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Found 87 result(s)
Swiss Institute of Bioinformatics (SIB) coordinates research and education in bioinformatics throughout Switzerland and provides bioinformatics services to the national and international research community. ExPASy gives access to numerous repositories and databases of SIB. For example: array map, MetaNetX, SWISS-MODEL and World-2DPAGE, and many others see a list here http://www.expasy.org/resources
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The RAMEDIS system is a platform independent, web-based information system for rare metabolic diseases based on filed case reports. It was developed in close cooperation with clinical partners to allow them to collect information on rare metabolic diseases with extensive details, e.g. about occurring symptoms, laboratory findings, therapy and molecular data.
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Open Government Data Portal of Tamil Nadu is a platform (designed by the National Informatics Centre), for Open Data initiative of the Government of Tamil Nadu. The portal is intended to publish datasets collected by the Tamil Nadu Government for public uses in different perspective. It has been created under Software as A Service (SaaS) model of Open Government Data (OGD) and publishes dataset in open formats like CSV, XLS, ODS/OTS, XML, RDF, KML, GML, etc. This data portal has following modules, namely (a) Data Management System (DMS) for contributing data catalogs by various state government agencies for making those available on the front end website after a due approval process through a defined workflow; (b) Content Management System (CMS) for managing and updating various functionalities and content types; (c) Visitor Relationship Management (VRM) for collating and disseminating viewer feedback on various data catalogs; and (d) Communities module for community users to interact and share their views and common interests with others. It includes different types of datasets generated both in geospatial and non-spatial data classified as shareable data and non-shareable data. Geospatial data consists primarily of satellite data, maps, etc.; and non-spatial data derived from national accounts statistics, price index, census and surveys produced by a statistical mechanism. It follows the principle of data sharing and accessibility via Openness, Flexibility, Transparency, Quality, Security and Machine-readable.
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The Health Atlas is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research projects from the partner institutions.
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AURIN is a collaborative national network of leading researchers and data providers across the academic, government, and private sectors. We provide a one-stop online workbench with access to thousands of multidisciplinary datasets, from over 100 different data sources.
Brain Image Library (BIL) is an NIH-funded public resource serving the neuroscience community by providing a persistent centralized repository for brain microscopy data. Data scope of the BIL archive includes whole brain microscopy image datasets and their accompanying secondary data such as neuron morphologies, targeted microscope-enabled experiments including connectivity between cells and spatial transcriptomics, and other historical collections of value to the community. The BIL Analysis Ecosystem provides an integrated computational and visualization system to explore, visualize, and access BIL data without having to download it.
The IMEx consortium is an international collaboration between a group of major public interaction data providers who have agreed to share curation effort and develop and work to a single set of curation rules when capturing data from both directly deposited interaction data or from publications in peer-reviewed journals, capture full details of an interaction in a “deep” curation model, perform a complete curation of all protein-protein interactions experimentally demonstrated within a publication, make these interaction available in a single search interface on a common website, provide the data in standards compliant download formats, make all IMEx records freely accessible under the Creative Commons Attribution License
OHSU Digital Commons is a repository for the scholarly and creative work of Oregon Health & Science University. Developed by the OHSU Library, Digital Commons provides the university community with a platform for publishing and accessing content produced by students, faculty, and staff. OHSU Digital Commons documents the history and growth of the university, as well as current progress in education, research, and health care.
All ADNI data are shared without embargo through the LONI Image and Data Archive (IDA), a secure research data repository. Interested scientists may obtain access to ADNI imaging, clinical, genomic, and biomarker data for the purposes of scientific investigation, teaching, or planning clinical research studies. "The Alzheimer’s Disease Neuroimaging Initiative (ADNI) unites researchers with study data as they work to define the progression of Alzheimer’s disease (AD). ADNI researchers collect, validate and utilize data, including MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors of the disease. Study resources and data from the North American ADNI study are available through this website, including Alzheimer’s disease patients, mild cognitive impairment subjects, and elderly controls. "
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, hhttps://www.ebrains.eu/data/find-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.
The PeptideAtlas validates expressed proteins to provide eukaryotic genome data. Peptide Atlas provides data to advance biological discoveries in humans. The PeptideAtlas accepts proteomic data from high-throughput processes and encourages data submission.
AceView provides a curated, comprehensive and non-redundant sequence representation of all public mRNA sequences (mRNAs from GenBank or RefSeq, and single pass cDNA sequences from dbEST and Trace). These experimental cDNA sequences are first co-aligned on the genome then clustered into a minimal number of alternative transcript variants and grouped into genes. Using exhaustively and with high quality standards the available cDNA sequences evidences the beauty and complexity of mammals’ transcriptome, and the relative simplicity of the nematode and plant transcriptomes. Genes are classified according to their inferred coding potential; many presumably non-coding genes are discovered. Genes are named by Entrez Gene names when available, else by AceView gene names, stable from release to release. Alternative features (promoters, introns and exons, polyadenylation signals) and coding potential, including motifs, domains, and homologies are annotated in depth; tissues where expression has been observed are listed in order of representation; diseases, phenotypes, pathways, functions, localization or interactions are annotated by mining selected sources, in particular PubMed, GAD and Entrez Gene, and also by performing manual annotation, especially in the worm. In this way, both the anatomy and physiology of the experimentally cDNA supported human, mouse and nematode genes are thoroughly annotated.
>>>!!!<<<2019-02-19: The repository is no longer available>>>!!!<<< >>>!!!<<<Data is archived at ChemSpider https://www.chemspider.com/Search.aspx?dsn=UsefulChem and https://www.chemspider.com/Search.aspx?dsn=Usefulchem Group Bradley Lab >>>!!!<<< see more information at the Standards tab at 'Remarks'
<<<!!!<<< This repository is no longer available. >>>!!!>>> Migration of the data, tools, and services from IRD and ViPR to BV-BRC is complete! We are now in the sunsetting phase of the transition. Starting on October 31, 2022, launching the IRD or ViPR home pages will redirect you to the new BV-BRC home page. The current plan is to completely shut down IRD and ViPR by the end of this calendar year. Although it will still be possible to use those sites until shutdown, we strongly encourage you to start using BV-BRC now.
MycoCosm, the DOE JGI’s web-based fungal genomics resource, which integrates fungal genomics data and analytical tools for fungal biologists. It provides navigation through sequenced genomes, genome analysis in context of comparative genomics and genome-centric view. MycoCosm promotes user community participation in data submission, annotation and analysis.
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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The Genome Warehouse (GWH) is a public repository housing genome-scale data for a wide range of species and delivering a series of web services for genome data submission, storage, release and sharing.
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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 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.
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Morph·D·Base has been developed to serve scientific research and education. It provides a platform for storing the detailed documentation of all material, methods, procedures, and concepts applied, together with the specific parameters, values, techniques, and instruments used during morphological data production. In other words, it's purpose is to provide a publicly available resource for recording and documenting morphological metadata. Moreover, it is also a repository for different types of media files that can be uploaded in order to serve as support and empirical substantiation of the results of morphological investigations. Our long-term perspective with Morph·D·Base is to provide an instrument that will enable a highly formalized and standardized way of generating morphological descriptions using a morphological ontology that will be based on the web ontology language (OWL - http://www.w3.org/TR/owl-features/). This, however, represents a project that is still in development.
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.
NeuGRID is a secure data archiving and HPC processing system. The neuGRID platform uses a robust infrastructure to provide researchers with a simple interface for analysing, searching, retrieving and disseminating their biomedical data. With hundreds of investigators across the globe and more than 10 million of downloadable attributes, neuGRID aims to become a widespread resource for brain analyses. NeuGRID platform guarantees reliability with a fault-tolerant network to prevent system failure.
The US BRAIN Initiative archive for publishing and sharing neurophysiology data including electrophysiology, optophysiology, and behavioral time-series, and images from immunostaining experiments.
The ABCD Data Repository houses all data generated by the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD Study is supported by NIH partners (the National Institute on Drug Abuse, the National Institute on Alcohol Abuse and Alcoholism, the National Cancer Institute, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Mental Health, the National Institute on Minority Health and Health Disparities, the National Institute of Neurological Disorders and Stroke, the NIH Office of Behavioral and Social Sciences Research, and the NIH Office of Research on Women’s Health), as well as the Centers for Disease Control and Prevention – Division of Adolescent and School Health. This repository will store data generated by ABCD investigators, serve as a collaborative platform for harmonizing these data, and share those data with qualified researchers.
MIDRC aims to develop a high-quality repository for medical images related to COVID-19 and associated clinical data, and develop and foster medical image-based artificial intelligence (AI) for use in the detection, diagnosis, prognosis, and monitoring of COVID-19.