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https://www.niaid.nih.gov/

National Institute of Allergy and Infectious Diseases is a leading research institution to understand, treat, and prevent infectious, immunologic, and allergic diseases.

Proper citation: NIAID (RRID:SCR_016598) Copy   

•   Source: SciCrunch Registry

https://niaid.github.io/spice/

Software application for data mining and visualization. Used for analyzes of large FLOWJO data sets from polychromatic flow cytometry and organizing the normalized data graphically.

Proper citation: SPICE (RRID:SCR_016603) Copy   

•   Source: SciCrunch Registry

https://sleepdata.org/datasets/cfs

Portal for family based study of sleep apnea. Contains data for quantifying the familial aggregation of sleep apnea. The polysomnographic (PSG) montage signals: EEG, ECG, EOG, EMG, SpO2, plethysmography, airflow (thermistor), nasal pressure, respiratory effort, position, snore.

Proper citation: Cleveland Family Study (RRID:SCR_016585) Copy   

•   Source: SciCrunch Registry

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.webarraydb.org/webarray/index.html

An open source integrated microarray database and analysis suite that features convenient uploading of data for storage in a MIAME (Minimal Information about a Microarray Experiment) compliant fashion. It allows data to be mined with a large variety of R-based tools, including data analysis across multiple platforms. Different methods for probe alignment, normalization and statistical analysis are included to account for systematic bias. Student's t-test, moderated t-tests, non-parametric tests and analysis of variance or covariance (ANOVA/ANCOVA) are among the choices of algorithms for differential analysis of data. Users also have the flexibility to define new factors and create new analysis models to fit complex experimental designs. All data can be queried or browsed through a web browser. The computations can be performed in parallel on symmetric multiprocessing (SMP) systems or Linux clusters.

Proper citation: WebArrayDB (RRID:SCR_005577) Copy   

•   Source: SciCrunch Registry

http://www.chori.org/

CHORI is the internationally renowned biomedical research institute of Children''s Hospital and Research Center at Oakland. With world-class scientists and research centers known both nationally and internationally in multiple fields, CHORI is 5th in the nation for National Institutes of Health pediatric research funding. Bridging basic science and clinical research in the treatment and prevention of human disease, CHORI is a leader in translational research, providing cures for blood diseases, developing new vaccines for infectious diseases, and discovering new treatment protocols for previously fatal or debilitating conditions. Striving to provide the highest standard of excellence and innovation, CHORI brings together a multidisciplinary collaborative of distinguished investigators in six different Centers of Research: The Center for Cancer Research, The Center for Genetics, The Center for Immunobiology & Vaccine Development, The Center for Nutrition & Metabolism, The Center for Prevention of Obesity, Cardiovascular Disease & Diabetes, and The Center for Sickle Cell Disease & Thalassemia. Within these major areas of focus, CHORI pushes the frontiers of science and of excellence beyond their borders. Among the leading biotech enterprises in the Bay Area, CHORI produced 25 patents in the last 5 years alone. In addition to providing world-class research, CHORI is also a teaching institute, offering unique educational opportunities to high school, college, doctoral and post-doctoral students.

Proper citation: Childrens Hospital Oakland Research Institute (RRID:SCR_005582) Copy   

•   Source: SciCrunch Registry

http://cs.nyu.edu/~bingsun/

NYU Bioinformatics group applies algorithmic, statistical, and mathematical techniques to solve problems of interest to biology, biotechnology and biomedicine. The group focuses on bioinformatics, computational biology and systems biology with many active projects in areas ranging from single molecules to entire populations: Analysis of Single-Molecule/Single-Cell Data, SPM-based Transcriptomic Profiling, Whole-Genome Haplotype Sequencing using SMASH (Single Molecule Approaches to Haplotype Sequencing), SUTTA (Scoring and Unfolding Trimmed Tree Assembler) assembly algorithm, Analysis of Spatio-Temporal Data, Model Checking and Model Building for Systems Biology, GOALIE-based Phenomenological Models and their Verification, Causality Analysis, Causal Models and their Verification, Analysis of EHR (Electronic Health Record Data) and Disease Models (e.g., Chronic Fatigue Syndrome, Congestive Heart Failure, Deep Vein Thrombosis, etc.), Models of Cancer, Applications to Pancreatic Cancer, Polymorphisms and Biomarkers, Strategies for Group Testing, Epidemiological and Bio-Warfare Models, Planning with Large Agent Networks against Catastrophes (PLAN C), Population Genomics, and Genome Wide Association Studies (GWAS). The group has received its funding from Air Force, Army, CCPR, DARPA, NIH, NIST, NSF, NYSTAR, etc. and various other governmental and commercial entities. Currently, the group is part of an NSF funded Expedition in Computing project (CMACS: Center for Modeling and Analysis of Complex Systems at CMU) and collaborates widely, both nationally and internationally. The group is highly multi-disciplinary, attracting researchers and students from mathematics, statistics, computer science, and biology who team up with physicians, physicists, and chemists as well as professionals in their own disciplines. This group is led by Prof. Bud Mishra, a professor of computer science and mathematics at NYU''s Courant Institute of Mathematical Sciences.

Proper citation: NYU Bioinformatics Group (RRID:SCR_005697) Copy   

•   Source: SciCrunch Registry

http://www.cmhd.ca/genetrap/

Generate gene trap insertions using mutagenic polyA trap vectors, followed by sequence tagging to develop a library of mutagenized ES cells freely available to the scientific community. This library is searchable by sequence or key word searches including gene name or symbol, chromosome location, or Gene Ontology (GO) terms. In addition,they offer a custom email alert service in which researchers are able to submit search criteria. Researchers will receive automated e-mail notification of matching gene trap clones as they are entered into the library and database. The resource features the use of complementary second and third generation polyA trap vectors developed by the Stanford lab and the laboratory of Professor Yasumasa Ishida of the Nara Institute of Science and Technology (NAIST) in Japan to mutagenize murine embryonic stem (ES) cells. CMHD gene trap clones are distributed by the Canadian Mouse Mutant Repository(CMMR). Information about ordering, services, and pricing can be found on their web site (http://www.cmmr.ca/services/index.html)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: Centre for Modeling Human Disease Gene Trap Resource (RRID:SCR_002785) Copy   

•   Source: SciCrunch Registry

https://simtk.org

A National NIH Center for Biomedical Computing that focuses on physics-based simulation of biological structures and provides open access to high quality simulation tools, accurate models and the people behind them. It serves as a repository for models that are published (as well as the associated code) to create a living archive of simulation scholarship. Simtk.org is organized into projects. A project represents a research endeavor, a software package or a collection of documents and publications. Includes sharing of image files, media, references to publications and manuscripts, as well as executables and applications for download and source code. Simulation tools are free to download and space is available for developers to manage, share and disseminate code.

Proper citation: Simtk.org (RRID:SCR_002680) Copy   

•   Source: SciCrunch Registry

https://simtk.org/home/foldvillin

An archive of hundreds of all-atom, explicit solvent molecular dynamics simulations that were performed on a set of nine unfolded conformations of a variant of the villin headpiece subdomain (HP-35 NleNle). It includes scripts for accessing the archive of villin trajectories as well as a VMD plug-in for viewing the trajectories. In addition, all starting structures used in the trajectories are also provided. The simulations were generated using a distributed computing method utilizing the symmetric multiprocessing paradigm for individual nodes of the Folding_at_home distributed computing network. The villin trajectories in the archive are divided into two projects: PROJ3036 and PROJ3037. PROJ3036 contains trajectories starting from nine non-folded configurations. PROJ3037 contains trajectories starting from the native (folded) state. Runs 0 through 8 (in PROJ3036) correspond to starting configurations 0 through 8 discussed in the paper in J. Mol. Biol. (2007) 374(3):806-816 (see the publications tab for a full reference), whereas RUN9 uses the same starting configuration as RUN8. Each run contains 100 trajectories (named clone 0-99), each with the same starting configuration but different random velocities. Trajectories vary in their length of time and are subdivided into frames, also known as a generation. Each frame contains around 400 configurational snapshots, or timepoints, of the trajectory, with the last configurational snapshot of frame i corresponding to the first configurational snapshot of generation i+1. The goal is to allow researchers to analyze and benefit from the many trajectories produced through the simulations.

Proper citation: Molecular Simulation Trajectories Archive of a Villin Variant (RRID:SCR_002704) Copy   

•   Source: SciCrunch Registry

http://hapmap.ncbi.nlm.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.

Proper citation: International HapMap Project (RRID:SCR_002846) Copy   

•   Source: SciCrunch Registry

http://www.compucell3d.org/

Open-source simulation environment for multi-cell, single-cell-based modeling of tissues, organs and organisms. It uses Cellular Potts Model to model cell behavior.

Proper citation: CompuCell3D (RRID:SCR_003052) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/gsea/

Software package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.

Proper citation: Gene Set Enrichment Analysis (RRID:SCR_003199) 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://ccdb.ucsd.edu/SAO

Ontology that describes structures from the dimensional range encompassing cellular and subcellular structure, supracellular domains, and macromolecules. It is built according to ontology development best practices (re-use of existing ontologies; formal definitions of terms; use of foundational ontologies). It describes the parts of neurons and glia and how these parts come together to define supracellular structures such as synapses and neuropil. Molecular specializations of each compartment and cell type are identified. The SAO was designed with the goal of providing a means to annotate cellular and subcellular data obtained from light and electron microscopy, including assigning macromolecules to their appropriate subcellular domains. The SAO thus provides a bridge between ontologies that describe molecular species and those concerned with more gross anatomical scales. Because it is intended to integrate into ontological efforts at these other scales, particular care was taken to construct the ontology in a way that supports such integration.

Proper citation: Subcellular Anatomy Ontology (RRID:SCR_003486) Copy   

•   Source: SciCrunch Registry

https://bioportal.bioontology.org/ontologies/NEMO/?p=summary

Ontology that describes classes of event-related brain potentials (ERP) and their properties, including spatial, temporal, and functional (cognitive / behavioral) attributes, and data-level attributes (acquisition and analysis parameters). Its aim is to support data sharing, logic-based queries and mapping/integration of patterns across data from different labs, experiment paradigms, and modalities (EEG/MEG).

Proper citation: NEMO Ontology (RRID:SCR_003386) Copy   

•   Source: SciCrunch Registry

https://dpcpsi.nih.gov/onr/nrcc

Coordinates nutritional sciences-related research and research training across the National Institutes of Health (NIH) and among Federal Agencies by providing mechanisms to communicate research, research training, policy, and education initiatives. The DNRC facilitates the exchange of information, coordinates workshops and seminars on critical issues, encourages national and international research collaborations, and serves as the NIH primary point of contact for the Department of Health and Human Services (DHHS) and other agencies, departments, and organizations in matters pertaining to nutritional sciences and physical activity. Through its dedicated efforts to promote scientific policy reviews, innovative research, interagency collaboration, and technical advancements, the DNRC strives to define the increasing roles of nutritional sciences and physical activity in health promotion and disease prevention and treatment.

Proper citation: NIH Division of Nutrition Research Coordination (RRID:SCR_001469) Copy   

•   Source: SciCrunch Registry

https://github.com/jefftc/changlab

Software system for performing bioinformatics analyses. System includes knowledge base where the capabilities of bioinformatics software is explicitly and formally encoded. Backwards-chaining rule-based expert system comprised of data model that can capture richness of biological data, and inference engine that reasons on knowledge base to produce workflows. Knowledge base is populated with rules to analyze microarray and next generation sequencing data.

Proper citation: BETSY (RRID:SCR_026239) Copy   

•   Source: SciCrunch Registry

http://nashua.case.edu/PathwaysWeb/Web/

An integrated software system for storing, managing, analyzing, and querying biological pathways at different levels of genetic, molecular, biochemical and organismal detail. The system contains a pathways database and associated tools to store, compare, query, and visualize metabolic pathways. The aim is to develop an integrated database and the associated tools to support computational analysis and visualization of biochemical pathways. At the computational level, PathCase allows users to visualize pathways in multiple abstraction levels, and to pose predetermined and ad hoc queries using a graphical user interface. Pathways are represented as graphs, and implemented as a relational database. The available functional annotations include the identity of the substrate(s), product(s), cofactors, activators, inhibitors, enzymes or other processing molecules, GO-categories of enzymes (as well as GO hierarchy visualizations two-way-linked to PathCase enzymes), EC number information and the associated links, and synonyms and encoding genes of gene products.

Proper citation: PathCase Pathways Database System (RRID:SCR_001835) Copy   

•   Source: SciCrunch Registry

http://videocast.nih.gov/

VideoCasting of special NIH events, seminars, conferences, meetings and lectures available to viewers on the NIH network and the Internet from the VideoCast web site. VideoCasting is the method of electronically streaming digitally encoded video and audio data from a server to a client. VideoCast is often referred to as streaming video. Streaming files are not downloaded, but rather are broadcast in a manner similar to television broadcasts. The videos are processed by a compression program into a streaming format and delivered in a staggered fashion to minimize impact upon the network and maximize the experience of the content for the viewer. When users request a streaming file they will receive an initial burst of data after a short delay (file latency). While content is being viewed, the streaming server machine and software continues to stream data in such a manner that the viewer experiences no break in the content. CIT can broadcast your seminar, conference or meeting live to a world-wide audience over the Internet as a real-time streaming video. The event can be recorded and made available for viewers to watch at their convenience as an on-demand video or a downloadable podcast. CIT can also broadcast NIH-only or HHS-only content.

Proper citation: NIH VideoCasting (RRID:SCR_001885) Copy   

•   Source: SciCrunch Registry