br> justification is provided, or if no statement is provided is provided "None" <br> is recorded. If the statement indicates openly available data or no data <br> produced, "N/A" is recorded.<br> 7) All data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access data associated with the article are given and there is no <br> indication that any data is not openly available; "n" is recorded if no means <br> to access data are given or there is some indication that some or all data is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 8) At least some data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access data associated with the article are given; "n" is <br> recorded if no means to access data are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 9) All code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access code associated with the article are given and there is no <br> indication that any code is not openly available; "n" is recorded if no means <br> to access code are given or there is some indication that some or all code is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 10) At least some code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access code associated with the article are given; "n" is <br> recorded if no means to access code are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 11) All data available upon request<br> If there is an availability statement indicating data is produced and no data <br> is openly available, "y" is recorded if any data is available upon request to <br> the authors of the examined journal article (not a request to any other <br> entity); "n" is recorded if no data is available upon request to the authors <br> of the examined journal article. If there is no availability statement, any <br> data is openly available, or no data is produced, the record is left blank.<br> 12) At least some data available upon request<br> If there is an availability statement indicating data is produced and not all <br> data is openly available, "y" is recorded if all data is available upon <br> request to the authors of the examined journal article (not a request to any <br> other entity); "n" is recorded if not all data is available upon request to <br> the authors of the examined journal article. If there is no availability <br> statement, all data is openly available, or no data is produced, the record<br> is left blank.<br> 13) no data produced<br> If there is an availability statement that indicates that no data was<br> produced for the examined journal article, "y" is recorded. Otherwise, the<br> record is left blank.<br> 14) links work<br> If the availability statement contains one or more links to a data or code <br> repository, "y" is recorded if all links work; "n" is recorded if one or more <br> links do not work. If there is no availability statement or the statement <br> does not contain any links to a data or code repository, the record is left <br> blank. </p>
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></p>
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Electronic representation of Russian journals on Earth Sciences
<p>The dataset describes the deprth of digital archives of the most authoritative Russian journal on Earth Sciences. The five figures demostrate the results of graphical processing while preparing digital archives of Geologiya i Geofizika and Zapiski Gornogo Instituta journals, as well as the forms of metadata presentation in electronic archives.</p>
Return on Investment Metrics for Data Repositories in Earth and Environmental Sciences
Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Model output and PTt marker data for van Agtmaal et al., 2022 (in review), Frontiers in Earth Science
<p>Model output for reproduction of key figures in the manuscript van Agtmaal et al. titled "Quantifying continental collision dynamics for Alpine-style orogens" currently under revision in Frontiers in Earth Science</p>
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
Linked collectors and determiners for: UAM Earth Sciences Collection (Arctos).
Natural history specimen data linked to collectors and determiners held within, "UAM Earth Sciences Collection (Arctos)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f">https://bionomia.net/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f">https://gbif.org/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f</a>. Formatted as a Frictionless Data package.
Mirror of data from NOAA U.S. Climate Reference Network for Research Computing in Earth Science
<p>This is a mirror of data from the NOAA U.S. Climate Reference Network (https://www.ncei.noaa.gov/products/land-based-station/us-climate-reference-network).</p> <p>It was created because outbound FTP access is not allowed from some cloud-based JupyterHub setups.</p>
Data for: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences
<p>Microscale processes in three-phase suspensions (mixtures of gas, liquids, and solids) can affect the macroscale behavior of the whole suspension. To visualize these small-scale processes at high speed and in 3D, we use a recently developed imaging system: Swept Confocally-Aligned Planar Excitation (SCAPE) microscopy. This dataset contains 3D videos taken with SCAPE microscopy of experiments where different phases interact with each other. Each zipped folder contains raw data and processed data for a single experiment. "Case 1" experiments show CO2 bubbles growing on PMMA (acrylic) particles in sparkling water. The "Case 2" experiment shows water droplets suspended in canola oil and flowing through a porous medium made of packed PMMA particles. "Case 3" experiments show growth of injected air bubbles in particle suspensions (either glass beads in immersion oil, or PMMA particles in a refractive index matched liquid).</p> <p>All scaling parameters are provided in Table 1. "info.txt" files contain metadata for the processed hyperstacks.</p> <p>The experiments provided here are discussed in the following publication:<br> Oppenheimer, J.*, Patel, K.*, Lindoo, A., Hillman, E. M. C., and Lev, E.: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences. <em>Geochemistry, Geophysics, Geosystems.</em> (In press, 12/2020)</p> <p><br> </p>
Global flow of earth science scientific articles based on several databases
<p>Global flow of earth science scientific articles based on several databases contains the summary of our findings from our search of earth science articles in ten databases:</p> <ol> <li>Google Scholar</li> <li>Lens</li> <li>Dimensions</li> <li>Korean Citation Index</li> <li>Russian Scientific Citation Index</li> <li>Garuda Ristekbrin</li> <li>HAL</li> <li>Scielo</li> <li>Scopus</li> <li>Web of Science</li> </ol> <p>The data are visualized using Datawrapper in the following links. All graphs contain links to the data sources (click "Get the data" under each graph):</p> <ol> <li><a href="https://www.datawrapper.de/_/YxTc9/">https://www.datawrapper.de/_/YxTc9/ (Scielo)</a></li> <li><a href="https://www.datawrapper.de/_/blOXM/">https://www.datawrapper.de/_/blOXM/ (Dimensions)</a></li> <li><a href="https://www.datawrapper.de/_/0fyDw/">https://www.datawrapper.de/_/0fyDw/ (Lens)</a></li> <li><a href="https://www.datawrapper.de/_/ad6c8/">https://www.datawrapper.de/_/ad6c8/ (Scopus)</a></li> <li><a href="https://www.datawrapper.de/_/NyypS/">https://www.datawrapper.de/_/NyypS/ (Maximum score for documents in rank promotion regulation of Indonesia)</a></li> <li><a href="https://www.datawrapper.de/_/s1KMD/">https://www.datawrapper.de/_/s1KMD/ (Percentage of OA earth sciences documents in several databases)</a></li> <li><a href="https://www.datawrapper.de/_/2nBnP/">https://www.datawrapper.de/_/2nBnP/ (Sum of earth sciences documents in several databases)</a></li> <li><a href="https://www.datawrapper.de/_/CgpLO/">https://www.datawrapper.de/_/CgpLO/ (Distribution of earth sciences documents by year in log scale)</a></li> </ol>
Japan Agency for Marine-Earth Science and Technology: jamstec DwCA file
Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has the main objective to contribute to the advancement of academic research in addition to the improvement of marine science and technology by proceeding the fundamental research and development on marine, and the cooperative activities on the academic research related to the Ocean for the benefit of the peace and human welfare. <p></p>http://www.jamstec.go.jp/<p></p>Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has the main objective to contribute to the advancement of academic research in addition to the improvement of marine science and technology by proceeding the fundamental research and development on marine, and the cooperative activities on the academic research related to the Ocean for the benefit of the peace and human welfare. <p></p>http://www.jamstec.go.jp/
Combined data file for Jilbert et al. "Anthropogenic Inputs of Terrestrial Organic Matter Influence Carbon Loading and Methanogenesis in Coastal Baltic Sea Sediments", Frontiers in Earth Science 9, 2021
<p>The datafile contains all the new raw data presented in the figures in the publication.</p>
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Benefits of Ontologies in Earth System Science
<p>The exponential growth of data due to technological developments along with an increased recognition of research data as relevant research output during the last decades substantiates fundamental challenges in terms of interoperability, reproducibility and reuse of scientific information. Being cross-disciplinary at its core, research in Earth System Science comprises divergent domains such as Paleontology, Marine Science, Atmospheric Sciences and Molecular Biology in addition to different types of data such as observation and simulation data. Within the various disciplines, distinct methods and terms for indexing, cataloguing, describing and finding scientific data have been developed, resulting in several controlled Vocabularies, Taxonomies and Thesauri. However, given the semantic heterogeneity across scientific domains, effective utilisation and (re)use of data is impeded while the importance of enhanced and improved interoperability across research areas will increase even further, considering the global impact of Climate Change to literally all aspects of everyday life. There is thus a clear need to harmonise practices around the development and usage of semantics in representing and describing information and knowledge. For a beneficial usage of semantic artefacts, sustainability is the key: any kind of terminology service must be maintained to guarantee that terms and relations are offered in a persistent way. But if they are, Vocabularies, Taxonomies, Thesauri and Ontologies can serve as a driving force for improved visibility and findability of research output within and across different research areas. Why Ontologies matter, what they are, and how they can be used will be depicted on our Poster in an easy-to-understand way.</p> <p> </p>
Fault Traces Dataset for Zou and Fialko Earth and Space Sciences Manuscript
<p>The 'xx_fault.dat' contains the linked fault traces of different regions (nz: Northern New Zealand; nv: Basin and Range Province; ca: Ventura County, California; np: Pennsylvania and Northern New Jersey); The 1st and 2nd columns are UTM coordinates; The 3rd column is the random number assigned for distinguishing each fault traces.</p> <p>The 'xx_len.dat' contains the length of each fault trace in the corresponding regions, in km.</p> <p>The other '.dat' files and the 'SunData.xls' contain the length of fractures from outcrop and lab data. All of them are frequency distribution, except the 'LaHouve_Villemin.dat' which is already in cumulative distribution. The unit of 'SunData.xls' is mm; for the two 'Bahat' datasets is cm; for the rest of the outcrop data is m.</p> <p>The .m files are the codes for calculating cumulative length distribution, frequency density distribution, and fault connection.</p> <p> </p> <p> </p> <p>For any questions please contact Xiaoyu Zou via x3zou@ucsd.edu</p>
Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels.</p> <p> </p> <p>The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. </p> <p> </p> <p>The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat.</p> <p><br> The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (<a href="https://github.com/ai2es/miles-guess">https://github.com/ai2es/miles-guess</a>).</p>
Features of research data policies of Earth science and biodiversity academic society journals
<p>Dataset related to Hübner (2020) Earth science and biodiversity journals can improve support for data publication. Preprint: https://doi.org/10.23689/fidgeo-3818</p> <p>This study reviews research data policies and author instructions of 31 journals from the Earth sciences and from biodiversity that are published by German learned societies or research institutions. The statements on data publishing of the journal´s data policies / author guidelines were matched to 14 pre-defined features of journal research data policies from Hrynaszkiewicz, I. et al. (2020) Developing a Research Data Policy Framework for All Journals and Publishers. Data Science Journal, 19: 5, pp. 1–15. <a href="https://doi.org/10.5334/dsj-2020-005">https://doi.org/10.5334/dsj-2020-005</a>.</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.