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https://triage.niaid.nih.gov

Platform to facilitate prediction, analysis, and hypothesis generation from genome wide perturbation studies like those designed with RNAi and CRISPR technologies.

Proper citation: TRIAGE (RRID:SCR_016609) Copy   

•   Source: SciCrunch Registry

https://commonfund.nih.gov/hubmap

Project to facilitate research on single cells within tissues by supporting data generation and technology development to explore the relationship between cellular organization and function, as well as variability in normal tissue organization at the level of individual cells. Framework for functional mapping the human body with cellular resolution.Designed to support diverse spatial and non-spatial omics and imaging data types and to integrate with a wide range of analysis workflows.

Proper citation: The Human BioMolecular Atlas Program (RRID:SCR_016922) Copy   

•   Source: SciCrunch Registry

https://github.com/dpeerlab/phenograph

Software tool as clustering method designed for high dimensional single cell data. Algorithmically defines phenotypes in high dimensional single cell data. Used for large scale analysis of single cell heterogeneity.

Proper citation: Phenograph (RRID:SCR_016919) 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://grantome.com/grant/NIH/U01-DK099919-04S1

Consortium to design and conduct pilot and feasibility studies of novel therapies to reduce morbidity and mortality for patients treated with maintenance hemodialysis. Data Coordinating Center (DCC) for consortium provides scientific expertise and operational support for pilot studies that will be conducted at HDPSC Participating Clinical Centers. Data Coordinating Center for Hemodialysis Pilot Studies Consortium.

Proper citation: Hemodialysis Pilot Studies Consortium (RRID:SCR_017468) Copy   

•   Source: SciCrunch Registry

https://bioinfo3d.cs.tau.ac.il/PatchDock/

Web server for molecular docking. Performs structure prediction of protein–protein and protein–small molecule complexes. Molecular docking algorithm based on shape complementarity principles.

Proper citation: PatchDock (RRID:SCR_017589) Copy   

•   Source: SciCrunch Registry

http://www.immunexpresso.org

Software tool as text-mining engine that structures and standardizes knowledge of immune intercellular communication. Knowledgebase contains interactions and separate mentions of cells or cytokines in context of thousands of diseases. Intercellular interactions were text-mined from all available PubMed abstracts across disease conditions.

Proper citation: immuneXpresso (RRID:SCR_017578) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/BepiPred/index.php

Sequential B-Cell Epitope Predictor. Web server predicts B-cell epitopes from protein sequence. Sequence-based B-cell epitope prediction using conformational epitopes. Sequences of protein of interest should be in fasta format. BepiPred 2.0 is available as stand alone software package, with same functionality as web service., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BepiPred-2.0 (RRID:SCR_018499) Copy   

•   Source: SciCrunch Registry

https://sedfitsedphat.nibib.nih.gov/software/default.aspx

Software tool for analytical ultracentrifugation developed by Dynamics of Macromolecular Assembly group of Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, NIH. Used for biophysical analysis of macromolecular assembly.

Proper citation: SEDFIT (RRID:SCR_018365) Copy   

•   Source: SciCrunch Registry

https://nemar.org

Portal for human electrophysiological data, supports, sharing and in depth analysis of identified human neuroelectromagnetic brain data including scalp EEG, its magnetic counterpart, MEG, and, intracranial iEEG and ECoG. Open access EEG and MEG data archives, analysis, and visualization. Neuroelectromagnetic data, tools, and compute resource.

Proper citation: NEMAR (RRID:SCR_019100) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://www.synapse.org/#!Synapse:syn22345748/wiki/605339

Reference dataset of multiplexed immunofluorescence microscopy images collected at HMS Laboratory of Systems Pharmacology. Includes set of images of different types for development and benchmarking of computational methods for image processing. As of 4/2/2021, EMIT comprises tissue microarray containing cores from 34 cancer, non-neoplastic diseases, and normal tissue collected from clinical discards under IRB supervised protocol. TMA was imaged using cyclic immunofluorescence method. Additional extensions of EMIT are currently in the planning stages. Long term goal is to compose ImageNet like resource for highly multiplexed images of tissues and tumors by consolidating high quality curated datasets.

Proper citation: Exemplar Microscopy Images of Tissues (RRID:SCR_021052) Copy   

•   Source: SciCrunch Registry

http://www.cbil.upenn.edu/RAD

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on March 24, 2014. A resource for gene expression studies, storing highly curated MIAME-compliant studies (i.e. experiments) employing a variety of technologies such as filter arrays, 2-channel microarrays, Affymetrix chips, SAGE, MPSS and RT-PCR. Data were available for querying and downloading based on the MGED ontology, publications or genes. Both public and private studies (the latter viewable only by users having appropriate logins and permissions) were available from this website. Specific details on protocols, biomaterials, study designs, etc., are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format. RAD is part of a more general Genomics Unified Schema (http://gusdb.org), which includes a richly annotated gene index (http://allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. NOTE: Due to changes in technology and funding, the RAD website is no longer available. RAD as a schema is still very much active and incorporated in the GUS (Genomics Unified Schema) database system used by CBIL (EuPathDB, Beta Cell Genomics) and others. The schema for RAD can be viewed along with the other GUS namespaces through our Schema Browser.

Proper citation: RNA Abundance Database (RRID:SCR_002771) Copy   

•   Source: SciCrunch Registry

http://www.credrivermice.org/

Project to provide Neuroscience Community with mouse strains that are suitable for tissue and cell-type-specific perturbation of gene function in nervous system. NIH Neuroscience Blueprint has established three centers in the USA for generation of genetically modified mice expressing CRE recombinases in nervous system on the C57BJ/6 genetic background. Mouse lines are generated at Cold Spring Harbor Lab, at Scripps Research Institute, and at Baylor College of Medicine.

Proper citation: CRE Driver Network (RRID:SCR_002720) Copy   

•   Source: SciCrunch Registry

http://insitu.fruitfly.org/cgi-bin/ex/insitu.pl

Database of embryonic expression patterns using a high throughput RNA in situ hybridization of the protein-coding genes identified in the Drosophila melanogaster genome with images and controlled vocabulary annotations. At the end of production pipeline gene expression patterns are documented by taking a large number of digital images of individual embryos. The quality and identity of the captured image data are verified by independently derived microarray time-course analysis of gene expression using Affymetrix GeneChip technology. Gene expression patterns are annotated with controlled vocabulary for developmental anatomy of Drosophila embryogenesis. Image, microarray and annotation data are stored in a modified version of Gene Ontology database and the entire dataset is available on the web in browsable and searchable form or MySQL dump can be downloaded. So far, they have examined expression of 7507 genes and documented them with 111184 digital photographs.

Proper citation: Patterns of Gene Expression in Drosophila Embryogenesis (RRID:SCR_002868) Copy   

•   Source: SciCrunch Registry

http://www.icpsr.umich.edu/

Data archive of more than 500,000 files of research in the social sciences, hosting 16 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields. ICPSR comprises a consortium of about 700 academic institutions and research organizations providing training in data access, curation, and methods of analysis for the social science research community. ICPSR welcomes and encourages deposits of digital data. ICPSR's educational activities include the Summer Program in Quantitative Methods of Social Research external link, a comprehensive curriculum of intensive courses in research design, statistics, data analysis, and social methodology. ICPSR also leads several initiatives that encourage use of data in teaching, particularly for undergraduate instruction. ICPSR-sponsored research focuses on the emerging challenges of digital curation and data science. ICPSR researchers also examine substantive issues related to our collections, with an emphasis on historical demography and the environment.

Proper citation: Inter-university Consortium for Political and Social Research (ICPSR) (RRID:SCR_003194) Copy   

•   Source: SciCrunch Registry

http://www.mybiosoftware.com/population-genetics/332

A tool for SNP Search and downloading with local management. It also offers flanking sequence downloading and automatic SNP filtering. It requires Windows and .NET Framework.

Proper citation: SNPHunter (RRID:SCR_002968) Copy   

•   Source: SciCrunch Registry

http://edoctoring.ncl.ac.uk/Public_site/

Online educational tool that brings challenging clinical practice to your computer, providing medical education that is engaging, challenging and interactive. While there is no substitute for real-life direct contact with patients or colleagues, research has shown that interactive online education can be a highly effective and enjoyable method of learning many components of clinical medicine, including ethics, clinical management, epidemiology and communication skills. eDoctoring offers 25 simulated clinical cases, 15 interactive tutorials and a virtual library containing numerous articles, fast facts and video clips. Their learning material is arranged in the following content areas: * Ethical, Legal and Social Implications of Genetic Testing * Palliative and End-of-Life Care * Prostate Cancer Screening and Shared Decision-Making

Proper citation: eDoctoring (RRID:SCR_003336) Copy   

•   Source: SciCrunch Registry

https://services.healthtech.dtu.dk/

Center for Biological Sequence Analysis of the Technical University of Denmark conducts basic research in the field of bioinformatics and systems biology and directs its research primarily towards topics related to the elucidation of the functional aspects of complex biological mechanisms. A large number of computational methods have been produced, which are offered to others via WWW servers. Several data sets are also available. The center also has experimental efforts in gene expression analysis using DNA chips and data generation in relation to the physical and structural properties of DNA. The on-line prediction services at CBS are available as interactive input forms. Most of the servers are also available as stand-alone software packages with the same functionality. In addition, for some servers, programmatic access is provided in the form of SOAP-based Web Services. The center also educates engineering students in biotechnology and systems biology and offers a wide range of courses in bioinformatics, systems biology, human health, microbiology and nutrigenomics.

Proper citation: DTU Center for Biological Sequence Analysis (RRID:SCR_003590) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/BIRN/Projects/Mouse/

Animal model data primarily focused on mice including high resolution MRI, light and electron microscopic data from normal and genetically modified mice. It also has atlases, and the Mouse BIRN Atlasing Toolkit (MBAT) which provides a 3D visual interface to spatially registered distributed brain data acquired across scales. The goal of the Mouse BIRN is to help scientists utilize model organism databases for analyzing experimental data. Mouse BIRN has ended. The next phase of this project is the Mouse Connectome Project (https://www.nitrc.org/projects/mcp/). The Mouse BIRN testbeds initially focused on mouse models of neurodegenerative diseases. Mouse BIRN testbed partners provide multi-modal, multi-scale reference image data of the mouse brain as well as genetic and genomic information linking genotype and brain phenotype. Researchers across six groups are pooling and analyzing multi-scale structural and functional data and integrating it with genomic and gene expression data acquired from the mouse brain. These correlated multi-scale analyses of data are providing a comprehensive basis upon which to interpret signals from the whole brain relative to the tissue and cellular alterations characteristic of the modeled disorder. BIRN's infrastructure is providing the collaborative tools to enable researchers with unique expertise and knowledge of the mouse an opportunity to work together on research relevant to pre-clinical mouse models of neurological disease. The Mouse BIRN also maintains a collaborative Web Wiki, which contains announcements, an FAQ, and much more.

Proper citation: Mouse Biomedical Informatics Research Network (RRID:SCR_003392) Copy   

•   Source: SciCrunch Registry