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https://github.com/ElementoLab/ChIPseeqer

Software that provides a comprehensive framework for the analysis of ChIP-seq data.

Proper citation: ChIPseeqer (RRID:SCR_001545) Copy   

•   Source: SciCrunch Registry

http://mobile.ebiocenter.com/ebionews/

eBioNews specializes in online information services and resource exchanges in the fields of life sciences and biotechnology. By applying its knowledge database and content management system (CMS), eBioNews offers readers and customers the organized and comprehensive information. eBioNews also provides a membership-based service to assist our customers in information and data search, processing, storage, and sharing. Generally, eBioNews covers the following areas: - life science frontiers - news and discussions - features and specials - resources and sourcing - career development - academic and industry - training and education Additionally, eBioNews information is organized into the following two clusters: - News Center: 1. Headlights 2. Research Frontiers 3. General Research 4. Clinical Development 5. Enterprise & Industry 6. Products & Services 7. Investment & Financials 8. Features 9. Newsletter The News Center consists of the elements and mechanisms that enable collecting, organizing, displaying, and delivering life science related information, data, and knowledge. - Resource Center: 1. eBioResources 2. Cooperation 3. Events 4. Human Resources 5. Intellectual Property 6. Finance & Legal 7. Operations 8. Organization 9. Publication The Resource Center is a system that hosts and facilitates the resource-related information between and among multiple parties, especially for promoting cooperation, collaboration, consortium, partnering, joint venture, licensing, out-sourcing, and trading. Sponsors: This resource is supported by eBioCenter Corporation.

Proper citation: eBioNews - A Subsidiary of eBioCenter (RRID:SCR_001717) 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

http://www.nitrc.org/projects/itk-snap/

Open source interactive software application for three dimentional medical images, manual delineation of anatomical regions of interest, and performing automatic image segmentation. Used for delineating anatomical structures and regions in MRI, CT and other 3D biomedical imaging data.WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons).

Proper citation: ITK-SNAP (RRID:SCR_002010) Copy   

•   Source: SciCrunch Registry

https://datashare.nida.nih.gov

Website which allows data from completed clinical trials to be distributed to investigators and public. Researchers can download de-identified data from completed NIDA clinical trial studies to conduct analyses that improve quality of drug abuse treatment. Incorporates data from Division of Therapeutics and Medical Consequences and Center for Clinical Trials Network.

Proper citation: NIDA Data Share (RRID:SCR_002002) 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://factominer.free.fr/index.html

Software R package for multivariate analysis which takes into account different types of data structure. Data can be organized in groups of variable, groups of individuals, or into hierarchy of variables.

Proper citation: FactoMineR (RRID:SCR_014602) Copy   

•   Source: SciCrunch Registry

http://www.heka.com/downloads/downloads_main.html#down_tida

A software which is used to acquire physiological data from the HEKA Patch Clamp Amplifiers and HEKA interfaces.

Proper citation: TIDA (RRID:SCR_014582) Copy   

•   Source: SciCrunch Registry

http://www.oas.samhsa.gov/nsduh.htm

NSDUH is the primary source of statistical information on the use of illegal drugs, alcohol, and tobacco by the U.S. civilian, noninstitutionalized population aged 12 or older. Conducted by the Federal Government since 1971, the survey collects data through face-to-face interviews with a representative sample of the population at the respondent''s place of residence. Correlates in OAS reports include the following: age, gender, pregnancy status, race / ethnicity, education, employment, geographic area, frequency of use, and association with alcohol, tobacco, & illegal drug use. NSDUH collects information from residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories) and from civilians living on military bases. The survey excludes homeless persons who do not use shelters, military personnel on active duty, and residents of institutional group quarters, such as jails and hospitals. Most of the questions are administered with audio computer-assisted self-interviewing (ACASI). ACASI is designed to provide the respondent with a highly private and confidential mode for responding to questions in order to increase the level of honest reporting of illicit drug use and other sensitive behaviors. Less sensitive items are administered by interviewers using computer-assisted personal interviewing (CAPI). The 2010 NSDUH employed a State-based design with an independent, multistage area probability sample within each State and the District of Columbia. The eight States with the largest population (which together account for about half of the total U.S. population aged 12 or older) were designated as large sample States (California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas) and had a sample size of about 3,600 each. For the remaining 42 States and the District of Columbia, the sample size was about 900 per State. The design oversampled youths and young adults; each State''s sample was approximately equally distributed among three age groups: 12 to 17 years, 18 to 25 years, and 26 years or older.

Proper citation: National Survey on Drug Use and Health (RRID:SCR_007031) Copy   

•   Source: SciCrunch Registry

http://physionet.org/physiobank/

Archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community. PhysioBank currently includes databases of multi-parameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and patients with a variety of conditions with major public health implications, including sudden cardiac death, congestive heart failure, epilepsy, gait disorders, sleep apnea, and aging. The PhysioBank Archives now contain over 700 gigabytes of data that may be freely downloaded. PhysioNet is seeking contributions of data sets that can be made freely available in PhysioBank. Contributions of digitized and anonymized (deidentified) physiologic signals and time series of all types are welcome. If you have a data set that may be suitable, please review PhysioNet''s guidelines for contributors and contact them.

Proper citation: Physiobank (RRID:SCR_006949) Copy   

•   Source: SciCrunch Registry

http://senselab.med.yale.edu/modeldb/

Curated database of published models so that they can be openly accessed, downloaded, and tested to support computational neuroscience. Provides accessible location for storing and efficiently retrieving computational neuroscience models.Coupled with NeuronDB. Models can be coded in any language for any environment. Model code can be viewed before downloading and browsers can be set to auto-launch the models. The model source code has to be available from publicly accessible online repository or WWW site. Original source code is used to generate simulation results from which authors derived their published insights and conclusions.

Proper citation: ModelDB (RRID:SCR_007271) Copy   

•   Source: SciCrunch Registry

http://www.biomanta.org/

This project encompasses development of novel biological network analysis methods and infrastructure for querying biological data in a semantically-enabled format, and aims to create a semantic interactome model. Research within the BioMANTA project will focus on computational modelling and analysis, primarily using Semantic Web technologies and Machine Learning methods, of large-scale protein-protein interaction and compound activity networks across a wide variety of species. A range of information such as kinetic activity, tissue expression, and subcellular localization and disease state attributes will be included in the resulting data model. Protein interactions are a fundamental component of biological processes. Many proteins are functional only in multimeric complexes, or require interaction partners to achieve their correct localisation or function. For this reason, the study of protein-protein interaction (PPI) networks has become an area of growing interest in computational biology. Through the use of Semantic Web technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL), interaction data is modelled to create a knowledge representation in which meaning is vested in the ontology rather than instances of data. Stochastic and computational intelligence methods are applied to this data to infer high coverage networks. Semantic inferencing is used to infer previously unknown and meaningful pathways. Major project components: - The BioMANTA Ontology:- An OWL DL ontology incorporating the PSI-MI Ontology, the NCBI Taxonomy, and elements of BioPax ontology and Gene Ontology (describing subcellular localisation). This allows us to re-use existing ontologies, thereby reducing overheads associated with knowledge acquisition in the ontology development process. We are able to integrate existing public data that contain annotation in these formats. - Data conversion & semantic protein integration:- A set of software components that convert protein-protein databases (DIP, MPact, IntAct, etc.) from PSI-MI XML to RDF compliant with the BioMANTA ontology. These software allow us to make these protein-protein interaction datasets (and more generally, any PSI-MI XML data) semantically available for querying and inference within BioMANTA. - A RDF triple store based on RDF Molecules and the MapReduce architecture:- A proof-of-concept RDF triple store using RDF molecules and Hadoop scale-out architectures. Regular RDF graphs are deconstructed into RDF molecules, which are distributed over distributed compute nodes in the MapReduce architecture, and are subsequently combined to form equivalent RDF graphs. Such an approach makes the distributed SPARQL querying and reasoning on RDF triple stores possible. - A quantitative framework to integrate networks extracted from independent data sources (gene expression, subcellular localization, and ortholog mapping):- The model is multi-layer, with a first layer based on Decision Trees where each Decision tree is built on each dataset independently. The tree nodes are cut using Shannon''s entropy (mutual information); the decision of these independent trees is integrated using logistic regression, and the parameters are optimised using maximum likelihood. Sponsors: This resource is supported by the Pfizer Global Research and Development, the Institute for Molecular Bioscience (IMB), and the University of Queensland, Australia.

Proper citation: BioMANTA (RRID:SCR_007177) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/collaborators/function-birn/

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

Proper citation: Function BIRN (RRID:SCR_007291) Copy   

•   Source: SciCrunch Registry

http://www.fei.com/software/amira-3d-for-life-sciences/

Software tool for visualizing, manipulating, and understanding data from tomography, microscopy, MRI and other imaging processes.Used to import and export options, to processes 3D image filtering and DTI based fiber tracking to visualization, volume and surface rendering, author tools for virtual reality navigation, video generation, and more.

Proper citation: Advanced 3D Visualization and Volume Modeling (RRID:SCR_007353) Copy   

•   Source: SciCrunch Registry

https://wiki.med.harvard.edu/SysBio/Megason/GoFigure

GoFigure is a software platform for quantitating complex 4d in vivo microscopy based data in high-throughput at the level of the cell. A prime goal of GoFigure is the automatic segmentation of nuclei and cell membranes and in temporally tracking them across cell migration and division to create cell lineages. GoFigure v2.0 is a major new release of our software package for quantitative analysis of image data. The research focuses on analyzing cells in intact, whole zebrafish embryos using 4d (xyzt) imaging which tends to make automatic segmentation more difficult than with 2d or 2d+time imaging of cells in culture. This resource has developed an automatic segmentation pipeline that includes ICA based channel unmixing, membrane nuclear channel subtraction, Gaussian correlation, shape models, and level set based variational active contours. GoFigure was designed to meet the challenging requirements of in toto imaging. In toto imaging is a technology that we are developing in which we seek to track all the cell movements and divisions that form structures during embryonic development of zebrafish and to quantitate protein expression and localization on top of this digital lineage. For in toto imaging, GoFigure uses zebrafish embryos in which the nuclei and cell membranes have been marked with 2 different color fluorescent proteins to allow cells to be segmented and tracked. A transgenic line in a third color can be used to mark protein expression and localization using a genetic approach that this resource developed called FlipTraps or using traditional transgenic approaches. Embryos are imaged using confocal or 2-photon microscopy to capture high-resolution xyzt image sets used for cell tracking. The GoFigure GUI will provide many tools for visualization and analysis of bioimages. Since fully automatic segmentation of cells is never perfect, GoFigure will provide easy to use tools for semi-automatically and manually adding, deleting, and editing traces in 2d (figures-xy, xz, or yz), 3d (meshes- xyz), 4d (tracks- xyzt) and 4d+cell division (lineages). GoFigure will also provide a number of views into complex image data sets including 3d XYZ and XYT image views, tabular list views of traces, histograms, and scattergrams. Importantly, all these views will be linked together to allow the user to explore their data from multiple angles. Data will be easily sorted and color-coded in many ways to explore correlations in higher dimensional data. The GoFigure architecture is designed to allow additional segmentation, visualization, and analysis filters to be plugged in. Sponsors: GoFigure is developed by Harvard University., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Harvard Medical School, Department of Systems Biology: The Megason Lab -GoFigure Software (RRID:SCR_008037) Copy   

•   Source: SciCrunch Registry

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