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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
A C++ software framework to develop, simulate and run magnetic resonance sequences on different platforms.
Proper citation: Object-Oriented Development Interface for NMR (RRID:SCR_005974) Copy
A Monte Carlo (MC) solver for photon migration in 3D turbid media. Different from existing MC software designed for layered (such as MCML) or voxel-based media (such as MMC or tMCimg), MMC can represent a complex domain using a tetrahedral mesh. This not only greatly improves the accuracy of the solutions when modeling objects with smooth/complex boundaries, but also gives an efficient way to sample the problem domain to use less memory. The current version of MMC support multi-threaded programming and can give a almost proportional speed-up when using multiple CPU cores.
Proper citation: Mesh-based Monte Carlo (MMC) (RRID:SCR_006950) Copy
http://www.nitrc.org/projects/papaya
A pure JavaScript medical research image viewer, compatible across a range of popular web browsers. The orthogonal viewer supports NIFTI and DICOM files, overlays and atlas labels. It requires Firefox (7+), Chrome (7+), Safari (6+), MobileSafari (iOS 6+), or IE (10+).
Proper citation: Papaya (RRID:SCR_014188) Copy
http://www.nitrc.org/projects/efficient_pt
A Matlab implementation for efficient permutation testing by using matrix completion.
Proper citation: Efficient Permutation Testing (RRID:SCR_014104) Copy
https://as.nyu.edu/research-centers/cbi/resources/Software.html
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Software which converts DICOM images to NIfTI format.
Proper citation: dinifti (RRID:SCR_000303) Copy
http://www.sci.utah.edu/cibc/software/131-shapeworks.html
THIS RESOURCE IS NO LONGER IN SERVICE.Documented on September 2, 2022. Software that is an open-source distribution of a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wide range of shape analysis problems, including nonmanifold surfaces and objects of arbitrary topology. The proposed correspondence point optimization uses an entropy-based minimization that balances the simplicity of the model (compactness) with the accuracy of the surface representations. The ShapeWorks software includes tools for preprocessing data, computing point-based shape models, and visualizing the results.
Proper citation: ShapeWorks (RRID:SCR_000424) Copy
http://sccn.ucsd.edu/wiki/BCILAB
Open Source MATLAB toolbox and EEGLAB plugin for the design, prototyping, testing, experimentation with, and evaluation of Brain-Computer Interfaces (BCIs), and other systems in the same computational framework. It facilitates the design and development of new methods for cognitive state estimation and their use in both offline data analysis and real-time applications. BCILAB includes an easily extensible collection of currently over 100 methods from the literature (covering signal processing, machine learning and BCI-specific methods). Aside from supporting advanced BCI research, a special aim of BCILAB is to facilitate the adoption of machine learning and advanced statistical modeling for functional neuroimaging purposes in tandem with the EEGLAB platform. The toolbox offers multiple different interfaces which link to the same backend functionality, including a GUI, scripting support (MATLAB-based), APIs for real-time processing, and a variety of extension component interfaces. MATLAB programming is not strictly necessary, as most BCILAB features can be accessed from the GUI, although it is required for batch scripting and custom extensions. The strength of MATLAB-based software lies in its resources for leading-edge scientific computing, as well as in the good support for rapid prototyping, but BCI systems developed in it can be used for real-time out-of-lab experimentation, and can in principle be deployed without the need for a MATLAB license. However, due to the complexity and overhead of the MATLAB environment, the system is best used as a research platform, and not as a product development environment -- end-user software is ideally re-implemented in a compiled language, after a suitable approach has been identified and extensively tested. The process of identifying and testing an approach involves more than just computation, but also data exploration and investigation - an area which is helped by the deep integration with the EEGLAB platform. In the future, this integration will be further strengthened, bringing rich statistical learning and signal processing into routine EEG analysis workflows. The toolbox has been developed by C. Kothe at the Swartz Center, inspired by the preceding PhyPA BCI toolbox created by C. Kothe and T. Zander at the Chair for Human-Machine Systems, Berlin Institute of Technology.
Proper citation: BCILAB (RRID:SCR_007013) Copy
http://www.pstnet.com/eprime.cfm
A suite of applications to fulfill all of your computerized experiment needs. Used by more than 15,000 professionals in the research community, E-Prime provides a truly easy-to-use environment for computerized experiment design, data collection, and analysis. E-Prime provides millisecond precision timing to ensure the accuracy of your data. E-Prime's flexibility to create simple to complex experiments is ideal for both novice and advanced users. The E-Prime suite of applications includes: * E-Studio ? Drag and drop graphical interface for experiment design * E-Basic ? Underlying scripting language of E-Prime * E-Run ? Once the experiment is generated with a single click, E-Run affords you the millisecond precision of stimulus presentation, synchronizations, and data collection. * E-Merge ? Merges your single session data files for group analysis * E-DataAid ? Data management utility * E-Recovery ? Recovers data files
Proper citation: E-Prime (RRID:SCR_009567) Copy
https://github.com/hjmjohnson/DTIPrep
DTIPrep performs a Study-specific Protocol based automatic pipeline for DWI/DTI quality control and preparation. This is both a GUI and command line tool. The configurable pipeline includes image/diffusion information check, padding/Cropping of data, slice-wise, interlace-wise and gradient-wise intensity and motion check, head motion and Eddy current artifact correction, and DTI computing.
Proper citation: DWI/DTI Quality Control Tool: DTIPrep (RRID:SCR_009562) Copy
http://genome.sph.umich.edu/wiki/Mach2dat:_Association_with_MACH_output
Software that performs logistic regression, using imputed SNP dosage data and adjusting for covariates.
Proper citation: Mach2dat (RRID:SCR_009599) Copy
http://www.nitrc.org/projects/fvlight/
Light version of the existing tool Fiber Viewer. It includes every clustering methods of Fiber Viewer such as : Lenght, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm. As in the full version you can also display a plane on the fiber. This tool works faster than the full version due to simplified visualizations.
Proper citation: FiberViewerLight (RRID:SCR_009476) Copy
http://www.unc.edu/~yunmli/MaCH-Admix/
A genotype imputation software that is an extension to MaCH for faster and more flexible imputaiton, especially in admixed populations. It has incorporated a novel piecewise reference selection method to create reference panels tailored for target individual(s). This reference selection method generates better imputation quality in shorter running time. MaCH-Admix also separates model parameter estimation from imputation. The separation allows users to perform imputation with standard reference panels + pre-calibrated parameters in a data independent fashion. Alternatively, if one works with study-specific reference panels, or isolated target population, one has the option to simultaneously estimate these model parameters while performing imputation. MaCH-Admix has included many other useful options and supports VCF input files. All existing MaCH documentation applies to MaCH-Admix.
Proper citation: MaCH-Admix (RRID:SCR_009598) Copy
http://www.nitrc.org/projects/finslerbacktr/
Software provided as a sub-project in the Finsler-tractography module: http://www.nitrc.org/projects/finslertract
Proper citation: Fiber-tracking based on Finsler distance (RRID:SCR_009475) Copy
http://www.nitrc.org/projects/fdrw/
Simple and efficient, this application performs the Weighted False Discovery Rate procedure of Benjamini and Hochberg (1997) to correct for multiple testing. The good think is that you can test virtually any number of p-values (even millions) obtained with any test-statistics for any data set. The bonus is that you can assign a-priori weights to give a better chance to those variables that you deem important. In practice, this procedure is powerful only with a relatively small number of p-values.
Proper citation: False Discovery Rate Weighted (RRID:SCR_009473) Copy
Software application which aims to assign metric distances on the space of anatomical images in Computational Anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. As part of these efforts the Center for Imaging Science at Johns Hopkins University developed techniques to not only compare images, but also to visualize the changes and differences. For additional information please refer to: Faisal Beg, Michael Miller, Alain Trouve, and Laurent Younes. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, Volume 61, Issue 2; February 2005. M.I. Miller and A. Trouve and L. Younes, On the Metrics and Euler-Lagrange Equations of Computational Anatomy, Annual Review of biomedical Engineering, 4:375-405, 2002. Software developed with support from National Institutes of Health NCRR grant P41 RR15241.
Proper citation: LDDMM (RRID:SCR_009590) Copy
http://www.loni.usc.edu/Software/moreinfo.php?package=BGE
A JAVA application designed to create taxonomies or hierarchies in order to classify and organize information.
Proper citation: BrainGraph Editor (RRID:SCR_009536) Copy
http://www.openbioinformatics.org/annovar/
An efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform: 1. gene-based annotation. 2. region-based annotation. 3. filter-based annotation. 4. other functionalities. (entry from Genetic Analysis Software)
Proper citation: ANNOVAR (RRID:SCR_012821) Copy
http://www.nitrc.org/projects/se_linux/
Software tools optimized for performing univariate and multivariate imaging genetics analyses while providing practical correction strategies for multiple testing. The goal of this project is to merge two important research directions in modern science, genetics and neuroimaging. This entails combining modern statistical genetic methods and quantitative phenotyping performed with high dimensional neuroimaging modalities. So far, however, standard imaging tools are unable to deal with large-scale genetics data, and standard genetics tools, in turn, are unable to accommodate large size and binary format of the image data. Their focus is to create imaging genetics tools for classical genetic and epigenetic epidemiological analyses such as heritability, pleiotropy, quantitative trait loci (QTL) and genome-wide association (GWAS), gene expression, and methylation analyses optimized for traits derived from structural and functional brain imaging data
Proper citation: Solar Eclipse Imaging Genetics tools (RRID:SCR_009645) Copy
http://www.openbioinformatics.org/gengen/
A suite of free software tools to facilitate the analysis of high-throughput genomics data sets. The package is currently a work-in-progress and infrequently updated.
Proper citation: GenGen (RRID:SCR_013447) Copy
http://www.multifactordimensionalityreduction.org/
Software application that is a data mining strategy for detecting and characterizing nonlinear interactions among discrete attributes (e.g. SNPs, smoking, gender, etc.) that are predictive of a discrete outcome (e.g. case-control status). The MDR software combines attribute selection, attribute construction and classification with cross-validation to provide a powerful approach to modeling interactions. (entry from Genetic Analysis Software)
Proper citation: MDR (RRID:SCR_013427) Copy
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