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https://singlecell.pitt.edu/

Core offers single cell sequencing. Provides analysis of transcriptomics, proteomics and epigenomics at single cell level. Provides comprehensive approach for cell characterization and gene expression profiling. Services include single cell gene expression, immune profiling, ATAC, and multiome analysis.

Proper citation: University of Pittsburgh Single Cell Core Facility (RRID:SCR_025110) Copy   

•   Source: SciCrunch Registry

https://shop.sartorius.com/ca/p/incucyte-ai-cell-health-analysis-software-module/BA-04871#

Software for analysis to determine live versus dead cells – no fluorescent dyes needed.

Proper citation: Incucyte Cell-By-Cell Analysis Software Module (RRID:SCR_025367) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages/release/bioc/html/singleCellTK.html

Software R package provides interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. Allows users to integrate tools from various packages at different stages of analysis workflow.

Proper citation: singleCellTK (RRID:SCR_026813) Copy   

•   Source: SciCrunch Registry

https://www.biochem.mpg.de/mass_spectrometry

Mass spectrometry core facility. Facility uses chromatography systems, mass spectrometers and workflows for in-depth analysis of biomolecules.

Proper citation: Max Planck Institute of Biochemistry Mass Spectrometry Core Facility (RRID:SCR_025745) Copy   

•   Source: SciCrunch Registry

https://tracedrawer.com/product/tracedrawer/

Software for evaluating, comparing and presenting real-time interaction data. Used for quantification of kinetics and affinity through curve fitting, with large number of binding models to choose from. Can extract experimental information from measurement, requiring minimal user input.

Proper citation: TraceDrawer (RRID:SCR_025782) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/jrobust/

Software application for analysis of force microscopy recordings, including images and force curves. Allows for fast and reliable processing of single force curves and force maps, providing estimation of mechanical properties of sample.

Proper citation: AtomicJ (RRID:SCR_026023) Copy   

•   Source: SciCrunch Registry

http://confocal.uconn.edu

Facility provides training and access to advanced light microscopy systems at an hourly rate. In addition, we are available to consult with and support users at every stage of a project including: experimental design, sample preparation, image acquisition, analysis, and data preparation.

Proper citation: University of Connecticut Advanced Light Microscopy Core Facility (RRID:SCR_027547) Copy   

•   Source: SciCrunch Registry

https://www.seattlechildrens.org/research/resources/behavioral-phenotyping-core/

Core dedicated to the protocol driven collection, analysis, and reporting of behavioral data using a blend of classic and innovative assays. Supports neuroscience, psychology, pharmacology, genetics, cancer, and development by providing advanced tools and expertise for the precise measurement and interpretation of behavior.

Proper citation: Seattle Childrens Research Institute Behavioral Phenotyping Core Facility (RRID:SCR_026371) Copy   

•   Source: SciCrunch Registry

http://www.synaptosoft.com/MiniAnalysis/

Software tool that detects peaks of any type, any shape, any direction, and any size for neuroscientists who are studying spontaneous activities. Allows detection of virtually any kind of peaks including spontaneous miniature synaptic currents and potentials, action potential spikes, calcium imaging peaks, amperometric peaks, ECG peaks etc. It includes the complex and multiple peak detection algorithm. Has post-detection analyses including essential plots and statistical parameters. Group Analysis provides specialized and detailed analysis options for action potentials, decay fitting, fEPSP/population spikes, amperometry, etc., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Mini Analysis Program (RRID:SCR_002184) Copy   

•   Source: SciCrunch Registry

http://brainproducts.com/productdetails.php?id=17

Software to manage the daily work of analyzing various neurophysiological data. Features include a history tree, automated analysis, various data format readers, and more.

Proper citation: BrainVision Analyzer (RRID:SCR_002356) Copy   

•   Source: SciCrunch Registry

http://perso.telecom-paristech.fr/~cardoso/guidesepsou.html

Blind Source Separation and Independent Component Analysis (ICA) algorithms including: An efficient batch algorithm: JADE and Adaptive algorithms: relative gradient algorithms. Associated papers / documentation are included as well as thoughts on Multi-dimensional independent component analysis. * An efficient batch algorithm: JADE - For off-line ICA, an algorithm has been developed based on the (joint) diagonalization of cumulant matrices. "Good" statistical performance is achieved by involving all the cumulants of order 2 and 4 while a fast optimization is obtained by the device of joint diagonalization. JADE has been successfully applied to the processing of real data sets, such as found in mobile telephony and in airport radar as well as to bio-medical signals (ECG, EEG, multi-electrode neural recordings). The strongest point of JADE for applications of ICA is that it works off-the-shelf (no parameter tuning). They advocate using the code provided as a plug-in replacement for PCA (whenever one is willing to investigate if such a replacement is appropriate). The weakest point of the current implementation is that the number of sources (but not of sensors) is limited in practice (by the available memory) to something like 40 or 50 depending on your computer. The JADE algorithm was originally developed to process complex signals, motivated by applications to digital communications. Another implementation is now available which is tuned to process more efficiently real-valued signals. * Adaptive algorithms: relative gradient algorithms - For adaptive source separation, they have developed a class of equivariant algorithms. This means that their performance is independent of the mixing matrix. They are obtained as stochastic relative gradient algorithms. * Multi-dimensional independent component analysis - Performing ICA on ECG signals with the JADE algorithm, it was realized that an interesting extension of the notion of independent component analysis would be to consider an analysis into linear components that would be "as independent as possible" as in ICA, but would be "livin" in subspaces of dimension greater than 1. This could be called "MICA" for Multi-dimensional Independent Component Analysis.

Proper citation: Blind Source Separation and Independent Component Analysis (RRID:SCR_002812) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.graphpad.com/

Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.

Proper citation: GraphPad Prism (RRID:SCR_002798) Copy   

•   Source: SciCrunch Registry

http://camera.calit2.net/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016; however, the URL provides links to associated projects and data. A suite of data query, download, upload, analysis and sharing tools serving the needs of the microbial ecology research community, and other scientists using metagenomics data.

Proper citation: Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (RRID:SCR_002676) Copy   

•   Source: SciCrunch Registry

https://github.com/UCSFBiomagneticImagingLab/nutmeg

Software MEG/EEG analysis toolbox for reconstructing neural activation and overlaying it onto structural MR images. Toolbox runs under MATLAB in conjunction with SPM2 and can be used with Linux/UNIX, Mac OS X, and Windows platforms.

Proper citation: NUTMEG (RRID:SCR_002748) Copy   

•   Source: SciCrunch Registry

http://tbi.ci.uchicago.edu/

Project to define a roadmap for diffusion MR imaging of traumatic brain imaging and design an infrastructure to implement the recommendations and tested to ensure feasibility, disseminate results, and facilitate deployment and adoption. The research roadmap and infrastructure development will concentrate on three areas: 1) standardization of diffusion imaging methodology, 2) trial design and patient selection for acute or chronic therapy, and 3) development of multi-center collaborations and repositories for evaluating whether advanced diffusion imaging does improve decision making and TBI patients' outcomes. # DTI MRI reproducability: One of the major areas of investigation in this project is to study the reproducibility of data acquisition and image analysis algorithms. Understanding reproducibility defines a base level of deviation from which scans can be analyzed with statistical significance. As part of this work they are also developing site qualification criteria with the intention of setting limits on the MR system minimal performance for acceptable use in TBI evaluation. # Infrastructure for image storage, analysis and visualization: There is a continuing need to refine and extend software methods for diffusion MRI data analysis and visualization. Not only to translate tools into clinical practice, but also to encourage continuation of the innovation and development of new tools and techniques. To deliver upon these goals they are designing and implementing a storage and computational infrastructure to provide access to shared datasets and intuitive interfaces for analysis and visualization through a variety of tools. A strong emphasis has been placed on providing secure data sharing and the ability to add community defined common data elements. The infrastructure is built upon a Software-as-a-Service model, in which tools are hosted and managed remotely allowing users access through well-defined interfaces. The final service will also facilitate composition or orchestration of workflows composed of different analysis and processing tasks (for example using LONI or XNAT pipelines) with the ultimate goal of providing automated no-click evaluations of diffusion MRI data. # Tool development: The final aspect of this project aims to facilitate and encourage tool development and contribution. By providing access to open datasets, they will create a platform on which tool developers can compare and improve and their tools. When tools are sufficiently mature they can be exposed in the infrastructure mentioned above and used by researchers and other developers.

Proper citation: Diffusion MRI of Traumatic Brain Injury (RRID:SCR_001637) Copy   

•   Source: SciCrunch Registry

https://www.cmtk.org/

A Python-based open source toolkit for magnetic resonance connectome mapping, data management, sharing, visualization and analysis. The toolkit includes the connectome mapper (a full DMRI processing pipeline), a new file format for multi modal data and metadata, and a visualization application.

Proper citation: Connectome Mapping Toolkit (RRID:SCR_001644) Copy   

•   Source: SciCrunch Registry

http://dti-tk.sourceforge.net/pmwiki/pmwiki.php

A spatial normalization and atlas construction toolkit optimized for examining white matter morphometry using DTI data with special care taken to respect the tensorial nature of the data. It implements a state-of-the-art registration algorithm that drives the alignment of white matter (WM) tracts by matching the orientation of the underlying fiber bundle at each voxel. The algorithm has been shown to both improve WM tract alignment and to enhance the power of statistical inference in clinical settings. A 2011 study published in NeuroImage ranks DTI-TK the top-performing tool in its class. Key features include: * open standard-based file IO support: NIfTI format for scalar, vector and tensor image volumes * tool chains for manipulating tensor image volumes: resampling, smoothing, warping, registration & visualization * pipelines for WM morphometry: spatial normalization & atlas construction for population-based studies * built-in cluster-computing support: support for open source Sun Grid Engine (SGE) * Interoperability with other popular DTI tools: AFNI, Camino, FSL & DTIStudio * Interoperability with ITK-SNAP: support multi-modal visualization and segmentation

Proper citation: Diffusion Tensor Imaging ToolKit (RRID:SCR_001642) Copy   

•   Source: SciCrunch Registry

http://neuroimage.usc.edu/brainstorm/

Software as collaborative, open source application dedicated to analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. User-Friendly Application for MEG/EEG Analysis.

Proper citation: Brainstorm (RRID:SCR_001761) Copy   

•   Source: SciCrunch Registry

https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/

Software tool to visualize and analyse high throughput mapped sequence data.

Proper citation: SeqMonk (RRID:SCR_001913) Copy   

•   Source: SciCrunch Registry

http://cvlab.epfl.ch/NeuroMorph

A toolset for the morphometric analysis and visualization of 3D models derived from electron microscopy image stacks. It is designed to import, analyze, and visualize mesh models. It has been designed specifically for the morphological analysis of 3D objects derived from serial electron microscopy images of brain tissue, although much of its functionality can be applied to any 3D mesh. These models can be generated by software that allows the images to be segmented so that 3D objects can be built. These objects can be generated by any 3D image segmentation software, such as ilastik or Fiji. The NeuroMorph toolset has been developed as a set of add-ons for Blender, a widely used free and open source 3D modeling software package.

Proper citation: NeuroMorph (RRID:SCR_002091) Copy   

•   Source: SciCrunch Registry