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http://depts.washington.edu/yeastrc/

Biomedical technology research center that (1) exploits the budding yeast Saccharomyces cerevisiae to develop novel technologies for investigating and characterizing protein function and protein structure (2) facilitates research and extension of new technologies through collaboration, and (3) actively disseminates data and technology to the research community. Through collaboration, the YRC freely provides resources and expertise in six core technology areas: Protein Tandem Mass Spectrometry, Protein Sequence-Function Relationships, Quantitative Phenotyping, Protein Structure Prediction and Design, Fluorescence Microscopy, Computational Biology.

Proper citation: Yeast Resource Center (RRID:SCR_007942) Copy   

•   Source: SciCrunch Registry

http://proteomics.ucsd.edu/

Biomedical technology research center that focuses on the computational bottlenecks that impair the interpretation of data, bringing modern algorithmic approaches to mass spectrometry and building a new generation of reliable, open-access software tools to support both new mass spectrometry instrumentation and emerging applications.

Proper citation: Center for Computational Mass Spectrometry (RRID:SCR_008161) Copy   

•   Source: SciCrunch Registry

https://github.com/ccipd/MRQy

Software quality assurance and checking tool for quantitative assessment of magnetic resonance imaging and computed tomography data. Used for quality control of MR imaging data.

Proper citation: MRQy (RRID:SCR_025779) Copy   

•   Source: SciCrunch Registry

https://github.com/Washington-University/HCPpipelines

Software package as set of tools, primarily shell scripts, for processing multi-modal, high-quality MRI images for the Human Connectome Project. Minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space.

Proper citation: HCP Pipelines (RRID:SCR_026575) Copy   

•   Source: SciCrunch Registry

http://www.usc.edu/dept/biomed/UTRC/

Biomedical technology research center focusing on the development of very high frequency (above 20 MHz) ultrasonic transducers/arrays for applications in medicine and biology that include ophthalmology, dermatology, vascular surgery, and small animal imaging. The research is pursued simultaneously in three directions: novel piezoelectric materials, very high frequency single element transducers and linear arrays, and finite element modeling and material property measurements. The Center also serves the community through collaborative efforts with investigators having a research interest in high-frequency ultrasound imaging. In addition, it performs the function of training and information dissemination by offering conferences, seminars and specialized courses at the University of Southern California. The Center has set forth a number of goals which define its mission: * Conduct novel research and development of very high frequency (>20MHz) ultrasonic transducers, arrays and imaging applications * Collaborate with other academic institutions, non-profit organizations, and small businesses supported by the NIH to further the development of these high-frequency applications and provide the expertise in transducers necessary for project success * Serve as an educational center for training scientists and engineers interested in ultrasonic transducer technology One of the primary goals of the Center is to provide service to outside investigators and small business. Often an investigator or company has a specific application in mind but is without the expertise to develop the necessary ultrasonic device. Investigators at academic institutions, research institutes, or small businesses supported by NIH grants who have a need for medical ultrasound transducers and are interested in a collaborative effort should contact Dr. Hyung Ham Kim or Dr. K. Kirk Shung. Ultrasound transducers and components can be fabricated either completely by center personnel or in a joint effort with other investigators. In addition, collaborators are encouraged to visit the facility for additional training in fabrication and assembly.

Proper citation: Resource Center for Medical Ultrasonic Transducer Technology (RRID:SCR_001404) Copy   

•   Source: SciCrunch Registry

https://omics.pnl.gov/software/ms-gf

Software that performs peptide identification by scoring MS/MS spectra against peptides derived from a protein sequence database.

Proper citation: MS-GF+ (RRID:SCR_015646) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/Debabeler

Software to manage the conversion of imaging data from one file format and convention to another. It consists of a graphical user interface to visually program the translations, and a data translation engine to read, sort and translate the input files, and write the output files to disk. The data translation engine: (1) Reads metadata from a set of image files on disk to identify the source that produced each file; (2) Groups the image files into user-defined collections using image metadata values; (3) Translates each image file collection by reading metadata and pixel data and mapping the data into the appropriate output file format through a programmable set of connected modules. The Debabeler uses the Java Image I/O Plugin Architecture to read and write a wide variety of common medical image file formats, including ANALYZE, MINC, and most variations of DICOM.

Proper citation: LONI Debabeler (RRID:SCR_001160) Copy   

•   Source: SciCrunch Registry

https://github.com/SciKnowEngine/kefed.io

Knowledge engineering software for reasoning with scientific observations and interpretations. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a "neural connection matrix" interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. The KEfED model is designed to provide a lightweight representation for scientific knowledge that is (a) generalizable, (b) a suitable target for text-mining approaches, (c) relatively semantically simple, and (d) is based on the way that scientist plan experiments and should therefore be intuitively understandable to non-computational bench scientists. The basic idea of the KEfED model is that scientific observations tend to have a common design: there is a significant difference between measurements of some dependent variable under conditions specified by two (or more) values of some independent variable.

Proper citation: Knowledge Engineering from Experimental Design (RRID:SCR_001238) Copy   

•   Source: SciCrunch Registry

http://www.michr.umich.edu/services/biorepository

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 24,2025. In 2009, the Medical School and the Michigan Institute for Clinical & Health Research (MICHR) unveiled a new biorepository for U-M researchers in need of a controlled storage environment for biological samples. MICHR is pleased to be able to add to its many services for the research community a centralized biological repository for controlled storage of biological samples, and related services (including DNA, RNA, and other downstream preparation) within the U-M campus. The biorepository, located in the CAP/CLIA-certified Michigan Center for Translational Pathology (MCTP) laboratory at the U-M Traverwood facility on Huron Parkway, will store biologic material, including blood and urine. Sample accessioning and tracking will be accomplished using the caTISSUE suite of programs, and samples will be processed and stored in compliance with CAP/CLIA guidelines. Initially, all samples will be used only with the authorization of the individual investigator who directed the project under which the samples were obtained. Samples will be used in accordance with the relevant informed consent. Long-term plans include federating the database in order to facilitate sharing of data and samples between research teams.

Proper citation: University of Michigan Biorepository (RRID:SCR_004643) Copy   

•   Source: SciCrunch Registry

http://researchiq.bmi.osumc.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented March 14, 2016. Research Integrative Query (ResearchIQ) tool, a semantically anchored resource discovery platform that facilitates semantic discovery of local and publicly available data through a single web portal designed for researchers in the biomedical informatics domain within The Ohio State University.

Proper citation: ResearchIQ (RRID:SCR_014304) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/X2K/

Software tool to produce inferred networks of transcription factors, proteins, and kinases predicted to regulate the expression of the inputted gene list by combining transcription factor enrichment analysis, protein-protein interaction network expansion, with kinase enrichment analysis. It provides the results as tables and interactive vector graphic figures.

Proper citation: eXpression2Kinases (RRID:SCR_016307) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/DiD

Software application for removing patient-identifying information from medical image files. Removing this information is often necessary for enabling investigators to share image files in a HIPAA compliant manner.

Proper citation: LONI De-identification Debablet (RRID:SCR_009593) Copy   

•   Source: SciCrunch Registry

http://www.sci.utah.edu/cibc/software/231-biomesh3d.html

A free, easy to use program for generating quality meshes for use in biological simulations. It is currently integrated with SCIRun and uses the SCIRun system to visualize the intermediate results. The BioMesh3D program uses a particle system to distribute nodes on the separating surfaces that separate the different materials and then uses the TetGen software package to generate a full tetrahedral mesh.

Proper citation: BioMesh3D (RRID:SCR_009534) Copy   

•   Source: SciCrunch Registry

http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) Copy   

•   Source: SciCrunch Registry

http://brainatlas.mbi.ufl.edu/Database/

Comprehensive three-dimensional digital atlas database of the C57BL/6J mouse brain based on magnetic resonance microscopy images acquired on a 17.6-T superconducting magnet. This database consists of: Individual MRI images of mouse brains; three types of atlases: individual atlases, minimum deformation atlases and probabilistic atlases; the associated quantitative structural information, such as structural volumes and surface areas. Quantitative group information, such as variations in structural volume, surface area, magnetic resonance microscopy image intensity and local geometry, have been computed and stored as an integral part of the database. The database augments ongoing efforts with other high priority strains as defined by the Mouse Phenome Database focused on providing a quantitative framework for accurate mapping of functional, genetic and protein expression patterns acquired by a myriad of technologies and imaging modalities. You must register First (Mandatory) and then you may Download Images and Data.

Proper citation: MRM NeAt (Neurological Atlas) Mouse Brain Database (RRID:SCR_007053) Copy   

•   Source: SciCrunch Registry

http://ncmir.ucsd.edu/downloads/manual_align_rts2000.shtm

Software program to adjust the alignment of two adjacent images. Allows to correct for any misalignment that may occur during auto-alignment step. Serves as a bootstrap to get the images in approximately the right place.

Proper citation: Manual Align RTS2000 (RRID:SCR_007107) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/collaborators/function-birn/

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

Proper citation: Function BIRN (RRID:SCR_007291) Copy   

•   Source: SciCrunch Registry

http://www.nbirn.net/research/bcc/index.shtm

THIS RESOURCE IS NO LONGER IN SERVICE, documented on September 06, 2013. It was established to develop, implement and support the information infrastructure necessary to achieve large-scale data sharing among the test bed participants (function, morphometry and mouse birn). The BIRN-CC consists of a unique and well-established partnership between computer scientists, neuroscientists and engineers. This partnership addresses a large array of technical, policy, and architectural issues to fundamentally enable a new suite of information technology supported database and analysis tools that allow scientists to analyze and interpret significantly larger sets of data than is possible in the traditional single-institution study paradigm.

Proper citation: BIRN Coordinating Center (RRID:SCR_007290) Copy   

•   Source: SciCrunch Registry

http://www.cmrr.umn.edu/stimulate

An fMRI analysis software package with a GUI (Graphical User Interface) front end. Stimulate offers a comprehensive set of fMRI analysis tools integrated into a single package for convenient and flexible data processing. Users can point and click with the mouse to modify analysis or display variables. Activation maps can be calculated from the fMRI data and overlaid onto structural MRI image displays.

Proper citation: Stimulate (RRID:SCR_007375) Copy   

•   Source: SciCrunch Registry

http://mmcd.nmrfam.wisc.edu/

A database which supports high-throughput NMR and MS approaches to the identification and quantification of metabolites present in biological samples. MMCD serves as a hub for information on small molecules of biological interest gathered from electronic databases and the scientific literature. Each metabolite entry in the MMCD is supported by information in separate data fields, which provide the chemical formula, names and synonyms, structure, physical and chemical properties, NMR and MS data on pure compounds under defined conditions where available, NMR chemical shifts determined by empirical and/or theoretical approaches, calculated isotopomer masses, information on the presence of the metabolite in different biological species, and links to images, references, and other public databases. The MMCD search engine supports versatile data mining and allows users to make individual or bulk queries on the basis of experimental NMR and/or MS data plus other criteria.

Proper citation: Madison Metabolomics Consortium Database (RRID:SCR_007803) Copy   

•   Source: SciCrunch Registry