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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://labs.nri.ucsb.edu/reese/benjamin/SA3D.html

A user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. The analyses use files containing sets of X, Y, Z coordinates. These point patterns are frequently coordinates of cells of specific cell classes within in volumes of tissue derived from microscopy analyses. The analyses are scale independent so spatial analyses of coordinates from larger and smaller scale distributions are possible. The software can also generate sample sets of X, Y, Z coordinates for program exploration and modeling purposes.

Proper citation: Spatial Analysis 3D (RRID:SCR_002563) Copy   

•   Source: SciCrunch Registry

http://mind.loni.usc.edu

The MiND: Metadata in NIfTI for DWI framework enables data sharing and software interoperability for diffusion-weighted MRI. This site provides specification details, tools, and examples of the MiND mechanism for representing important metadata for DWI data sets at various stages of post-processing. MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users. To assist both users and developers in working with MiND-formatted files, we provide a number of software tools for download. * MiNDHeader A utility for inspecting MiND-extended files. * I/O Libraries Programming libraries to simplify writing and parsing MiND-formatted data. * Sample Files Example files for each MiND schema. * DIRAC LONI''s Diffusion Imaging Reconstruction and Analysis Collection is a DWI processing suite which utilizes the MiND framework.

Proper citation: LONI MiND (RRID:SCR_004820) Copy   

•   Source: SciCrunch Registry

http://ki.se/meb/star

Large, ongoing, multifactorial study based on nation-wide ascertainment of patients with schizophrenia and bipolar disorder through the Swedish Twin Registry to include both neuroimaging data, neurocognitive function, molecular genetic data and early adverse environmental factors in the same model in a genetic sensitive design. Swedish schizophrenia research will benefit from this large study database of in total 240 affected and healthy twin pairs collected over a 5 year period. The specific aims are: * To elucidate neural endophenotypes for schizophrenia and bipolar disorder and to clarify the extent of overlap in these features between the two syndromes. * To investigate candidate genes and genomic regions for linkage and association with neural endophenotypes for schizophrenia and bipolar disease. * To determine the contributions of adverse prenatal and perinatal conditions to neural changes associated with schizophrenia and bipolar disease. Types of samples * EDTA whole blood * DNA * RNA Number of sample donors: 251 (June 2010)

Proper citation: KI Biobank - STAR (RRID:SCR_005923) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/pediatric_mri

A database which contains longitudinal structural MRIs, spectroscopy, DTI and correlated clinical/behavioral data from approximately 500 healthy, normally developing children, ages newborn to young adult.

Proper citation: NIH Pediatric MRI Data Repository (RRID:SCR_014149) Copy   

•   Source: SciCrunch Registry

http://pklab.med.harvard.edu/scde/pagoda.links.html

Software tool for analyzing transcriptional heterogeneity to detect statistically significant ways in which measured cells can be classified. Used to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Proper citation: PAGODA (RRID:SCR_017099) Copy   

•   Source: SciCrunch Registry

https://github.com/FeeLab/seqNMF

Software tool for unsupervised discovery of sequential structure. Used to detect sequences in neural data generated by internal behaviors, such as animal thinking or sleeping. Used for unsupervised discovery of temporal sequences in high dimensional datasets in neuroscience without reference to external markers.

Proper citation: seqNMF (RRID:SCR_017068) Copy   

•   Source: SciCrunch Registry

http://erpinfo.org/erplab

A set of open source, freely available Matlab routines for analyzing Event Related Potential (ERP) data. It is tightly integrated with the EEGLAB Toolbox. ERPLAB routines can be accessed from the Matlab command window and from Matlab scripts in addition to being accessed from the EEGLAB GUI. Consequently, ERPLAB provides the ease of learning of a GUI-based system but also provides the power and flexibility of a scripted system.The development of ERPLAB Toolbox is being coordinated by Steve Luck and Javier Lopez-Calderon at the UC-Davis Center for Mind & Brain, with financial support from NIMH.

Proper citation: ERPLAB (RRID:SCR_009574) Copy   

•   Source: SciCrunch Registry

https://github.com/hakyimlab/PrediXcan

Software tool to detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. Used to test the molecular mechanisms through which genetic variation affects phenotype.

Proper citation: PrediXcan (RRID:SCR_016739) Copy   

•   Source: SciCrunch Registry

http://neuroexpresso.org/

Database of mouse brain cell type-specific gene expression datasets. NeuroExpresso is able to demonstrate the use of marker genes for acquiring cell type specific information from whole tissue expression.

Proper citation: NeuroExpresso (RRID:SCR_015724) 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://www.mitre.org/news/digest/archives/2002/neuroinformatics.html

This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality.

Proper citation: MITRE Neuroinformatics (RRID:SCR_006508) Copy   

•   Source: SciCrunch Registry

https://www.phenxtoolkit.org/

Set of measures intended for use in large-scale genomic studies. Facilitate replication and validation across studies. Includes links to standards and resources in effort to facilitate data harmonization to legacy data. Measurement protocols that address wide range of research domains. Information about each protocol to ensure consistent data collection.Collections of protocols that add depth to Toolkit in specific areas.Tools to help investigators implement measurement protocols.

Proper citation: Phenotypes and eXposures Toolkit (RRID:SCR_006532) Copy   

•   Source: SciCrunch Registry

http://intramural.nimh.nih.gov/

The Division of Intramural Research Programs (DIRP) at the National Institute of Mental Health (NIMH) is the internal research division of the NIMH. NIMH DIRP scientists conduct research ranging from studies into mechanisms of normal brain function, conducted at the behavioral, systems, cellular, and molecular levels, to clinical investigations into the diagnosis, treatment and prevention of mental illness. Major disease entities studied throughout the lifespan include mood disorders and anxiety, schizophrenia, obsessive-compulsive disorder, attention deficit hyperactivity disorder, and pediatric autoimmune neuropsychiatric disorders. Because of its outstanding resources, unique funding mechanisms, and location in the nation''s capital, the DIRP is viewed as a national resource, providing unique opportunities in mental health research and research training. Training is conducted in all the Institute''s clinical branches and basic neuroscience laboratories located on the 305-acre National Institutes of Health campus in Bethesda, Maryland. In addition to individualized trainee/mentor-driven postdoctoral training opportunities in the clinical and basic sciences, the DIRP offers Postbaccalaureate Research Training Awards, a Clinical Electives Program, as well as a variety of Summer Research Fellowships and an Undergraduate Internship Program. The mission of the division is to plan and conduct basic, clinical, and translational research to advance understanding of the diagnosis, causes, treatment, and prevention of mental disorders through the study of brain function and behavior; conduct state-of-the-art research that, in part, complements extramural research activities and exploits the special resources of the National Institutes of Health; and provide an environment conducive to the training and development of clinical and basic scientists. In addition the DIRP fosters standards of excellence in the ethical treatment and the provision of clinical care to research subjects; serve as a resource to the NIMH in responding to requests made by the Administration, members of Congress, and citizens'' groups for information regarding mental disorders; and analyzes and evaluates national needs and research opportunities and provides advice to the Institute Director on matters of scientific interest. Core Facilities: * Functional MRI Core * Magnetic Resonance Core * Magnetoencephalography Core * Microarray Core * Neurophysiology Imaging Facility * Non-Human Primate Core * Scientific and Statistical Computing Core * Section on Instrumentation Core * Transgenic Core * Veterinary Medicine Resources

Proper citation: NIMH Division of Intramural Research Programs (RRID:SCR_006860) Copy   

•   Source: SciCrunch Registry

http://brainmaps.org

An interactive multiresolution brain atlas that is based on over 20 million megapixels of sub-micron resolution, annotated, scanned images of serial sections of both primate and non-primate brains and integrated with a high-speed database for querying and retrieving data about brain structure and function. Currently featured are complete brain atlas datasets for various species, including Macaca mulatta, Chlorocebus aethiops, Felis catus, Mus musculus, Rattus norvegicus, Tyto alba and many other vertebrates. BrainMaps is currently accepting histochemical, immunocytochemical, and tracer connectivity data, preferably whole-brain. In addition, they are interested in EM, MRI, and DTI data.

Proper citation: BrainMaps.org (RRID:SCR_006878) Copy   

•   Source: SciCrunch Registry

http://www.webgestalt.org/

Web based gene set analysis toolkit designed for functional genomic, proteomic, and large-scale genetic studies from which large number of gene lists (e.g. differentially expressed gene sets, co-expressed gene sets etc) are continuously generated. WebGestalt incorporates information from different public resources and provides a way for biologists to make sense out of gene lists. This version of WebGestalt supports eight organisms, including human, mouse, rat, worm, fly, yeast, dog, and zebrafish.

Proper citation: WebGestalt: WEB-based GEne SeT AnaLysis Toolkit (RRID:SCR_006786) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/cn3/index3.html

Multidisciplinary research team devoted to the study of basic neuroscience with a specific interest in the description and generation of dendritic morphology, and in its effect on neuronal electrophysiology. In the long term, they seek to create large-scale, anatomically plausible neural networks to model entire portions of a mammalian brain (such as a hippocampal slice, or a cortical column). Achievements by the CNG include the development of software for the quantitative analysis of dendritic morphology, the implementation of computational models to simulate neuronal structure, and the synthesis of anatomically accurate, large scale neuronal assemblies in virtual reality. Based on biologically plausible rules and biophysical determinants, they have designed stochastic models that can generate realistic virtual neurons. Quantitative morphological analysis indicates that virtual neurons are statistically compatible with the real data that the model parameters are measured from. Virtual neurons can be generated within an appropriate anatomical context if a system level description of the surrounding tissue is included in the model. In order to simulate anatomically realistic neural networks, axons must be grown as well as dendrites. They have developed a navigation strategy for virtual axons in a voxel substrate.

Proper citation: Computational Neuroanatomy Group (RRID:SCR_007150) 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://intramural.nimh.nih.gov/sscc/index.html

Scientific and Statistical Computing Core of the NIMH Intramural Research Program supporting functional neuroimaging research at the NIH. This includes development of new data analysis techniques, their implementation in the AFNI software, advising researchers on the analysis methods, and instructing them in the use of software tools. Support methods: A. Provision of software for analysis for FMRI data (AFNI package: http://afni.nimh.nih.gov) * AFNI has been developed for the last 10 years by Dr Cox, et al. (6 years in Milwaukee, 4 years at NIMH) * Formal and informal instruction in the use of AFNI, including outlines of the statistical methods used in the programs * Installation of AFNI on NIH computers (Mac OS X, Unix, Linux) approximately 120 NIH systems have used AFNI in the last month (80 NIMH, 20 NINDS, 20 other) * Realtime monitoring of FMRI data at scanners * Continuing development of new modules for AFNI to meet needs of NIH researchers B. Consulting with NIH researchers about FMRI data analysis issues, concerns, and methods

Proper citation: NIMH DIRP Scientific and Statistical Computing Core (RRID:SCR_006958) Copy   

•   Source: SciCrunch Registry

http://brainml.org/goto.do?page=.home

Set of standards and practices for using XML to facilitate information exchange between user application software and neuroscience data repositories. It allows for common shared library routines to handle most of the data processing, but also supports use of structures specialized to the needs of particular neuroscience communities. This site also serves as a repository for BrainML models. (A BrainML model is an XML Schema and optional vocabulary files describing a data model for electronic representation of neuroscience data, including data types, formats, and controlled vocabulary. ) It focuses on layered definitions built over a common core in order to support community-driven extension. One such extension is provided by the new NIH-supported neuroinformatics initiative of the Society for Neuroscience, which supports the development of expert-derived terminology sets for several areas of neuroscience. Under a cooperative agreement, these term lists will be made available Open Source on this site.
The repository function of this site includes the following features:
* BrainML models are published in searchable, browsable form.
* Registered users may submit new models or new versions of existing models to accommodate data of interest. * BrainML model schema and vocabulary files are made available at fixed URLs to allow software applications to reference them.
* Users can check models and/or instance documents for correct format before submitting them using an online validation service.
To complement the BrainML modeling language, a set of protocols have been developed for BrainML document exchange between repositories and clients, for indexing of repositories, and for data query.

Proper citation: BrainML (RRID:SCR_007087) Copy   

•   Source: SciCrunch Registry