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361 results for “January”
Hyporheic diatom community assemblages from Von Guerard Stream, Taylor Valley, McMurdo Dry Valleys, Antarctica in January 2019
In this data package, we present diatom community assemblages from hyporheic sediments collected in January 2019 from six transects across Von Guerard Stream, Taylor Valley, Antarctica. These samples were collected to address questions about the retention and processing of particulate organic matter in the hyporheic zone of McMurdo Dry Valley streams. The six transects were located at pools, riffles, and meanders (three of each geomorphology type) along Von Guerard Stream and extended across the stream channel to the edges of the wetted zone, ranging from 6.6 to 13.6 m in length. At each sampling location, we collected subsurface sediment sample that was preserved in formalin directly after sample collection. We characterized diatom assemblages by counting a total of 300 diatom valves from each preserved diatom sample. This data package is associated with a complementary data package that contains hyporheic sediment chemistry for the same samples.
MCR LTER: Coral Reef: pH Time Series from Bottom-mounted SeaFET on the Fringing Reef, January-February 2011
Bottom-mounted instrumentation (SeaFET, Seabird thermistors) sampled for 6 weeks on the fringing reef of Moorea Island, French Polynesia at site LTER Fringe 1. Sampling began in January 2011. The instruments were secured to a cement piling at 3.3 meters depth and 0.7 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. The Seabird thermistors provided measures of seawater temperature at 10-minute sampling intervals. These data are published in Rivest, E.B. and G.E. Hofmann. 2014. Responses of the metabolism of the larvae of Pocillopora damicornis to ocean acidification and warming. PLoS ONE DOI:10.1371/journal.pone.0096172
Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.</p> <p>Objectives:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid >>> file "January_2020_HY_nut01_10x10.tif"<br> - Downscaling to 2x2 km grid >>> file "January_2020_HY_nut00_2x2.tif"</p> <p><br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p>Conclusions guiding future methodological steps:<br> - To update wild boar hunting yield data for some specific regions<br> - To increase hunting yield data resolution<br> - To explore model independent parametrization for each bioregion</p> <p>For further details and methodological approach see the paper:</p> <p>ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787.</p> <p>Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.<br> </p>
Cumulative PLOS ALM Report - January 2016
Article-Level Metrics (ALM) measure the reach and online engagement of scholarly works. This PLOS ALM report contains the cumulative stats collected for all works through January 10, 2016. Data are generated by the Lagotto open source software. Go to the Lagotto forum for questions or comments.
TESS January 2017
<p>Dataset with the measures taken by STARS4ALL photometers network</p>
Level A Pan Europe Solar Index for estimation of Potential evaporation January
Solar Index for estimation of Potential evaporation January. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
UEX1 - January 2022
<p>Measurements of UEX1 (University of Extremadura) in January 2022</p><p>Extremadura</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/UEX1">info</a></p>
LPL2 - January 2022
<p>Measurements of LPL2 (Caldera de Taburiente NP) in January 2022</p><p>La Palma</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></p>
Dataset of the deleted DOIs extracted from the difference set between Crossref DOIs as of March 2017 and January 2021
<p><strong>Abstract</strong></p> <p>Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we identified the deleted DOIs by from the difference set between Crossref DOIs as of March 2017 and January 2021. We note that the file "00_Non-Crossref_DOIs.jsonl.gz" is not deleted DOIs but other files are deleted DOIs. Please refer the conference paper shown in the references for details. Sample of the record is the following.</p> <ul> <li>doi -- DOI name (String), e.g., "10.xxxx/xxxx"</li> <li>whichRA -- Registration agency name or error message for the DOI name according to the “<a href="https://www.doi.org/factsheets/DOIProxy.html#whichra">whichRA?</a>.” (String). "Airiti," "Crossref," "DOI does not exist," "DataCite," "KISTI," "Public," or "mEDRA."</li> <li>redirects -- Redirected URIs for the DOI name obtained by curl command (Array of String), e.g., ["https://doi.org/10.1001/archinte.166.4.387","http://archinte.jamanetwork.com/article.aspx?doi=10.1001/archinte.166.4.387"]</li> <li>redirect_to_other_doi -- The other DOI when the DOI link redirects to. (Array of String), e.g., "[10.1001/archinte.166.4.387]"</li> <li>timestamp -- Date the data was retrieved (Datetime), "2022-01-30T02:10:45Z"</li> <li>label -- The group where the DOI belongs to. Alias DOIs," "DOIs with Deleted Description in Metadata," "DOIs without Redirects," "Defunct DOIs," "Non-Crossref DOIs," "Non-existing DOIs," or "Other DOIs."</li> </ul> <p>As for the file "04_DOIs_with_Deleted_Description_on_Metadata.jsonl.gz," additional records are available as follows.</p> <ul> <li>alias_doi -- Alias DOI name, the same as the value of "doi." (String), e.g., "10.1007/bf00400428."</li> <li>primary_doi -- Primary DOI name for the alias DOI name, the same as the first value of "redirect_to_other_doi". (String), e.g., "10.1007/bf00400429"</li> <li>container_title_of_alias_doi -- the container title for the alias DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>title_of_alias_doi -- the title for the alias DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>container_title_of_primary_doi -- the container title for the primary DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>title_of_alias_doi -- the title for the primary DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> </ul> <p><strong>References</strong></p> <ul> <li>Kikkawa, J., Takaku, M. & Yoshikane, F. "Analysis of the deletions of DOIs: What factors undermine their persistence and to what extent?", Proceedings of the 26th International Conference on Theory and Practice of Digital Libraries (<a href="http://tpdl2022.dei.unipd.it/"><em>TPDL 2022</em></a>), (to appear), 2022.</li> </ul> <p><strong>FUNDING</strong></p> <ul> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K21303">JP21K21303</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K18147">JP22K18147</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12543">JP20K12543</a>, and <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K12592">JP21K12592</a></li> </ul>
Yearly CDIP:MOP-alongshore modeled wave statistics for California, January 2000 - July 2022
<p><strong>Overview</strong></p> <ul> <li>Yearly wave averages for all 11,594 CDIPS-MOPS alongshore sites in California.</li> <li>Sites are defined in the files "CDIP_Transects.csv" and "CDIP_Transects.geojson". The bounds of each site are listed in the file "CA_region_bounds.csv"</li> <li>Data are described here: https://cdip.ucsd.edu/documents/index/product_docs/mops/mop_intro.html</li> <li>Data are obtained from here: https://thredds.cdip.ucsd.edu/thredds/catalog.html</li> </ul> <p><strong>Methods</strong></p> <ul> <li>Data are computed from hourly inshore wave hindcasts and nowcasts. Data download script is the file "CDIP_MassDownloader.ipynb"</li> <li>wave summary statistics have been computed using the file "Create_stats.ipynb". All yearly data are simple averages (i.e. mean values) of the hourly data</li> </ul> <p><strong>Data files</strong><br> Data have been split into 25 regions, defined in "CA_regions.json"</p> <p>Data are provided in geoJSON format, in the form of one file per region, and one file for all regions</p> <p><strong>Data fields</strong></p> <ul> <li>Hs: significant wave height [meters]</li> <li>Tp: peak wave period [seconds]</li> <li>Ta: average wave period [seconds]</li> <li>Dp: peak wave direction [degrees]</li> <li>Da: average wave direction [degrees]</li> <li>Ea: wave energy density, averaged over wave frequencies</li> <li>Es: wave energy density, summed over wave frequencies</li> <li>QC: quality flag</li> <li>waveTime: UTC time string</li> <li>metaWaterDepth: water depth of modeled wave data (range is 10-15m)</li> </ul>
Monthly CDIP:MOP-alongshore modeled wave statistics for California, January 2000 - July 2022
<p><strong>Overview</strong></p> <ul> <li>Monthly wave averages for all 11,594 CDIPS-MOPS alongshore sites in California.</li> <li>Jan 2000 to July 2022 inclusive</li> <li>Sites are defined in the files "CDIP_Transects.csv" and "CDIP_Transects.geojson". The bounds of each site are listed in the file "CA_region_bounds.csv"</li> <li>Data are described here: https://cdip.ucsd.edu/documents/index/product_docs/mops/mop_intro.html</li> <li>Data are obtained from here: https://thredds.cdip.ucsd.edu/thredds/catalog.html</li> </ul> <p><strong>Methods</strong></p> <ul> <li>Data are computed from hourly inshore wave hindcasts and nowcasts. Data download script is the file "CDIP_MassDownloader.ipynb"</li> <li>wave summary statistics have been computed using the file "Create_stats.ipynb". All monthly data are simple averages (i.e. mean values) of the hourly data</li> </ul> <p><strong>Data files</strong></p> <ul> <li>Data have been split into 25 regions, defined in "CA_regions.json"</li> <li>Data are provided in geoJSON format, in the form of one file per region, and one file for all regions</li> </ul> <p><strong>Data fields</strong></p> <ul> <li>Hs: significant wave height [meters]</li> <li>Tp: peak wave period [seconds]</li> <li>Ta: average wave period [seconds]</li> <li>Dp: peak wave direction [degrees]</li> <li>Da: average wave direction [degrees]</li> <li>Ea: wave energy density, averaged over wave frequencies</li> <li>Es: wave energy density, summed over wave frequencies</li> <li>QC: quality flag</li> <li>waveTime: UTC time string</li> <li>metaWaterDepth: water depth of modeled wave data (range is 10-15m)</li> </ul>
SuperDARN meteor wind data for January 2019
<p>SuperDARN meteor wind data</p> <p>*.m.* - meridional</p> <p>*.z.* - zonal</p> <p>X/Y are in radar coordinates - most users can disregard. </p> <p> </p> <p>Supported by NSF #1934973</p> <p>Collaborative Research: Super Dual Auroral Radar Network (SuperDARN) Operations, Research and Community Support</p> <p> </p> <p> </p> <p>SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgment of data by its users. Guidelines for data acknowledgment are as follows:</p> <p>When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection.</p> <p>For all usage of SuperDARN data, users are asked to include the following standard acknowledgment text: “The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.”</p> <p>While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data.</p> <p>The SuperDARN Executive Council (see list in the README) must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.</p> <p> </p>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (January 2022)
<p>This report is one of a set of outputs from the Arts and Humanities Research Council funded project ‘Preserving and sharing born-digital and hybrid objects from and across the National Collection’. It has been designed to provide an extensive account of the project research activities and findings, to be useful to museum, heritage, and preservation professionals, as well as to scholars interested in born-digital materials.</p>
RAL IMS retrieval of SO2 and sulphates (January to April 2022)
<p><strong>RAL IMS retrieval of SO2 and sulphates</strong></p> <p><strong>Description of the product</strong></p> <p>The RAL (Rutherford Appleton Laboratory) Infrared/Microwave Sounder (IMS) retrieval core scheme (Siddans, 2019) uses an optimal estimation spectral fitting procedure to retrieve atmospheric and surface parameters jointly from co-located measurements by IASI (Infrared Atmospheric Sounding Interferometer), AMSU (Advanced Microwave Sounding Unit) and MHS(Microwave Humidity Sounder) on MetOp-B spacecraft, using RTTOV 12 (Radiative Transfer for TOVS)(Saunders et al., 2017) as the forward radiative transfer model. The use of RTTOV 12 enables the quantitative retrieval of volcanic-specific aerosols (sulphate aerosol) and trace gases (SO2). The present dataset includes IMS SO2 and sulphate aerosols retrievals from its near-real time implementation. The IMS scheme retrieves the SO2 in the sensitive region around 1100-1200 cm<sup>−1</sup>, in ppbv assuming a uniform vertical mixing ratio. It retrieves sulphate-specific AOD (Aerosol Optical Depth) at 1170 cm<sup>−1</sup> (i.e. the peak of the mid-infrared extinction cross section (Sellitto and Legras, 2016)), assuming a Gaussian extinction coefficient profile shape peaking at 20 km altitude, with 2 km full-width half-maximum. The bulk of the spectroscopic information on SO2 and sulphate aerosols, in the IMS scheme, thus comes from the IASI Fourier transform spectrometer (Clerbaux et al., 2009).</p> <p>We refer to the two retrieved products as IMS SO2 and IMS SA OD.</p> <p><strong>References</strong></p> <p>Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmospheric Chemistry and Physics, 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009.</p> <p>Saunders, R., Hocking, J., Rundle, D., Rayer, P., Hayemann, S., Matricardi, A., Lupu, C., Brunel, P., and Vidot, J.: RTTOV-12 SCIENCE AND VALIDATION REPORT; Version : 1.0, Doc ID : NWPSAF-MO-TV-41, https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov12/rttov12_svr.pdf, 2017.</p> <p>Sellitto, P. and Legras, B.: Sensitivity of thermal infrared nadir instruments to the chemical and microphysical properties of UTLS secondary sulfate aerosols, Atmospheric Measurement Techniques, 9, 115–132, https://doi.org/10.5194/amt-9-115-2016, 2016.</p> <p>Siddans, R.: Water Vapour Climate Change Initiative (WV-CCI) - Phase One, Deliverable 2.2; Version 1.0, https://climate.esa.int/documents/1337/Water_Vapour_CCI_D2.2_ATBD_Part2-IMS_L2_product_v1.0.pdf, 2019.</p> <p><strong>Description of the data</strong></p> <p>The archive IMS-2022.tgz contains level 3 daily gridded files for the two retrieved products IMS SO2 and IMS SA OD in the period 13 January to 30 April 2022. A few days are missing between 9 March and 13 March. The first 8 letters of the name of each file contain the date. There are 4 files per day as the two products are in separate files and there is a file collecting day-time orbits and another one for night-time orbits every day.</p> <p>For the 28 April 2022, the four files are</p> <p>20220428_ims_metopb_tir_qnrt_aot0_day_global_g0.5_qc0.nc SA OD day-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_aot0_night_global_g0.5_qc0.nc SA OD night-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_so2_day_global_g0.5_qc0.nc SO2 day-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_so2_night_global_g0.5_qc0.nc SO2 night-time orbits</p> <p>The names for other dates can be derived by changing the first 8 letters.</p> <p>The format is netdcf4 that is readable with many programming languages and graphics packages.</p> <p>The data are on a [-90,90] x [-180,180] lat x lon grid with resolution 0.25°, that is a 720 x 1440 array of centered values.</p> <p>For both SO2 and SA OD, the values are in the ‘data’ variable. The variable ‘qa_value’ is a quality control value used to screen values for plotting; 0 means do not plot; -1 means mask is not defined so the mask is not used (data will be plotted).</p> <p>SO2 units are ppbv (assuming a uniform mixing ratio vertical profile ). SA OD is an optical depth with no unit.</p> <p><strong>Reading software</strong></p> <p>A python package to read and process the data is available at https://github.com/bernard-legras/ASTuS/tree/master/IMS and in the IMS-reader.tgz archive</p>
Air quality and noise data in Santo Domingo and Santiago de los Caballeros, Dominican Republic from May 18,2020 to January 26, 2021
<p>Air and noise pollution affect the quality of life of any community. The data presented observes the noise level, eight air quality parameters and three weather parameters. The air quality parameters are carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2); three particle-matter variables: ultrafine particulate matter (PM1), fine particulate matter (PM2.5), and coarse particulate matter (PM10). The weather parameters are temperature, relative humidity and atmospheric pressure. The empirical measurements were collected every ten or twenty minutes in two cities of the Dominican Republic from May 18th, 2020 to January 26th, 2021. The data can provide insight on the changes in air quality and noise level and can be used to compare with other variables such as traffic conditions. </p>
Daily meteorological data from January 2005 to February 18, 2022 from Viladrau Meteorological Station (Catalonia, Spain)
<p>Daily meteorological Data from Viladrau WS meteorological Station (Catalonia, Spain) from January 1, 2005 to February 18, 2022. Data includes the following variables: Date, Average, Maximal and Minimal daily temperatures, Maximal and Average of Relative humidity, Average of Atmospheric Pressure, Accumulated rain and maximal speed of wind. <strong>Surviving on the edge: present and future effects of climate warming on the common frog (Rana temporaria) population in the Montseny massif (NE Iberia).</strong></p>
Zenodo Public Metadata Records until 17 January 2017
<p>This dataset contains metadata of all publicly available records on Zenodo (n=142,117 data records), downloaded on 17 January 2017. The data was retrieved via the Zenodo OAI-PMH interface using the R package oai (Chamberlain & Bojanowski, 2016).</p> <p>The data is provided in two formats:</p> <ol> <li>The original XML in the format OAI DataCite v3, see <em> zenodo-all-metadata-records_oai-datacite3_2017-01-17.xml</em></li> <li>Converted to a tab-separated file, see <em>zenodo-all-metadata-records_2017-01-17.tsv</em></li> </ol>
Figure 7 in Observations of five litle-known tubenoses from Melanesia in January 2017
Figure 7. Moulting Heinroth's Shearwater Puffinus heinrothi, Blackett Strait, between Kolombangara and Kohinggo Islands, Solomons, 29 January 2017; outer three primaries old and moult ongoing in middle primaries (Kirk Zufelt)
Figure 6 in Observations of five litle-known tubenoses from Melanesia in January 2017
Figure 6. Map of the study area for Heinroth's Shearwater Puffinus heinrothi showing Blackett Strait, Vella Gulf and Fergusson Passage.
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.