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Found 46 result(s)
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The German Neuroinformatics Node's data infrastructure (GIN) services provide a platform for comprehensive and reproducible management and sharing of neuroscience data. Building on well established versioning technology, GIN offers the power of a web based repository management service combined with a distributed file storage. The service addresses the range of research data workflows starting from data analysis on the local workstation to remote collaboration and data publication.
The Research Collection is ETH Zurich's publication platform. It unites the functions of a university bibliography, an open access repository and a research data repository within one platform. Researchers who are affiliated with ETH Zurich, the Swiss Federal Institute of Technology, may deposit research data from all domains. They can publish data as a standalone publication, publish it as supplementary material for an article, dissertation or another text, share it with colleagues or a research group, or deposit it for archiving purposes. Research-data-specific features include flexible access rights settings, DOI registration and a DOI preview workflow, content previews for zip- and tar-containers, as well as download statistics and altmetrics for published data. All data uploaded to the Research Collection are also transferred to the ETH Data Archive, ETH Zurich’s long-term archive.
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The Universitat de Barcelona Digital Repository is an institutional resource containing open-access digital versions of publications related to the teaching, research and institutional activities of the UB's teaching staff and other members of the university community, including research data.
Data deposit is supported for University of Ottawa faculty, students, and affiliated researchers. The repository is multidisciplinary and hosted on Canadian servers. It includes features such as permanent links (DOIs) which encourage citation of your dataset and help you set terms for access and reuse of your data. uOttawa Dataverse is currently optimal for small to medium datasets.
The ENCODE Encyclopedia organizes the most salient analysis products into annotations, and provides tools to search and visualize them. The Encyclopedia has two levels of annotations: Integrative-level annotations integrate multiple types of experimental data and ground level annotations. Ground-level annotations are derived directly from the experimental data, typically produced by uniform processing pipelines.
NCBI Datasets is a continually evolving platform designed to provide easy and intuitive access to NCBI’s sequence data and metadata. NCBI Datasets is part of the NIH Comparative Genomics Resource (CGR). CGR facilitates reliable comparative genomics analyses for all eukaryotic organisms through an NCBI Toolkit and community collaboration.
This Web resource provides data and information relevant to SARS coronavirus. It includes links to the most recent sequence data and publications, to other SARS related resources, and a pre-computed alignment of genome sequences from various isolates. The genome of SARS-CoV consists of a single, positive-strand RNA that is approximately 29,700 nucleotides long. The overall genome organization of SARS-CoV is similar to that of other coronaviruses. The reference genome includes 13 genes, which encode at least 14 proteins. Two large overlapping reading frames (ORFs) encompass 71% of the genome. The remainder has 12 potential ORFs, including genes for structural proteins S (spike), E (small envelope), M (membrane), and N (nucleocapsid). Other potential ORFs code for unique putative SARS-CoV-specific polypeptides that lack obvious sequence similarity to known proteins.
A place where researchers can publicly store and share unthresholded statistical maps, parcellations, and atlases produced by MRI and PET studies.
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Welcome to the National Yang Ming Chiao Tung University Dataverse research data knowledge management website, where you can learn how to obtain, upload, cite and explore research data in the National Yang Ming Chiao Tung University Dataverse.
The Harvard Dataverse Repository is a free data repository open to all researchers from any discipline, both inside and outside of the Harvard community, where you can share, archive, cite, access, and explore research data. Each individual Dataverse collection is a customizable collection of datasets (or a virtual repository) for organizing, managing, and showcasing datasets.
Open access to macromolecular X-ray diffraction and MicroED datasets. The repository complements the Worldwide Protein Data Bank. SBDG also hosts reference collection of biomedical datasets contributed by members of SBGrid, Harvard and pilot communities.
RIVMdata is a metadata catalog. This catalog is filled with the metadata of RIVM datasets. ISO 19115 and DCAT standards are used as the metadata standards. The catalog consists of an internal site, which is only accessible to RIVM employees, and an external site, in which the metadata is accessible to the general public.
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DataverseNO (https://dataverse.no) is a curated, FAIR-aligned national generic repository for open research data from all academic disciplines. DataverseNO commits to facilitate that published data remain accessible and (re)usable in a long-term perspective. The repository is owned and operated by UiT The Arctic University of Norway. DataverseNO accepts submissions from researchers primarily from Norwegian research institutions. Datasets in DataverseNO are grouped into institutional collections as well as special collections. The technical infrastructure of the repository is based on the open source application Dataverse (https://dataverse.org), which is developed by an international developer and user community led by Harvard University.
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Edmond is the institutional repository of the Max Planck Society for public research data. It enables Max Planck scientists to create citable scientific assets by describing, enriching, sharing, exposing, linking, publishing and archiving research data of all kinds. Further on, all objects within Edmond have a unique identifier and therefore can be clearly referenced in publications or reused in other contexts.
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A machine learning data repository with interactive visual analytic techniques. This project is the first to combine the notion of a data repository with real-time visual analytics for interactive data mining and exploratory analysis on the web. State-of-the-art statistical techniques are combined with real-time data visualization giving the ability for researchers to seamlessly find, explore, understand, and discover key insights in a large number of public donated data sets. This large comprehensive collection of data is useful for making significant research findings as well as benchmark data sets for a wide variety of applications and domains and includes relational, attributed, heterogeneous, streaming, spatial, and time series data as well as non-relational machine learning data. All data sets are easily downloaded into a standard consistent format. We also have built a multi-level interactive visual analytics engine that allows users to visualize and interactively explore the data in a free-flowing manner.
The KNB Data Repository is an international repository intended to facilitate ecological, environmental and earth science research in the broadest senses. For scientists, the KNB Data Repository is an efficient way to share, discover, access and interpret complex ecological, environmental, earth science, and sociological data and the software used to create and manage those data. Due to rich contextual information provided with data in the KNB, scientists are able to integrate and analyze data with less effort. The data originate from a highly-distributed set of field stations, laboratories, research sites, and individual researchers. The KNB supports rich, detailed metadata to promote data discovery as well as automated and manual integration of data into new projects. The KNB supports a rich set of modern repository services, including the ability to assign Digital Object Identifiers (DOIs) so data sets can be confidently referenced in any publication, the ability to track the versions of datasets as they evolve through time, and metadata to establish the provenance relationships between source and derived data.
Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.
The NIH 3D Print Exchange (the “Exchange”) is an open, comprehensive, and interactive website for searching, browsing, downloading, and sharing biomedical 3D print files, modeling tutorials, and educational material. "Biomedical" includes models of cells, bacteria, or viruses, molecules like proteins or DNA, and anatomical models of organs, tissue, and body parts. The NIH 3D Print Exchange provides models in formats that are readily compatible with 3D printers, and offers a unique set of tools to create and share 3D-printable models related to biomedical science.
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ISTA Research Explorer is an online digital repository of multi-disciplinary research datasets as well as publications produced at IST Austria, hosted by the Library. ISTA researchers who have produced research data associated with an existing or forthcoming publication, or which has potential use for other researches, are invited to upload their dataset for sharing and safekeeping. A persistent identifier and suggested citation will be provided.
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The Dutch Trial Register (LTR) is a register in which a part of the clinical studies in The Netherlands are registered. This currently includes all data from the former National Trial Register (NTR).