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7,195
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Dataset results
7,195 results for “SET”
IG receptor germline set for species: Mouse subgroup: CBA/J set_name: CBA/J IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: DBA/2J set_name: DBA/2J IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: CBA/J set_name: CBA/J IGKV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: CAST/EiJ set_name: CAST/EiJ IGKV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: CAST/EiJ set_name: CAST/EiJ IGH
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: CAST/EiJ set_name: CAST/EiJ IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: NOR/LtJ set_name: NOR/LtJ IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: LEWES/EiJ set_name: LEWES/EiJ IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: NOR/LtJ set_name: NOR/LtJ IGKV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: MSM/MsJ set_name: MSM/MsJ IGH
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: MRL/MpJ set_name: MRL/MpJ IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: C3H/HeJ set_name: C3H/HeJ IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: C57BL/6J set_name: C57BL/6J IGKV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: C57BL/6J set_name: C57BL/6J IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
Data Set "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"
<p>This data set accompanies the publication "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"</p> <p>It contains:</p> <p>- xyz files of all considered molecular structures.</p> <p>- PyADF input scripts for running the eb-MBE and db-MBE calculations.</p> <p>- raw results data from the eb-MBE and db-MBE calculations</p> <p>- Jupyter notebooks for generating the plots and tables</p>
Cloud-based User Entity Behavior Analytics Log Data Set
<p>This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1].</p><p>The logs are provided in JSON format with the following attributes in the first level:</p><ul><li><strong>id</strong>: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1.</li><li><strong>time</strong>: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z.</li><li><strong>uid</strong>: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer.</li><li><strong>uidType</strong>: Specifier for uid, which is either the user name or IP address for logged out users.</li><li><strong>type</strong>: The action carried out by the user, e.g., file_accessed.</li><li><strong>params</strong>: Additional event parameters (e.g., paths, groups) stored in a nested dictionary.</li><li><strong>isLocalIP</strong>: Optional flag for event origin, which is either internal (true) or external (false).</li><li><strong>role</strong>: Optional user role: consulting, administration, management, sales, technical, or external.</li><li><strong>location</strong>: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc.</li></ul><p>In the following data sample, the first object depicts a successful user login (see <i>type: login_successful</i>) and the second object depicts a file access (see <i>type: file_accessed</i>) from a remote location:</p><blockquote><p>{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}</p><p> {"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"}</p></blockquote><p>The data set was generated at the premises of <a href="https://www.huemer-group.com/">Huemer Group</a>, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called <a href="https://www.huemer-group.com/hbox/">hBOX</a> enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project <a href="https://nextcloud.com">Nextcloud</a> with interfaces and functionalities tailored to the requirements of customers.</p><p>The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our <a href="https://github.com/ait-aecid/clue-lds">github repo</a>.</p><p>Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set.</p><p><strong>If you use the dataset, please cite the following publication:</strong></p><p>[1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. <a href="https://doi.org/10.1109/BigData55660.2022.10020672">"A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing"</a>. <a href="http://bigdataieee.org/BigData2022/"><i>2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022)</i></a>, December 17-20, 2022, Osaka, Japan. IEEE. [<a href="https://www.skopik.at/ait/2022_bigdata.pdf">PDF</a>]</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 07
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 07 comprises three stitched image montages recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (control). Ciliated cells and extracellular material, such as vesicles and needle-like crystals, are visible, but no coronavirus particles.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 06
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 06 comprises three stitched image montages recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (control). Ciliated cells and extracellular material, such as vesicles and needle-like crystals, are visible, but no coronavirus particles.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
ArtTabGen Benchmarking Data Sets V1.0
<p>These datasets can be used to benchmark information extraction systems. To see an exemplary benchmarking script, please<br> refer to our PLIX publication. For more information regarding the generation of this dataset, please check out our<br> ArtTabGen Generation Code repository and our paper.</p>
Rotterdam EyePACS AIROGS Lite development and test set
<p>Development and test set for the AIROGS Lite challenge (<a href="https://airogs-lite.grand-challenge.org/">https://airogs-lite.grand-challenge.org/</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.