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https://www.uniklinik-freiburg.de/mr-en/research-groups/diffperf/fibertools.html

Implemented under MATLAB, this DTI image processing toolbox provides import-filters for several MR file standards, a processing unit to calculate the diffusion tensors; several GUI based tools to calculate fiber tracks and to evaluate the DTI dataset. The results can be filed as images with 3D impression or can be logged in formatted ASCII files. Tools and features: * DTI Processing Unit: Calculates the diffusion tensors and their eigenvalues and eigenvectors. Different file formats are supported (like DICOM, Bruker, binary files, Matlab structures). The standard SIEMENS and GE diffusion encoding schemes are supported; other schemes have to be defined in a separate text, .m or .mat file. * FiberTracking: ** Fiber tracking is realized by using the FACT algorithm (Mori et al., Annal. Neurol 1999). ** Probabilistic tracking realized by using the PiCo (Parker et al., JMRI 2003) approach but with DTI data as basis. It is possible to extract pathways between two seeds by combining two maps (Kreher et al., NeuroImage 2008). ** Global Fiber Tracking on basis of HARDI or DTI data. The method is based on the approach reported in (Marco Reisert et al: Global fiber reconstruction becomes practical. NeuroImage 54(2):955-62) * FiberViewer: ** Visualization and Navigation through different data modalities like DTI maps, fiber tracks, diffusion main directions. ** Supports different kinds of DTI maps (e.g. FA, Trace, lambda images ) ** Creation and manipulation of mask based ROIs. ** Selection of streamline fibers ** Visualization of probabilistic fiber tracking results ** Documentation by logging statistics of ROIs and fiber tracks into text files. ** Import/Export from/to ANALYZE or Nifti * 3D Visualizer: Visualization of map slices, ROIs, and fiber tracks with 3D impression. * Batch Editor: Automatic processing of high amounts of data. Possibility to link processing with SPM8 easily.

Proper citation: DTI and Fibertools Software Package (RRID:SCR_001641) Copy   

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http://isp.imm.dtu.dk/thor/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022.Center hosting a number of related projects concerning neural networks, functional neuroimaging, multimedia signal processing, and biomedical signal processing. Neuroinformatics is a research field rooted in classical disciplines like signal processing, biology, physics, computer science and engineering. Neuroinformatics combines learning from the brain and learning about the brain. By studying information processing in the brain neuroinformatics invents new computing paradigms (e.g., artificial neural networks) with the objective of understanding the dynamics of the conscious mind. Artificial neural networks is an active neuroinformatics research field, which combines many approaches to adaptive signal processing in solving real world problems. They began using neural networks for general nonlinear adaptive signal processing. Since 1991 the CONNECT groups have participated in the development of neural computing as an advanced, non-linear statistical tool, which has been applied to forecasting within dynamical systems, pattern recognition, and medical image analysis, particularly functional neuroimages. While neural computing has largely been viewed as a black box approach, they have initiated research aimed at opening this black box, using hypertext, multimedia, and interactivity. Their key objective is to convert abstract models into intuitive knowledge through interactive visualization.

Proper citation: THOR Center for Neuroinformatics (RRID:SCR_001400) Copy   

•   Source: SciCrunch Registry

http://globin.cse.psu.edu/

Data and tools for studying the function of DNA sequences, with an emphasis on those involved in the production of hemoglobin. It includes information about naturally-occurring human hemoglobin mutations and their effects, experimental data related to the regulation of the beta-like globin gene cluster, and software tools for comparing sequences with one another to discover regions that are likely to play significant roles.

Proper citation: Globin Gene Server (RRID:SCR_001480) Copy   

•   Source: SciCrunch Registry

https://jhuccs1.us/nash/

Clinical research network to focus on the etiology, contributing factors, natural history, complications, and therapy of nonalcoholic steatohepatitis. They research the nature and underlying cause of Nonalcoholic Steatohepatitis (NASH) and conduct clinical studies on prevention and treatment. Approximately 1,500 pediatric and adult participants throughout the United States and Canada with nonalcoholic fatty liver disease (NAFLD) have enrolled into a database. The NASH CRN has recently reopened the database to enroll additional pediatric and adult participants with NAFLD. Serum, liver tissue, and genomic DNA samples are being collected and stored in the NIDDKrepository for ongoing as well as future studies. A three-arm randomized, placebo-controlled clinical trial of pioglitazone versus vitamin E completed enrollment in 2009. In addition to this adult trial, a similar trial in pediatric NASH patients randomized 180 children to receive treatment with vitamin E, metformin, or placebo.

Proper citation: Nonalcoholic Steatohepatitis Clinical Research Network (RRID:SCR_001519) Copy   

•   Source: SciCrunch Registry

http://www.stjudebgem.org/web/mainPage/mainPage.php

This database contains gene expression patterns assembled from mouse nervous tissues at 4 time points throughout brain development including embryonic (e) day 11.5, e15.5, postnatal (p) day 7 and adult p42. Using a high throughput in situ hybridization approach we are assembling expression patterns from selected genes and presenting them in a searchable database. The database includes darkfield images obtained using radioactive probes, reference cresyl violet stained sections, the complete nucleotide sequence of the probes used to generate the data and all the information required to allow users to repeat and extend the analyses. The database is directly linked to Pubmed, LocusLink, Unigene and Gene Ontology Consortium housed at the National Center for Biotechnology Information (NCBI) in the National Library of Medicine. These data are provided freely to promote communication and cooperation among research groups throughout the world.

Proper citation: Brain Gene Expression Map (RRID:SCR_001517) Copy   

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http://www.well.ox.ac.uk/happy/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package for Multipoint QTL Mapping in Genetically Heterogeneous Animals (entry from Genetic Analysis Software) The method is implemented in a C-program and there is now an R version of HAPPY. You can run HAPPY remotely from their web server using your own data (or try it out on the data provided for download).

Proper citation: Happy (RRID:SCR_001395) Copy   

•   Source: SciCrunch Registry

http://www.retinalmaps.com.au/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. A database of over 700 retinal topography maps of a wide variety of species published in a diversity of journals. It has been assembled to assist vision and neuroscience researchers to locate and compare the distribution of retinal neurons within and across species. The maps can be searched by taxonomic or common name classification, cell type sampled, type of retinal specialization and staining/visualization method. Maps can be compared by selecting multiple maps and clicking the Compare Selected button. An interactive spreadsheet can be also downloaded.

Proper citation: Retinal Topography Maps Database (RRID:SCR_001399) Copy   

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http://www.gudmap.org

Project aggregates and provides experimental gene expression data from genito-urinary system. International consortium providing molecular atlas of gene expression for developing organs of GenitoUrinary (GU) tract. Mouse strains to facilitate developmental and functional studies within GU system. Experimental protocols and standard specifications. Tutorials describing GU organogenesis and primary data via database. Data are from large-scale in situ hybridization screens (wholemount and section) and microarray gene expression data of microdissected, laser-captured and FACS-sorted components of developing mouse genitourinary (GU) system.

Proper citation: GenitoUrinary Development Molecular Anatomy Project (RRID:SCR_001554) Copy   

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http://www.nb.uw.edu/

Biomedical technology research center that provides state-of-the-art surface analysis expertise, instrumentation, experimental protocols, and data analysis methods to address surface-related biomedical problems. NESAC/BIO develops and applies surface science methodologies that produce a full understanding of the surface composition, structure, spatial distribution, and orientation of biomaterials and adsorbed biomolecules. The NESAC/BIO program identifies areas where surface science must evolve to keep pace with the growth in biochemical knowledge and biomaterial fabrication technology, and develops instrumentation, experimental protocols, and data analysis methods to achieve this evolution. NESAC/BIO provides state-of-the-art surface analysis tools to researchers in the biomedical community. You can gain access to the NESAC/BIO facilities in one of the following ways: * Collaborative: Propose a project to collaborate on with NESAC/BIO. The project should be rewarding for both groups, and the results should reflect the utility of surface analysis for biomedical research * Service: Ask NESAC/BIO to analyze your biomaterial specimens. The spectra obtained from the analyses will be interpreted for you. * Training: Visit the University of Washington to receive training in surface analysis and personally run experiments for your individual research projects. These experiments should have a high probability for yielding useful information and should not involve the development of new ESCA techniques or methodologies.

Proper citation: National ESCA and Surface Analysis Center for Biomedical Problems (RRID:SCR_001430) Copy   

•   Source: SciCrunch Registry

https://www.mristudio.org/

An image processing program running under Windows suitable for such tasks as tensor calculation, color mapping, fiber tracking, and 3D visualization. Most of operations can be done with only a few clicks. This tool evolved from DTI Studio. Tools in the program can be grouped in the following way: * Image Viewer * Diffusion Tensor Calculations * Fiber Tracking and Editing * 3D Visualization * Image File Management * Region of Interesting (ROI) Drawing and Statistics * Image Registration

Proper citation: MRI Studio (RRID:SCR_001398) Copy   

•   Source: SciCrunch Registry

http://www.opencolleges.edu.au/informed/learning-strategies/

Interactive infographic of a brain exploring more than 100,000 chemical reactions, highlighted by areas and explanations of what that area is known to do.

Proper citation: Open Colleges Interactive Brain (RRID:SCR_001427) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/study/67

Clinical trial under the Urinary Incontinence Treatment Network to compare the treatment success for two surgical procedures that are frequently used and have similar cure rates, yet have not been compared directly to each other in a large, rigorously conducted randomized trial. The secondary aims of the trial are to compare other outcomes for the two surgical procedures, including quality of life, sexual function, satisfaction with treatment outcomes, complications, and need for other treatment(s) after surgery. Follow-up will be a minimum of two years and up to four years.

Proper citation: SISTEr (RRID:SCR_001542) Copy   

•   Source: SciCrunch Registry

https://github.com/vital-ai/vital-documentation/wiki/Vital-AI-Ontology

Ontology for the four consensus human vital signs: blood pressure, body temperature, respiration rate, pulse rate. It provides a controlled structured vocabulary for describing vital signs measurement data, the various processes of measuring vital signs, and the various devices and anatomical entities participating in such measurements.

Proper citation: Vital Signs Ontology (RRID:SCR_001422) Copy   

•   Source: SciCrunch Registry

http://mugsy.sourceforge.net/

Software resource for multiple whole genome alignment. It uses Nucmer, a custom graph-based segmentation procedure, for pairwise alignment, and the Seqan:TCoffee's multiple alignment strategy.

Proper citation: Mugsy (RRID:SCR_001414) Copy   

•   Source: SciCrunch Registry

http://www.immunetolerance.org/

International clinical research consortium dedicated to the clinical evaluation of novel tolerogenic approaches for the treatment of autoimmune diseases, asthma and allergic diseases, and the prevention of graft rejection. They aim to advance the clinical application of immune tolerance by performing high quality clinical trials of emerging therapeutics integrated with mechanism-based research. In particular, they aim to: * Establish new tolerance therapeutics * Develop a better understanding of the mechanisms of immune function and disease pathogenesis * Identify new biomarkers of tolerance and disease Their goals are to identify and develop treatment game changers for tolerance modulating therapies for the treatment of immune mediated diseases and disabling conditions, and to conduct high quality, innovative clinical trials and mechanistic studies not likely to be funded by other sources or to be conducted by private industry that advance our understanding of immunological disorders. In the Immune Tolerance Network's (ITN) unique hybrid academic/industry model, the areas of academia, government and industry are integral to planning and conducting clinical studies. They develop and fund clinical trials and mechanistic studies in partnership. Their development model is a unique, interactive process. It capitalizes on their wide-ranging, multidisciplinary expertise provided by an advisory board of highly respected faculty from institutions worldwide. This model gives investigators special insight into developing high quality research studies. The ITN is comprised of leading scientific and medical faculty from more than 50 institutions in nine countries worldwide and employs over 80 full-time staff at the University of California San Francisco (UCSF), Bethesda, Maryland and Benaroya Research Institute in Seattle, Washington.

Proper citation: Immune Tolerance Network (ITN) (RRID:SCR_001535) Copy   

•   Source: SciCrunch Registry

http://www.ncibi.org/

The Center develops conceptual models, computational infrastructure, an integrated knowledge repository, and query and analysis tools that enable scientists to effectively access and integrate the wealth of biological data. The National Center for Integrative Biomedical Informatics (NCIBI) was founded in October 2005 and is one of seven National Centers for Biomedical Computing (NCBC) in the NIH Roadmap. NCIBI is based at the University of Michigan as a part of the Center for Computational Medicine and Biology (CCMB). NCIBI is composed of biomedical researchers, computational biologists, computer scientists, developers and human-computer interaction specialists organized into seven major core functions. They work in interdisciplinary teams to collectively develop tools that are not only computationally powerful but also biologically relevant and meaningful. The four initial Driving Biological Projects (prostate cancer progression, Type 1 and type 2 diabetes and bipolar disorder) provide the nucleation point from which tool development is informed, launched, and tested. In addition to testing tools for function, a separate team is dedicated to testing usability and user interaction that is a unique feature of this Center. Once tools are developed and validated the goal of the Center is to share and disseminate data and software throughout the research community both internally and externally. This is achieved through various mechanisms such as training videos, tutorials, and demonstrations and presentations at national and international scientific conferences. NCIBI is supported by NIH Grant # U54-DA021519.

Proper citation: National Center for Integrative Biomedical Informatics (RRID:SCR_001538) Copy   

•   Source: SciCrunch Registry

http://www.farsight-toolkit.org/wiki/FARSIGHT_Toolkit

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23, 2022. A collection of software modules for image data handling, pre-processing, segmentation, inspection, editing, post-processing, and secondary analysis. These modules can be scripted to accomplish a variety of automated image analysis tasks. All of the modules are written in accordance with software practices of the Insight Toolkit Community. Importantly, all modules are accessible through the Python scripting language which allows users to create scripts to accomplish sophisticated associative image analysis tasks over multi-dimensional microscopy image data. This language works on most computing platforms, providing a high degree of platform independence. Another important design principle is the use of standardized XML file formats for data interchange between modules.

Proper citation: Farsight Toolkit (RRID:SCR_001728) Copy   

•   Source: SciCrunch Registry

http://www.stat.cmu.edu/~fiasco/

Collection of software designed to analyze fMRI data using a series of processing steps. The input is the raw data, and the outputs are statistical brain maps showing regions of neural activation. Corrections for different systematic variations in the k-space (raw) data obtained from an fMRI session (head motion, ghosting, etc) are performed first. The image is then reconstructed (using the Fast Fourier Transform) and statistical analyses run. The user has a great deal of flexibility in choosing which corrections and statistics are executed. FIASCO emphasizes correct statistical models, for example for group comparisons.

Proper citation: Functional Image Processing software Computational Olio (RRID:SCR_001689) Copy   

•   Source: SciCrunch Registry

http://talasso.cnb.csic.es/

Tool for quantification of human miRNA-mRNA Interactions. TaLasso is also available as Matlab or R code.

Proper citation: TaLasso (RRID:SCR_001726) Copy   

•   Source: SciCrunch Registry

http://surfer.nmr.mgh.harvard.edu/

Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.

Proper citation: FreeSurfer (RRID:SCR_001847) Copy   

•   Source: SciCrunch Registry