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

Simulation software that generates pathological ground truth from a healthy ground truth. The software requires an input directory that describes a healthy anatomy (anatomical probabilities, mesh, diffusion tensor image, etc) and then outputs simulation images.

Proper citation: TumorSim (RRID:SCR_002604) Copy   

•   Source: SciCrunch Registry

http://wiki.na-mic.org/Wiki/index.php/2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3

3D Slicer module for automated segmentation of the spine. This is an implementation of a novel model-based segmentation algorithm. This work was presented at the NA-MIC Week in Salt Lake City, Jan 2010.

Proper citation: SpineSegmentation module for 3DSlicer (RRID:SCR_002593) Copy   

•   Source: SciCrunch Registry

http://nipy.org/nitime/

Software library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.

Proper citation: Nitime (RRID:SCR_002504) Copy   

•   Source: SciCrunch Registry

http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

•   Source: SciCrunch Registry

https://neuroscienceblueprint.nih.gov/Resources-Tools/Blueprint-Resources-Tools-Library

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 22, 2023. National initiative to advance biomedical research through data sharing and online collaboration that provides data sharing infrastructure, software tools, strategies and advisory services. Groups may choose whether to share data internally or with external audiences. Hardware and data remain under control of individual user groups.

Proper citation: Biomedical Informatics Research Network (RRID:SCR_005163) Copy   

•   Source: SciCrunch Registry

Issue

https://github.com/spm

Software package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.

Proper citation: SPM (RRID:SCR_007037) Copy   

•   Source: SciCrunch Registry

http://www.pstnet.com/software.cfm?ID=96

Designed for use in an MRI simulator, MoTrak software uses Ascension Technology?s Flock of Birds. The sensor attaches to the subject?s head and determines the position of the head in space relative to the transmitter. The sensor records angular rotations as well as positional displacements from an initially calibrated position. This information is displayed and logged by the program in real-time, allowing observation of head motion in an MRI simulator. In the simulator, the participant can simultaneously be habituated to the MRI environment, while being trained to remain still via feedback from the MoTrak system.

Proper citation: MoTrak Head Motion Tracking System (RRID:SCR_009607) Copy   

•   Source: SciCrunch Registry

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

Aa brain network visualization tool, which can help researchers to visualize structural and functional connectivity patterns from different levels in a quick, easy, and flexible way.

Proper citation: BrainNet Viewer (RRID:SCR_009446) Copy   

•   Source: SciCrunch Registry

http://cytoseg.googlecode.com

A tool for automatic segmentation of 3D biological datasets, with emphasis on 3D electron microscopy. It works best for 3D blob shaped objects like mitochondria, lysosomes, etc. The project is written in Python and uses the pythonxy platform (which includes scipy and ITK image processing tools).

Proper citation: Cytoseg (RRID:SCR_009553) Copy   

•   Source: SciCrunch Registry

http://www.math.mcgill.ca/keith/surfstat

A Matlab toolbox for the statistical analysis of univariate and multivariate surface data using linear mixed effects models and random field theory.

Proper citation: SurfStat (RRID:SCR_007081) Copy   

•   Source: SciCrunch Registry

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

A segmentation tool for the segmentation of a lung from CT images. The sofware can be run in two modes: fully automatic and semi-automatic with manual seeding by the user. The software also allows the user to perform basic filtering operations and manual correction to the segmentation. The VTK-based rendering implementation, along with option to view in axial, coronal, and sagittal, provides the user with better visualization of the segmented lung.

Proper citation: NIH-CIDI Lung Segmentation Tool (RRID:SCR_014150) Copy   

•   Source: SciCrunch Registry

http://cocomac.g-node.org/main/index.php?

Online access (html or xml) to structural connectivity ("wiring") data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies. The main database, contains data from tracing studies on anatomical connectivity in the macaque cerebral cortex. Also available are a variety of tools including a graphical simulation workbench, map displays and the CoCoMac-Paxinos-3D viewer. Submissions are welcome. To overcome the problem of divergent brain maps ORT (Objective Relational Transformation) was developed, an algorithmic method to convert data in a coordinate- independent way based on logical relations between areas in different brain maps. CoCoMac data is used to analyze the organization of the cerebral cortex, and to establish its structure- function relationships. This includes multi-variate statistics and computer simulation of models that take into account the real anatomy of the primate cerebral cortex. This site * Provides full, scriptable open access to the data in CoCoMac (you must adhere to the citation policy) * Powers the graphical interface to CoCoMac provided by the Scalable Brain Atlas * Sports an extensive search/browse wizard, which automatically constructs complex search queries and lets you further explore the database from the results page. * Allows you to get your hands dirty, by using the custom SQL query service. * Displays connectivity data in tabular form, through the axonal projections service. CoCoMac 2 was initiated at the Donders Institute for Brain, Cognition and Behaviour, and is currently supported by the German neuroinformatics node and the Computational and Systems Neuroscience group at the Juelich research institute.

Proper citation: CoCoMac (RRID:SCR_007277) Copy   

•   Source: SciCrunch Registry

http://www.oasis-brains.org/

Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.

Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/colin3t7t

High-field extension of the Colin27 single-subject atlas with additional high-resolution, quantitative, averaged scans at both 3T and 7T.

Proper citation: Colin 3T/7T High-resolution Atlas (RRID:SCR_000160) Copy   

•   Source: SciCrunch Registry

http://www.dian-info.org/default.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. An international research partnership of leading scientists determined to understand a rare form of Alzheimers disease that is caused by a gene mutation and to establish a research database and tissue repository to support research on Alzheimers disease by other investigators around the world. One goal of DIAN is to study possible brain changes that occur before Alzheimers disease is expressed in people who carry an Alzheimers disease mutation. Other family members without a mutation will serve as a comparison group. People in families in which a mutation has been identified will be tracked in order to detect physical or mental changes that might distinguish people who inherited the mutation from those who did not. DIAN currently involves eleven outstanding research institutions in the United States, United Kingdom, and Australia. John C. Morris, M.D., Friedman Distinguished Professor of Neurology at Washington University School of Medicine in St. Louis, is the principal investigator of the project., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DIAN - Dominantly Inherited Alzheimer Network (RRID:SCR_000812) Copy   

•   Source: SciCrunch Registry

http://datacite.labs.orcid-eu.org

Service (Beta) that allows users to search the DataCite Metadata Store, and add their research outputs including datasets, software, and others to their ORCID profile. This should increase the visibility of these research data, and will make it easier to use these data citations in applications that connect to the ORCID Registry. In addition, the service is also providing formatted citations in several popular citation styles, supports COinS, links to related resources, and displays the attached Creative Commons license where this information is available. The DataCite Metadata Store of course also contains many text documents from academic publishers and services such as figshare or PeerJ Preprints, and these works can also be claimed. This tool is a collaborative effort by ORCID, CrossRef and DataCite.

Proper citation: ODIN (RRID:SCR_001386) Copy   

•   Source: SciCrunch Registry

http://ibvd.virtualbrain.org/

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

Proper citation: Internet Brain Volume Database (RRID:SCR_002060) Copy   

•   Source: SciCrunch Registry

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

A set of tools for deriving region of interest (ROI) atlases by whole brain clustering of task or resting state data. This resource also contains several atlases derived by parcellating publicly available resting state fMRI datasets. The initial release will include python scripts and ROI atlases developed to perform the analyses described in Craddock et. al., A whole brain fMRI atlas generated via spatially constrained spectral clustering, which is currently in revision in Human Brain Mapping. The scripts provide all of the tools necessary to derive an ROI atlases using spatially constrained Ncut spectral clustering. The scripts require python, numpy and scipy to run. Source code and parcellations now available! Go to http://ccraddock.github.io/cluster_roi/ for more information.

Proper citation: Spatially Constrained Parcellation (RRID:SCR_002198) Copy   

•   Source: SciCrunch Registry

http://mindboggle.info/data.html

Complete set of free, publicly accessible, downloadable atlases, templates, and individual manually labeled brain image data, the largest collection of publicly available, manually labeled human brains in the world! http://journal.frontiersin.org/article/10.3389/fnins.2012.00171/full

Proper citation: Mindboggle-101 atlases (RRID:SCR_002439) Copy   

•   Source: SciCrunch Registry

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

Symmetric atlas of the primate brain created using 18 cases of rhesus macaques aged 16-34 months. It includes the T1-weighted image (with and without skull), and also tissue segmentation probability maps (white matter, gray matter, CSF, rest), subcortical structures segmentation (amygdala, caudate, hippocampus, pallidus, putamen), and a lobar parcellation map. You can find more details about the creation of this atlas in the following paper : M. Styner, R. Knickmeyer, S. Joshi, C. Coe, S. J. Short, and J. Gilmore. Automatic brain segmentation in rhesus monkeys. In Proc SPIE Vol 6512, Medical Imaging, 2007, pp. 65122 L1-8

Proper citation: UNC Primate Brain Atlas (RRID:SCR_002570) Copy   

•   Source: SciCrunch Registry