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http://ods.od.nih.gov/Research/CARDS_Database.aspx

Database of federally funded research projects pertaining to dietary supplements. CARDS contains projects funded by the United States Department of Agriculture (USDA), the Department of Defense (DOD) and the Institutes and Centers (ICs) of the National Institutes of Health (NIH) beginning with fiscal year 1999, the first year that NIH ICs began reporting research related to dietary supplements. Projects funded by other Federal agencies will be added to CARDS as they become available. The Office of Dietary Supplements (ODS) will post notices on its website and listserv when CARDS updates are completed. Codes assigned to each research project allow the CARDS user to identify: * research related to specific dietary supplement ingredients; for example, vitamin E or St. John''''s wort * the type of study; for example, a Phase III study or an animal study * health outcomes or biological effects; for example, osteoporosis or antioxidant function * whether the research is directly related or indirectly related to dietary supplements. For example, a clinical trial comparing bone density in women given a daily calcium supplement versus a placebo would be classified as directly related to dietary supplements. A study examining the activation of steroid hormone receptors by supplemental vitamin D in cell culture would be classified as indirectly related to dietary supplements because the direct physiological or health effects of vitamin D supplementation are not being studied. A search of the CARDS database can be used to sort and tabulate information for a variety of purposes. For example, a researcher may want to know which ICs at the NIH fund research on herbal supplement ingredients. A consumer may want to know if the Federal government is supporting research on a popular dietary supplement ingredient such as vitamin C.

Proper citation: CARDS Database (RRID:SCR_009011) Copy   

•   Source: SciCrunch Registry

http://immport.org/immport-open/public/reference/cytokineRegistry

A registry of cytokines, chemokines, and receptors generated for the purpose of collecting, integrating, and mapping between entity names and synonyms from several resources. These resources include MeSH, the Protein Ontology, EntrezGene, HGNC, MGI, UniProt and others.

Proper citation: Cytokine Registry (RRID:SCR_014368) Copy   

•   Source: SciCrunch Registry

https://bioinformatics.niaid.nih.gov/chemokinedb/

Resource of chemokines and receptors with detailed information including taxonomy, nomenclature, structure, physiological function, tissue information, and phenotype, collected from IUPHAR/BPS, UniGene, and UniProt public databases.

Proper citation: ChemokineDB (RRID:SCR_016593) Copy   

•   Source: SciCrunch Registry

http://software.broadinstitute.org/gsea/msigdb/index.jsp

Collection of annotated gene sets for use with Gene Set Enrichment Analysis (GSEA) software.

Proper citation: Molecular Signatures Database (RRID:SCR_016863) Copy   

•   Source: SciCrunch Registry

https://github.com/VH-Lab/vhlab-microscopyimageanalysis-matlab

Software Matlab app for analysis of high density imaging data like that from Array Tomography.

Proper citation: vhlab-microscopyimageanalysis-matlab (RRID:SCR_024450) Copy   

•   Source: SciCrunch Registry

http://www.biosyn.com/

A commercial supplier of custom synthetic molecules. They specialize in peptides, oligonucleotides, bioconjugation, molecular biology services, proteins and specialty chemistry.

Proper citation: Bio-Synthesis (RRID:SCR_000820) Copy   

•   Source: SciCrunch Registry

http://www.midasplatform.org/

Open-source toolkit that enables the rapid creation of tailored, web-enabled data storage and provides a cohesive system for data management, visualization, and processing. At its core, Midas Platform is implemented as a PHP modular framework with a backend database (PostGreSQL, MySQL and non-relational databases). While the Midas Platform system can be installed and deployed without any customization, the framework has been designed with customization in mind. As building one system to fit all is not optimal, the framework has been extended to support plugins and layouts. Through integration with a range of other open-source toolkits, applications, or internal proprietary workflows, Midas Platform offers a solid foundation to meet the needs of data-centric computing. Midas Platform provides a variety of data access methods, including web, file system and DICOM server interfaces, and facilitates extending the methods in which data is stored to other relational and non-relational databases.

Proper citation: Midas Platform (RRID:SCR_002186) 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://bard.nih.gov/

Database that allows scientists without specialized training to effectively utilize Molecular Libraries Program (MLP) data. It allows the research community to utilize and develop new chemical probes to explore biological functions by building a central, permanently accessible link to all aspects of chemical biology data and analyses. The project is split into two basic segments, the first segment delivering functionality for a data dictionary, as well as assay protocol and data entry tools. The second builds a data warehouse for analysis and visualization, accessible through a public RESTful API. They will initially deploy two clients that will use this API - a web-based interface and a desktop application. Advanced access to data and the platforms will also be available to support plug-in development and the repackaging of data by others. Initially the project will focus on small molecule assays. Features: * allow scientists to annotate assay data using a common, shared language * provide facile access to data, integrating existing chemical biology and computational resources * enable meaningful analysis and interpretation of discovery data by the research community * support hypothesis generation for iterative probe- and drug-discovery projects * inform the entire small molecule discovery and development process, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BARD (RRID:SCR_006283) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

http://www.cpc.unc.edu/projects/addhealth

Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.

Proper citation: Add Health (National Longitudinal Study of Adolescent Health) (RRID:SCR_007434) Copy   

•   Source: SciCrunch Registry

http://compbio.soe.ucsc.edu/yeast_introns.html

Database of information about the spliceosomal introns of the yeast Saccharomyces cerevisiae. Listed are known spliceosomal introns in the yeast genome and the splice sites actually used are documented. Through the use of microarrays designed to monitor splicing, they are beginning to identify and analyze splice site context in terms of the nature and activities of the trans-acting factors that mediate splice site recognition. In version 3.0, expression data that relates to the efficiency of splicing relative to other processes in strains of yeast lacking nonessential splicing factors is included. These data are displayed on each intron page for browsing and can be downloaded for other types of analysis.

Proper citation: Yeast Intron Database (RRID:SCR_007144) Copy   

•   Source: SciCrunch Registry

https://www.pathology.umn.edu/research/liver-tissue-cell-distribution-system

Tissue bank that provides human liver tissue from regional centers for distribution to scientific investigators throughout the United States. These USA regional centers have active liver transplant programs with human subjects approval to provide portions of the resected pathologic liver for which the transplant is performed.

Proper citation: Minnesota Liver Tissue Cell Distribution System (RRID:SCR_004840) Copy   

•   Source: SciCrunch Registry

http://udn.nichd.nih.gov/brainatlas_home.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 1, 2019. The first brain atlas for the common marmoset to be made available since a printed atlas by Stephan, Baron and Schwerdtfeger published in 1980. It is a combined histological and magnetic resonance imaging (MRI) atlas constructed from the brains of two adult female marmosets. Histological sections were processed from Nissl staining and digitized to produce an atlas in a large format that facilitates visualization of structures with significant detail. Naming of identifiable brain structures was performed utilizing current terminology. For the present atlas, an adult female was perfused through the heart with PBS followed by 10% formalin. The brain was then sent to Neuroscience Associates of Knoxville, TN, who prepared the brain for histological analysis. The brain was cut in the coronal (frontal) plane at 40 microns, every sixth section stained for Nissl granules with thionine and every seventh section stained for myelinated fibers with the Weil technique. The mounted sections were photographed at the NIH (Medical Arts and Photography Branch). The equipment used was a Nikon Multiphot optical bench with Zeiss Luminar 100 mm lens, and scanned with a Better Light 6100 scan back driven by Better Light Viewfinder 5.3 software. The final images were saved as arrays of 6000x8000 pixels in Adobe Photoshop 6.0. A scale in mm provided with these images permitted construction of the final Nissl atlas files with a horizontal and vertical scale. Some additional re-touching (brightness and contrast) was done with Adobe Photoshop Elements 2.0. The schematic (labeled) atlas plates were created from the Nissl images. The nomenclature came almost exclusively from brainmaps.org, where a rhesus monkey brain with structures labeled can be found. The labels for the MRI images were placed by M. R. Zametkin, under supervision from Dr. Newman.

Proper citation: Brain atlas of the common marmoset (RRID:SCR_005135) Copy   

•   Source: SciCrunch Registry

http://frederick.cancer.gov/

A federally funded research and development center dedicated to biomedical research. NCI-Frederick partners with university, government, and corporate scientists to speed the translation of laboratory research into new diagnostic tests and treatments for cancer and HIV/AIDS. NCI-Frederick is comprised of more than 2,800 government- and contractor-employed biomedical researchers, laboratory technicians, and support staff and several cancer research centers. The FNLCR provides quick response capabilities and meets special long-term research and development needs for NCI that cannot be met as effectively by existing in-house or contractor resources.

Proper citation: NCI-Frederick (RRID:SCR_004880) Copy   

•   Source: SciCrunch Registry

http://science.education.nih.gov/SciEdBlog

A blog put out by the NIH Office of Science Education.

Proper citation: NIH SciEd Blog (RRID:SCR_005499) Copy   

•   Source: SciCrunch Registry

http://www.neuroepigenomics.org/methylomedb/

A database containing genome-wide brain DNA methylation profiles for human and mouse brains. The DNA methylation profiles were generated by Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS) method and analyzed by Methyl-Analyzer software package. The methylation profiles cover over 80% CpG dinucleotides in human and mouse brains in single-CpG resolution. The integrated genome browser (modified from UCSC Genome Browser allows users to browse DNA methylation profiles in specific genomic loci, to search specific methylation patterns, and to compare methylation patterns between individual samples. Two species were included in the Brain Methylome Database: human and mouse. Human postmortem brain samples were obtained from three distinct cortical regions, i.e., dorsal lateral prefrontal cortex (dlPFC), ventral prefrontal cortex (vPFC), and auditory cortex (AC). Human samples were selected from our postmortem brain collection with extensive neuropathological and psychopathological data, as well as brain toxicology reports. The Department of Psychiatry of Columbia University and the New York State Psychiatric Institute have assembled this brain collection, where a validated psychological autopsy method is used to generate Axis I and II DSM IV diagnoses and data are obtained on developmental history, history of psychiatric illness and treatment, and family history for each subject. The mouse sample (strain 129S6/SvEv) DNA was collected from the entire left cerebral hemisphere. The three human brain regions were selected because they have been implicated in the neuropathology of depression and schizophrenia. Within each cortical region, both disease and non-psychiatric samples have been profiled (matching subjects by age and sex in each group). Such careful matching of subjects allows one to perform a wide range of queries with the ability to characterize methylation features in non-psychiatric controls, as well as detect differentially methylated domains or features between disease and non-psychiatric samples. A total of 14 non-psychiatric, 9 schizophrenic, and 6 depression methylation profiles are included in the database.

Proper citation: MethylomeDB (RRID:SCR_005583) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

•   Source: SciCrunch Registry

https://github.com/YuanXue1993/SegAN

Image analysis software for medical image segmentation. The software is fueled by an end-to-end adversarial neural network that generates segmentation label maps.

Proper citation: SegAN (RRID:SCR_016215) Copy   

•   Source: SciCrunch Registry

https://cic.ini.usc.edu/

Project to create complete mesoscale connectivity atlas of the C57Black/6 mouse brain and to subsequently generate its global neural networks.

Proper citation: Mouse Connectome Project (RRID:SCR_017313) Copy   

•   Source: SciCrunch Registry