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https://opticalmapping.info

Platform to provide tutorials and resources in experimental design and data analysis to researchers interested in using optical mapping data.

Proper citation: OpticalMapping.info (RRID:SCR_017146) Copy   

•   Source: SciCrunch Registry

http://www.imgt.org/HighV-QUEST/home.action

Next generation B and T cell sequence alignment and characterization online surface by IMGT. Web portal for immunoglobulin (IG) or antibody and T cell receptor (TR) analysis from NGS high throughput and deep sequencing.

Proper citation: IMGT HighV-QUEST (RRID:SCR_018196) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/factoextra/index.html

R package from CRAN to extract and visualize the results of multivariate data analysis.

Proper citation: factoextra (RRID:SCR_016692) Copy   

•   Source: SciCrunch Registry

https://github.com/neurostuff/NiMARE

Software Python package for coordinate and image based meta analysis of neuroimaging data.

Proper citation: NiMARE (RRID:SCR_017398) Copy   

•   Source: SciCrunch Registry

https://www.iconplc.com/innovation/nonmem/

Software tool for nonlinear mixed effects modelling. Used for population pharmacokinetic and pharmacodynamic analysis and to simulate data and to fit data. Used in the development of new drugs. NONMEM versions up through 6 are the property of the Regents of the University of California, San Francisco, but ICON Development Solutions has exclusive rights to license their use. NONMEM 7 up to the current version is the property of ICON Development Solutions.

Proper citation: NONMEM (RRID:SCR_016986) Copy   

•   Source: SciCrunch Registry

http://redrocksw.com/

Software tool for statistics and data visualization by Red Rock Software, Inc. Provides unparalleled chart selection, data analysis and graph customization capabilities.

Proper citation: Deltagraph (RRID:SCR_018084) Copy   

•   Source: SciCrunch Registry

http://rfmri.org/DPARSF

A MATLAB toolbox forpipeline data analysis of resting-state fMRI that is based on Statistical Parametric Mapping (SPM) and a plug-in software within DPABI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient images interactively or define regions of interest interactively. Users can skip or combine the processing steps in DPARSF advanced edition freely.

Proper citation: DPARSF (RRID:SCR_002372) Copy   

•   Source: SciCrunch Registry

https://www.upf.edu/web/ntsa/downloads/-/asset_publisher/xvT6E4pczrBw/content/2012-nonrandomness-nonlinear-dependence-and-nonstationarity-of-electroencephalographic-recordings-from-epilepsy-patients

THIS RESOURCE IS NO LONGER IN SERVICE, documented November 23, 2020; EEG data set, source code, and results from 7500 signal pairs from 5 epilepsy patients analyzed in the manuscript, Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86, 046206, 2012. All Matlab source codes are included in the file ASR_Sources_2012_10_16.zip. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control.

Proper citation: Bern-Barcelona EEG database (RRID:SCR_001582) Copy   

•   Source: SciCrunch Registry

http://www.genetrap.org/

Consortium represents all publicly available gene trap cell lines, which are available on non-collaborative basis for nominal handling fees. Researchers can search and browse IGTC database for cell lines of interest using accession numbers or IDs, keywords, sequence data, tissue expression profiles and biological pathways, can find trapped genes of interest on IGTC website, and order cell lines for generation of mutant mice through blastocyst injection. Consortium members include: BayGenomics (USA), Centre for Modelling Human Disease (Toronto, Canada), Embryonic Stem Cell Database (University of Manitoba, Canada), Exchangeable Gene Trap Clones (Kumamoto University, Japan), German Gene Trap Consortium provider (Germany), Sanger Institute Gene Trap Resource (Cambridge, UK), Soriano Lab Gene Trap Resource (Mount Sinai School of Medicine, New York, USA), Texas Institute for Genomic Medicine - TIGM (USA), TIGEM-IRBM Gene Trap (Naples, Italy).

Proper citation: International Gene Trap Consortium (RRID:SCR_002305) Copy   

•   Source: SciCrunch Registry

http://icatb.sourceforge.net/fusion/fusion_startup.php

A MATLAB toolbox which implements the joint Independent Component Analysis (ICA), parallel ICA and CCA with joint ICA methods. It is used to to extract the shared information across modalities like fMRI, EEG, sMRI and SNP data. * Environment: Win32 (MS Windows), Gnome, KDE * Operating System: MacOS, Windows, Linux * Programming Language: MATLAB * Supported Data Format: ANALYZE, NIfTI-1

Proper citation: Fusion ICA Toolbox (RRID:SCR_003494) Copy   

•   Source: SciCrunch Registry

http://gephi.org/

Open-source software for network visualization and analysis helping data analysts to intuitively reveal patterns and trends, highlight outliers and tells stories with their data. It uses a 3D render engine to display large graphs in real-time and to speed up the exploration. Gephi combines built-in functionalities and flexible architecture to: explore, analyze, spatialize, filter, cluterize, manipulate and export all types of networks. Gephi runs on Windows, Linux and Mac OS X. Gephi is based on a visualize-and-manipulate paradigm which allow any user to discover networks and data properties. Moreover, it is designed to follow the chain of a case study, from data file to nice printable maps. It is open-source and free (GNU General Public License). Applications: * Exploratory Data Analysis: intuition-oriented analysis by networks manipulations in real time. * Link Analysis: revealing the underlying structures of associations between objects, in particular in scale-free networks. * Social Network Analysis: easy creation of social data connectors to map community organizations and small-world networks. * Biological Network analysis: representing patterns of biological data. * Poster creation: scientific work promotion with hi-quality printable maps. Gephi 0.7 architecture is modular and therefore allows developers to add and extend functionalities with ease. New features like Metrics, Layout, Filters, Data sources and more can be easily packaged in plugins and shared. The built-in Plugins Center automatically gets the list of plugins available from the Gephi Plugin portal and takes care of all software updates. Download, comment, and rate plugins provided by community members and third-party companies, or post your own contributions!

Proper citation: Gephi (RRID:SCR_004293) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.rug.nl/standalone/fiva/

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Currently, seven different modules containing functional information have been implemented: (i) gene regulatory interactions, (ii) cluster of orthologous groups (COG) of proteins, (iii) gene ontologies (GO), (iv) metabolic pathways (v) Swiss Prot keywords, (vi) InterPro domains - and (vii) generic functional categories. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FIVA - Functional Information Viewer and Analyzer (RRID:SCR_005776) Copy   

•   Source: SciCrunch Registry

http://ftp://ftp.geneontology.org/pub/go/www/GO.tools_by_type.term_enrichment.shtml#gobean

GoBean is a Java application for gene ontology enrichment analysis. It utilizes the NetBeans platform framework. Features * Graphical comparison of multiple enrichment analysis results * Versatile filter facility for focused analysis of enrichment results * Effective exploitation of the graphical/hierarchical structure of GO * Evidence code based association filtering * Supports local data files such as the ontology obo file and gene association files * Supports late enrichment methods and multiple testing corrections * Built-in ID conversion for common species using Ensembl biomart service Platform: Windows compatible, Mac OS X compatible, Linux compatible

Proper citation: GoBean - a Java application for Gene Ontology enrichment analysis (RRID:SCR_005808) Copy   

•   Source: SciCrunch Registry

http://www.seqexpress.com/

A comprehensive analysis and visualization software package for gene expression experiments that provides: a number of clustering and analysis techniques; integrated gene expression and analysis result visualizations, integration with the Gene Expression Omnibus; and an optional data sharing architecture. GO is used to assign functional enrichment scores to clusters, using a combination of specially developed techniques and general statistical methods. These results can be explored using the in built ontology browsing tool or through the generated web pages. SeqExpress also supports numerous data transformation, projection, visualization, file export/import, searching, integration (with R), and clustering options.

Proper citation: SeqExpress (RRID:SCR_007075) Copy   

•   Source: SciCrunch Registry

http://www.nmr.mgh.harvard.edu/DOT/resources/homer2/home.htm

Software matlab scripts used for analyzing fNIRS data to obtain estimates and maps of brain activation. Graphical user interface (GUI) for visualization and analysis of functional near-infrared spectroscopy (fNIRS) data.

Proper citation: Homer2 (RRID:SCR_009586) Copy   

•   Source: SciCrunch Registry

http://dsi-studio.labsolver.org

A software for diffusion MR images analysis. The provided functions include reconstruction (DTI, QBI, DSI, and GQI), deterministic fiber tracking, and 3D visualization. It has a window-based interface and operates on Microsoft Windows system.

Proper citation: DSI Studio (RRID:SCR_009557) Copy   

•   Source: SciCrunch Registry

http://rfmri.org/dpabi

Software toolbox for data processing and analysis of brain imaging, evolved from DPARSF (Data Processing Assistant for Resting-State fMRI).

Proper citation: DPABI (RRID:SCR_010501) Copy   

•   Source: SciCrunch Registry

http://www.ntnu.edu/hunt

International biobank storing whole blood and DNA from 200,000 individuals, serum and plasma samples from more than 100,000 individuals as well as urine, RNA tubes, cells, buffy coat and Na-heparin tubes for environmental analysis for as many as 50,000 individuals. All bio-specimens from the HUNT surveys are collected, processed and stored at the HUNT Biobank in Levanger. The National CONOR Biobank is located on the same site, where it serves as a central research repository for DNA samples from all the largest Norwegian health surveys. These make up the Cohorts of Norway (CONOR), which include samples from more than 200,000 individuals. * HUNT 1 was carried out in 1984-1986 to establish the health history of 75,000 people. * HUNT 2, carried out in 1995-1997, focused on the evolution of the health history of 74,000 people. This included blood sample collection from 65,000 people. The data that accompany biospecimens in the biobank are stored in secured computer systems that run complex database management and analysis software. * HUNT 3 was completed in June 2008. 93,210 people were invited to participate in the study, and as of the 6th of June, 2008, 48,289 people participated (52% participation rate). The data, collected by means of questionnaires, interviews, clinical examinations and collection of blood and urine samples, will be ready for analysis in January 2009. * Young-HUNT is the adolescent part of HUNT including participants aged 13-19 years. Young-HUNT1 (1995-97) was conducted as part of HUNT2, 9141 adolescents participated (90% response rate). Young-HUNT2 (2000-01) was a follow-up study of Young-HUNT1, 2400 students participated in both studies (77% of the invited). Young-HUNT3 (2006-08) was a new cross-sectional study as part of HUNT3. This time 8677 adolescents participated (87% response rate). Data collection included self-reported questionnaires, structured interviews, clinical measurements and, in Young-HUNT3, buccal smears. All institutions with research expertise can apply for access to analyze HUNT data. Projects must have recommendations from The Regional Committee for Medical Research in Norway (REK) and be registered with The Norwegian Social Science Data Services (NSD).

Proper citation: Hunt Biobank (RRID:SCR_010626) Copy   

•   Source: SciCrunch Registry

https://github.com/neuropoly/qMRLab

Software for quantitative MR image analysis, simulation, and protocol optimization. It aims to provide the community with a tool for data fitting, plotting, simulation and protocol optimization for a variety of different quantitative models.

Proper citation: qMRLab (RRID:SCR_016256) Copy   

•   Source: SciCrunch Registry

https://biocodecommons.org/

Platform provides resources for genomic observations from collection to analysis and publication. Works with standards community to ensure clear vocabularies and useful ontologies for biological resources and related assets. Biocode Commons is also collaborating on development of Biological Collections Ontology, working to better integrate ontologies, vocabularies, and relevant standards that are related to BCO.

Proper citation: Biocode Commons (RRID:SCR_024553) Copy   

•   Source: SciCrunch Registry