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Found 123 result(s)
Yoda publishes research data on behalf of researchers that are affiliated with Utrecht University, its research institutes and consortia where it acts as a coordinating body. Data packages are not limited to a particular field of research or license. Yoda publishes data packages via Datacite. To find data publications use: https://public.yoda.uu.nl/ , or the Datacite search engine: https://search.datacite.org/repositories/delft.uu
Sharing and preserving data are central to protecting the integrity of science. DataHub, a Research Computing endeavor, provides tools and services to meet scientific data challenges at Pacific Northwest National Laboratory (PNNL). DataHub helps researchers address the full data life cycle for their institutional projects and provides a path to creating findable, accessible, interoperable, and reusable (FAIR) data products. Although open science data is a crucial focus of DataHub’s core services, we are interested in working with evidence-based data throughout the PNNL research community.
Museum explorers travel to ocean depths, the peaks of the Andes, Africa's Rift Valley, the rainforests of South America, and the deserts of Central Asia. Perhaps even to a field site or research institution in your own state, territory or country. In each area, researchers collect specimens: fossils, minerals, and rocks, plants and animals, tools and artworks. Collections care professionals have meticulously preserved, labeled, cataloged, and organized items of this kind for more than 150 years. Taken together, the NMNH collections form the largest, most comprehensive natural history collection in the world. By comparing items gathered in different eras and regions, scientists learn how our world has varied across time and space.
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KU Leuven RDR (pronounced "RaDaR") is KU Leuven's Research Data Repository, built on Dataverse.org - open source repository software built by Harvard University. RDR gives KU Leuven researchers a one-stop platform to upload, describe, and share their research data, conveniently and with support from university staff.
EBRAINS offers one of the most comprehensive platforms for sharing brain research data ranging in type as well as spatial and temporal scale. We provide the guidance and tools needed to overcome the hurdles associated with sharing data. The EBRAINS data curation service ensures that your dataset will be shared with maximum impact, visibility, reusability, and longevity, https://ebrains.eu/services/data-knowledge/share-data. Find data - the user interface of the EBRAINS Knowledge Graph - allows you to easily find data of interest. EBRAINS hosts a wide range of data types and models from different species. All data are well described and can be accessed immediately for further analysis.
ARCHE (A Resource Centre for the HumanitiEs) is a service aimed at offering stable and persistent hosting as well as dissemination of digital research data and resources for the Austrian humanities community. ARCHE welcomes data from all humanities fields. ARCHE is the successor of the Language Resources Portal (LRP) and acts as Austria’s connection point to the European network of CLARIN Centres for language resources.
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bonndata is the institutional, FAIR-aligned and curated, cross-disciplinary research data repository for the publication of research data for all researchers at the University of Bonn. The repository is fully embedded into the University IT and Data Center and curated by the Research Data Service Center (https://www.forschungsdaten.uni-bonn.de/en). The software that bonndata is based on is the open source software Dataverse (https://dataverse.org)
OrthoMCL is a genome-scale algorithm for grouping orthologous protein sequences. It provides not only groups shared by two or more species/genomes, but also groups representing species-specific gene expansion families. So it serves as an important utility for automated eukaryotic genome annotation. OrthoMCL starts with reciprocal best hits within each genome as potential in-paralog/recent paralog pairs and reciprocal best hits across any two genomes as potential ortholog pairs. Related proteins are interlinked in a similarity graph. Then MCL (Markov Clustering algorithm,Van Dongen 2000; www.micans.org/mcl) is invoked to split mega-clusters. This process is analogous to the manual review in COG construction. MCL clustering is based on weights between each pair of proteins, so to correct for differences in evolutionary distance the weights are normalized before running MCL.
ToxoDB is a genome database for the genus Toxoplasma, a set of single-celled eukaryotic pathogens that cause human and animal diseases, including toxoplasmosis.
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The Marine Data Portal is a product of the “Underway”- Data initiative of the German Marine Research Alliance (Deutsche Allianz Meeresforschung - DAM) and is supported by the marine science centers AWI, GEOMAR and Hereon of the Helmholtz Association. This initiative aims to improve and standardize the systematic data collection and data evaluation for expeditions with German research vessels and marine observation. It supports scientists in their data management duties and fosters (data) science through FAIR and open access to marine research data. AWI, GEOMAR and Hereon develop this marine data hub (Marehub) to build a decentralized data infrastructure for processing, long-term archiving and dissemination of marine observation and model data and data products. The Marine Data Portal provides user-friendly, centralized access to marine research data, reports and publications from a wide range of data repositories and libraries in the context of German marine research and its international collaboration. The Marine Data Portal is developed by scientists for scientists in order to facilitate Findability and Access of marine research data for Reuse. It supports machine-readable and data driven science. Please note that the quality of the data may vary depending on the purpose for which it was originally collected.
The Duke Research Data Repository is a service of the Duke University Libraries that provides curation, access, and preservation of research data produced by the Duke community. Duke's RDR is a discipline agnostic institutional data repository that is intended to preserve and make public data related to the teaching and research mission of Duke University including data linked to a publication, research project, and/or class, as well as supplementary software code and documentation used to provide context for the data.
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The Repositori Ilmiah Nasional (RIN) is a means for storing, preserving, citing, analyzing and sharing research data. RIN acts as an online media in managing, storing and sharing research data. Researchers, data writers, publishers, data distributors, and affiliated institutions all receive academic credit and web visibility. Researchers, agencies, and funders have full control over research data.
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FDAT is a research data repository hosted by the University of Tübingen, designed to facilitate long-term archiving and publication of research data. Managed by the Information, Communication and Media Center (IKM), it primarily caters to the humanities and social sciences, while welcoming researchers from all scientific disciplines at the university. Committed to high-quality data management, FDAT emphasizes the importance of adhering to the FAIR Data Principles, promoting findability, accessibility, interoperability, and reusability of the research data it contains.
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This data repository allows users to publish animal tracking datasets that have been uploaded to Movebank (https://www.movebank.org/ ). Published datasets have gone through a submission and review process, and are typically associated with a written study published in an academic journal. All animal tracking data in this repository are available to the public.
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The NOMAD Repository and Archive stands for open access of scientific materials data. It enables the confirmatory analysis of materials data, their reuse, and repurposing. All data is available in their raw format as produced by the underlying code (Repository) and in a common, machine-processable, and well-defined data format (Archive).
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The institutional research data repository of the Universität Innsbruck is a service to enable storing, sharing and publishing of research data according to the FAIR principles for its employees and project partners.
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B2SHARE allows publishing research data and belonging metadata. It supports different research communities with specific metadata schemas. This server is provided for researchers of the Research Centre Juelich and related communities.
The Virtual Research Environment (VRE) is an open-source data management platform that enables medical researchers to store, process and share data in compliance with the European Union (EU) General Data Protection Regulation (GDPR). The VRE addresses the present lack of digital research data infrastructures fulfilling the need for (a) data protection for sensitive data, (b) capability to process complex data such as radiologic imaging, (c) flexibility for creating own processing workflows, (d) access to high performance computing. The platform promotes FAIR data principles and reduces barriers to biomedical research and innovation. The VRE offers a web portal with graphical and command-line interfaces, segregated data zones and organizational measures for lawful data onboarding, isolated computing environments where large teams can collaboratively process sensitive data privately, analytics workbench tools for processing, analyzing, and visualizing large datasets, automated ingestion of hospital data sources, project-specific data warehouses for structured storage and retrieval, graph databases to capture and query ontology-based metadata, provenance tracking, version control, and support for automated data extraction and indexing. The VRE is based on a modular and extendable state-of-the art cloud computing framework, a RESTful API, open developer meetings, hackathons, and comprehensive documentation for users, developers, and administrators. The VRE with its concerted technical and organizational measures can be adopted by other research communities and thus facilitates the development of a co-evolving interoperable platform ecosystem with an active research community.