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AbDb processed and pickled for use in deep learning CDR-H3 Structure...
- data-staging.niaid.nih.gov
- data.niaid.nih.gov
Updated Jan 24, 2020
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EmailClick to copy linkLink copiedCiteBlundell, Benjamin; Martin, Andrew (2020). AbDb processed and pickled for use in deep learning CDR-H3 Structure prediction [Dataset]. https://data-staging.niaid.nih.gov/resources?id=zenodo_2560435Dataset updatedJan 24, 2020Dataset provided byKing's College London
University College LondonAuthorsBlundell, Benjamin; Martin, AndrewLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis is a pickle file, ready for training by the neural network described in "Improving CDR-H3 modelling in Antibodies" found at the following URL:
https://github.com/OniDaito/MRes
The data is derived from the AbDb dataset found at:
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eu_df55d864-17a3-4647-abdb-5377ba446092_1 | gimi9.com
- gimi9.com
ShareFacebookTwitterEmailClick to copy linkLink copiedCiteeu_df55d864-17a3-4647-abdb-5377ba446092_1 | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_df55d864-17a3-4647-abdb-5377ba446092_1/LicenseCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automaticallyDescriptionThis training survey in Geological Exploration took place on board the Marine Institute's R.V. Celtic Voyager in Cork Harbour. Three instructors delivered 6 days of training to postgraduate and undergraduate students from University College Cork (UCC) from the 24th to 29th of November 2017. These training surveys arise from a learning module in multidisciplinary offshore marine science developed by the Strategic Marine Alliance for Research and Training (SMART) in 2011 . The objective of this SMART UCC training programme was to provide offshore training for Geology and Earth and Environmental Science undergraduates and Exploration Field Geology postgraduates in offshore geoscience. Overall, the training provided practical training in: • seabed mapping techniques and practice; • seabed sampling techniques and practice; • sub-seabed imaging techniques and practice; • offshore geological data collection; • evaluation of resource and resource assessment.
FBIP: IZIKO Marine Bony Fish (1884-2013)
- gbif.org
- demo.gbif.org
Updated Oct 7, 2019ShareFacebookTwitterEmailClick to copy linkLink copiedCiteAlbe Bosman; Albe Bosman (2019). FBIP: IZIKO Marine Bony Fish (1884-2013) [Dataset]. http://doi.org/10.15468/rnpcwhUnique identifierhttps://doi.org/10.15468/rnpcwhDataset updatedOct 7, 2019Dataset provided byGlobal Biodiversity Information Facilityhttps://www.gbif.org/
South African National Biodiversity InstituteAuthorsAlbe Bosman; Albe BosmanLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredDescriptionThe Iziko South African Museum has a comprehensive holdings comprising of identified bony and cartilaginous fish, mostly from Cape waters, but extending to Angola and Mozambique and the Southern, Indian and Atlantic Oceans
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Inspire data set BPL “Im Friederikele 2.Change”
- data.europa.eu
- gimi9.com
wfs, wmsUpdated Jan 9, 2021ShareFacebookTwitterEmailClick to copy linkLink copiedCite(2021). Inspire data set BPL “Im Friederikele 2.Change” [Dataset]. https://data.europa.eu/data/datasets/ca8a6eb9-f80a-4beb-abdb-d3f9d4afd776wms, wfsAvailable download formatsDataset updatedJan 9, 2021DescriptionAccording to INSPIRE transformed development plan “Im Friederikele 2.Change” of the city of Bietigheim-Bissingen based on an XPlanung dataset in version 5.0.
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Raw data from external antibody databases and scripts to homogenize and...
- entrepot.recherche.data.gouv.fr
application/x-gzip +1Updated Feb 4, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteNicolas MAILLET; Nicolas MAILLET; Simon MALESYS; Simon MALESYS (2025). Raw data from external antibody databases and scripts to homogenize and standardize them used to build AntiBody Sequence Database (for reproducibility) [Dataset]. http://doi.org/10.57745/DDLHWUapplication/x-gzip(620431), application/x-gzip(163643), application/x-gzip(6833391387), text/markdown(12475), application/x-gzip(80726198), application/x-gzip(65497009)Available download formatsUnique identifierhttps://doi.org/10.57745/DDLHWUDataset updatedFeb 4, 2025Dataset provided byRecherche Data GouvAuthorsNicolas MAILLET; Nicolas MAILLET; Simon MALESYS; Simon MALESYSLicensehttps://entrepot.recherche.data.gouv.fr/api/datasets/:persistentId/versions/3.1/customlicense?persistentId=doi:10.57745/DDLHWUhttps://entrepot.recherche.data.gouv.fr/api/datasets/:persistentId/versions/3.1/customlicense?persistentId=doi:10.57745/DDLHWU
DescriptionReproducibility data for the AntiBody Sequence Database (ABSD) article. This dataset contains the raw data (antibody sequences) extracted on June 20, 2024, from various databases, as well as the several scripts, to ensure the reproducibility of our results. External databases used: ABDB, AbPDB, CoV-AbDab, Genbank, IMGT, PDB, SACS, SAbDab, TheraSAbDab, UniProt, KABAT Scripts usage: each external database has a corresponding script to format all antibody sequences extracted from it. A last script enable merging all extracted antibody sequences while removing redundancy, standardizing and cleaning data.
Meps 2.5 km pressure level parameters from ensemble member 4...
- data.met.no
Updated Jun 18, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteSenter for utvikling av værvarslingstjenesten (2025). Meps 2.5 km pressure level parameters from ensemble member 4 2025-06-18T10:00:00Z + 66 hours [Dataset]. https://data.met.no/dataset/8956e8bf-abdb-42b4-b102-05765ba06caeDataset updatedJun 18, 2025AuthorsSenter for utvikling av værvarslingstjenestenLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyTime period coveredJun 18, 2025 - Jun 21, 2025Area coveredDescriptionThis file contains pressure level parameters from ensemble member 4. For more information, please visit https://github.com/metno/NWPdocs/wiki
Meps 2.5 km pressure level parameters from ensemble member 1...
- data.met.no
Updated Aug 23, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteSenter for utvikling av værvarslingstjenesten (2024). Meps 2.5 km pressure level parameters from ensemble member 1 2024-08-23T18:00:00Z + 66 hours [Dataset]. https://data.met.no/dataset/46a17fd4-abdb-4303-a236-b54c028339d9Dataset updatedAug 23, 2024AuthorsSenter for utvikling av værvarslingstjenestenLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyTime period coveredAug 23, 2024 - Aug 26, 2024Area coveredDescriptionThis file contains pressure level parameters from ensemble member 1. For more information, please visit https://github.com/metno/NWPdocs/wiki
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Kirchstraße 2 (1. Änderung)
- service.tib.eu
- data.europa.eu
Updated Feb 4, 2025+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCite(2025). Kirchstraße 2 (1. Änderung) [Dataset]. https://service.tib.eu/ldmservice/dataset/govdata_ea6a007b-7b37-498c-abdb-4a5d973e2e60--1Dataset updatedFeb 4, 2025DescriptionKirchstraße 2 (1. Änderung) im Datenformat XPlanGML Version 5.1.2
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This is a pickle file, ready for training by the neural network described in "Improving CDR-H3 modelling in Antibodies" found at the following URL:
https://github.com/OniDaito/MRes
The data is derived from the AbDb dataset found at: