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http://www.cng.fr/cagekid

The Cancer Genomics of the Kidney (CAGEKID) consortium brings together expertise in clinical care and epidemiology with genomic expertise. This consortium is an effort of the larger International Cancer Genome Consortium (ICGC), which has the goal of obtaining a comprehensive description of genomic, transcriptomic and epigenomic changes in 50 different tumour types and/or subtypes with the aim of elucidating the genomic changes present in the many forms of cancers that contribute to the burden of disease throughout the world.

Proper citation: Cancer Genomics of the Kidney (CAGEKID) (RRID:SCR_013670) Copy   

•   Source: SciCrunch Registry

http://www.careconnectivity.org/

The Care Connectivity Consortium (CCC) is working to address and overcome the most critical and key challenges to effective health information exchange (HIE), to make HIE technologies more versatile and valuable for patients, caregivers, and health care organizations. By developing improved technologies for comprehensive, secure, reliable and innovative HIE, the CCC aims to enable health care provider organizations nationwide to deliver better, timelier, and more informed patient care.

Proper citation: Care Connectivity Consortium (RRID:SCR_013671) Copy   

•   Source: SciCrunch Registry

https://gin.g-node.org/

Data services that provides 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.

Proper citation: G-Node Data Infrastructure Services (RRID:SCR_015864) Copy   

•   Source: SciCrunch Registry

https://www.globus.org/

Software-as-a-service for big data management offering fast, reliable, secure file transfer and sharing services to non-profit researchers. It combines state-of-the-art algorithms, data management tools, a graphical workflow environment, and an elastic computing infrastructure making it easy to manipulate, store, and share your data, no matter how big it gets.

Proper citation: Globus Genomics (RRID:SCR_011887) Copy   

•   Source: SciCrunch Registry

https://www.rd-alliance.org/groups/rda-covid19

Guidelines for data deposition in any common data hub or platform to facilitate data sharing in public health emergencies for scientific research.

Proper citation: Research Data Alliance Working Group (RRID:SCR_018283) Copy   

•   Source: SciCrunch Registry

https://github.com/SouthGreenPlatform/metaXplor

Web interfaced application to store, share, explore and manipulate metagenomic data. Interactive viral and microbial metagenomic data manager. Stores large volumes of user defined sample, sequence and assignment information while providing filtering web interface. Offers means to share datasets with collaborators, BLAST external sequences against them, and confirm assignments by running phylogenetic placement. Available as set of Docker containers that make it simple to deploy on various infrastructures.

Proper citation: metaXplor (RRID:SCR_019025) Copy   

•   Source: SciCrunch Registry

https://edal.ipk-gatersleben.de

Software tool as Java based framework to store, share and publish research data. Free software infrastructure to FAIRly maintain and publish in-house stored research data. Used for version tracking, metadata management, information retrieval, journal and founding agency proven registration of persistent identifiers (DOI), embedded HTTP(S) server for public data access, access as network file system, and scalable storage backend.

Proper citation: electronic Data Archive Library (RRID:SCR_019017) Copy   

•   Source: SciCrunch Registry

http://dataup.cdlib.org/

An open source tool to help researchers document, manage, and archive their tabular data that integrates with Microsoft Excel. The tool will parse .xlsx or .csv file to detect the presence of potential issues that do not comply with data management best practices, assign a unique identifier to a data set and deposit it within the DataONE repository system.

Proper citation: DataUp (RRID:SCR_006905) Copy   

•   Source: SciCrunch Registry

http://btris.nih.gov

Provides NIH clinical investigators with access to identifiable data for the subjects on their own active protocols, while providing all NIH investigators with access to de-identified data across all protocols. BTRIS provides users with advanced search, filtering, and aggregation methods to create data sets to support ongoing studies and stimulate ideas for new research. BTRIS is two distinct but interrelated applications, BTRIS Data Access and BTRIS Preferences. * BTRIS Data Access is the data repository where principal investigators or their designee create reports on their active protocols with identified subject data. Reports include the IRB Inclusion Enrollment Report, demographics, patient lists, laboratory and microbiology results, vital signs, medication orders and administration, diagnoses, and radiology reports (with links to images in the CC PACS system). * BTRIS Preferences is a Web based application that allows principal investigators or their designees to verify subject enrollment in their protocol(s). This ensures that reports created in BTRIS Data Access include all subjects. It also allows the principal investigator to designate an alternate investigator from the protocol to manage subject enrollment and create reports in BTRIS Data Access. BTRIS contains subject data from CRIS/MIS (the Clinical Center Medical Information Systems) and research data from NIAID (Crimson), NIAAA, and NCI. Data are available from 1976 to the present.

Proper citation: BTRIS: NIH Biomedical Translational Research Information System (RRID:SCR_006838) Copy   

•   Source: SciCrunch Registry

http://www.1000genomes.org/

International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes

Proper citation: 1000 Genomes: A Deep Catalog of Human Genetic Variation (RRID:SCR_006828) Copy   

•   Source: SciCrunch Registry

http://brainml.org/goto.do?page=.home

Set of standards and practices for using XML to facilitate information exchange between user application software and neuroscience data repositories. It allows for common shared library routines to handle most of the data processing, but also supports use of structures specialized to the needs of particular neuroscience communities. This site also serves as a repository for BrainML models. (A BrainML model is an XML Schema and optional vocabulary files describing a data model for electronic representation of neuroscience data, including data types, formats, and controlled vocabulary. ) It focuses on layered definitions built over a common core in order to support community-driven extension. One such extension is provided by the new NIH-supported neuroinformatics initiative of the Society for Neuroscience, which supports the development of expert-derived terminology sets for several areas of neuroscience. Under a cooperative agreement, these term lists will be made available Open Source on this site.
The repository function of this site includes the following features:
* BrainML models are published in searchable, browsable form.
* Registered users may submit new models or new versions of existing models to accommodate data of interest. * BrainML model schema and vocabulary files are made available at fixed URLs to allow software applications to reference them.
* Users can check models and/or instance documents for correct format before submitting them using an online validation service.
To complement the BrainML modeling language, a set of protocols have been developed for BrainML document exchange between repositories and clients, for indexing of repositories, and for data query.

Proper citation: BrainML (RRID:SCR_007087) Copy   

•   Source: SciCrunch Registry

http://mipav.cit.nih.gov

A Java-based application that enables quantitative analysis and visualization of medical images of numerous modalities such as DTI, PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders. MIPAV can be run on any Java-enabled platform such as Windows, UNIX, or Macintosh OS X. Functionality includes segmentation, inter- and intra multi-modality registration, surface rendering, volume rendering and reading and writing a large number of biomedical file formats including: DICOM 3.0, Analyze, NIFTI, SPM, MINC, Phillips, GE, Zeiss, Biorad, jpeg, png, tiff, mrc, fits, interfile, and many more.

Proper citation: MIPAV: Medical Image Processing and Visualization (RRID:SCR_007371) Copy   

•   Source: SciCrunch Registry

http://www.acttion.org/

A public-private partnership with the United States Food and Drug Administration (FDA) to identify, prioritize, sponsor, coordinate, and promote innovative activities - with a special interest in optimizing clinical trials - that will expedite the discovery and development of improved analgesic, anesthetic, and addiction medications and to more generally accelerate the development of treatments with improved efficacy and safety. This multi-year, multi-phase initiative is closely aligned with the FDA''''s Critical Path Initiative. The key objectives of ACTTION involve initiating and supporting strategic collaborations among a broad spectrum of stakeholders - including, but not limited to, academia, the FDA and other government agencies, industry, professional organizations, patient advocacy groups, foundations, and philanthropic organizations - with the goals of sharing data and innovative thinking about the development of novel therapeutics. These strategic collaborations involve a wide range of research projects and other activities, for example, scientific workshops, consensus meetings, and in-depth analyses of clinical trial data to determine the effects of research methods on study assay sensitivity and efficiency. ACTTION launched an initiative to develop training materials for clinical trial subjects (and staff) to be used with pain rating scales and other pain-related patient reported outcomes. Once materials have been developed and evaluated, proof-of-concept trials will be conducted to test the hypothesis that patient and staff training can improve the assay sensitivity of analgesic trials.

Proper citation: ACTTION (RRID:SCR_004003) Copy   

•   Source: SciCrunch Registry

http://www.wikigenes.org/e/art/e/258.html

Consortium to discover and map the genes that contribute to Alzheimer's disease and completely understand the role inheritance plays. To achieve this goal, they will work to identify all the genes that contribute to the risk of developing this disease. Investigators will have access to combined genetic data from a large number of Alzheimer's disease subjects and compare it to genetic data from an equally large number of elderly people who do not have Alzheimer's. In the initial phase of the work, more than 20,000 people with Alzheimer's and about 20,000 healthy elderly subjects will be compared. As the study progresses, 10,000 additional people with Alzheimer's and the same number of healthy elderly subjects will be added to the study. The subjects for these studies come from different Alzheimer research project locations across Europe, the UK, the US, and Canada. Data is available from their 2014 publication in Translational Psychiatry at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944635/ (http://www.ncbi.nlm.nih.gov/pubmed/24495969) Currently, there is no public access to the raw individual level genetic data because of privacy considerations. Researchers working with US cohorts deposit data in the database of genotypes and phenotypes (dbGaP), where it is available to all researchers who can show that they are able to guarantee the security of the data. After scanning the DNA of over 74,000 patients and controls from 15 countries, the IGAP consortium reported 11 new regions of the genome involved in late-onset Alzheimer's disease. IGAP published its results in Nature Genetics on October 27, http://www.ncbi.nlm.nih.gov/pubmed/24162737

Proper citation: International Genomics of Alzheimers Project (RRID:SCR_004029) Copy   

•   Source: SciCrunch Registry

http://www.calliope-network.eu/

Consortium to act as a forum for the diversity of stakeholders in the implementation of electronic health systems. It comprises a dedicated forum where decision makers, implementers, professionals, patients and other stakeholders can share visions, experiences and good practices on how to establish interoperable eHealth services. The process of consensus-building has reached six main achievements. # Offering support to European decision-makers with regard to EU level actions on eHealth. # Enlarging active representation of EU and European Free Trade Association (EFTA) activities. CALLIOPE established a roster of 22 national members in 2010 # Enlarging active involvement of European eHealth stakeholder organizations. # Developing and validating an open working method among stakeholders # Creating a working collaboration method between the appropriate eHealth large-scale pilot(s) and a wider range of Member States & and sta-keholders CALLepSO collaboration # Building an eHealth Interoperability roadmapping process. A full iterative cycle of the Roadmapping process has been now completed and the second issue of the EU eHealth Interoperability Roadmap has been delivered.

Proper citation: CALLIOPE Network (RRID:SCR_003845) Copy   

•   Source: SciCrunch Registry

http://www.brainnet-europe.org/

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on July 7, 2022. Consortium of 19 brain banks across Europe with an aim to harmonize neuropathological diagnostic criteria and develop gold standards for quality, safety and ethics standards for brain banking. BrainNet Europe also contributes to research on rare diseases, such as: Pick''s disease or other rare forms of dementia, as well as to questions after the events in the aging brain. Anyone can be a donor - irrespective of disease of the central nervous system or not, because for research purposes, one does not only need tissue samples from ill donors, but also from healthy ones for comparison.

Proper citation: BrainNet Europe (RRID:SCR_004461) Copy   

•   Source: SciCrunch Registry

http://sidr-dr.inist.fr/

A searchable public data repository for multi-assay studies in the field of high-throughput biology. Studies are annotated and curated using ontologies and controlled vocabularies according to the recommendations of the OBO foundry (Open Biological and Biomedical Ontologies) and the NCBO bioportal. Each data set is assigned a digital object identifier (DOI) provided by DataCite. In addition, the SIDR team has developed an ISA-XML (ISA-ML) version of the ISA-TAB format and the mapping to the FuGE object model (ISA-TABtoFuGE); these improvements should facilitate further tool integration, including web services. SIDR contributes to the proof-of-concept addressing the critical success factor for data integration. The SIDR team will continue to put in place the building blocks for giving access to interoperable data through a world-wide network of national hubs in the context of the community-based ISA infrastructure.

Proper citation: Standards-based Infrastructure with Distributed Resources (RRID:SCR_004532) Copy   

•   Source: SciCrunch Registry

http://www.science3point0.com/coaspedia/index.php/Welcome

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 08, 2013. Wiki serving as a demo for integrating wikis with scientific workflows, extending peer review and open access from formal scholarly publishing to the whole research cycle. Just imagine you could browse science as it happens. It is intended to be launched, as a prototype, in early 2011, and any help with it is welcome just create an account and mention here how you would like to get involved. In the initial phase, the platform will primarily be used to simply archive copies of scholarly articles that were formally published under an Open license. This WikiRepository shall provide a unified front-end to search, discover and sort all these Open Access papers, and to annotate, share, bookmark and discuss them. Later on, it is intended to be complemented by a formal publishing platform, WikiJournal. From the start on, emphasis will be placed on updatability of published research, on placing new research in the context of existing knowledge as well as ongoing and even planned research on the matter, and of the communities concerned.

Proper citation: Daniel Mietchens demo wiki: What would scholarly communication look like if it were invented today? (RRID:SCR_004805) Copy   

•   Source: SciCrunch Registry

http://datasharing.net

The U.S. National Institutes of Health Final NIH Statement on Sharing Research Data (NIH-OD-03-032) is now in effect. It specifies that all high-direct-cost NIH grant applications include plans for sharing of research data. To support and encourage collegial, enabling, and rewarding data sharing for neuroscience and beyond, the Laboratory of Neuroinformatics at Weill Medical College of Cornell University has established this site. A source of, and portal to, tools and proposals supporting the informed exchange of neuroscience data.

Proper citation: Datasharing.net (RRID:SCR_003312) Copy   

•   Source: SciCrunch Registry

http://www.humanvariomeproject.org/

Project facilitating the establishment and maintenance of standards systems and infrastructure for the worldwide collection and sharing of all genetic variations effecting human disease. The Human Variome Project produces two categories of recommendations: HVP Standards and HVP Guidelines. HVP Standards are those systems, procedures and technologies that the Human Variome Project Consortium has determined should be used by the community. These carry more weight than the less prescriptive HVP Guidelines, which cover those systems, procedures and technologies that the Human Variome Project Consortium has determined would be beneficial for the community to adopt. HVP Standards and Guidelines are central to supporting the work of the Human Variome Project Consortium and cover a wide range of fields and disciplines, from ethics to nomenclature, data transfer protocols to collection protocols from clinics. They can be thought of as both technical manuals and scientific documents, and while the impact of HVP Standards and Guidelines differ, they are both generated in a similar fashion. A document has been generated both as a guide for those collecting and distributing data and for those developing policy. Items should include those generated by HGVS/HVP collaborators as well as those generated by groups of individual Societies and Standards bodies in all relevant fields worldwide.

Proper citation: Human Variome Project (RRID:SCR_003492) Copy   

•   Source: SciCrunch Registry