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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
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
Integrated genomic and functional genomic database for Entamoeba and Acanthamoeba parasites. Contains genomes of three Entamoeba species and microarray expression data for E. histolytica. Integrates whole genome sequence and annotation and includes experimental data and environmental isolate sequences provided by community researchers.
Proper citation: AmoebaDB (RRID:SCR_017592) Copy
Free, cloud-based platform for publishing, sharing, and processing standardized neurophysiology data, primarily using the Neurodata Without Borders (NWB) format. Supported by the BRAIN Initiative, it enables researchers to collaborate, reuse datasets, and adhere to FAIR data principles.
Proper citation: Distributed Archives for Neurophysiology Data Integration (RRID:SCR_017571) Copy
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
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
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
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
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
Workstation is built on Bravo automated liquid handling robot preconfigured for library prep and target enrichment using Next-Generation Sequencing protocols. Workstation modules add microplate handling. Intuitive Agilent VWorks software enables setup of preprogrammed protocols and allows users to create custom protocols., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Agilent: Bravo NGS (RRID:SCR_019473) Copy
https://metagenote.niaid.nih.gov/
Quick and intuitive way to annotate data from genomics studies including microbiome. Project to aid researchers in applying standardized metadata describing what, where, how, and when of samples collected in genomics study. Collection of METAdata of GEnomics studies on web based NOTEbook. Metadata are stored in centralized repository and validated according to guidelines from Genomics Standard Consortium, which are also supported by repositories and large microbiome initiatives such as NCBI, European Bioinformatics Institute (EBI), and Earth Microbiome Project. Upon request from researchers, data will also be submitted for publication via NCBI Sequence Read Archive (SRA) repository.
Proper citation: METAGENOTE (RRID:SCR_018494) Copy
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
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
http://cole-trapnell-lab.github.io/monocle-release/docs/
Software package for analyzing single cell gene expression, classifying and counting cells, performing differential expression analysis between subpopulations of cells, and reconstructing cellular trajcectories. Works well with very large single-cell RNA-Seq experiments containing tens of thousands of cells or more. Used in computational analysis of gene expression data in single cell gene expression studies to profile transcriptional regulation in complex biological processes and highly heterogeneous cell populations.
Proper citation: Monocle2 (RRID:SCR_016339) Copy
https://bioconductor.org/packages/release/bioc/html/MAST.html
Software as an open source package for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.
Proper citation: MAST (RRID:SCR_016340) Copy
http://genes.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html
Software tool as a framework for modeling the sequences of short sequence motifs based on the maximum entropy principle (MEP). Used for sequence motifs such as those involved in RNA splicing.
Proper citation: MAxEntScan (RRID:SCR_016707) Copy
Open source neurostimulation and recording hardware instrument platform. Part of the SPARC project. COSMIIC is based on the Networked Neuroprosthesis developed at Case Western Reserve University.
Proper citation: COSMIIC HORNET (RRID:SCR_023679) Copy
http://ccb.jhu.edu/software/glimmerhmm/
A gene finder based on a Generalized Hidden Markov Model (GHMM). Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models . Currently, GlimmerHMM's GHMM structure includes introns of each phase, intergenic regions, and four types of exons (initial, internal, final, and single).
Proper citation: GlimmerHMM (RRID:SCR_002654) Copy
http://www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html
A listing of NIH supported data sharing repositories that make data accessible for reuse. Most accept submissions of appropriate data from NIH-funded investigators (and others), but some restrict data submission to only those researchers involved in a specific research network. Also included are resources that aggregate information about biomedical data and information sharing systems. The table can be sorted according by name and by NIH Institute or Center and may be searched using keywords so that you can find repositories more relevant to your data. Links are provided to information about submitting data to and accessing data from the listed repositories. Additional information about the repositories and points-of-contact for further information or inquiries can be found on the websites of the individual repositories.
Proper citation: NIH Data Sharing Repositories (RRID:SCR_003551) Copy
neurospy is a free software for functional imaging of fast neuronal activity. neurospy is a modular cross-platform application framework written in Java for the NetBeans Platform. At this time it runs on Windows XP-based LeCroy oscilloscopes and drives acousto-optic scanners via USB using the Analog Devices 9959 Direct Digital Synthesis chip. This combination makes one of the most powerful systems for scanning microscopy available today at any price. neurospy is very easy to port to other kinds of acquisition and scanning hardware.
Proper citation: neurospy (RRID:SCR_007016) Copy
http://dockground.bioinformatics.ku.edu/
Data sets, tools and computational techniques for modeling of protein interactions, including docking benchmarks, docking decoys and docking templates. Adequate computational techniques for modeling of protein interactions are important because of the growing number of known protein 3D structures, particularly in the context of structural genomics. The first release of the DOCKGROUND resource (Douguet et al., Bioinformatics 2006; 22:2612-2618) implemented a comprehensive database of cocrystallized (bound) protein-protein complexes in a relational database of annotated structures. Additional releases added features to the set of bound structures, such as regularly updated downloadable datasets: automatically generated nonredundant set, built according to most common criteria, and a manually curated set that includes only biological nonobligate complexes along with a number of additional useful characteristics. Also included are unbound (experimental and simulated) protein-protein complexes. Complexes from the bound dataset are used to identify crystallized unbound analogs. If such analogs do not exist, the unbound structures are simulated by rotamer library optimization. Thus, the database contains comprehensive sets of complexes suitable for large scale benchmarking of docking algorithms. Advanced methodologies for simulating unbound conformations are being explored for the next release. The Dockground project is developed by the Vakser lab at the Center for Bioinformatics at the University of Kansas. Parts of Dockground were co-developed by Dominique Douguet from the Center of Structural Biochemistry (INSERM U554 - CNRS UMR5048), Montpellier, France.
Proper citation: Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING (RRID:SCR_007412) Copy
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Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
