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Found 10 result(s)
The WashU Research Data repository accepts any publishable research data set, including textual, tabular, geospatial, imagery, computer code, or 3D data files, from researchers affiliated with Washington University in St. Louis. Datasets include metadata and are curated and assigned a DOI to align with FAIR data principles.
Country
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.
Country
Swedish National Data Service (SND) is a research data infrastructure designed to assist researchers in preserving, maintaining, and disseminating research data in a secure and sustainable manner. The SND Search function makes it easy to find, use, and cite research data from a variety of scientific disciplines. Together with an extensive network of almost 40 Swedish higher education institutions and other research organisations, SND works for increased access to research data, nationally as well as internationally.
This website makes data available from the first round of data sharing projects that were supported by the CRCNS funding program. To enable concerted efforts in understanding the brain experimental data and other resources such as stimuli and analysis tools should be widely shared by researchers all over the world. To serve this purpose, this website provides a marketplace and discussion forum for sharing tools and data in neuroscience. To date we host experimental data sets of high quality that will be valuable for testing computational models of the brain and new analysis methods. The data include physiological recordings from sensory and memory systems, as well as eye movement data.
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The RAM Legacy Stock Assessment Database is a compilation of stock assessment results for commercially exploited marine populations from around the world. The recently updated database offers many graphical and analytic tools to explore the data, as well as new data sets including; assessments from N.W. Africa, assessments from the Mediterranean Sea, assessments from Chile, data sets on Pacific salmon. The database is seeking collaborators to cover parts of the world that we are missing.
Welcome to the York Research Database, where you can find all our research staff, projects, publications and organisational units, and explore the connections between them all. The university of York is a member of the Russell Group of research-intensive universities.
The aim of the Freshwater Biodiversity Data Portal is to integrate and provide open and free access to freshwater biodiversity data from all possible sources. To this end, we offer tools and support for scientists interested in documenting/advertising their dataset in the metadatabase, in submitting or publishing their primary biodiversity data (i.e. species occurrence records) or having their dataset linked to the Freshwater Biodiversity Data Portal. This information portal serves as a data discovery tool, and allows scientists and managers to complement, integrate, and analyse distribution data to elucidate patterns in freshwater biodiversity. The Freshwater Biodiversity Data Portal was initiated under the EU FP7 BioFresh project and continued through the Freshwater Information Platform (http://www.freshwaterplatform.eu). To ensure the broad availability of biodiversity data and integration in the global GBIF index, we strongly encourages scientists to submit any primary biodiversity data published in a scientific paper to national nodes of GBIF or to thematic initiatives such as the Freshwater Biodiversity Data Portal.
PhysioBank is a large and growing archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community.
Modern signal processing and machine learning methods have exciting potential to generate new knowledge that will impact both physiological understanding and clinical care. Access to data - particularly detailed clinical data - is often a bottleneck to progress. The overarching goal of PhysioNet is to accelerate research progress by freely providing rich archives of clinical and physiological data for analysis. The PhysioNet resource has three closely interdependent components: An extensive archive ("PhysioBank"), a large and growing library of software ("PhysioToolkit"), and a collection of popular tutorials and educational materials