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http://connectomics.org/viewer

Extensible, scriptable, pythonic software tool for visualization and analysis in structural neuroimaging research on many spatial scales. Employing the Connectome File Format, diverse data such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The field of Connectomics research benefits from recent advances in structural neuroimaging technologies on all spatial scales. The need for software tools to visualize and analyze the emerging data is urgent. The ConnectomeViewer application was developed to meet the needs of basic and clinical neuroscientists, as well as complex network scientists, providing an integrative, extensible platform to visualize and analyze Connectomics data. With the Connectome File Format, interlinking different datatypes such as hierarchical networks, surface data, volumetric data is easy and might provide new ways of analyzing and interacting with data. Furthermore, ConnectomeViewer readily integrates with: * ConnectomeWiki: a semantic knowledge base representing connectomics data at a mesoscale level across various species, allowing easy access to relevant literature and databases. * ConnectomeDatabase: a repository to store and disseminate Connectome files.

Proper citation: ConnectomeViewer: Multi-Modal Multi-Level Network Visualization and Analysis (RRID:SCR_008312) Copy   

•   Source: SciCrunch Registry

http://www.archer.edu.au/

The ARCHER project is built upon the prototype software developed by the DART (http://dart.edu.au) and ARROW (http://arrow.edu.au) projects to produce a robust set of software tools. These tools: - may be customised to suit the needs of diverse research areas - automate the collection and management of instrument generated data - enable the repository storage of research data and associated metadata - enable collection and tagging of research data in a collaborative environment, and - provide these capabilities in a secure end-to-end proces. :ARCHER developed a ''production-ready'' software tools, operating in a secure environment, to assist researchers to: - collect, capture and retain large data sets from a range of different sources including scientific instruments - deposit data files and data sets to eResearch storage repositories - populate these eResearch data repositories with associated metadata - permit data set annotation and discussion in a collaborative environment, and - support next-generation methods for research publication, dissemination and access.

Proper citation: Australian ResearCH Enabling enviRonment (RRID:SCR_008390) Copy   

•   Source: SciCrunch Registry

http://tuna.tamu.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Map improvement server that returns a bias minimized, 6-fold averaged map generated from a model and diffraction data (with optional preceding Molecular Replacement). It does not build or repair the model for you (yet). For automated model building, you need to install a local copy of CCP4 and ARP/wARP (aka wARP&Trace), RESOLVE, MAID, or TEXTAL.

Proper citation: TB Consortium Bias Removal Server (RRID:SCR_008425) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding supervised classification, gene selection, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting (WV) and k-nearest neighbor (KNN) algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. It enhances the clustering capabilities of GeneCluster 1.0 by adding a module for batch SOM clustering, and also includes a marker gene finder based on a KNN analysis and a visualization module. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.

Proper citation: GeneCluster 2: An Advanced Toolset for Bioarray Analysis (RRID:SCR_008446) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/

Non profit research organization for genome sequences to advance understanding of biology of humans and pathogens in order to improve human health globally. Provides data which can be translated for diagnostics, treatments or therapies including over 100 finished genomes, which can be downloaded. Data are publicly available on limited basis, and provided more extensively upon request.

Proper citation: Wellcome Trust Sanger Institute; Hinxton; United Kingdom (RRID:SCR_011784) Copy   

•   Source: SciCrunch Registry

https://www.immport.org/home

Data sharing repository of clinical trials, associated mechanistic studies, and other basic and applied immunology research programs. Platform to store, analyze, and exchange datasets for immune mediated diseases. Data supplied by NIAID/DAIT funded investigators and genomic, proteomic, and other data relevant to research of these programs extracted from public databases. Provides data analysis tools and immunology focused ontology to advance research in basic and clinical immunology.

Proper citation: The Immunology Database and Analysis Portal (ImmPort) (RRID:SCR_012804) Copy   

•   Source: SciCrunch Registry

http://web.bioinformatics.ic.ac.uk/eqtlexplorer/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 1,2023. eQTL Explorer was developed as a computational resource to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. This visualization tool stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer also provides a new and powerful tool to interrogate these very large and complex datasets. eQTLexplorer allows users to mine and understand data from a repository of genetical genomics experiments. It will graphically display eQTL information based on a certain number of selection criteria, including: tissue type, p-value, cis/trans, probeset Affymetrix id and PQTL type. Sponsors: This work was funded by the MRC Clinical Sciences Centre and the Wellcome Trust programme for Cardiovascular Functional Genomics.

Proper citation: eQTL Visualization Tool (RRID:SCR_013413) Copy   

•   Source: SciCrunch Registry

http://www.hedtags.org/

Strategy guide for HED Annotation. Framework for systematically describing laboratory and real world events.HED tags are comma separated path strings. Organized in forest of groups with roots Event, Item, Sensory presentation, Attribute, Action, Participant, Experiment context, and Paradigm. Used for preparing brain imaging data for automated analysis and meta analysis. Applied to brain imaging EEG, MEG, fNIRS, multimodal mobile brain or body imaging, ECG, EMG, GSR, or behavioral data. Part of Brain Imaging Data Structure standard for brain imaging.

Proper citation: HED Tags (RRID:SCR_014074) Copy   

•   Source: SciCrunch Registry

https://skyline.gs.washington.edu/labkey/project/home/software/Skyline/begin.view

Software tool as Windows client application for targeted proteomics method creation and quantitative data analysis. Open source document editor for creating and analyzing targeted proteomics experiments. Used for large scale quantitative mass spectrometry studies in life sciences.

Proper citation: Skyline (RRID:SCR_014080) Copy   

•   Source: SciCrunch Registry

http://www.originlab.com/index.aspx?go=PRODUCTS/Origin

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 4, 2025.Software application for data analysis and graphing. Origin contains a variety of different graph types, including statistical plots, 2D and 3D vector graphs, and counter graphs. More advance version is OriginPro which offers advanced analysis tools and Apps for Peak Fitting, Surface Fitting, Statistics and Signal Processing.

Proper citation: Origin (RRID:SCR_014212) Copy   

•   Source: SciCrunch Registry

http://mouse.brain-map.org/static/atlas

Allen Mouse Brain Atlas includes full color, high resolution anatomic reference atlas accompanied by systematic, hierarchically organized taxonomy of mouse brain structures. Enables interactive online exploration of atlas and to provide deeper level of 3D annotation for informatics analysis and viewing in Brain Explorer 3D viewer.

Proper citation: Allen Mouse Brain Reference Atlas (RRID:SCR_002978) Copy   

•   Source: SciCrunch Registry

http://www.biojava.org

Project dedicated to providing Java framework for processing biological data. It provides analytical and statistical routines, parsers for common file formats and allows the manipulation of sequences and 3D structures. The goal of the biojava project is to facilitate rapid application development for bioinformatics. Sponsor: BioJava is not formally funded by any grants. Through the OBF they have received sponsorship from Sun Microsystems, Apple Computers and NESCent. The initial development of the phylogenetics module was undertaken as a Google Summer of Code 2007 project in collaboration with NESCent.

Proper citation: BioJava Project (RRID:SCR_007180) Copy   

•   Source: SciCrunch Registry

http://nirlweb.duhs.duke.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Neuropsychiatric Imaging Research Laboratory (NIRL) analyze magnetic resonance images to research numerous psychiatric disorders including depression, bipolar disorder, and post traumatic stress disorder. NIRL also develop new methods for MR image processing to improve quality and reliability of research in the field of neuroimaging. The laboratory computer resources include Sun MicroSystems SPARC workstations, Windows PCs, over 3 terabytes of online hard disk space, and a web server system. The lab has a site filtered anonymous ftp server system for data transfer. There are individual offices for visiting fellows and analysts for image processing as well as shared work-study rooms and conference facilities.

Proper citation: Duke University Medical Center Neuropsychiatric Imaging Research Laboratory (RRID:SCR_007124) Copy   

•   Source: SciCrunch Registry

https://commonfund.nih.gov/hmp/

NIH Project to generate resources to characterize the human microbiota and to analyze its role in human health and disease at several different sites on the human body, including nasal passages, oral cavities, skin, gastrointestinal tract, and urogenital tract using metagenomic and traditional approach to genomic DNA sequencing studies.HMP was supported by the Common Fund from 2007 to 2016.

Proper citation: Human Microbiome Project (RRID:SCR_012956) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.12/bioc/html/PICS.html

R package with tools that use probabilistic inference of ChIP-Seq. It follows an empirical Bayes mixture model approach.

Proper citation: PICS (RRID:SCR_001093) Copy   

•   Source: SciCrunch Registry

https://imagej.net/

Open source Java based image processing software program designed for scientific multidimensional images. ImageJ has been transformed to ImageJ2 application to improve data engine to be sufficient to analyze modern datasets.

Proper citation: ImageJ (RRID:SCR_003070) Copy   

•   Source: SciCrunch Registry

http://www.type2diabetesgenetics.org/

Portal and database of DNA sequence, functional and epigenomic information, and clinical data from studies on type 2 diabetes and analytic tools to analyze these data. .Provides data and tools to promote understanding and treatment of type 2 diabetes and its complications. Used for identifying genetic biomarkers correlated to Type 2 diabetes and development of novel drugs for this disease.

Proper citation: Accelerating Medicines Partnership Type 2 Diabetes Knowledge Portal (AMP-T2D) (RRID:SCR_003743) Copy   

•   Source: SciCrunch Registry

http://www.biomart.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4,2023.Platform provides free software and data services to international scientific community in order to foster scientific collaboration and facilitate scientific discovery process. Project adheres to open source philosophy that promotes collaboration and code reuse.

Proper citation: BioMart Project (RRID:SCR_002987) Copy   

•   Source: SciCrunch Registry

http://www.alzforum.org/res/com/ant/

The Alzheimer Research Forum is the web''s most dynamic scientific community dedicated to understanding Alzheimer''s disease and related disorders. It also contains a database of providers of antibodies directed against several hundred molecules and proteins of relevant to research on Alzheimer and other neurodegenerative diseases. The web site reports on the latest scientific findings, from basic research to clinical trials; creates and maintains public databases of essential research data and reagents, and produces discussion forums to promote debate, speed the dissemination of new ideas, and break down barriers across the numerous disciplines that can contribute to the global effort to cure Alzheimer''s disease. The ARF team of professional science writers and editors, information technology experts, web developers and producers all work closely with our distinguished and diverse Advisory Board to ensure a high-quality of information and services. We very much welcome our readers'' participation in all aspects of the web site. Sponsors: The Alzheimer Research Forum is an independent nonprofit organization. It is supported by grants and individual donations.

Proper citation: Alzforum Antibody Directory for Neuroscience Research (RRID:SCR_013601) Copy   

•   Source: SciCrunch Registry

http://web.stanford.edu/group/barres_lab/brain_rnaseq.html

Database containing RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of cerebral cortex. Collection of RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of mouse cerebral cortex. RNA-Seq of cell types isolated from mouse and human brain.

Proper citation: Brain RNA-Seq (RRID:SCR_013736) Copy   

•   Source: SciCrunch Registry