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https://brainlife.io/

Free cloud platform for secure neuroscience data analysis. Allows to manage data, processing and results, sharing projects privately with collaborators or publicly with brainlife.io community.Promotes engagement and education in reproducible neuroscience.You can share your neuroimaging data publicly or privately. Data on brainlife.io is organized as Datatypes to allow interoperability between Apps.

Proper citation: brainlife (RRID:SCR_020940) Copy   

•   Source: SciCrunch Registry

https://www.bcforms.org

Software toolkit for concretely describing non-canonical polymers and complexes to facilitate global biochemical networks. Web tool for describing molecular structure of macromolecular complexes, including non canonical monomeric forms, circular topologies, and crosslinks. Describes semantic meaning of whole cell computational models.

Proper citation: BcForms (RRID:SCR_018654) Copy   

•   Source: SciCrunch Registry

https://www.datanator.info

Software toolkit for discovering data needed to build, calibrate, and validate mechanistic models of cells. Integrated database of molecular data for quantitatively modeling cellular behavior. Web application for identifying relevant data for modeling specific organism in specific environmental condition.

Proper citation: Datanator (RRID:SCR_018651) Copy   

•   Source: SciCrunch Registry

http://www.fz-juelich.de/ime/spm_anatomy_toolbox

A MATLAB toolbox which uses three dimensional probabilistic cytoarchitechtonic maps to correlate microscopic, anatomic and functional data of the cerebral cortex. Correlating the activation foci identified in functional imaging studies of the human brain with structural (e.g., cytoarchitectonic) information on the activated areas is a major methodological challenge for neuroscience research. We here present a new approach to make use of three-dimensional probabilistic cytoarchitectonic maps, as obtained from the analysis of human post-mortem brains, for correlating microscopical, anatomical and functional imaging data of the cerebral cortex. We introduce a new, MATLAB based toolbox for the SPM2 software package which enables the integration of probabilistic cytoarchitectonic maps and results of functional imaging studies. The toolbox includes the functionality for the construction of summary maps combining probability of several cortical areas by finding the most probable assignment of each voxel to one of these areas. Its main feature is to provide several measures defining the degree of correspondence between architectonic areas and functional foci. The software, together with the presently available probability maps, is available as open source software to the neuroimaging community. This new toolbox provides an easy-to-use tool for the integrated analysis of functional and anatomical data in a common reference space.

Proper citation: SPM Anatomy Toolbox (RRID:SCR_013273) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/MAST.html

Software as an open source package for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

Proper citation: MAST (RRID:SCR_016340) Copy   

•   Source: SciCrunch Registry

http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Rician_Noise_Removal

Two Slicer3 modules removing rician noise in diffusion tensor MRI

Proper citation: Slicer3 Module Rician noise filter (RRID:SCR_009614) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/BrainParser

Software that uses a novel statistical-learning technique to segment brain regions of interest (ROIs) based on a training set of data and generates 3D MRI volumes. The software comes pre-trained on a provided data set but can be retrained to work with your desired regions of interest.

Proper citation: LONI Brain Parser (RRID:SCR_009572) Copy   

•   Source: SciCrunch Registry

https://github.com/qiicr/dcmqi

Software library to help with the conversion between imaging research formats and the standard DICOM representation for image analysis results. Used to implement conversion of the data stored in commonly used research formats into the standard DICOM representation. Available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer.

Proper citation: dcmqi (RRID:SCR_016933) 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

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM

Software tool for inference using permutation methods. Requires Matlab or Octave. Can be executed from inside either environment, or directly from the shell and can be called from scripts. For users who are familiar with statistics and willing to use experimental analysis tools.

Proper citation: PALM (RRID:SCR_017029) Copy   

•   Source: SciCrunch Registry

https://crispresso.pinellolab.partners.org/submission

Software suite of tools to qualitatively and quantitatively evaluate outcomes of genome editing experiments in which target loci are subject to deep sequencing and provides integrated, user friendly interface. Used for analysis of CRISPR-Cas9 genome editing outcomes from sequencing data. CRISPResso2 provides accurate and rapid genome editing sequence analysis.Used for analysis of deep sequencing data for rapid and intuitive interpretation of genome editing experiments.

Proper citation: CRISPResso (RRID:SCR_021538) Copy   

•   Source: SciCrunch Registry

http://bmsr.usc.edu/software/lysis/

Interactive software of a set of modular programs (each performing a specific task) that provide an integrated computing environment for data analysis and system modeling. Unique capabilities of LYSIS include input-output nonlinear system modeling and the novel methodology of Principal Dynamic Modes (PDMs). LYSIS is currently available in two versions: one for LYSIS 7.1 Windows and one for LYSIS 7.2 Matlab. Early versions are also available for UNIX environments, distributed as source code that can be compiled for each UNIX implementation (e.g., Solaris, HPUX, Linux). Specific features of LYSIS that cannot be found in commercially available packages include the efficient kernel estimation using Laguerre expansions and the use of Principal Dynamic Modes (PDMs). These enable input-output modeling of dynamic nonlinear systems with relatively short data-records (even in the presence of considerable noise). System Requirements * Operating System ** Windows XP/Vista/7 ** Sun/Unix: Solaris 2.x

Proper citation: LYSIS (RRID:SCR_001385) Copy   

•   Source: SciCrunch Registry

https://www.loni.usc.edu/research/software?name=WAIR

A software tool for the quantitative analysis of various n-dimensional (n-D) image registration techniques. The series of 'C' subroutines which comprise the WAIR library can be easily incorporated into the user's site specific programs and adapted to their particular needs. Wavelet-space triangle analysis is applicable for studying a family of warps on single or multiple n-D data sets. For each data set the WAIR routine assigns a positive real number to every warp alignment in the family, and the best warp for the given data will be the one with the smallest value. It uses the original data prior to warping and the target of the warp in determining warp ranking in reduced wavelet space. Cluster group classification (CGC) is applicable for analyzing the overall performance of a family of warps of a group of data sets. A single number is assigned to each registration alignment, based on its group-clustering characteristics. Spread group classification (SGC) gives preference to registration techniques that spread apart baseline versus activation functional signal for group data.

Proper citation: Wavelet Analysis of Image Registration (RRID:SCR_000172) Copy   

•   Source: SciCrunch Registry

http://cng.gmu.edu/brava

A database of digital reconstructions of the human brain arterial arborizations from 61 healthy adult subjects along with extracted morphological measurements. The arterial arborizations include the six major trees stemming from the circle of Willis, namely: the left and right Anterior Cerebral Arteries (ACAs), Middle Cerebral Arteries (MCAs), and Posterior Cerebral Arteries (PCAs).

Proper citation: BraVa (RRID:SCR_001407) Copy   

•   Source: SciCrunch Registry

https://bams1.org/cells/list.php, https://bams1.org/cells/search_bams_ref.php, https://bams1.org/cells/search_by_brain_region.php

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023.BAMS is an online resource for information about neural circuitry. The BAMS Cell view focuses on the major brain regions and which cells are contained therein.

Proper citation: BAMS Cells (RRID:SCR_003531) Copy   

•   Source: SciCrunch Registry

http://www.emouseatlas.org

Detailed multidimensional digital multimodal atlas of C57BL/6J mouse nervous system with data and informatics pipeline that can automatically register, annotate, and visualize large scale neuroanatomical and connectivity data produced in histology, neuronal tract tracing, MR imaging, and genetic labeling. MAP2.0 interoperates with commonly used publicly available databases to bring together brain architecture, gene expression, and imaging information into single, simple interface.Resource to visualise mouse development, identify anatomical structures, determine developmental stage, and investigate gene expression in mouse embryo. eMouseAtlas portal page allows access to EMA Anatomy Atlas of Mouse Development and EMAGE database of gene expression.EMAGE is freely available, curated database of gene expression patterns generated by in situ techniques in developing mouse embryo. EMA, e-Mouse Atlas, is 3-D anatomical atlas of mouse embryo development including histology and includes EMAP ontology of anatomical structure, provides information about shape, gross anatomy and detailed histological structure of mouse, and framework into which information about gene function can be mapped.

Proper citation: eMouseAtlas (RRID:SCR_002981) Copy   

•   Source: SciCrunch Registry

http://nmrresource.ucsd.edu/

Biomedical technology research center that develops new technology for NMR spectroscopy and makes it available to the biomedical research community for structure determination of proteins in biological supramolecular assemblies, such as membrane proteins or virus particles. The principal applications are to membrane-associated proteins; however, the approach is generally applicable to polypeptides that cannot be prepared in forms suitable for X-ray crystallography or multidimensional solution NMR spectroscopy. As a result, there are also applications to viruses and other biological systems. The principal instrumentation consists of high-field NMR spectrometers dedicated to high-resolution solid-state NMR spectroscopy. The spectrometers are capable of the full-range of multiple-resonance experiments on stationary and spinning samples; however, the major emphasis is on methods that utilize mechanically or magnetically oriented samples. Development encompasses preparation of samples, including: * Expression and purification of membrane proteins * Design and construction of instrumentation, especially probes * Implementation of new pulse sequences and other experimental protocols for solid-state NMR spectroscopy * Calculations for the processing of experimental data and protein structure determination from the orientational constraints derived from these data

Proper citation: UCSD Center for NMR Spectroscopy and Imaging of Proteins (RRID:SCR_001401) Copy   

•   Source: SciCrunch Registry

https://med.stanford.edu/lucasmri.html

Biomedical technology research center that develops innovative technologies in five core research areas of magnetic resonance imaging and spectroscopy (MRI/MRS): # image reconstruction, fast imaging and radiofrequency (RF) pulse design methods, # R hardware development, # body imaging methods, # neuroimaging methods. # MR spectroscopy methods. In each of these areas, they capitalize on the long-standing, successful partnership and extensive experience in Stanford's Radiology and Electrical Engineering departments to improve and expand imaging technology for use in basic research and clinical care, and to provide cutting edge opportunities to the extramural community for biomedical research with MRI. Over its more than 18 years of existence, CAMRT has been motivated by and has served a wide base of extramurally sponsored collaborators and service users from leading medical and research institutions. Examples of collaborative projects are the development of real-time functional MRI biofeedback methods for neuroscience and clinical applications such as pain remediation, development of methods to mitigate metal artifacts in musculoskeletal imaging, development of novel RF pulses for many applications, and studies of breast cancer with efficient MRS methods.

Proper citation: Richard M. Lucas Center for Imaging (RRID:SCR_001406) Copy   

•   Source: SciCrunch Registry

http://web.mit.edu/fbml/cmr.shtml#

Biomedical technology research center designated as a biotechnology nuclear magnetic resonance (NMR) resource that hosts research efforts into cancer, neurological diseases, and many other areas. The heart of the 900 MHz magnetic resonance instrument is a superconducting magnet with a field strength of 21 Tesla, the highest field currently available for magnetic resonance spectroscopy, roughly 400,000 times stronger than the earth's magnetic field. Magnetic resonance is a powerful tool for determining the structure of molecules, and has proven especially useful for elucidating the role of proteins in biological processes and diseases. The state of the art facility includes two 17.6 T magnets with a third to be delivered in 2000. With multiple 17.6 T NMR magnets, the CMR is a valuable resource in New England and will continue to serve this research community for years to come. The advent of high magnetic fields has placed demands on the hardware needed to run experiments in these very high fields. The CMR has recently produced new advances in NMR probe technology, in electron magnetic resonance, and in using microwaves to enhance NMR experiments. The results are powerful new methods for performing biological research and they are looking forward to the continued growth of these exciting new areas!

Proper citation: MIT/ Harvard Center for Magnetic Resonance (RRID:SCR_001412) Copy   

•   Source: SciCrunch Registry

http://www.nrims.harvard.edu/

Biomedical technology research center with the focus on the application to biomedical research of a new generation of secondary ion mass spectrometer (SIMS), the Multi-Isotope Imaging Mass Spectrometer (MIMS). MIMS is an ion microscope and an ion counter. MIMS provides high mass separation at high transmission (M/lambdaM > 10,000), high spatial resolution (< 40 nm) and has the unique capability of simultaneously recording several atomic mass images. Of the utmost importance, MIMS makes it possible for the first time (and at the intracellular level) to simultaneously image the distribution and measure the accumulation of molecules labeled with any isotopes, in particular with stable isotopes, for example with 15N. Thus, MIMS allows one to study localization, accumulation and turnover of proteins, fats, sugars and foreign molecules in cellular microdomains, donor-receiver cellular trafficking, stem cell nesting and localization of drugs. Their aim is to be a technological, methodological, and intellectual resource for researchers from a variety of disciplines. They seek to explore and develop the unique capabilities of MIMS and to bring cutting-edge information to biology and medicine that is currently unobtainable using existing technologies.

Proper citation: National Resource for Imaging Mass Spectrometry (RRID:SCR_001416) Copy   

•   Source: SciCrunch Registry