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https://www.leica-microsystems.com/products/confocal-microscopes/p/stellaris-8/

STELLARIS 5 microscope has core system with integrated WLL, combined with proprietary Acousto-Optical Beam Splitter and Power HyD S detectors. Together with the TauSense technology, STELLARIS 5 sets a new standard for the quality of images and quantity of information generated. Imaging with user interface, ImageCompass.

Proper citation: Leica: STELLARIS 5 microscope (RRID:SCR_024663) Copy   

•   Source: SciCrunch Registry

https://www.leica-microsystems.com/products/confocal-microscopes/p/stellaris-8/

STELLARIS 8 microscope with core system addition of spectrum WLL and specialized detector options of Power HyD family to expand range of confocal applications. Can be combined with all Leica Microsystems modalities, including FAst Lifetime CONtrast (FALCON), Deep In Vivo Explorer (DIVE), STED, Digital Light Sheet (DLS) and CARS. STELLARIS 8 new features maximize the potential of these modalities.

Proper citation: Leica: STELLARIS 8 microscope (RRID:SCR_024664) Copy   

•   Source: SciCrunch Registry

https://www.leica-microsystems.com/products/light-microscopes/p/leica-lmd7/

Laser Microdissection system enables users to isolate specific single cells or entire areas of tissue. Powered by unique laser design and dynamic software, Leica LMD systems allow users to easily isolate Regions of Interest from entire areas of tissue down to single cells or even subcellular structures such as chromosomes. LMD7 provides higher laser power. Suited to dissect any kind of tissue independent of its size or shape.

Proper citation: Leica: LMD7 Laser Microdissection microscope (RRID:SCR_024657) Copy   

•   Source: SciCrunch Registry

https://www.leica-microsystems.com/products/light-microscopes/p/leica-lmd7/

Laser Microdissection system enables users to isolate specific single cells or entire areas of tissue. Powered by unique laser design and dynamic software, Leica LMD systems allow users to easily isolate Regions of Interest from entire areas of tissue down to single cells or even subcellular structures such as chromosomes. Leica LMD6 is for standard tissue dissection. Used for standard applications dissecting soft tissues such as brain, liver, or kidney.

Proper citation: Leica: LMD6 Laser Microdissection microscope (RRID:SCR_024658) Copy   

•   Source: SciCrunch Registry

https://webprotege.stanford.edu

Web based platform for editing biomedical ontologies. Web application for editing OWL 2 ontologies. Open source, lightweight, web based ontology editor implemented in Java and JavaScript using OWL API and Google Web Toolkit. For users who do not wish to host their ontologies on Stanford servers, WebProtégé is available as Web app that can be run locally using Servlet container such as Tomcat.

Proper citation: WebProtege (RRID:SCR_024627) Copy   

•   Source: SciCrunch Registry

https://p2sl.berkeley.edu/about/

Research institute dedicated to developing and deploying knowledge and tools for project management. Projects are temporary production systems. Dedicated to developing and deploying knowledge and tools for management of project production systems and the management of organizations that produce and deliver goods and services through such systems.

Proper citation: Project Production Systems Laboratory (RRID:SCR_024641) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/lumina/

A reliable patient response system designed specifically for use in an fMRI. Lumina was developed to satisfy the requirements of both the clinical and research fields.

Proper citation: Lumina LP- 400 Response System (RRID:SCR_009596) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/diffusion-mri/

This program contains Python modules for modeling and reconstruction of diffusion weighted MRI data. It is a subset of the code internally used in the CVGMI lab at the University of Florida. Three different reconstruction methods are currently included in this program, namely, Mixture of Wisharts (MOW), Diffusion Orientation Transform (DOT) and Q-ball Imaging (QBI). This program is mainly developed and maintained by Bing Jian, as part of his Ph.D. research, supervised by Prof. Baba Vemuri. Please note that the source code of this program is hosted at Google Code, see the Source Code link on the left.

Proper citation: Multi-fiber Reconstruction from DW-MRI (RRID:SCR_009509) Copy   

•   Source: SciCrunch Registry

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

This repository stores and provides opportunities for collaboration through Matlab code, libraries, and configuration information for projects in early stage development. The MASI research laboratory concentrates on analyzing large-scale cross-sectional and longitudinal neuroimaging data. Specifically, they are interested in population characterization with magnetic resonance imaging (MRI), multi-parametric studies (DTI, sMRI, qMRI), and shape modeling.

Proper citation: MASIMatlab (RRID:SCR_009506) Copy   

•   Source: SciCrunch Registry

http://www.smivision.com/en/gaze-and-eye-tracking-systems/products/iview-x-mri-meg.html

A non-invasive, long-range eye tracking system for use in the fMRI environment. Some features of the system include: * Elaborate faraday shielding and fiber optics to avoid noise in high-field magnets. * Includes stimulus presentation software ?Experiment Center? and is compatible with 3rd party products such as ?Presentation? by NeuroBS. * Utilizes mirror box customized for large field of view. * Includes powerful analysis software ?BeGaze2? for graphical and statistical analysis of eye movements. * Includes fixation, saccade and blink detection, and area-of-interest based statistics * Real-time data available via digital or analog output

Proper citation: iView X MRI-LR - Eye Tracking for fMRI (RRID:SCR_009627) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/masi-fusion/

Tool that provides a unified framework for testing and applying statistical and voting label fusion techniques. The project will include implementations of several different voting techniques including majority vote, weighted voting, and regionally weighted voting. Additionally, multiple statistical fusion methods will be included, notably, STAPLE, Spatial STAPLE, STAPLER and COLLATE. In addition to the fusion algorithms, code for running specialized simulations and various tools and utilities to test the efficacy of the algorithms will be provided.

Proper citation: MASI Label Fusion (RRID:SCR_009505) Copy   

•   Source: SciCrunch Registry

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

Automatically creates a mask based on the "foreground" of an anatomical scan volume.

Proper citation: BRAINSROIAuto (RRID:SCR_009501) Copy   

•   Source: SciCrunch Registry

http://scalce.sourceforge.net/Home

A FASTQ compression tool that uses locally consistent parsing to obtain better compression rate.

Proper citation: SCALCE (RRID:SCR_009658) 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.labmedmolge.unisa.it/inglese/research/imir

A modular pipeline for comprehensive analysis of smallRNA-Seq data, comprising specific tools for adapter trimming, quality filtering, DE analysis, target prediction by integrating multiple open source modules and resources in an automated workflow.

Proper citation: iMir (RRID:SCR_009496) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/wonglab/SpliceMap/

A de novo splice junction discovery and alignment tool.

Proper citation: SpliceMap (RRID:SCR_009650) Copy   

•   Source: SciCrunch Registry

http://www.vpixx.com/products/visual-stimulators/datapixx.html

Supplies a complete multi-function data and video processing USB peripheral for vision research. In addition to a dual-display video processor, the DATAPixx includes an array of peripherals which often need to be synchronized to video during an experiment, including a stereo audio stimulator, a button box port for precise reaction-time measurement, triggers for electrophysiology equipment, and even a complete analog I/O subsystem. Because we implemented the video controller and peripheral control on the same circuit board, you can now successfully synchronize all of your subject I/O to video refresh with microsecond precision.

Proper citation: DATAPixx (RRID:SCR_009648) Copy   

•   Source: SciCrunch Registry

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

To enable widespread application of the Biological parametric mapping (BPM) approach, they introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Biological parametric mapping (BPM) has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects.

Proper citation: Robust Biological Parametric Mapping (RRID:SCR_009642) Copy   

•   Source: SciCrunch Registry

http://www.connectomics.org/cfflib/

A container format for multi-modal neuroimaging data. It comprises connectome objects of type: CMetadata, CNetwork, CVolume, CSurface, CTrack, CScript, CData, CTimeseries, CImagestack. The Python library cfflib provides read/write functionality.

Proper citation: Connectome File Format (RRID:SCR_009551) Copy   

•   Source: SciCrunch Registry

http://www.cise.ufl.edu/~tichen/cdfHC.zip

A Matlab demo for group wise point set registration using a novel CDF-based Havrda-Charvat Divergence, which is based on the paper: Ting Chen, Baba C. Vemuri, Anand Rangarajan and Stephan J. Eisenschenk, Group-wise Point-set registration using a novel CDF-based Havrda-Charvat Divergence. In IJCV : International Journal of Computer Vision, 86(1):111-124, January, 2010.

Proper citation: CDF-HC PointSetReg (RRID:SCR_009544) Copy   

•   Source: SciCrunch Registry