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http://images.nigms.nih.gov/

Database of scientific photos, illustrations, and videos made available by the National Institute of General Medical Sciences.

Proper citation: National Institute of General Medical Sciences Image Gallery (RRID:SCR_003480) Copy   

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http://www.idoimaging.com/program/280

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023.Comprised of a large array of sophisticated programs, this comprehensive software package with tools based around the MINC file format. Utilities are provided for conversion, viewing, editing, registering, segmentation, and a wide array of analysis. Many programs are in Perl. MINC software tools for neurological imaging are free. Input format: Analyze, DICOM, Minc

Proper citation: MINC Brain Imaging Toolbox (RRID:SCR_003519) Copy   

•   Source: SciCrunch Registry

http://neuroscienceblueprint.nih.gov/

Collaborative framework that includes the NIH Office of the Director and the 14 NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, the Blueprint identifies cross-cutting areas of research, and confronts challenges too large for any single Institute or Center. The Blueprint makes collaboration a day-to-day part of how the NIH does business in neuroscience, complementing the basic missions of Blueprint partners. During each fiscal year, the partners contribute a small percentage of their funds to a common pool. Since the Blueprint's inception in 2004, this pool has comprised less than 1 percent of the total neuroscience research budget of the partners. In 2009, the Blueprint Grand Challenges were launched to catalyze research with the potential to transform our basic understanding of the brain and our approaches to treating brain disorders. * The Human Connectome Project is an effort to map the connections within the healthy brain. It is expected to help answer questions about how genes influence brain connectivity, and how this in turn relates to mood, personality and behavior. The investigators will collect brain imaging data, plus genetic and behavioral data from 1,200 adults. They are working to optimize brain imaging techniques to see the brain's wiring in unprecedented detail. * The Grand Challenge on Pain supports research to understand the changes in the nervous system that cause acute, temporary pain to become chronic. The initiative is supporting multi-investigator projects to partner researchers in the pain field with researchers in the neuroplasticity field. * The Blueprint Neurotherapeutics Network is helping small labs develop new drugs for nervous system disorders. The Network provides research funding, plus access to millions of dollars worth of services and expertise to assist in every step of the drug development process, from laboratory studies to preparation for clinical trials. Project teams across the U.S. have received funding to pursue drugs for conditions from vision loss to neurodegenerative disease to depression. Since its inception in 2004, the Blueprint has supported the development of new resources, tools and opportunities for neuroscientists. For example, the Blueprint supports several training programs to help students pursue interdisciplinary areas of neuroscience, and to bring students from underrepresented groups into the neurosciences. The Blueprint also funds efforts to develop new approaches to teaching neuroscience through K-12 instruction, museum exhibits and web-based platforms. From fiscal years 2007 to 2009, the Blueprint focused on three major themes of neuroscience - neurodegeneration, neurodevelopment, and neuroplasticity. These efforts enabled unique funding opportunities and training programs, and helped establish new resources including the Blueprint Non-Human Primate Brain Atlas.

Proper citation: NIH Blueprint for Neuroscience Research (RRID:SCR_003670) Copy   

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http://www.wikisurgery.com/index.php?title=Main_Page

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. A free surgical encyclopedia for surgeons and their patients. Contributions in the form of new articles and editing can be made by anyone at anytime anywhere in the world. With over 33,000 articles for surgeons and patients, including news, articles, operation scripts, biographies and images. The website is a "wiki" which allows for the collaborative editing and building of content. Contributions in the form of new articles and editing can be made by those who register on the site, confirm their email address and whose application is accepted by an administrator. Wikisurgery is more than just a depot of surgical knowledge, not just articles about facts but also articles about controversy, debates, with none of the usual editorial limits on space. Indeed we hope it will become a record of surgical thought, experience and progression. Key Topics: Operation Scripts, Basic Surgical Skills Training Program, Basic Laparoscopic Training Program, Operative Images, Patient Information; there is also a Featured Videos section.

Proper citation: WikiSurgery - The Free Surgical Encyclopedia (RRID:SCR_003556) Copy   

•   Source: SciCrunch Registry

http://c-path.org/programs/pkd/

Consortium to develop evidence supporting the use of imaging Total Kidney Volume (TKV) as a prognostic biomarker that predicts the progression of Autosomal Dominant Polycystic Kidney Disease (ADPKD) to select patients likely to respond to therapy into clinical trials. It aims to replace the currently used measurement of glomerular filtration rate (GFR). Scientists will use the data collected to develop a disease progression model that will evaluate the relationship between TKV and the known complications of ADPKD, including rate of loss of kidney function, hypertension, gross hematuria, kidney stones, urinary tract infections, development of end-stage renal disease, and mortality. These analyses will be used to support the regulatory qualification of TKV as an accepted measure for assessing the progression of ADPKD in clinical trials in which new therapies are tested. PKDOC has the following goals: # Develop standard clinical data elements and definitions that are specific to ADPKD # Create a database of aggregated data from existing multiple, longitudinal, and well-characterized research registries maintained over decades by the leading institutions in ADPKD clinical investigation # Advance and harmonize the missions of regulatory agencies by creating tools that help with the evaluation of new pharmaceutical compounds # Develop a quantitative disease progression model to examine the linkage between TKV and disease outcomes

Proper citation: Polycystic Kidney Disease Outcomes Consortium (RRID:SCR_003674) Copy   

•   Source: SciCrunch Registry

http://vano.cellexplorer.org/

VANO is a Volume image object AnNOtation System for 3D multicolor image stacks, developed by Hanchuan Peng, Fuhui Long, and Gene Myers. VANO provides a well-coordinated way to annotate hundreds or thousands of 3D image objects. It combines 3D views of images and spread sheet neatly, and is just easy to manage 3D segmented image objects. It also lets you incorporate your segmentation priors, and lets you edit your segmentation results! This system has been used in building the first digital nuclei atlases of C. elegans at the post-embryonic stage (joint work with Stuart Kim lab, Stanford Univ), the single-neuron level fruit fly neuronal atlas of late embryos (with Chris Doe lab, Univ of Oregon, HHMI), and the compartment-level of digital map(s) of adult fruit fly brains (several labs at Janelia Farm, HHMI). VANO is cross-platform software. Currently the downloadable versions are for Windows (XP and Vista) and Mac (Intel-chip based, Leopard or Tiger OS). If you need VANO for different systems (such as 64bit or 32bit, Redhat Linux, Ubuntu, etc), you can either compile the software, or send an email to pengh (at) janelia.hhmi.org. VANO is Open-Source. You can download both the source code files and pre-complied versions at the Software Downloads page.

Proper citation: Volume image object AnNOtation System (RRID:SCR_003393) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/BIRN/Projects/Mouse/

Animal model data primarily focused on mice including high resolution MRI, light and electron microscopic data from normal and genetically modified mice. It also has atlases, and the Mouse BIRN Atlasing Toolkit (MBAT) which provides a 3D visual interface to spatially registered distributed brain data acquired across scales. The goal of the Mouse BIRN is to help scientists utilize model organism databases for analyzing experimental data. Mouse BIRN has ended. The next phase of this project is the Mouse Connectome Project (https://www.nitrc.org/projects/mcp/). The Mouse BIRN testbeds initially focused on mouse models of neurodegenerative diseases. Mouse BIRN testbed partners provide multi-modal, multi-scale reference image data of the mouse brain as well as genetic and genomic information linking genotype and brain phenotype. Researchers across six groups are pooling and analyzing multi-scale structural and functional data and integrating it with genomic and gene expression data acquired from the mouse brain. These correlated multi-scale analyses of data are providing a comprehensive basis upon which to interpret signals from the whole brain relative to the tissue and cellular alterations characteristic of the modeled disorder. BIRN's infrastructure is providing the collaborative tools to enable researchers with unique expertise and knowledge of the mouse an opportunity to work together on research relevant to pre-clinical mouse models of neurological disease. The Mouse BIRN also maintains a collaborative Web Wiki, which contains announcements, an FAQ, and much more.

Proper citation: Mouse Biomedical Informatics Research Network (RRID:SCR_003392) Copy   

•   Source: SciCrunch Registry

http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/UCM382536.pdf

Urinary kidney biomarkers (KIM-1, albumin, total protein, 2-microglobulin, cystatin C, clusterin and trefoil factor-3) that are considered acceptable biomarkers for the detection of acute drug-induced nephrotoxicity in rats and can be included along with traditional clinical chemistry markers and histopathology in toxicology studies. These biomarkers may be used voluntarily as additional evidence of nephrotoxicity in nonclinical safety assessment studies to complement the standard data (BUN and sCr). In ROC analyses, some of these biomarkers showed better sensitivity and specificity than BUN and sCr relative to histopathological alterations considered to be the gold standard when tested with a limited number of nephrotoxicant and control compounds.

Proper citation: PSTC Nephrotoxicity Biomarkers (RRID:SCR_003709) Copy   

•   Source: SciCrunch Registry

http://www.mindtouch.com/

A web based social authoring and publishing environment that adheres to open standards and RESTful design principals. It provides wiki-like ease of use with a sophisticated web services framework for rapid application development, creating flexible workflows and rapid integration. MindTouch creates a vibrant real-time information fabric by federating content from across enterprise silos, such as CRM, ERP, file servers, email, databases, web services and more.

Proper citation: Mindtouch DekiWiki (RRID:SCR_003425) Copy   

•   Source: SciCrunch Registry

http://www.addconsortium.org/

A collaborative network among university-led drug discovery centers and programs to allow scientists to exchange technical expertise on drug discovery and development strategies as well as form partnerships with each other, biopharma companies, and drug discovery-focused contract service organizations and consultants. The website will also serve as a repository for drug discovery events, educational material, job postings, and partnership opportunities. Through active member participation this website will become a valuable tool for every scientist working in the drug discovery arena. In addition, involvement of members will enable them to effectively advocate to the NIH and other funding agencies to increase the awareness of the growing number of academic drug discovery scientists and their success as well as their needs.

Proper citation: Academic Drug Discovery Consortium (RRID:SCR_003706) Copy   

•   Source: SciCrunch Registry

http://www.nanostring.com/products/nSolver

Data analysis software program that offers nCounter users the ability to QC, normalize, and analyze data without having to purchase additional software packages.

Proper citation: nSolver Analysis Software (RRID:SCR_003420) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/mimiplugin.html

The Cytoscape MiMI Plugin is an open source interactive visualization tool that you can use for analyzing protein interactions and their biological effects. The Cytoscape MiMI Plugin couples Cytoscape, a widely used software tool for analyzing bimolecular networks, with the MiMI database, a database that uses an intelligent deep-merging approach to integrate data from multiple well-known protein interaction databases. The MiMI database has data on 119,880 molecules, 330,153 interactions, and 579 complexes. By querying the MiMI database through Cytoscape you can access the integrated molecular data assembled in MiMI and retrieve interactive graphics that display protein interactions and details on related attributes and biological concepts. You can interact with the visualization by expanding networks to the next nearest neighbors and zooming and panning to relationships of interest. You also can perceptually encode nodes and links to show additional attributes through color, size and the visual cues. You can edit networks, link out to other resources and tools, and access information associated with interactions that has been mined and summarized from the research literature information through a biology natural language processing database (BioNLP) and a multi-document summarization system, MEAD. Additionally, you can choose sub-networks of interest and use SAGA, a graph matching tool, to match these sub-networks to biological pathways.

Proper citation: MiMI Plugin for Cytoscape (RRID:SCR_003424) Copy   

•   Source: SciCrunch Registry

http://graphml.graphdrawing.org/

A file format for graphs that consists of a language core to describe the structural properties of a graph and a flexible extension mechanism to add application-specific data. It is based on XML and is ideally suited as a common denominator for all kinds of services generating, archiving, or processing graphs. Its main features include support of * directed, undirected, and mixed graphs, * hypergraphs, * hierarchical graphs, * graphical representations, * references to external data, * application-specific attribute data, and * light-weight parsers.

Proper citation: GraphML (RRID:SCR_003545) Copy   

•   Source: SciCrunch Registry

http://wiki.healthgrid.org/Main_Page

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 6, 2016. HealthGrid is a wiki dedicated to grids for health. It is maintained as a dynamic knowledge resource for the healthgrid community. The HealthGrid community (a world-wide initiative) gathers individuals from the public and private domain world-wide who are actively exploring the beneficial impact of healthgrid technology on healthcare provision and research.

Proper citation: HealthGrid Wiki (RRID:SCR_003572) Copy   

•   Source: SciCrunch Registry

http://edoctoring.ncl.ac.uk/Public_site/

Online educational tool that brings challenging clinical practice to your computer, providing medical education that is engaging, challenging and interactive. While there is no substitute for real-life direct contact with patients or colleagues, research has shown that interactive online education can be a highly effective and enjoyable method of learning many components of clinical medicine, including ethics, clinical management, epidemiology and communication skills. eDoctoring offers 25 simulated clinical cases, 15 interactive tutorials and a virtual library containing numerous articles, fast facts and video clips. Their learning material is arranged in the following content areas: * Ethical, Legal and Social Implications of Genetic Testing * Palliative and End-of-Life Care * Prostate Cancer Screening and Shared Decision-Making

Proper citation: eDoctoring (RRID:SCR_003336) Copy   

•   Source: SciCrunch Registry

http://www.asf.alaska.edu/

Satellite facility that downlinks, processes, archives, and distributes remote-sensing data to scientific users around the world. Three major components: * Satellite Tracking Ground Station: Part of NASA?s Near Earth Network system of ground stations around the world. * Synthetic Aperture Radar Distributed Active Archive Center (SAR DAAC): ASF maintains the NASA archive of SAR data from a variety of satellites and aircraft, and provides these data and associated specialty support services to U.S. Government-approved researchers in support of NASA?s Earth Science Data and Information System project. * ASF Enterprise Center (ASFE): In support of UAF?s mission to be a student-centered research university, the ASF-E focuses on applications of remote-sensing data, specifically for UAF research. The ASF-E includes the GeoData Center (GDC), which provides data management and archive services for UAF principal investigators and maintains a variety of geophysical data collections in support of scientific research.

Proper citation: Alaska Satellite Facility (RRID:SCR_003610) Copy   

•   Source: SciCrunch Registry

http://core.biotech.hawaii.edu/Bioinformatics.htm

THIS RESOURCE IS NO LONGER IN SERVCE, documented January 28, 2019. Core Facility provides the software and support for computer assisted protein and DNA sequence analysis and database access. The Genetics Computer Group GCG-Wisconsin package is currently available on PBRC's UNIX platform that is accessible via modem or direct connection. The package can be accessed via three interfaces: the command-line interface (UNIX C-shell), the web-based interface (SeqWeb) and the X-Windows based graphics interface (SeqLab). Applications in the package include sequence editing, alignment, comparison, primer design, restriction analysis, mapping, data presentation, database browsing, etc. In addition to local databases, access to remote databases (BLAST) is integrated into the package. The local databases are updated quarterly. Databases available include GenBank, EMBL, PIR-Protein, SWISS-PROT and Restriction Enzymes (REBASE).

Proper citation: GCG/SeqWeb (RRID:SCR_003454) Copy   

•   Source: SciCrunch Registry

https://github.com/alyssafrazee/polyester

An R package designed to simulate RNA sequencing experiments with differential transcript expression. Given a set of annotated transcripts, it will simulate the steps of an RNA-seq experiment (fragmentation, reverse-complementing, and sequencing) and produce files containing simulated RNA-seq reads. Simulated reads can be analyzed using a choice of downstream analysis tools. Polyester has a built-in wrapper function to simulate a case/control experiment with differential transcript expression and biological replicates. Users are able to set the levels of differential expression at transcripts of their choosing. This means they know which transcripts are differentially expressed in the simulated dataset, so accuracy of statistical methods for differential expression detection can be analyzed. Polyester offers several unique features: * Built-in functionality to simulate differential expression at the transcript level * Ability to explicitly set differential expression signal strength * Simulation of small datasets, since large RNA-seq datasets can require lots of time and computing resources to analyze * Generation of raw RNA-seq reads, as opposed to alignments or transcript-level abundance estimates * Transparency/open-source code

Proper citation: Polyester (RRID:SCR_003602) Copy   

•   Source: SciCrunch Registry

http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry

http://caties.cabig.upmc.edu/

The Cancer Text Information Extraction System (caTIES) provides tools for de-identification and automated coding of free-text structured pathology reports. It also has a client that can be used to search these coded reports. The client also supports Tissue Banking and Honest Broker operations. caTIES focuses on two important challenges of bioinformatics * Information extraction (IE) from free text * Access to tissue. Regarding the first challenge, information from free-text pathology documents represents a vital and often underutilized source of data for cancer researchers. Typically, extracting useful data from these documents is a slow and laborious manual process requiring significant domain expertise. Application of automated methods for IE provides a method for radically increasing the speed and scope with which this data can be accessed. Regarding the second challenge, there is a pressing need in the cancer research community to gain access to tissue specific to certain experimental criteria. Presently, there are vast quantities of frozen tissue and paraffin embedded tissue throughout the country, due to lack of annotation or lack of access to annotation these tissues are often unavailable to individual researchers. caTIES has three goals designed to solve these problems: * Extract coded information from free text Surgical Pathology Reports (SPRs), using controlled terminologies to populate caBIG-compliant data structures. * Provide researchers with the ability to query, browse and create orders for annotated tissue data and physical material across a network of federated sources. With caTIES the SPR acts as a locator to tissue resources. * Pioneer research for distributed text information extraction within the context of caBIG. caTIES focuses on IE from SPRs because they represent a high-dividend target for automated analysis. There are millions of SPRs in each major hospital system, and SPRs contain important information for researchers. SPRs act as tissue locators by indicating the presence of tissue blocks, frozen tissue and other resources, and by identifying the relationship of the tissue block to significant landmarks such as tumor margins. At present, nearly all important data within SPRs are embedded within loosely-structured free-text. For these reasons, SPRs were chosen to be coded through caTIES because facilitating access to information contained in SPRs will have a powerful impact on cancer research. Once SPR information has been run through the caTIES Pipeline, the data may be queried and inspected by the researcher. The goal of this search may be to extract and analyze data or to acquire slides of tissue for further study. caTIES provides two query interfaces, a simple query dashboard and an advanced diagram query builder. Both of these interfaces are capable of NCI Metathesaurus, concept-based searching as well as string searching. Additionally, the diagram interface is capable of advanced searching functionalities. An important aspect of the interface is the ability to manage queries and case sets. Users are able to vet query results and save them to case sets which can then be edited at a later time. These can be submitted as tissue orders or used to derive data extracts. Queries can also be saved, and modified at a later time. caTIES provides the following web services by default: MMTx Service, TIES Coder Service

Proper citation: caTIES - Cancer Text Information Extraction System (RRID:SCR_003444) Copy   

•   Source: SciCrunch Registry