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Found 10 result(s)
mentha archives evidence collected from different sources and presents these data in a complete and comprehensive way. Its data comes from manually curated protein-protein interaction databases that have adhered to the IMEx consortium. The aggregated data forms an interactome which includes many organisms. mentha is a resource that offers a series of tools to analyse selected proteins in the context of a network of interactions. Protein interaction databases archive protein-protein interaction (PPI) information from published articles. However, no database alone has sufficient literature coverage to offer a complete resource to investigate "the interactome". mentha's approach generates every week a consistent interactome (graph). Most importantly, the procedure assigns to each interaction a reliability score that takes into account all the supporting evidence. mentha offers eight interactomes (Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli K12, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae) plus a global network that comprises every organism, including those not mentioned. The website and the graphical application are designed to make the data stored in mentha accessible and analysable to all users. Source databases are: MINT, IntAct, DIP, MatrixDB and BioGRID.
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SILVA is a comprehensive, quality-controlled web resource for up-to-date aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains alongside supplementary online services. In addition to data products, SILVA provides various online tools such as alignment and classification, phylogenetic tree calculation and viewer, probe/primer matching, and an amplicon analysis pipeline. With every full release a curated guide tree is provided that contains the latest taxonomy and nomenclature based on multiple references. SILVA is an ELIXIR Core Data Resource.
Central data management of the USGS for water data that provides access to water-resources data collected at approximately 1.5 million sites in all 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, American Samoa and the Commonwealth of the Northern Mariana Islands. Includes data on water use and quality, groundwater, and surface water.
Earthdata powered by EOSDIS (Earth Observing System Data and Information System) is a key core capability in NASA’s Earth Science Data Systems Program. It provides end-to-end capabilities for managing NASA’s Earth science data from various sources – satellites, aircraft, field measurements, and various other programs. EOSDIS uses the metadata and service discovery tool Earthdata Search https://search.earthdata.nasa.gov/search. The capabilities of EOSDIS constituting the EOSDIS Science Operations are managed by NASA's Earth Science Data and Information System (ESDIS) Project. The capabilities include: generation of higher level (Level 1-4) science data products for several satellite missions; archiving and distribution of data products from Earth observation satellite missions, as well as aircraft and field measurement campaigns. The EOSDIS science operations are performed within a distributed system of many interconnected nodes - Science Investigator-led Processing Systems (SIPS), and distributed, discipline-specific, Earth science Distributed Active Archive Centers (DAACs) with specific responsibilities for production, archiving, and distribution of Earth science data products. The DAACs serve a large and diverse user community by providing capabilities to search and access science data products and specialized services.
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The CliSAP-Integrated Climate Data Center (ICDC) allows easy access to climate relevant data from satellite remote sensing and in situ and other measurements in Earth System Sciences. These data are important to determine the status and the changes in the climate system. Additionally some relevant re-analysis data are included, which are modeled on the basis of observational data. ICDC cooperates with the "Zentrum für Nachhaltiges Forschungsdatenmanagement "https://www.fdr.uni-hamburg.de/ to publish observational data with a doi.