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Found 93 result(s)
The Pennsieve platform is a cloud-based scientific data management platform focused on integrating complex datasets, fostering collaboration and publishing scientific data according to all FAIR principles of data sharing. The platform is developed to enable individual labs, consortiums, or inter-institutional projects to manage, share and curate data in a secure cloud-based environment and to integrate complex metadata associated with scientific files into a high-quality interconnected data ecosystem. The platform is used as the backend for a number of public repositories including the NIH SPARC Portal and Pennsieve Discover repositories. It supports flexible metadata schemas and a large number of scientific file-formats and modalities.
IEDB offers easy searching of experimental data characterizing antibody and T cell epitopes studied in humans, non-human primates, and other animal species. Epitopes involved in infectious disease, allergy, autoimmunity, and transplant are included. The IEDB also hosts tools to assist in the prediction and analysis of B cell and T cell epitopes.
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The MDR harvests metadata on data objects from a variety of sources within clinical research (e.g. trial registries, data repositories) and brings that together in a single searchable portal. The metadata is concerned with discoverability, access and provenance of the data objects (which because the data may be sensitive will often be available under a controlled access regime). At the moment (01/2021) the MDR obtains study data from: Clinical Trials.gov (CTG), The European Clinical Trials Registry (EUCTR), ISRCTN, The WHO ICTRP
The Coronavirus Antiviral Research Database is designed to expedite the development of SARS-CoV-2 antiviral therapy. It will benefit global coronavirus drug development efforts by (1) promoting uniform reporting of experimental results to facilitate comparisons between different candidate antiviral compounds; (2) identifying gaps in coronavirus antiviral drug development research; (3) helping scientists, clinical investigators, public health officials, and funding agencies prioritize the most promising compounds and repurposed drugs for further development; (4) providing an objective, evidenced-based, source of information for the public; and (5) creating a hub for the exchange of ideas among coronavirus researchers whose feedback is sought and welcomed. By comprehensively reviewing all published laboratory, animal model, and clinical data on potential coronavirus therapies, the Database makes it unlikely that promising treatment approaches will be overlooked. In addition, by making it possible to compare the underlying data associated with competing treatment strategies, stakeholders will be better positioned to prioritize the most promising anti-coronavirus compounds for further development.
>>>>!!!!<<<< AspGD data are being integrated into FungiDB. Please click here for additional details http://fungidb.org/ . Discussion of how to maximize the value of FungiDB for the Aspergillus research community will be a major topic at the upcoming AsperFest12 meeting at Asilomar (March 16-17, 2015). >>>>!!!!<<<< AspGD is an organized collection of genetic and molecular biological information about the filamentous fungi of the genus Aspergillus. Among its many species, the genus contains an excellent model organism (A. nidulans, or its teleomorph Emericella nidulans), an important pathogen of the immunocompromised (A. fumigatus), an agriculturally important toxin producer (A. flavus), and two species used in industrial processes (A. niger and A. oryzae). AspGD contains information about genes and proteins of multiple Aspergillus species; descriptions and classifications of their biological roles, molecular functions, and subcellular localizations; gene, protein, and chromosome sequence information; tools for analysis and comparison of sequences; and links to literature information; as well as a multispecies comparative genomics browser tool (Sybil) for exploration of orthology and synteny across multiple sequenced Aspergillus species.
LONI’s Image and Data Archive (IDA) is a secure data archiving system. The IDA uses a robust infrastructure to provide researchers with a flexible and simple interface for de-identifying, searching, retrieving, converting, and disseminating their biomedical data. With thousands of investigators across the globe and more than 21 million data downloads to data, the IDA guarantees reliability with a fault-tolerant network comprising multiple switches, routers, and Internet connections to prevent system failure.
The long-term vision of the NMDC is to support microbiome data exploration through a sustainable data discovery platform that promotes open science and shared-ownership across a broad and diverse community of researchers, funders, publishers, and societies. The NMDC is developing a distributed data infrastructure while engaging with the research community to enable multidisciplinary and FAIR microbiome data.
Ever growing search and retrieval site for a comprehensive set of Heliophysics data from NASA and other spacecraft and ground-based observatories.
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Welcome to the District of North Vancouver’s Open Data portal. Here you have access to many free datasets which you can use in your printed products or online services – completely free of charge. Our datasets are updated automatically and refreshed each week. Every dataset comes with its own metadata providing valuable information on the origin, history, accuracy and completeness of the dataset.
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The Maya Image Archive is intended to host research materials provided by various scholars, such as Karl Herbert Mayer, Berthold Riese, Stephan Merk and the members of the project among others. Comprising image collections with photographs, drawings, notes and manuscripts, the Maya Image Archive allows the user to browse through the results of several decades of research trips through the entire Maya region.
IMGT/mAb-DB provides a unique expertised resource on monoclonal antibodies (mAbs) with diagnostic or therapeutic indications, fusion proteins for immune applications (FPIA), composite proteins for clinical applications (CPCA) and relative proteins of the immune system (RPI) with clinical indications.
EuPathDB (formerly ApiDB) is an integrated database covering the eukaryotic pathogens in the genera Acanthamoeba, Annacaliia, Babesia, Crithidia, Cryptosporidium, Edhazardia, Eimeria, Encephalitozoon, Endotrypanum, Entamoeba, Enterocytozoon, Giardia, Gregarina, Hamiltosporidium, Leishmania, Nematocida, Neospora, Nosema, Plasmodium, Theileria, Toxoplasma, Trichomonas, Trypanosoma and Vavraia, Vittaforma). While each of these groups is supported by a taxon-specific database built upon the same infrastructure, the EuPathDB portal offers an entry point to all of these resources, and the opportunity to leverage orthology for searches across genera.
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A genome database for the genus Piroplasma. PiroplasmaDB is a member of pathogen-databases that are housed under the NIAID-funded EuPathDB Bioinformatics Resource Center (BRC) umbrella.
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The CEBS database houses data of interest to environmental health scientists. CEBS is a public resource, and has received depositions of data from academic, industrial and governmental laboratories. CEBS is designed to display data in the context of biology and study design, and to permit data integration across studies for novel meta analysis.
Patients-derived tumor xenograft (PDX) mouse models are an important oncology research platform to study tumor evolution, drug response and personalised medicine approaches. We have expanded to organoids and cell lines and are now called CancerModels.Org
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This purpose of this repository is to share Ontario's government data sets online to increase transparency and accountability. We're adding to the hundreds of records we’ve released so far to create an inventory of known government data. Data will either be open, restricted, under review or in the process of being made open, depending on the sensitivity of the information.
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times.
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The Marine Data Archive (MDA) is an online repository specifically developed to independently archive data files in a fully documented manner. The MDA can serve individuals, consortia, working groups and institutes to manage data files and file versions for a specific context (project, report, analysis, monitoring campaign), as a personal or institutional archive or back-up system and as an open repository for data publication.
The Materials Data Facility (MDF) is set of data services built specifically to support materials science researchers. MDF consists of two synergistic services, data publication and data discovery (in development). The production-ready data publication service offers a scalable repository where materials scientists can publish, preserve, and share research data. The repository provides a focal point for the materials community, enabling publication and discovery of materials data of all sizes.
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The GIGA (German Institute of Global and Area Studies) researchers generate a large number of qualitative and quantitative research data. On this page you will find descriptions of this research data ("metadata") as well as information about the available access options. To facilitate its reuse, and to enhance research transparency, a large part of the GIGA research data is published in datorium, a repository hosted by the GESIS Leibniz Institute for the Social Sciences: https://www.re3data.org/repository/r3d100011062 Our objective is to offer free access to as much of our data as possible, to guarantee the possibility of its citation, and to secure its safe storage. Metadata of research data that cannot be published open access due to its sensitivity is also shown on this page.
!!! >>> merged with https://www.re3data.org/repository/r3d100012653 <<< !!! RDoCdb is an informatics platform for the sharing of human subjects data generated by investigators as part of the NIMH's Research Domain Criteria initiative, and to support this initiative's aims. It also accepts and shares appropriate data related to mental health from other sources.
CottonGen is a new cotton community genomics, genetics and breeding database being developed to enable basic, translational and applied research in cotton. It is being built using the open-source Tripal database infrastructure. CottonGen consolidates and expands the data from CottonDB and the Cotton Marker Database, providing enhanced tools for easy querying, visualizing and downloading research data.
The Deep Carbon Observatory (DCO) is a global community of multi-disciplinary scientists unlocking the inner secrets of Earth through investigations into life, energy, and the fundamentally unique chemistry of carbon. Deep Carbon Observatory Digital Object Registry (“DCO-VIVO”) is a centrally-managed digital object identification, object registration and metadata management service for the DCO. Digital object registration includes DCO-ID generation based on the global Handle System infrastructure and metadata collection using VIVO. Users will be able to deposit their data into the DCO Data Repository and have that data discoverable and accessible by others.