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http://www2.bsc.gwu.edu/bsc/oneproj.php?pkey=28

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Collect, store, and distribute genetic samples from cases and controls of type 1 diabetes and diabetic nephropathy for investigator-driven research into the genetic basis of diabetic nephropathy. As the risk of kidney complications in type 1 diabetes appears to have a considerable genetic component, this study assembled a large data resource for researchers attempting to identify causative genetic variants. The types of data collected allowed traditional case-control testing, a rapid and often powerful approach, and family-based analysis, a robust approach that is not influenced by population substructure.

Proper citation: Genetics of Kidneys in Diabetes (RRID:SCR_000133) Copy   

•   Source: SciCrunch Registry

http://www.asn-online.org/

Society leading the fight against kidney disease by educating health professionals, sharing new knowledge, advancing research, and advocating the highest quality care for patients. To accomplish its mission, ASN will: # Educate health professionals by increasing the value of ASN education. # Share new knowledge by improving the quality and expanding the reach of ASN''s communications, including maintaining the premier publications in kidney disease. # Promote the highest quality care by serving as the professional organization informing health policy in kidney disease. # Advance patient care and research in kidney disease by strengthening the pipeline of clinicians, researchers, and educators. To accomplish this goal, ASN will: ## Implement a strategy to increase interest in nephrology careers, which includes promoting diversity within the nephrology workforce. ## Help fund travel to ASN educational activities for physicians and researchers training in the field of kidney disease. ## Use the ASN Grants Program to support outstanding research and foster career development. # Continue to bolster the ASN infrastructure, which includes: ## Increasing diversityincluding age and experience, ethnicity, and genderat all levels of the society. ## Providing avenues for helping ASN members facilitate professional exchange. ## Expanding ASN membership. ## Increasing the ASN Council-Designated Endowment Fund (independent of operational budget) to support grants and other priorities

Proper citation: ASN - American Society of Nephrology (RRID:SCR_006709) Copy   

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https://usdrn.org/

Portal for research on urinary stones in adults and children in order to learn more about who forms kidney stones, treatments and prevention. Network comprises of experts including adult and pediatric urologists, adult and pediatric nephrologists, pediatricians, emergency department physicians, clinical trialists, nutritionists, behavioral scientists, and radiologists. Duke Clinical Research Institute is Scientific Data Research Center and with clinical sites including University of Pennsylvania Children Hospital of Philadelfia, University of Texas Southwestern Medical Center, University of Washington, Washington University in St. Louis, work together in planning, executing, and analyzing results from USDRN studies.

Proper citation: Urinary Stone Disease Research Network (RRID:SCR_019059) Copy   

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http://www.nih.gov/science/amp/autoimmune.htm

The autoimmune disease arm of the Accelerating Medicine Partnership (AMP), which aims to identify and validate the most promising biological targets of disease for new diagnostic and drug development, that is focused on rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). They seek to identify shared common flaws in inflammation, particularly those that are shared with a larger number of autoimmune disorders which can cause severe disability, greatly affect quality of life, and are associated with an increased risk of death. This project aims to reveal biomarkers and biological targets for drug development, matching existing drugs to patients with specific molecular profiles who are most likely to benefit. The research plan proposes a 5 year process. Year one will include startup activities such as validation of tissue acquisition processes and analytic technologies, and the development of operating procedures. The second year will focus on identification of disease specific pathways by comparing data from patients and healthy individuals. Years 3-5 will expand the scale to include comparisons of different subsets of patients with RA or lupus to allow molecularly based patient stratification for precise treatment. The final 12 months (2019) will also include preliminary target validation. The data will be made publicly available through an internet-based information portal.

Proper citation: Accelerating Medicines Partnership Autoimmune Diseases of Rheumatoid Arthritis and Lupus (RRID:SCR_003731) Copy   

•   Source: SciCrunch Registry

http://science.education.nih.gov/home2.nsf/feature/index.htm

The NIH Office of Science Education (OSE) coordinates science education activities at the NIH and develops and sponsors science education projects in house. These programs serve elementary, secondary, and college students and teachers and the public. Activities * Develop curriculum supplements and other educational materials related to medicine and research through collaborations with scientific experts at NIH * Maintain a website as a central source of information about NIH science education resources * Establish national model programs in public science education, such as the NIH Mini-Med School and Science in the Cinema * Promote science education reform as outlined in the National Science Education Standards and related guidelines The OSE was established in 1991 within the Office of Science Policy of the Office of the Director of the National Institutes of Health. The NIH is the world''s foremost biomedical research center and the U.S. federal government''s focal point for such research. It is one of the components of the Department of Health and Human Services (HHS). The Office of Science Education (OSE) plans, develops, and coordinates a comprehensive science education program to strengthen and enhance efforts of the NIH to attract young people to biomedical and behavioral science careers and to improve science literacy in both adults and children. The function of the Office is as follows: (1) develops, supports, and directs new program initiatives at all levels with special emphasis on targeting students in grades kindergarten to 16, their educators and parents, and the general public; (2) advises NIH leadership on science education issues; (3) examines and evaluates research and emerging trends in science education and literacy for policy making; (4) works closely with the NIH extramural, intramural, women''s health, laboratory animal research, and minority program offices on science education special issues and programs to ensure coordination of NIH efforts; (5) works with NIH institutes, centers, and divisions to enhance communication of science education activities; and (6) works cooperatively with other public- and private-sector organizations to develop and coordinate activities.

Proper citation: NIH Office of Science Education (RRID:SCR_005603) Copy   

•   Source: SciCrunch Registry

http://rarediseasesnetwork.epi.usf.edu/index.htm

The Rare Diseases Clinical Research Network (RDCRN) was created to facilitate collaboration among experts in many different types of rare diseases. Our goal is to contribute to the research and treatment of rare diseases by working together to identify biomarkers for disease risk, disease severity and activity, and clinical outcome, while also encouraging development of new approaches to diagnosis, prevention, and treatment. The Rare Diseases Clinical Research Network (RDCRN) is made up of 19 distinctive consortia that are working in concert to improve availability of rare disease information, treatment, clinical studies, and general awareness for both patients and the medical community. The RDCRN also aims to provide up-to-date information for patients and to assist in connecting patients with advocacy groups, expert doctors, and clinical research opportunities.

Proper citation: Rare Diseases Clinical Research Network (RRID:SCR_004372) Copy   

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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://clinicaltrials.gov/

Registry and results database of federally and privately supported clinical trials conducted in United States and around world. Provides information about purpose of trial, who may participate, locations, and phone numbers for more details. This information should be used in conjunction with advice from health care professionals.Offers information for locating federally and privately supported clinical trials for wide range of diseases and conditions. Research study in human volunteers to answer specific health questions. Interventional trials determine whether experimental treatments or new ways of using known therapies are safe and effective under controlled environments. Observational trials address health issues in large groups of people or populations in natural settings. ClinicalTrials.gov contains trials sponsored by National Institutes of Health, other federal agencies, and private industry. Studies listed in database are conducted in all 50 States and in 178 countries.

Proper citation: ClinicalTrials.gov (RRID:SCR_002309) Copy   

•   Source: SciCrunch Registry

https://www.immport.org/home

Data sharing repository of clinical trials, associated mechanistic studies, and other basic and applied immunology research programs. Platform to store, analyze, and exchange datasets for immune mediated diseases. Data supplied by NIAID/DAIT funded investigators and genomic, proteomic, and other data relevant to research of these programs extracted from public databases. Provides data analysis tools and immunology focused ontology to advance research in basic and clinical immunology.

Proper citation: The Immunology Database and Analysis Portal (ImmPort) (RRID:SCR_012804) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/pdbe/

The European resource for the collection, organization and dissemination of data on biological macromolecular structures. In collaboration with the other worldwide Protein Data Bank (wwPDB) partners - the Research Collaboratory for Structural Bioinformatics (RCSB) and BioMagResBank (BMRB) in the USA and the Protein Data Bank of Japan (PDBj) - they work to collate, maintain and provide access to the global repository of macromolecular structure data. The main objectives of the work at PDBe are: * to provide an integrated resource of high-quality macromolecular structures and related data and make it available to the biomedical community via intuitive user interfaces. * to maintain in-house expertise in all the major structure-determination techniques (X-ray, NMR and EM) in order to stay abreast of technical and methodological developments in these fields, and to work with the community on issues of mutual interest (such as data representation, harvesting, formats and standards, or validation of structural data). * to provide high-quality deposition and annotation facilities for structural data as one of the wwPDB deposition sites. Several sophisticated tools are also available for the structural analysis of macromolecules.

Proper citation: PDBe - Protein Data Bank in Europe (RRID:SCR_004312) 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

https://nih.figshare.com/

Repository to make datasets resulting from NIH funded research more accessible, citable, shareable, and discoverable. Data submitted will be reviewed to ensure there is no personally identifiable information in data and metadata prior to being published and in line with FAIR -Findable, Accessible, Interoperable, and Reusable principles. Data published on Figshare is assigned persistent, citable DOI (Digital Object Identifier) and is discoverable in Google, Google Scholar, Google Dataset Search, and more.Complited on July,2020. Researches can continue to share NIH funded data and other research product on figshare.com.

Proper citation: NIH Figshare Archive (RRID:SCR_017580) 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

http://www.rcsb.org/#Category-welcome

Collection of structural data of biological macromolecules. Database of information about 3D structures of large biological molecules, including proteins and nucleic acids. Users can perform queries on data and analyze and visualize results.

Proper citation: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) (RRID:SCR_012820) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/pdbe/emdb/

Repository for electron microscopy density maps of macromolecular complexes and subcellular structures at Protein Data Bank in Europe. Covers techniques, including single-particle analysis, electron tomography, and electron (2D) crystallography.

Proper citation: Electron Microscopy Data Bank at PDBe (MSD-EBI) (RRID:SCR_006506) Copy   

•   Source: SciCrunch Registry

http://www.physionet.org/

Collection of dissemination and exchange recorded biomedical signals and open-source software for analyzing them. Provides facilities for cooperative analysis of data and evaluation of proposed new algorithm. Providies free electronic access to PhysioBank data and PhysioToolkit software. Offers service and training via on-line tutorials to assist users at entry and more advanced levels. In cooperation with annual Computing in Cardiology conference, PhysioNet hosts series of challenges, in which researchers and students address unsolved problems of clinical or basic scientific interest using data and software provided by PhysioNet. All data included in PhysioBank, and all software included in PhysioToolkit, are carefully reviewed. Researchers are further invited to contribute data and software for review and possible inclusion in PhysioBank and PhysioToolkit. Please review guidelines before submitting material.

Proper citation: PhysioNet (RRID:SCR_007345) Copy   

•   Source: SciCrunch Registry

https://anvilproject.org/

Portal to facilitate integration and computing on and across large datasets generated by NHGRI programs, as well as initiatives funded by National Institutes of Health or by other agencies that support human genomics research. Resource for genomic scientific community, that leverages cloud based infrastructure for democratizing genomic data access, sharing and computing across large genomic, and genomic related data sets. Component of federated data ecosystem, and is expected to collaborate and integrate with other genomic data resources through adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles, as their specifications emerge from scientific community. Will provide collaborative environment, where datasets and analysis workflows can be shared within consortium and be prepared for public release to broad scientific community through AnVIL user interfaces.

Proper citation: Analysis, Visualization, and Informatics Lab-space (AnVIL) (RRID:SCR_017469) Copy   

•   Source: SciCrunch Registry

http://www.wwpdb.org/

Public global Protein Data Bank archive of macromolecular structural data overseen by organizations that act as deposition, data processing and distribution centers for PDB data. Members are: RCSB PDB (USA), PDBe (Europe) and PDBj (Japan), and BMRB (USA). This site provides information about services provided by individual member organizations and about projects undertaken by wwPDB. Data available via websites of its member organizations.

Proper citation: Worldwide Protein Data Bank (wwPDB) (RRID:SCR_006555) Copy   

•   Source: SciCrunch Registry