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http://www.na-mic.org/Wiki/index.php/SoftwareInventory

A free open source software platform consisting of the 3D Slicer application software, a number of tools and toolkits such as VTK and ITK, and a software engineering methodology that enables multiplatform implementations. It also draws on other best practices from the community to support automatic testing for quality assurance. The NA-MIC kit uses a modular approach, where the individual components can be used by themselves or together. The NA-MIC kit is fully-compatible with local installation (behind institutional firewalls) and installation as an internet service. Significant effort has been invested to ensure compatibility with standard file formats and interoperability with a large number of external applications. Users of the NAMIC Kit will typically use a combination of its many modular components. * 3D Slicer is a general purpose application. Biomedical researchers will typically use this software tool to load, view, analyze, process and save image data. Slicer has been implemented to interoperate with many other tools, including XNAT, which is an open source image database. * Slicer modules, which are dynamically loaded by Slicer at run-time, can be used to extend Slicer''''s core functionality including defining graphical user interfaces. Modules are typically used by algorithms and application developers. * Application and algorithms developers may also use NA-MIC Kit toolkits and libraries. For example, the Insight Segmentation and Registration Toolkit ITK can be used to develop slicer modules for medical image analysis. The Visualization Toolkit can be used to process, visualize and graphically interact with data. KWWidgets is a 2D graphical user interface toolset that can be used to build applications. Teem is a library of general purpose command-line tools that are useful for processing data. Finally, those individuals wishing to create and manage complex software, the NAMIC-Kit software process is available as embodied in CMake, CTest, CPack, DART and the various documentation, bug tracking and communication tools.

Proper citation: NA-MIC Kit (RRID:SCR_005616) Copy   

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http://www.scripps.edu/research/

Nonprofit American medical research facility that focuses on research and education in the biomedical sciences. Headquartered in San Diego, California with a sister facility in Jupiter, Florida, the institute has laboratories employing scientists, technicians, graduate students, and administrative and other staff, making it the largest private, non-profit biomedical research organization in the United States and among the largest in the world.

Proper citation: Scripps Research Institute (RRID:SCR_001907) Copy   

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https://www.agilent.com/en/product/next-generation-sequencing/hybridization-based-next-generation-sequencing-ngs/ngs-automation-platforms/bravo-ngs-232819

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   

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http://www.stanford.edu/group/brainsinsilicon/neurogrid.html

A specialized hardware platform that will perform cortex-scale emulations while offering software-like flexibility. With sixteen 12x14 sq-mm chips (Neurocores) assembled on a 6.5x7.5 sq-in circuit board that can model a slab of cortex with up to 16x256x256 neurons - over a million! The chips are interconnected in a binary tree by 80M spike/sec links. An on-chip RAM (in each Neurocore) and an off-chip RAM (on a daughterboard, not shown) softwire vertical and horizontcal cortical connections, respectively. It provides an affordable option for brain simulations that uses analog computation to emulate ion-channel activity and uses digital communication to softwire synaptic connections. These technologies impose different constraints, because they operate in parallel and in serial, respectively. Analog computation constrains the number of distinct ion-channel populations that can be simulatedunlike digital computation, which simply takes longer to run bigger simulations. Digital communication constrains the number of synaptic connections that can be activated per secondunlike analog communication, which simply sums additional inputs onto the same wire. Working within these constraints, Neurogrid achieves its goal of simulating multiple cortical areas in real-time by making judicious choices.

Proper citation: Neurogrid (RRID:SCR_005024) Copy   

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http://www.pbrc.edu/default.asp

Research institute which investigates chronic disease and its triggers.

Proper citation: Pennington Biomedical Research Center (RRID:SCR_002946) Copy   

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http://compgen.bscb.cornell.edu/phast/

A freely available software package for comparative and evolutionary genomics that consists of about half a dozen major programs, plus more than a dozen utilities for manipulating sequence alignments, phylogenetic trees, and genomic annotations. For the most part, PHAST focuses on two kinds of applications: the identification of novel functional elements, including protein-coding exons and evolutionarily conserved sequences; and statistical phylogenetic modeling, including estimation of model parameters, detection of signatures of selection, and reconstruction of ancestral sequences. It consists of over 60,000 lines of C code.

Proper citation: PHAST (RRID:SCR_003204) Copy   

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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   

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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   

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https://obofoundry.org/ontology/cl.html

Ontology designed as a structured controlled vocabulary for cell types. It was constructed for use by the model organism and other bioinformatics databases. It includes cell types from prokaryotes, mammals, and fungi. The ontology is available in the formats adopted by the Open Biological Ontologies umbrella and is designed to be used in the context of model organism genome and other biological databases.

Proper citation: Cell Type Ontology (RRID:SCR_004251) Copy   

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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   

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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   

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http://hnrim.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database of human nutrition research and research training activities supported by the federal government. Information regarding trends in nutrition research, specific institutions and investigators involved in this research, or areas of agency emphases can be obtained from database searches or from published summary reports. Data for the system is prepared and submitted by participating agencies, and is updated annually. The database contains several thousand projects for each of fiscal years 1985present. Participating agencies include the Department of Health and Human Services, the U.S. Department of Agriculture, the Department of Veteran Affairs, the Agency for International Development, the Department of Defense, Department of Commerce, National Science Foundation, and the National Aeronautics and Space Administration.

Proper citation: Human Nutrition Research Information Management (RRID:SCR_001471) Copy   

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https://github.com/wlloyduw/ContainerProfiler

Software tool supports profiling resource utilization including CPU, memory, disk, and network metrics of containerized tasks. Resource utilization metrics are obtained across three levels: virtual machine (VM)/host, container, and process. Implementation leverages facilities provided by Linux operating system that is integral with Docker containers.

Proper citation: ContainerProfiler (RRID:SCR_023770) Copy   

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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   

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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   

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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://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   

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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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https://cosmiic.org/

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   

•   Source: SciCrunch Registry

http://www.orbitproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 29, 2014. The orbit project was a registry of biomedical resources.

Proper citation: OrbitProject (RRID:SCR_010463) Copy   

•   Source: SciCrunch Registry