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http://www.textpresso.org/

An information extracting and processing package for biological literature that can be used online or installed locally via a downloadable software package, http://www.textpresso.org/downloads.html Textpresso's two major elements are (1) access to full text, so that entire articles can be searched, and (2) introduction of categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., methods, etc). A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature. The Textpresso project serves the biological and biomedical research community by providing: * Full text literature searches of model organism research and subject-specific articles at individual sites. Major elements of these search engines are (1) access to full text, so that the entire content of articles can be searched, and (2) search capabilities using categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or identify one (e.g., cell, gene, allele, etc). The search engines are flexible, enabling users to query the entire literature using keywords, one or more categories or a combination of keywords and categories. * Text classification and mining of biomedical literature for database curation. They help database curators to identify and extract biological entities and facts from the full text of research articles. Examples of entity identification and extraction include new allele and gene names and human disease gene orthologs; examples of fact identification and extraction include sentence retrieval for curating gene-gene regulation, Gene Ontology (GO) cellular components and GO molecular function annotations. In addition they classify papers according to curation needs. They employ a variety of methods such as hidden Markov models, support vector machines, conditional random fields and pattern matches. Our collaborators include WormBase, FlyBase, SGD, TAIR, dictyBase and the Neuroscience Information Framework. They are looking forward to collaborating with more model organism databases and projects. * Linking biological entities in PDF and online journal articles to online databases. They have established a journal article mark-up pipeline that links select content of Genetics journal articles to model organism databases such as WormBase and SGD. The entity markup pipeline links over nine classes of objects including genes, proteins, alleles, phenotypes, and anatomical terms to the appropriate page at each database. The first article published with online and PDF-embedded hyperlinks to WormBase appeared in the September 2009 issue of Genetics. As of January 2011, we have processed around 70 articles, to be continued indefinitely. Extension of this pipeline to other journals and model organism databases is planned. Textpresso is useful as a search engine for researchers as well as a curation tool. It was developed as a part of WormBase and is used extensively by C. elegans curators. Textpresso has currently been implemented for 24 different literatures, among them Neuroscience, and can readily be extended to other corpora of text.

Proper citation: Textpresso (RRID:SCR_008737) Copy   

•   Source: SciCrunch Registry

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

A suite of tools for efficient management of neuroimaging project data. Specifically, DFBIdb was designed to allow users to quickly perform routine management tasks of sorting, archiving, exploring, exporting and organising raw data. DFBIdb was implemented as a collection of Python scripts that maintain a project-based, centralised database that is based on the XCEDE 2 data model. Project data is imported from a filesystem hierarchy of raw files, which is an often-used convention of imaging devices, using a single script that catalogues meta-data into a modified XCEDE 2 data model. During the import process data are reversibly anonymised, archived and compressed. The import script was designed to support multiple file formats and features an extensible framework that can be adapted to novel file formats. Graphical user interfaces are provided for data exploration. DFBIdb includes facilities to export, convert and organise customisable subsets of project data according to user-specified criteria.

Proper citation: DFBIdb (RRID:SCR_009456) Copy   

•   Source: SciCrunch Registry

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

Software to: view dicom files and assemble them into 3D volumes. View and convert between Analyze, Nifti, and Interfile. Classify and organize dicoms and 3D volumes using metadata. Search and report on a collection of scans.

Proper citation: DIAMOND (RRID:SCR_009457) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cbs-tools/

A fully automated processing pipeline for cortical analysis of structural MR images at a resolution of up to 400������m, including skull stripping, whole brain segmentation, cortical extraction, surface inflation and mapping, as well as dedicated tools for profile estimation across the cortical thickness. The tools are released as a set of plug-ins for the MIPAV software package and the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: CBS High-Res Brain Processing Tools (RRID:SCR_009452) Copy   

•   Source: SciCrunch Registry

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

An open-source C++-based application that allows automatic as well as user-interactive segmentation of the Corpus Callosum. Via a Qt-based graphical user interface, CCSeg also performs semi-automatic segmentation.

Proper citation: CCSeg - Corpus Callosum Segmentation (RRID:SCR_009453) Copy   

•   Source: SciCrunch Registry

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

A collection of tools for MRI T1 brain image segmentation in the Windows environment. It helps construct a complete pipeline with necessary preprocessing and postprocessing procedures besides brainparser, the core program of our fast brain segmentation. The execution of the whole pipeline can be completed in 2 hours with good segmentation results. Execution requires: FSL

Proper citation: BrainSolution (RRID:SCR_009447) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE, documented on November 05, 2013. It has been superseeded by the CALATK, available here http://www.calatk.org c++ libraries and applications for performing fluid registration based operations on 2D and 3D images. The registration method is based on the large displacement diffeomorphic mapping (LDDM) registration method and implements discretized fluid registration. This registration method is then applied to time series analysis, cross-sectional atlas building, and longitudinal atlas building. The individual tool components are: * LDDM: Fluid registration between two images. * TimeSeries: Time series analysis of longitudinal data for a single subject. * AtlasBuilder: Cross-sectional atlas building for a population of images. * LongitudinalAtlasBuilder: Longitudinal atlas building for a population of subjects, each with a longitudinal data set. * FRATUtils: A collection of utility functions for working with volumes and time series files

Proper citation: Fluid Registration and Atlas Toolkit (RRID:SCR_009478) Copy   

•   Source: SciCrunch Registry

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

A FSL package for the comprehensive management of large-scale multi-site fMRI projects, including data storage, retrieval, calibration, analysis, multi-modal integration, and quality control.

Proper citation: FBIRN Image Processing Scripts (RRID:SCR_009471) Copy   

•   Source: SciCrunch Registry

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

This code registers linearly and non-linearly Diffusion Weighted Magnetic Resonance Images (DW-MRIs) by extending FLIRT (linear registration of 3D scalar volumes) and FNIRT (non-linear registration of 3D scalar volumes) in the FMRIB Software Library (FSL) to work with 4D volumes. The basis for registering DW-MRIs is the concept of Angular Interpolation (Tao, X., Miller, J. V., 2006. A method forregistering diffusion weighted magnetic resonance images. In: MICCAI. Vol. 9. pp. 594?602), which is implemented and extended to non-linear registration, based on the FLIRT and FNIRT models in FSL. See http://www.frontiersin.org/Brain_Imaging_Methods/10.3389/fnins.2013.00041/abstract. The code does not overwrite FLIRT, FNIRT or any of the FSL C++ code. It is added as FLIRT4D, FNIRT4D and supporting cost functions. The makefiles will however be overwritten to compile the new code, without affecting any version of FSL.

Proper citation: DW-MRI registration in FSL (RRID:SCR_009461) Copy   

•   Source: SciCrunch Registry

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

Tool to assist site staff with curation of data dictionary, data item, and subject item files for preparation to uploading and sharing data with DbGaP resource.

Proper citation: DbGaP Cleaner (RRID:SCR_009462) Copy   

•   Source: SciCrunch Registry

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

This project is used for students enrolled in courses using the JIST framework. Content in this CVS is freely available, but it is not intended for any specific purpose.

Proper citation: JHU Proj. in Applied Medical Imaging (RRID:SCR_009499) Copy   

•   Source: SciCrunch Registry

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

Program to develop generic standards and tools to facilitate the recording, sharing, and reporting of neuroimaging metadata. It is expected that these efforts will greatly improve upon current practices for archiving and sharing neuroscience data. Neuroscience data, particularly those in neuroinformatics related areas such as neuroimaging and electrophysiology, are associated with a rich set of descriptive information often called metadata. For data archive, storage, sharing and re-use, metadata are of equal importance to primary data, as they define the methods and conditions of data acquisition (such as device characteristics, study/experiment protocol and parameters, behavioral paradigms, and subject/patient information), and statistical procedures. A further challenge for datasharing is the rapidly evolving nature of investigative methods and scientific applications.

Proper citation: INCF Neuroimaging Data Sharing (RRID:SCR_009497) Copy   

•   Source: SciCrunch Registry

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

Suite of tools for brain image analysis. Image manipulation, 2D visualization, linear alignment, BBSI, template-based bias correction, skullstrip. GUI Image analysis tools. Now modified to read/write single file nifti (.nii) format. Other packages to be added.

Proper citation: IDeA Lab brain image processing suite (RRID:SCR_009495) Copy   

•   Source: SciCrunch Registry

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

A method for group-wise image registration by pairwisely registering similar images identified using graph theoretic techniques. Particularly, they use sparse coding to estimate image similarity measures among images to be registered, yielding asymmetric, group-wise image similarity measures for each image to others in the group.

Proper citation: Groupwise Image Registration Toolbox (RRID:SCR_009492) Copy   

•   Source: SciCrunch Registry

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

An international effort to establish resources necessary to study the application of neuroimaging measures as (surrogate) biomarkers in Huntington''s Disease (HD). The primary aims are to develop and apply software tools, imaging protocols, quality control procedures, data archiving, data distribution, and participation guidelines that will accelerate existing and prospective imaging studies.

Proper citation: HD Neuro-Informatics (RRID:SCR_009493) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools/tree/master/BRAINSMush

Tool to generate brain volume mask from input of T1 and T2-weighted images alongside a region of interest brain mask. This volume mask omits dura, skull, eyes, etc. The program is built upon ITK and uses the Slicer3 execution model framework to define the command line arguments and can be fully integrated with Slicer3 using the module discovery capabilities of Slicer3.

Proper citation: BRAINSMush (RRID:SCR_009485) Copy   

•   Source: SciCrunch Registry

http://www.pstnet.com/hardware.cfm?ID=91

Instrument that accurately gathers participant responses and verifies signals. The Celeritas Series response units are assembled using high-impact, chemical resistant, medical grade plastic. The response units include a tactile indicator to ensure correct finger placement during experiments and comfortably attach to the participant?s wrists. The units communicate button presses through fiber optic cabling which connects to a Fiber Optic Interface Console located in the control room through an available wave guide. The interface console provides real-time feedback of participant responses via LED indicators and includes a set of switches which can be used to make responses for the participant as needed.

Proper citation: Fiber Optic Button Response System (RRID:SCR_009577) Copy   

•   Source: SciCrunch Registry

http://nrg.wustl.edu/projects/fiv

A tool for visualizing functional and anatomic MRI data.

Proper citation: FIV (RRID:SCR_009575) Copy   

•   Source: SciCrunch Registry

http://www.ncigt.org/pages/Research_Projects/ImagingCoreToolbox/Imaging_Toolkit

This software provides algorithms for the reconstruction of raw MR data. In particular, it supports the reconstruction of accelerated data acquisitions where k-space is subsampled and the Fourier domain encoding is complemented by temporal encoding, spatial encoding, or and/or a constrained reconstruction. This library of functions provides a number of reconstruction algorithms that accurately employ advanced MR imaging methods including: UNFOLD; parallel imaging methods such as SENSE and GRAPPA; Homodyne processing of partial-Fourier data, and gradient field inhomogeneity correction (gradwarp); EPI Nyquist Ghost correction and ramp-sampling gridding. The target audience is research groups who may be interested in exploring or employing advanced MR reconstruction techniques, but don't have the necessary expertise in-house. Inquires may be directed to: ncigt-imaging-toolkit -at- bwh.harvard.edu

Proper citation: NCIGT Fast Imaging Library (RRID:SCR_009609) Copy   

•   Source: SciCrunch Registry

http://www.mazesuite.com/

A complete set of tools that enables researchers to perform spatial and navigational behavior experiments within interactive, easy to create, and extendable (e.g., multiple rooms) 3D virtual environments. MazeSuite can be used to design/edit adapted 3D environments where subjects? behavioral performance can be tracked. Maze Suite consists of three main applications; an editing program to create and alter maps (MazeMaker), a visualization/rendering module (MazeWalker), and finally an analysis/mapping tool (MazeAnalyzer). Additionally, MazeSuite has the capabilities of sending signal pulses to physiological recording devices using standard computer ports. MazeSuite, with all 3 applications, is a unique and complete toolset for researchers who want to easily and rapidly deploy interactive 3D environments. Requirements Maze Suite is designed for Windows 7, Windows Vista and Windows XP. 3D rendering quality depends on available graphics card hardware; OpenGL 2.1 or above compliant is recommended. For Windows XP systems, .NET Framework Version 2.0 or above is required and can be downloaded from Microsoft's website.

Proper citation: MazeSuite (RRID:SCR_009606) Copy   

•   Source: SciCrunch Registry