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http://www.imi.europa.eu/

Initiative to improve health by speeding up the development of, and patient access to, innovative medicines, particularly in areas where there is an unmet medical or social need. It does this by initiating and managing consortia composed of the key players involved in healthcare research, including universities, the pharmaceutical and other industries, small and medium-sized enterprises (SMEs), patient organizations, and medicines regulators. IMI is a public-private partnership between the European Union and the European pharmaceutical industry, represented by the European Federation of Pharmaceutical Industries and Associations (EFPIA), with a timeframe separated into two phases (2008-2013, 2014-2024) that are each defined by unique research agendas. The first phase (2008 2013) had four pillars that defined the focus of its research agenda: * Predicting safety: evaluating the safety of a compound during the pre-clinical phase of the development process and the later phases in clinical development. * Predicting efficacy: improving the ability to predict how a drug will interact in humans and how it may produce a change in function. * Knowledge management: utilization of information and data for predicting safety and efficacy. * Education and training: closing existing training gaps in the drug development process. Some of the consortia managed under IMI focused on specific health issues while others focused on broader challenges in drug development. Additionally, IMI launched a number of education and training projects during its first phases. The goal of the second phase (IMI2, 2014-2024) is to develop next generation vaccines and drugs. The focus is on delivering the right prevention and treatment for the right patient at the right time. There is a strong focus on the development of new medicines with an emphasis on tools and methods that accelerate patient access to new medicines. IMI2's agenda can be defined by four axes of research: * target validation and biomarker research (efficacy and safety) * adoption of innovative clinical trial paradigms * innovative medicines * patient-tailored adherence programs As part of its distinct goals, IMI2 aims to deliver: * 30% better success rate in clinical trials of priority medicines identified by the WHO; clinical proof of concept in immunological, respiratory, neurological and neurodegenerative diseases in five years; * new and approved diagnostic markers for four of these diseases and at least two new medicines which could either be new antibiotics or new therapies for Alzheimer's disease.

Proper citation: Innovative Medicines Initiative (RRID:SCR_003754) Copy   

•   Source: SciCrunch Registry

http://www.big-project.eu/

A consortium working towards the definition and implementation of a clear strategy that tackles the necessary efforts of Big Data (in terms of research and innovation) while also providing supporting actions for the successful implementation of the Big Data economy. Building an industrial community around Big Data in Europe is the priority of this project, together with setting up the necessary collaboration and dissemination infrastructure to link technology suppliers, integrators and leading user organizations.

Proper citation: Big Data Public Private Forum (RRID:SCR_003837) Copy   

•   Source: SciCrunch Registry

http://www.mrn.org/

Non-profit organization focused on imaging technology that is dedicated to advancing the diagnosis and treatment of mental illness and brain injury. MRN consists of an interdisciplinary association of scientists located at universities, national laboratories and research centers around the world and is focused on imaging technology and its emergence as an integral element of neuroscience investigation. The MRNs initial plan called for the building of state-of-the-art magnetic resonance imaging (MRI) and magnetoencephalogram (MEG) neuroimaging systems to be applied to studies of mental illness. This important task was carried out by Minds initial collaborators: Massachusetts General Hospitals Martinos Biomedical Imaging Center (Harvard and MIT), the University of Minnesota, the University of New Mexico, and Los Alamos National Laboratory. Since both the Network and the mission have expanded beyond building neuroimaging tools, a comprehensive understanding of mental illness and more fundamental and systematic understanding of the brain, is possible. The MRN Mobile Imaging system is a custom designed one-of-a-kind facility.

Proper citation: Mind Research Network (RRID:SCR_002925) Copy   

•   Source: SciCrunch Registry

http://bionimbus.opensciencedatacloud.org/

A cloud-based infrastructure for managing, analyzing and sharing genomics datasets.

Proper citation: Bionimbus (RRID:SCR_001189) Copy   

•   Source: SciCrunch Registry

http://www.cns.atr.jp/dni/en/downloads/tools-for-brain-behavior-data-sharing/

This is MATLAB library to create Neuroshare data format. You can convert your own data into Neuroshare format file.

Proper citation: Matlab Neuroshare Library (RRID:SCR_006957) Copy   

•   Source: SciCrunch Registry

http://www.sharmuk.org/

A not for profit organization to accelerate research into aging by sharing resources: providing access to cost and time effective, aged murine tissue through a biorepository and database of live ageing colonies, as well as promoting the networking of researchers and dissemination of knowledge through its online collaborative environment; MiCEPACE. ShARM will provide valuable resources for the scientific community while helping to reduce the number of animals used in vital research into aging. The biobank of tissue and networking facility will enable scientists to access shared research material and data. By making use of collective resources, the number of individual animals required in research experiments can be minimized. The project also has the added value of helping to reduce the costs of research by connecting scientists, pooling resource and combining knowledge. ShARM works in partnership with MRC Harwell and the Centre for Intergrated Research into Musculoskeletal Ageing (CIMA).

Proper citation: ShARM (RRID:SCR_003120) Copy   

•   Source: SciCrunch Registry

http://www.cristudy.org/Chronic-Kidney-Disease/Chronic-Renal-Insufficiency-Cohort-Study/

A prospective observational national cohort study poised to make fundamental insights into the epidemiology, management, and outcomes of chronic kidney disease (CKD) in adults with intended long-term follow up. The major goals of the CRIC Study are to answer two important questions: * Why does kidney disease get worse in some people, but not in others? * Why do persons with kidney disease commonly experience heart disease and stroke? The CRIC Scientific and Data Coordinating Center at Penn receives data and provides ongoing support for a number of Ancillary Studies approved by the CRIC Cohort utilizing both data collected about CRIC study participants as well as their biological samples. The CRIC Study has enrolled over 3900 men and women with CKD from 13 recruitment sites throughout the country. Following this group of individuals over the past 10 years has contributed to the knowledge of kidney disease, its treatment, and preventing its complications. The NIDDKwill be extending the study for an additional 5 years, through 2018. An extensive set of study data is collected from CRIC Study participants. With varying frequency, data are collected in the domains of medical history, physical measures, psychometrics and behaviors, biomarkers, genomics/metabolomics, as well as renal, cardiovascular and other outcomes. Measurements include creatinine clearance and iothalamate measured glomerular filtration rate. Cardiovascular measures include blood pressure, ECG, ABI, ECHO, and EBCT. Clinical CV outcomes include MI, ischemic heart disease-related death, acute coronary syndromes, congestive heart failure, cerebrovascular disease, peripheral vascular disease, and composite outcomes. The CRIC Study has delivered in excess of 150,000 bio-samples and a dataset characterizing all 3939 CRIC participants at the time of study entry to the NIDDKnational repository. The CRIC Study will also be delivering a dataset to NCBI''''s Database for Genotypes and Phenotypes.

Proper citation: Chronic Renal Insufficiency Cohort Study (RRID:SCR_009016) Copy   

•   Source: SciCrunch Registry

http://dsarm.niapublications.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 18, 2014.

A networking site for investigators using animal models to study aging, developed to provide a venue for sharing information about research models for aging studies. If you have tissue or data from animal models relevant to aging research that you are willing to share with other investigators, D-SARM allows you to identify the model and provides a secure, blinded email contact for investigators who would like to contact you about acquiring tissue or related resources. Investigators looking for resources from a particular model enter search terms describing the model of interest and then use the provided link to send emails to the contacts (names blinded) listed in the search results to initiate dialog about tissue or resources available for sharing. The database is housed on a secure server and admission to the network is moderated by the NIA Project Officer and limited to investigators at academic, government and non-profit research institutions. The goal is to provide a secure environment for sharing information about models used in aging research, promoting the sharing of resources, facilitating new research on aging in model systems, and increasing the return on the investment in research models.

Proper citation: Database for Sharing Aging Research Models (RRID:SCR_008691) Copy   

•   Source: SciCrunch Registry

http://www.betacell.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented on August 1, 2015. Consortium that aims to facilitate interdisciplinary collaborations to advance the understanding of pancreatic islet development and function, with the goal of developing innovative therapies to correct the loss of beta cell mass in diabetes, including cell reprogramming, regeneration and replacement. They are responsible for collaboratively generating the necessary reagents, mouse strains, antibodies, assays, protocols, technologies and validation assays that are beyond the scope of any single research effort. The scientific goals for the BCBC are to: * Use cues from pancreatic development to directly differentiate pancreatic beta cells and islets from stem / progenitor cells for use in cell-replacement therapies for diabetes, * Determine how to stimulate beta cell regeneration in the adult pancreas as a basis for improving beta cell mass in diabetic patients, * Determine how to reprogram progenitor / adult cells into pancreatic beta-cells both in-vitro and in-vivo as a mean for developing cell-replacement therapies for diabetes, and * Investigate the progression of human type-1 diabetes using patient-derived cells and tissues transplanted in humanized mouse models. Many of the BCBC investigator-initiated projects involve reagent-generating activities that will benefit the larger scientific community. The combination of programs and activities should accelerate the pace of major new discoveries and progress within the field of beta cell biology.

Proper citation: Beta Cell Biology Consortium (RRID:SCR_005136) Copy   

•   Source: SciCrunch Registry

http://www.incf.org/resources/data-space/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented August 21, 2017.

Repository for sharing of neuroscience data, text, images, sounds, movies, models and simulations.

Proper citation: INCF Dataspace (RRID:SCR_000158) Copy   

•   Source: SciCrunch Registry

http://www.cdisc.org/

A global, open, multidisciplinary, non-profit organization that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata. Its mission is to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare. CDISC standards are vendor-neutral, platform-independent and freely available via the CDISC website.

Proper citation: Clinical Data Interchange Standards Consortium (RRID:SCR_000219) Copy   

•   Source: SciCrunch Registry

http://www.g-node.org/projects/odml

Mark up language for collecting and exchanging metadata in an automated, computer-based fashion, developed for neuroscience, specifically, neurophysiology experiments. In odML arbitrary metadata information is stored as extended key-value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability.

Proper citation: Open metadata mark up language (RRID:SCR_001376) Copy   

•   Source: SciCrunch Registry

http://datashare.ucsf.edu/

Platform to facilitate sharing, discovery, and secure access to UCSF biomedical data. It''s powered by the Dataverse Network platform, which supports a variety of data types, as well as attribution and licensing needs. Researchers may share datasets, discover data from other labs, and reuse data. Links to tools and information that help scientists properly organize, manage, and document their datasets are also provided.

Proper citation: UCSF DataShare (RRID:SCR_004340) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://stemcellcommons.org/

Open source environment for sharing, processing and analyzing stem cell data bringing together stem cell data sets with tools for curation, dissemination and analysis. Standardization of the analytical approaches will enable researchers to directly compare and integrate their results with experiments and disease models in the Commons. Key features of the Stem Cell Commons * Contains stem cell related experiments * Includes microarray and Next-Generation Sequencing (NGS) data from human, mouse, rat and zebrafish * Data from multiple cell types and disease models * Carefully curated experimental metadata using controlled vocabularies * Export in the Investigation-Study-Assay tabular format (ISA-Tab) that is used by over 30 organizations worldwide * A community oriented resource with public data sets and freely available code in public code repositories such as GitHub Currently in development * Development of Refinery, a novel analysis platform that links Commons data to the Galaxy analytical engine * ChIP-seq analysis pipeline (additional pipelines in development) * Integration of experimental metadata and data files with Galaxy to guide users to choose workflows, parameters, and data sources Stem Cell Commons is based on open source software and is available for download and development.

Proper citation: Stem Cell Commons (RRID:SCR_004415) Copy   

•   Source: SciCrunch Registry

http://www.cvrgrid.org/

Infrastructure for sharing cardiovascular data and data analysis tools. Human ExVivo heart data set and canine ExVivo normal and failing heart data sets are available. Canine hearts atlas and human InVivo atlases are available.

Proper citation: CardioVascular Research Grid (CVRG) (RRID:SCR_004472) Copy   

•   Source: SciCrunch Registry

https://www.med.unc.edu/pgc/

Consortium conducting meta-analyses of genome-wide genetic data for psychiatric disease. Focused on autism, attention-deficit hyperactivity disorder, bipolar disorder, major depressive disorder, schizophrenia, anorexia nervosa (AN), Tourette syndrome (TS), and obsessive-compulsive disorder (OCD). Used to investigate common single nucleotide polymorphisms (SNPs) genotyped on commercial arrays, structural variation (copy number variation) and uncommon or rare genetic variation. To participate you are asked to upload data from your study to central computer used by this consortium. Genetic Cluster Computer serves as data warehouse and analytical platform for this study . When data from your study have been incorporated, account will be provided on central server and access to all GWAS genotypes, phenotypes, and meta-analytic results relevant to deposited data and participation aims. NHGRI GWAS Catalog contains updated information about all GWAS in biomedicine, and is usually excellent starting point to find comprehensive list of studies. Files can be obtained by any PGC member for any disease to which they contributed data. These files can also be obtained by application to NIMH Genetics Repository. Individual-level genotype and phenotype data requires application, material transfer agreement, and informed consent consideration. Some datasets are also in controlled-access dbGaP and Wellcome Trust Case-Control Consortium repositories. PGC members can also receive back cleaned and imputed data and results for samples they contributed to PGC analyses.

Proper citation: Psychiatric Genomics Consortium (RRID:SCR_004495) Copy   

•   Source: SciCrunch Registry

http://brainliner.jp

Portal and tools for sharing and editing neurophysiological and behavioral data for brain-machine interface research. Users can search for existing data or login with their Google, Facebook, or Twitter account and upload new data. Their main focus is on supporting brain-machine interface research, so we encourage users to not just provide recordings of brain activity data, but also information about stimuli, etc., so that statistical relationships can be found between stimuli and/or subject behavior and brain activity. The Matlab tools are for writing, reading, and converting Neuroshare files, the common file format. A free, open source desktop tool for editing neurophysiological data for brain-machine interface research is also available: https://github.com/ATR-DNI/BrainLiner Since data formats aren''''t standardized between programs and researchers, data and analysis programs for data cannot be easily shared. Neuroshare was selected as the common file format. Neuroshare can contain several types of neurophysiological data because of its high flexibility, including analog time-series data and neuronal spike timing. Some applications have plug-ins or libraries available that can read Neuroshare format files, thus making Neuroshare somewhat readily usable. Neuroshare can contain several types of neurophysiological data, but there were no easy tools to convert data into the Neuroshare format, so they made and are providing a Neuroshare Converter Library and Simple Converter using the library. In future work they will make and provide many more useful tools for data sharing. Shared experiments include: EMG signal, Takemiya Exp, Reconstruct (Visual image reconstruction from human brain activity using a combination of multi-scale local image decoders), SPIKE data, Speech Imagery Dataset (Single-trial classification of vowel speech imagery using common spatial patterns), Functional Multineuron Calcium Imaging (fMCI), Rock-paper-scissors (The data was obtained from subject while he make finger-form of rock/paper/scissors). They also have a page at https://www.facebook.com/brainliner where you can contact us

Proper citation: BrainLiner (RRID:SCR_004951) Copy   

•   Source: SciCrunch Registry

http://www.methodbox.org/

Collaboration environment for sharing variable sets and statistical methods for analysis across social science survey data. MethodBox enables you to browse and download datasets, share methods and scripts, find fellow researchers with similar interests and share your knowledge. MethodBox source available on Google code. Finding the variables you need to support a particular research question can be time consuming. Wading through hundreds of pages of PDF documents, codebooks and metadata and then trying to find the exact column in a huge spreadsheet can be very frustrating. MethodBox gets you to the variables faster and lets you download only the data you need. You can also share your scripts with others to allow them to adopt best practice quicker than before.

Proper citation: MethodBox (RRID:SCR_004928) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/

Collection of resting state fMRI (R-fMRI) datasets from sites around world. It demonstrates open sharing of R-fMRI data and aims to emphasize aggregation and sharing of well-phenotyped datasets.

Proper citation: 1000 Functional Connectomes Project (RRID:SCR_005361) Copy   

•   Source: SciCrunch Registry