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http://c-path.org/programs/pkd/

Consortium to develop evidence supporting the use of imaging Total Kidney Volume (TKV) as a prognostic biomarker that predicts the progression of Autosomal Dominant Polycystic Kidney Disease (ADPKD) to select patients likely to respond to therapy into clinical trials. It aims to replace the currently used measurement of glomerular filtration rate (GFR). Scientists will use the data collected to develop a disease progression model that will evaluate the relationship between TKV and the known complications of ADPKD, including rate of loss of kidney function, hypertension, gross hematuria, kidney stones, urinary tract infections, development of end-stage renal disease, and mortality. These analyses will be used to support the regulatory qualification of TKV as an accepted measure for assessing the progression of ADPKD in clinical trials in which new therapies are tested. PKDOC has the following goals: # Develop standard clinical data elements and definitions that are specific to ADPKD # Create a database of aggregated data from existing multiple, longitudinal, and well-characterized research registries maintained over decades by the leading institutions in ADPKD clinical investigation # Advance and harmonize the missions of regulatory agencies by creating tools that help with the evaluation of new pharmaceutical compounds # Develop a quantitative disease progression model to examine the linkage between TKV and disease outcomes

Proper citation: Polycystic Kidney Disease Outcomes Consortium (RRID:SCR_003674) Copy   

•   Source: SciCrunch Registry

http://www.lamhdi.org/

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Proper citation: LAMHDI: The Initiative to Link Animal Models to Human DIsease (RRID:SCR_008643) Copy   

•   Source: SciCrunch Registry

http://consortiapedia.fastercures.org/

Project that aims to provide structure and clarity to the research-by-consortium model, presenting both the consortia framework and partnership components in an effort to guide and inform emerging and existing collaborative efforts. Their goal is to ensure that research-by-consortium efforts are at their highest performance and achieving the best possible outcomes. A consortium will thrive if its leadership and governance structure is able to define a mission that's shared by all stakeholders and articulate the desired outcomes of the effort early in the process. For collaborative efforts to move forward, consortia must recognize and leverage the unique strengths and resources that each partner can contribute and bring to fruition. They have found that managing expectations and establishing transparency measures are essential to building trust among all participating stakeholders. Transparency curbs potential conflicts of interest and creates a culture that allows for open sharing of data and a responsible approach to intellectual property negotiations. They found that for collaborations in the medical research and development ecosystem to be effective and sustainable, they must be driven by the ultimate goal of delivering a medical solution that could improve or save lives. Through the project, they are creating several tools for the biomedical research community: * Framework report: This series of reports analyzes a diversity of existing consortia to understand the operational management and framework used to initiate and manage these complex collaborations. * Consortium database * Landscape analysis trends and intended output * Educational webinars and spotlights

Proper citation: Consortia-pedia (RRID:SCR_003705) Copy   

•   Source: SciCrunch Registry

http://www.wormbase.org

Central data repository for nematode biology including complete genomic sequence, gene predictions and orthology assignments from range of related nematodes.Data concerning genetics, genomics and biology of C. elegans and related nematodes. Derived from initial ACeDB database of C. elegans genetic and sequence information, WormBase includes genomic, anatomical and functional information of C. elegans, other Caenorhabditis species and other nematodes. Maintains public FTP site where researchers can find many commonly requested files and datasets, WormBase software and prepackaged databases.

Proper citation: WormBase (RRID:SCR_003098) Copy   

•   Source: SciCrunch Registry

http://www.neuroinf.de

Portal used to coordinate activities of the German portion of the INCF group. The main focus is the development and free distribution of software tools for handling and analyzing neurophysiological data.

Proper citation: German Neuroinformatics Node (G-Node) (RRID:SCR_007279) Copy   

•   Source: SciCrunch Registry

http://flowrepository.org

A database of flow cytometry experiments where users can query and download data collected and annotated according to the MIFlowCyt data standard.

Proper citation: FLOWRepository (RRID:SCR_013779) Copy   

•   Source: SciCrunch Registry

http://www.metabolomicsworkbench.org

Repository for metabolomics data and metadata which provides analysis tools and access to various resources. NIH grantees may upload data and general users can search metabolomics database. Provides protocols for sample preparation and analysis, information about NIH Metabolomics Program, data sharing guidelines, funding opportunities, services offered by its Regional Comprehensive Metabolomics Resource Cores (RCMRC)s, and training workshops.

Proper citation: Metabolomics Workbench (RRID:SCR_013794) Copy   

•   Source: SciCrunch Registry

http://www.type2diabetesgenetics.org/

Portal and database of DNA sequence, functional and epigenomic information, and clinical data from studies on type 2 diabetes and analytic tools to analyze these data. .Provides data and tools to promote understanding and treatment of type 2 diabetes and its complications. Used for identifying genetic biomarkers correlated to Type 2 diabetes and development of novel drugs for this disease.

Proper citation: Accelerating Medicines Partnership Type 2 Diabetes Knowledge Portal (AMP-T2D) (RRID:SCR_003743) Copy   

•   Source: SciCrunch Registry

https://neuinfo.org/about/sources/nlx_143622-1

International registry of biomaterial supply resources both for transplantation and research. Contributions to this resource are welcome. The database is searchable through NIF and is updated regularly.

Proper citation: One Mind Biospecimen Bank Listing (RRID:SCR_004193) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/chembl/

Collection of bioactive drug-like small molecules that contains 2D structures, calculated properties and abstracted bioactivities. Used for drug discovery and chemical biology research. Clinical progress of new compounds is continuously integrated into the database.

Proper citation: ChEMBL (RRID:SCR_014042) Copy   

•   Source: SciCrunch Registry

https://bmrb.io

Public depository that collects, annotates, archives, and disseminates important spectral and quantitative data derived from nuclear magnetic resonance spectroscopic investigations of biological macromolecules and metabolites. Provides reference information and maintains a collection of NMR pulse sequences and computer software for biomolecular NMR.

Proper citation: Biological Magnetic Resonance Data Bank (BMRB) (RRID:SCR_002296) Copy   

•   Source: SciCrunch Registry

http://www.geneontology.org/

Computable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.

Proper citation: Gene Ontology (RRID:SCR_002811) Copy   

•   Source: SciCrunch Registry

http://www.mmrrc.org/

National public repository system for mutant mice. Archives and distributes scientifically valuable spontaneous and induced mutant mouse strains and ES cell lines for use by biomedical research community. Includes breeding/distribution facilities and information coordinating center. Mice strains are cryopreserved, unless live colony must be established. Live mice are supplied from production colony, from colony recovered from cryopreservation, or via micro-injection of cell line into host blastocysts. MMRRC member facilities also develop technologies to improve handling of mutant mice, including advances in assisted reproductive techniques, cryobiology, genetic analysis, phenotyping and infectious disease diagnostics.

Proper citation: Mutant Mouse Resource and Research Center (RRID:SCR_002953) Copy   

•   Source: SciCrunch Registry

http://openneuro.org

Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.

Proper citation: OpenNeuro (RRID:SCR_005031) Copy   

•   Source: SciCrunch Registry

http://www.kegg.jp/

Integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

Proper citation: KEGG (RRID:SCR_012773) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/metabolights/

A cross-species, cross-technique database for metabolomics experiments, data, and derived information. It includes metabolite structures and their reference spectra, their biological roles, locations and concentrations, and experimental data from metabolic experiments.

Proper citation: MetaboLights (RRID:SCR_014663) Copy   

•   Source: SciCrunch Registry

http://cxidb.org

Database of Coherent X-ray Imaging (CXI) experiments. This data is widely accessible to researchers worldwide.

Proper citation: Coherent X-Ray Imaging Data Bank (CXIDB) (RRID:SCR_014722) Copy   

•   Source: SciCrunch Registry

http://www.proteomexchange.org

A data repository for proteomic data sets. The ProteomeExchange consortium, as a whole, aims to provide a coordinated submission of MS proteomics data to the main existing proteomics repositories, as well as to encourage optimal data dissemination. ProteomeXchange provides access to a number of public databases, and users can access and submit data sets to the consortium's PRIDE database and PASSEL/PeptideAtlas.

Proper citation: ProteomeXchange (RRID:SCR_004055) Copy   

•   Source: SciCrunch Registry