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https://github.com/gelles-brandeis/CoSMoS_Analysis

Software tools for analyzing co-localization single-molecule spectroscopy image data.

Proper citation: CoSMoS_Analysis (RRID:SCR_016896) Copy   

•   Source: SciCrunch Registry

http://www.bx.psu.edu/~giardine/vision/

International project to analyze mouse and human hematopoiesis, and provide a tractable system with clear clinical significance and importance to NIDDK. Collection of information from the flood of epigenomic data on hematopoietic cells as catalogs of validated regulatory modules, quantitative models for gene regulation, and a guide for translation of research insights from mouse to human.

Proper citation: ValIdated Systematic IntegratiON of epigenomic data (RRID:SCR_016921) Copy   

•   Source: SciCrunch Registry

https://icite.od.nih.gov/covid19/search/

NIH comprehensive, curated source for publications related to COVID-19. Includes articles from PubMed and pre-prints from arXiv, medRxiv, bioRxiv, and ChemRxiv. Updated daily. NIH Office of Portfolio Analysis has developed this resource to explore and analyze set of advances in COVID‑19 research as they accumulate in real time, and complements efforts by NLM to aggregate full text documents broadly related to COVID-19 and other outbreaks, and articles on COVID‑19 specific to PubMed database.

Proper citation: NIH COVID-19 Portfolio (RRID:SCR_018295) Copy   

•   Source: SciCrunch Registry

https://ncats.nih.gov/n3c/about

Portal for centralized national data to study COVID-19 and identify potential treatments.Centralized, secure analytics platform where patient privacy is protected. Enables collection and analysis of clinical, laboratory and diagnostic data from hospitals and health care plans. Data are provided after executing data transfer agreement with National Center for Advancing Translational Sciences. N3C is partnership among NCATS supported Clinical and Translational Science Awards Program hubs and National Center for Data to Health with overall stewardship by NCATS.

Proper citation: National COVID Cohort Collaborative (RRID:SCR_018757) Copy   

•   Source: SciCrunch Registry

https://bivi.co/visualisation/apinatomy

Software toolkit for visualizing multiscale anatomy schematics with phenotype related information. Used for visualisation of multiscale physiology circuitboards and to support clinical and scientific graphical user interfaces and dashboards for biomedical resource management and data analytics. Creates FAIR models of vascular and neural connectivity information for molecular, subcellular, cellular and tissue conduits across multiple scales. Provides interface between physiology knowledge and data relevant to physiology through intuitive graphical interface for managing semantic metadata and ontologies relevant to physiology. Brings together expertise in computer science, image processing, bioengineering and medicine to manage knowledge in physiology and pathology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ApiNATOMY (RRID:SCR_018998) Copy   

•   Source: SciCrunch Registry

https://portal.imaging.datacommons.cancer.gov

Portal for finding and analyzing cancer imaging data. Part of Cancer Research Data Commons to support cancer imaging research. Provides cloud based access to medical imaging data and library of analytical tools and workflows to share, analyze, and visualize multi modal imaging data from both clinical and basic cancer research studies.

Proper citation: NCI Imaging Data Commons (RRID:SCR_019127) Copy   

•   Source: SciCrunch Registry

https://datacommons.cancer.gov

Cloud based data science infrastructure that provides secure access to cancer research data from NCI programs and key external cancer programs. Serves as coordinated resource for public data sharing of NCI funded programs. Users can explore and use analytical and visualization tools for data analysis. Enables to search and aggregate data across repositories including Cancer Data Service, Clinical Trial Data Commons, Genomic Data Commons, Imaging Data Commons, Integrated Canine Data Commons, Proteomic Data Commons.

Proper citation: Cancer Research Data Commons (RRID:SCR_019128) Copy   

•   Source: SciCrunch Registry

https://www.fdilab.org

UCSD based bioinformatics lab composed of several projects in different biomedical disciplines. Established in 2008 as Neuroscience Information Framework and has since expanded to include broader field of biomedical research. Leader in developing and providing novel informatics infrastructure and tools for making data FAIR: Findable, Accessible, Interoperable and Reusable. FAIR Data informatics laboratory develops SciCrunch.org platform.

Proper citation: FAIR Data Informatics Laboratory (RRID:SCR_019235) Copy   

•   Source: SciCrunch Registry

http://drugcentral.org/

Database of drug information created and maintained by the Division of Translational Informatics at University of New Mexico. It provides information on active ingredients chemical entities, pharmaceutical products, drug mode of action, indications, and pharmacologic action.

Proper citation: DrugCentral (RRID:SCR_015663) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.hungry.com/clearcut/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023.Software as a stand-alone reference implementation for the Relaxed Neighbor Joining (RNJ) algorithm. Used in distance-based phylogenetic tree reconstruction method to process large sequence datasets., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Clearcut (RRID:SCR_016059) Copy   

•   Source: SciCrunch Registry

https://www.researchmatch.org/

Free and secure registry to bring together two groups of people who are looking for one another: (1) people who are trying to find research studies, and (2) researchers who are looking for people to participate in their studies. It has been developed by major academic institutions across the country who want to involve you in the mission of helping today''''s studies make a real difference for everyone''''s health in the future. Anyone can join ResearchMatch. Many studies are looking for healthy people of all ages, while some are looking for people with specific health conditions. ResearchMatch can help ''''match'''' you with any type of research study, ranging from surveys to clinical trials, always giving you the choice to decide what studies may interest you.

Proper citation: ResearchMatch (RRID:SCR_006387) Copy   

•   Source: SciCrunch Registry

http://www.jobs.nih.gov/

A listing of all current openings across the NIH. You may search for NIH Jobs, browse job descriptions, view all descriptions or use the quick links.

Proper citation: Jobs(at)NIH (RRID:SCR_006471) Copy   

•   Source: SciCrunch Registry

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

The Alzheimer's disease arm of the Accelerating Medicines Partnership (AMP) that will identify biomarkers that can predict clinical outcomes, conduct a large scale analysis of human AD patient brain tissue samples to validate biological targets, and to increase the understanding of molecular pathways involved in the disease to identify new potential therapeutic targets. The initiative will deposit all data in a repository that will be accessible for use by the biomedical community. The five year endeavor, beginning in 2014, will result in several sets of project outcomes. For the biomarkers project, tau imaging and EEG data will be released in year two, as baseline data becomes available. Completed data from the randomized, blinded trials will be added after the end of the five year studies. This will include both imaging data and data from blood and spinal fluid biomarker studies. For the network analysis project, each project will general several network models of late onset AD (LOAD) and identify key drivers of disease pathogensis by the end of year three. Years four and five will be dedicated to validating the novel targets and refining the network models of LOAD, including screening novel compounds or drugs already in use for other conditions that may have the ability to modulate the likely targets.

Proper citation: Accelerating Medicines Partnership - Alzheimers (RRID:SCR_003742) Copy   

•   Source: SciCrunch Registry

http://www.humanconnectomeproject.org/

A multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and USC; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization. The mapping of the complete structural and functional neural connections in vivo within and across individuals provides unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve conclusions about the living human brain. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway. Working with other HCP partners based at Washington University in St. Louis they will provide rich data, essential imaging protocols, and sophisticated connectivity analysis tools for the neuroscience community. This project is working to achieve the following: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials.

Proper citation: MGH-USC Human Connectome Project (RRID:SCR_003490) Copy   

•   Source: SciCrunch Registry

http://elementsofmorphology.nih.gov/

Data set of standardized terms used to describe human morphology including definitions of terms for the craniofacies in general, the major components of the face, and the hands and feet. This provides a uniform and internationally accepted terms to describe the human phenotype.

Proper citation: elements of morphology (RRID:SCR_003707) Copy   

•   Source: SciCrunch Registry

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://www.med.uc.edu/cardio_bio/

Our 24 faculty members approach the Research and Training in Cardiovascular Biology program from different subspecialties that include genetics, metabolism, development, cellular biology, systems biology, structural biology, biophysics, pharmacology, molecular biology, bioinformatics and biochemistry. While these subspecialties are clearly diverse, our faculty collaboratively leverages these areas toward the common goal of understanding cardiovascular disease from the gene all the way up to integrated organism function (systems biology). This diverse array of subspecialties provides a truly unique training environment that few centers can match. Another critical aspect of our training program is our steadfast commitment to a superior and nurturing training environment for our predoctoral trainees, postdoctoral trainees and clinician-scientists. Our training faculty are uniformly committed to monitoring our personnel for success in every way possible, to not only ensure their future placement in the academic ranks but to also build a stronger cardiovascular community around the country. The current National Institutes of Health-sponsored Research and Training in Cardiovascular Biology was instituted in 1978 by Arnold Schwartz, MD, PhD. This program has trained more than 120 scientists, who have pursued independent research careers and are holding prominent scientific positions worldwide. Our trainees have been distinguished as chairs of basic science departments, directors of centers or pharmaceutical companies, clinical directors and tenured faculty members in academic research. The overall emphasis continues to focus on integrative training and well-rounded knowledge of the fundamentals in biochemical, molecular, physiological and pharmacological underpinnings of cardiovascular disease. Dr. Schwartz has been a constant guiding force since the program was established. The University of Cincinnati, with Cincinnati Children's, has also developed a reputation as a leading center for the generation and analysis of genetically modified mouse models for interrogation of gene-disease relationships in the heart. This theme has been expanded to incorporate molecular genomics, proteomics and bioinformatics, as we continue to be among the leaders in the nation in molecular pathway analysis associated with single gene manipulations in the hearts of mice. Most faculty and trainees are using these approaches, but they are also well-versed in many other aspects of cardiovascular science, including excellence in basic physiology, pharmacology, biochemistry, structural biology and molecular biology. Thus, we are a rare conglomeration of faculty in which all aspects of cardiovascular biology are practiced, starting with cutting-edge molecular and genetic approaches, spanning more traditional cellular and whole animal approaches to build an integrated network of functional and disease-relevant data and extending to translational research incorporating cell therapy.

Proper citation: University of Cincinnati Research and Training in Cardiovascular Biology (RRID:SCR_003860) Copy   

•   Source: SciCrunch Registry

https://neurowiki.case.edu/

Curriculum materials for an Introduction to Neurobiology course for undergraduate and graduate students.

The course focuses on the analysis of neurons and neural circuits for behavior using the fundamental principles of neuroscience. From the online course syllabus, the 24 units that make up the course may be directly accessed. Each unit contains a reading, links to at least one simulation, and a problem set.

A list of all available simulations can be found here: https://neurowiki.case.edu/wiki/Simulations. * 25 simulations are written in JavaScript and will run in any browser.
Source code: https://github.com/CWRUChielLab/JSNeuroSim * Pre-compiled executables (Windows, Mac, Linux) are available for 1 desktop simulation, the Nernst Potential Simulator.
Source code: https://github.com/CWRUChielLab/Nernst Structure of the Course * Solving problems based on simulations of neuronal components, neurons, and simple circuits to understand how they work. * For advanced students, writing a neuroscience Wikipedia article, critical review, or grant, in stages.

Proper citation: NeuroWiki (RRID:SCR_004066) 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.mprc.umaryland.edu/mbc.asp

The Maryland Brain Collection (MBC), a resource of the Maryland Psychiatric Research Center (MPRC), is dedicated to promoting research with brain tissue obtained post-mortem from individuals with schizophrenia or related disorders. The primary goal of the MBC is to provide high-quality tissue, along with comprehensive clinical information, for hypothesis-driven research. The MBC is not conceptualized as a Brain Bank with open access but is maintained and funded through collaborative research. The Maryland Brain Collection is managed by researchers at the Maryland Psychiatric Research Center (MPRC). MPRC scientists are dedicated to understanding the causes and improving the treatment of mental illness. The Maryland Brain Collection is associated with the Office of the Chief Medical Examiner for the State of Maryland and other donor sources. MPRC scientists collaborate with scientists from around the world to understand how abnormalities in brain tissue relate to mental illness. The purpose of the MBC is to study the following: Schizophrenia, Bipolar Disorder, Depression, Suicide/Teen suicide, Substance Abuse.

Proper citation: Maryland Brain Collection (RRID:SCR_004384) Copy   

•   Source: SciCrunch Registry