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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://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

https://www.bio.aps.anl.gov/

Biomedical technology research center and training resource for the study of the structure of partially ordered biological molecules, complexes of biomolecules and cellular structures under conditions similar to those present in living cells and tissues. The goal of research at BioCAT is to determine the detailed structure and mechanism of action of biological systems at the molecular level. The techniques used are X-ray fiber diffraction, X-ray solution scattering and X-ray micro-emission and micro-absorption spectroscopy, with an emphasis on time-resolved studies and the development of novel techniques.

Proper citation: BioCAT (RRID:SCR_001440) Copy   

•   Source: SciCrunch Registry

http://www.nbcr.net/

Biomedical technology research center that conducts, catalyzes and enables multiscale biomedical research, focusing on four key activities: 1) integrating computational, data and visualization resources in a transparent, advanced grid environment to enable better access to distributed data, computational resources, instruments and people; 2) developing and deploying advanced computational tools for modeling and simulation, data analysis, query and integration, three-dimensional image processing and interactive visualization; 3) delivering and supporting advanced grid/cyberinfrastructure for biomedical researchers; and 4) training a cadre of new researchers to have an interdisciplinary, working knowledge of computational technology relevant to biomedical scientists. NBCR enables biomedical scientists to address the challenge of integrating detailed structural measurements from diverse scales of biological organization that range from molecules to organ systems in order to gain quantitative understanding of biological function and phenotypes. Predictive multi-scale models and their driving biological research problems together address issues in modeling of sub-cellular biophysics, building molecular modeling tools to accelerate discovery, and defining tools for patient-specific multi-scale modeling. NBCR furthers these driving problems by developing tools and models based on rapid advances in mathematics and information technology, incorporating them into NBCR pipelines or problem solving environments, and addressing the inevitable changes in the underlying cyber-infrastructure technologies and continually adapting codes over time. Their technology focus integrates both the biological applications and the underlying support software into reproducible science workflows that can function across a number of physical infrastructures.

Proper citation: National Biomedical Computation Resource (RRID:SCR_002656) Copy   

•   Source: SciCrunch Registry

http://ncmir.ucsd.edu/

Biomedical technology research center that develops computer-aided, advanced microscopy for the acquisition of structural and functional data in the dimensional range of 1 nm to 100 um, a range encompassing macromolecules, subcellular structures and cells. Novel specimen-staining methods, imaging instrumentsincluding intermediate high-voltage transmission electron microscopes (IVEMs) and high-speed, large-format laser-scanning light microscopesand computational capabilities are available for addressing mesoscale biological microscopy of proteins and macromolecular complexes in their cellular and tissue environments. These technologies are developed to bridge understanding of biological systems between the gross anatomical and molecular scales and to make these technologies broadly available to biomedical researchers. NCMIR provides expertise, infrastructure, technological development, and an environment in which new information about the 3D ultrastructure of tissues, cells, and macromolecular complexes may be accurately and easily obtained and analyzed. NCMIR fulfills its mission through technology development, collaboration, service, training, and dissemination. It aims to develop preparative methods and analytical approaches to 3D microscopy applicable to neurobiology and cell biology, incorporating equipment and implementing software that expand the analysis of 3D structure. The core research activities in the areas of specimen development, instrument development, and software infrastructures maximize the advantages of higher voltage electron microscopy and correlated light microscopies to make ambitious imaging studies across scales routine, and to facilitate the use of resources by biomedical researchers. NCMIR actively recruits outside users who will not only make use of these resources, but who also will drive technology development and receive training.

Proper citation: National Center for Microscopy and Imaging Research (RRID:SCR_002655) Copy   

•   Source: SciCrunch Registry

http://www.mri-resource.kennedykrieger.org/

Biomedical technology research center that provides expertise for the design of quantitative magnetic resonance imaging (MRI) and spectroscopy (MRS) data acquisition and processing technologies that facilitate the biomedical research of a large community of clinicians and neuroscientists in Maryland and throughout the USA. These methods allow noninvasive assessment of changes in brain anatomy as well as in tissue metabolite levels, physiology, and brain functioning while the brain is changing size during early development and during neurodegeneration, i.e. the changing brain throughout the life span. The Kirby Center has 3 Tesla and 7 Tesla state of the art scanners equipped with parallel imaging (8, 16, and 32-channel receive coils) and multi-transmit capabilities. CIS has an IBM supercomputer that is part of a national supercomputing infrastructure. Resources fall into the following categories: * MRI facilities, image acquisition, and processing * Computing facilities and image analysis * Novel statistical methods for functional brain imaging * Translating laboratory discoveries to patient treatment

Proper citation: National Resource for Quantitative Functional MRI (RRID:SCR_006716) Copy   

•   Source: SciCrunch Registry

http://msf.ucsf.edu/

Provides high-performance tandem mass spectrometry and proteomics, including multiplexed quantitative comparative analysis of protein and post-translational modifications, and a suite of tools for the analysis of mass spectrometry proteomics data. It provides both scientific and technical expertise and state-of-the-art high-performance, tandem mass spectrometric instrumentation. The facility also provides a service for small molecule analysis. Significant instrumentation in the facility includes three QSTAR quadrupole orthogonal time of flight instruments, and both an LTQ-Orbitrap platform with electron transfer dissociation (ETD) and an LTQ-FT linear ion trap FT-ICR instrument equipped with the ability to perform electron capture dissociation (ECD). The Center also has a 4700 Proteomic Analyzer MALDI tandem time of flight instrument; as well as a QTRAP 5500 hybrid triple quadrupole linear ion trap instrument; and a Thermo Fisher LTQ Orbitrap Velos. Major research focuses within the Center are the analysis of post-translational modifications, including phosphorylation and O-GlcNAcylation and development of methods for quantitative comparative analysis of protein and post-translational modification levels. The program also continues to develop one of the leading suites of tools for analysis of mass spectrometry proteomics data, Protein Prospector. The current web-based release allows unrestricted searching of MS and MSMS data, as well as the ability to perform comparative quantitative analysis of samples using isotopic-labeling reagents. It is the only freely-available web-based resource that allows this type of analysis.

Proper citation: National Bio-Organic Biomedical Mass Spectrometry Resource Center (RRID:SCR_009004) 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.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/

Software program that automatically annotates a protein list with information that helps in the meaningful interpretation of data from mass spectrometry and other techniques. It takes protein lists as input, in the form of plain text files, protXML files (usually from the TPP), or Dat files from MASCOT search results. From this, it generates protein annotation tables, and a variety of GO charts to aid individual and differential analysis of proteomics data. It downloads information from mainly the Uniprot and EBI QuickGO databases. STRAP requires Windows XP or higher with at least version 3.5 of the Microsoft .NET Framework installed. Platform: Windows compatible

Proper citation: STRAP (RRID:SCR_005675) Copy   

•   Source: SciCrunch Registry

http://www.autopack.org/

An open-source general packing algorithm that packs 3D objects onto surfaces, into volumes, and around volumes. It provides a general architecture to allow various packing algorithms to interoperate efficiently in the same model. autoPack can incorporate any packing solution into its modular python program architecture, but is currently optimized to provide a novel solution to the loose packing problem which places objects of discrete size into place (compared to advancing front, popcorn, or other fast tight-packing solutions that allow objects to scale to arbitrary masses.) Most popular 3D software programs now contain robust physics engines based on Bullet that can separate small collections of overlapping objects or allow volumes to be filled by pouring shapes from generators, but these approaches fails for large complex systems and result in either overlapping geometry, crashed software, or non-random gradients. Most packing algorithms are designed to position objects as efficiently as possible, but autoPack allows the user to select from random loose packing to highly organized packing methods����??even to choose both methods at the same time. autoPack positions 3D geometries into, onto, and around volumes with minimal to zero overlap. autoPack mixes several packing approaches and procedural growth algorithms. autoPack can thus place objects with forces and constraints to allow a high degree of control ranging from completely random distributions to highly ordered structures. * zero to minimal overlaps depending on the method used * accuracy vs speed parameters selected by the user * zero edge effects * complete control, from fully random to fully ordered distributions * agent-based interaction, weighting, and collision control

Proper citation: Autopack (RRID:SCR_006830) Copy   

•   Source: SciCrunch Registry

http://www.imagevis3d.org/

A new volume rendering program developed by the NIH/NCRR Center for Integrative Biomedical Computing (CIBC). The main design goals of ImageVis3D are: simplicity, scalability, and interactivity. Simplicity is achieved with a new user interface that gives an unprecedented level of flexibility (as shown in the images). Scalability and interactivity for ImageVis3D mean that both on a notebook computer as well as on a high end graphics workstation, the user can interactively explore terabyte sized data sets. Finally, the open source nature as well as the strict component-by-component design allow developers not only to extend ImageVis3D itself but also reuse parts of it, such as the rendering core. This rendering core, for instance, is planned to replace the volume rendering subsystems in many applications at the SCI Institute and with their collaborators.

Proper citation: ImageVis3D (RRID:SCR_009566) Copy   

•   Source: SciCrunch Registry

http://www.cbioportal.org/

A portal that provides visualization, analysis and download of large-scale cancer genomics data sets.

Proper citation: cBioPortal (RRID:SCR_014555) Copy   

•   Source: SciCrunch Registry

http://www.ctsaip.org/

Database that aggregates and markets technologies from CTSA institutions as well as those of the National Institutes of Health, with the goal of enhancing research activity and private partnerships across the CTSA consortium. Regular, automatic updating with a standardized template facilitates broad participation by CTSA consortium members. Currently, there are over a dozen CTSAs contributing information on their technologies to the site. CTSA-IP Mission * Intellectual Property information exchange * Links publicly available licensing opportunities from CTSI Institutions in an easily searchable format that connects providers & users. * Aim of creating a consortium view of IP, licensing & sponsored research opportunities. * Stimulus to collaboration and partnering with and between CTSA member institutions.

Proper citation: CTSA-IP (RRID:SCR_006380) Copy   

•   Source: SciCrunch Registry

http://martinos.org/mne/

Software suite for processing magnetoencephalography and electroencephalography data. Open source Python software for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, ECoG . Implements all functionality of MNE Matlab tools in Python and extends capabilities of MNE Matlab tools to, e.g., frequency-domain and time-frequency analyses and non-parametric statistics.

Proper citation: MNE software (RRID:SCR_005972) Copy   

•   Source: SciCrunch Registry

https://www.researchmatch.org/

Free and secure registry to bring together two groups of people who are looking for one another: (1) people who are trying to find research studies, and (2) researchers who are looking for people to participate in their studies. It has been developed by major academic institutions across the country who want to involve you in the mission of helping today''''s studies make a real difference for everyone''''s health in the future. Anyone can join ResearchMatch. Many studies are looking for healthy people of all ages, while some are looking for people with specific health conditions. ResearchMatch can help ''''match'''' you with any type of research study, ranging from surveys to clinical trials, always giving you the choice to decide what studies may interest you.

Proper citation: ResearchMatch (RRID:SCR_006387) Copy   

•   Source: SciCrunch Registry

http://users.loni.ucla.edu/~shattuck/brainsuite/

Suite of image analysis tools designed to process magnetic resonance images (MRI) of the human head. BrainSuite provides an automatic sequence to extract genus-zero cortical surface mesh models from the MRI. It also provides a set of viewing tools for exploring image and surface data. The latest release includes graphical user interface and command line versions of the tools. BrainSuite was specifically designed to guide its users through the process of cortical surface extraction. NITRC has written the software to require minimal user interaction and with the goal of completing the entire process of extracting a topologically spherical cortical surface from a raw MR volume within several minutes on a modern workstation. The individual components of BrainSuite may also be used for soft tissue, skull and scalp segmentation and for surface analysis and visualization. BrainSuite was written in Microsoft Visual C using the Microsoft Foundation Classes for its graphical user interface and the OpenGL library for rendering. BrainSuite runs under the Windows 2000 and Windows XP Professional operating systems. BrainSuite features include: * Sophisticated visualization tools, such as MRI visualization in 3 orthogonal views (either separately or in 3D view), and overlayed surface visualization of cortex, skull, and scalp * Cortical surface extraction, using a multi-stage user friendly approach. * Tools including brain surface extraction, bias field correction, voxel classification, cerebellum removal, and surface generation * Topological correction of cortical surfaces, which uses a graph-based approach to remove topological defects (handles and holes) and ensure a tessellation with spherical topology * Parameterization of generated cortical surfaces, minimizing a harmonic energy functional in the p-norm * Skull and scalp surface extraction

Proper citation: BrainSuite (RRID:SCR_006623) Copy   

•   Source: SciCrunch Registry

http://ctdbase.org/

A public database that enhances understanding of the effects of environmental chemicals on human health. Integrated GO data and a GO browser add functionality to CTD by allowing users to understand biological functions, processes and cellular locations that are the targets of chemical exposures. CTD includes curated data describing cross-species chemical–gene/protein interactions, chemical–disease and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data will also provide insights into complex chemical–gene and protein interaction networks.

Proper citation: Comparative Toxicogenomics Database (CTD) (RRID:SCR_006530) Copy   

•   Source: SciCrunch Registry