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https://marmotgraph.org

Knowledge graph system developed for managing and organizing rich metadata objects, initially for the Human Brain Project (HBP) and now extended to be a more generic, domain-agnostic solution. It is associated with CSCS (Swiss National Supercomputing Centre) and aims to provide a comprehensive toolset and API for working with knowledge graphs.

Proper citation: MarmotGraph (RRID:SCR_027452) Copy   

•   Source: SciCrunch Registry

https://www.bwhneurosciences.org/neurotechnology-studio/

Offers advanced instrumentation and expert support to advance understanding of brain function and brain disease by providing researchers with access to cutting-edge technologies. Provides access to advanced instrumentation for optical microscopy, genomics, metabolic imaging and other technologies, as well as image analysis software. Provides expert support and training, data quality and interpretation.

Proper citation: Brigham and Women’s Hospital NeuroTechnology Studio Core Facility (RRID:SCR_027687) Copy   

•   Source: SciCrunch Registry

http://www.concepthub.org/wiki/Main_Page

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 18, 2016. A database built up with software and services that maintains multiple versions of terminologies, map sets, and values sets concurrently. HDD Access has superseded Concept Hub.

Proper citation: Concept Hub (RRID:SCR_013586) Copy   

•   Source: SciCrunch Registry

https://www.digitalpsych.org/mindlamp1.html

Core collects data to capture and consider the real-time lived experiences of patients. Uses open-source digital platform and mobile app for neuropsychiatric research and clinical care, to monitor, support, and improve brain health by collecting real-time data (like location, activity, heart rate via sensors), conducting digital assessments (surveys, cognitive tests), and delivering interventions (meditation, journaling, psychoeducation) to patients and clinicians. It helps study behavioral patterns, track symptoms, and personalize mental health treatment through features organized around Learn, Assess, Manage, and Prevent.

Proper citation: BIDMC Division of Digital Psychiatry LAMP platform Core Facility (RRID:SCR_027767) 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://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.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

http://medblast.sibsnet.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. An algorithm that finds articles most relevant to a genetic sequence. In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here, a new literature-mining tool MedBlast is developed, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. The genome sequencing projects generate such a large amount of data every day that many molecular biologists often encounter some sequences that they know nothing about. Literature is usually the principal resource of such information. It is relatively easy to mine the articles cited by the sequence annotation; however, it is a difficult task to retrieve those relevant articles without direct citation relationship. The related articles are those described in the given sequence (gene/protein), or its redundant sequences, or the close homologs in various species. They can be divided into two classes: direct references, which include those either cited by the sequence annotation or citing the sequence in its text; indirect references, those which contain gene symbols of the given sequence. A few additional issues make the task even more complicated: (1) symbols may have aliases; and (2) one sequence may have a couple of relatives that we want to take into account too, which include redundant (e.g. protein and gene sequences) and close homologs. Here the issues are addressed by the development of the software MedBlast, which can retrieve the related articles of the given sequence automatically. MedBlast uses BLAST to extend homology relationships, precompiled species-specific thesauruses, a useful semantics technique in natural language processing (NLP), to extend alias relationship, and EUtilities toolset to search and retrieve corresponding articles of each sequence from PubMed. MedBlast take a sequence in FASTA format as input. The program first uses BLAST to search the GenBank nucleic acid and protein non-redundant (nr) databases, to extend to those homologous and corresponding nucleic acid and protein sequences. Users can input the BLAST results directly, but it is recommended to input the result of both protein and nucleic acid nr databases. The hits with low e-values are chosen as the relatives because the low similarity hits often do not contain specific information. Very long sequences, e.g. 100k, which are usually genomic sequences, are discarded too, for they do not contain specific direct references. User can adjust these parameters to meet their own needs.

Proper citation: MedBlast (RRID:SCR_008202) 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://www.affymetrix.com/support/developer/powertools/apt_archive.affx

Affymetrix Power Tools (APT) are a set of cross-platform command line programs that implement algorithms for analyzing and working with Affymetrix GeneChip arrays. APT programs are intended for power users who prefer programs that can be utilized in scripting environments and are sophisticated enough to handle the complexity of extra features and functionality. APT provides platform for developing and deploying new algorithms without waiting for the GUI implementations. This resource is supported by Affymetrix, Inc.

Proper citation: Affymetrix Power Tools (RRID:SCR_008401) 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

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://eddylab.org/software.html

Software library containing tools for statistical manipulations of data. Tools include profile hidden Markov models for biological sequence analysis, RNA structure analysis, and a prototype noncoding RNA genefinder.

Proper citation: Eddy Lab Software (RRID:SCR_001458) Copy   

•   Source: SciCrunch Registry

http://www.pd-doc.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on December 02, 2011. Notice: This domain name expired on 10/29/11 and is pending renewal or deletion PD-DOC is a portal and a database resource, hosting a database and linking to other databases and data sets of clinical and translational data. PD-DOC functions to organize and facilitate clinical and translational research in Parkinson's disease. The PD-DOC Database contains standardized data collected by user institutions on large numbers of patients with Parkinsons disease and other parkinsonian disorders. In some cases, data is obtained at a single point in time, while in others data is collected repeatedly over time. The PD-DOC Database is composed of the Core Data Set (CDS) which consists of those variables required to be gathered for each subject whose data is entered into the PD-DOC database. In 2005, working groups of Udall Center and invited experts deliberated to establish the components of each CDS section (e.g. General Clinical, Cognitive/Behavioral, Postmortem Brain Neuropathological Findings). The PD-DOC CDS was established and designed to optimize data analyses and data mining for large numbers of subjects participating in a variety of research studies. In most cases corresponding DNA samples are available form the NINDS Human Genetic Repository (at Coriell). Much of the website is publicly available for viewing. To request access to sections of the website dealing with downloading or requesting data, requesting a consultation, or submitting data or other information you will need to register. Before registering, you should read the PD-DOC Policies. Note that PD-DOC data can be used for research purposes only. Once your registration is successfully completed you will be automatically logged into the website.

Proper citation: PD-DOC (RRID:SCR_001596) Copy   

•   Source: SciCrunch Registry

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