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http://ctsaconnect.org/

THIS RESOURCE IS NO LONGER IS SERVICE. Documented on December 5th, 2022. Semantic framework to integrate information about research activities, clinical activities, and scientific resources to facilitate the production and consumption of Linked Open Data about investigators, physicians, biomedical research resources, services, and clinical activities. The goal is to enable software to consume data from multiple sources and allow the broadest possible representation of researchers'''' and clinicians'''' activities and research products. Current research tracking and networking systems rely largely on publications, but clinical encounters, reagents, techniques, specimens, model organisms, etc., are equally valuable for representing expertise. CTSAConnect will provide linkage between semantic representations of a wide range of clinical and research data using controlled vocabularies mapped to the Unified Medical Language System (UMLS) as a bridge between the two subject areas. The data sources include data from Medicaid, hospital billing systems, CTSAShareCenter, and other CTSA resource data, eagle-i and VIVO. It allows institutions to leverage existing tools and data sources by making the information they contain more discoverable and easier to integrate. For instance, with the ISF, researchers can be characterized by organizational affiliations, grant and project participation, research resources that they have generated, and publications that they have (co)-authored. Clinicians can be characterized by training and credentials, by clinical research topic, and by the kinds of procedures and specialization that can be inferred from encounter data. LOD refers to data that has been given a specific Uniform Resource Identifier (URI), for the purpose of sharing and linking data and information on the Semantic Web. While a large amount of data is published as LOD, there remains a significant gap in the representation of research resources and clinical expertise. Researchers can be characterized by the organization to which they belong, the grants and research in which they have participated, the research topics and research resources (reagents, biospecimens, animal models) they have generated, as well as the publications they have (co)-authored. Clinician profiles on the other hand, can be defined by their credentials, clinical research topics, and the kinds of procedures and specialization that can be inferred from clinical encounter data. They believe that integrating and relating this diversity of information sources and platforms requires addressing the overlap between research resources and the attributes and activities of researchers and clinicians. CTSAconnect aims to promote integration and discovery of research activities, resources, and clinical expertise. To this end, they will publish their ontologies and LOD via their website, which will also illustrate repeatable methods and examples of how to extract, consume, and utilize this valuable new LOD using freely available tools like VIVO, eagle-i, and Google APIs. CTSAconnect is a collaboration between Oregon Health & Science University, Stony Brook University, Cornell University, Harvard University, University at Buffalo, and the University of Florida, and leverages the work of eagle-i (eagle-i.net), VIVO (vivoweb.org), and ShareCenter (ctsasharecenter.org).

Proper citation: CTSAconnect (RRID:SCR_005225) Copy   

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http://slicer.org/

A free, open source software package for visualization and image analysis including registration, segmentation, and quantification of medical image data. Slicer provides a graphical user interface to a powerful set of tools so they can be used by end-user clinicians and researchers alike. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X. Slicer is based on VTK (http://public.kitware.com/vtk) and has a modular architecture for easy addition of new functionality. It uses an XML-based file format called MRML - Medical Reality Markup Language which can be used as an interchange format among medical imaging applications. Slicer is primarily written in C++ and Tcl.

Proper citation: 3D Slicer (RRID:SCR_005619) Copy   

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http://motif-x.med.harvard.edu

motif-x (short for motif extractor) is a software tool designed to extract overrepresented patterns from any sequence data set. The algorithm is an iterative strategy which builds successive motifs through comparison to a dynamic statistical background.

Proper citation: Motif Extractor (RRID:SCR_008628) Copy   

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http://compbio.dfci.harvard.edu/

A computational biology laboratory that builds and redistributes genetic software tools.

Proper citation: Computational Biology and Functional Genomics Laboratory at Harvard (RRID:SCR_010240) Copy   

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https://paperpile.com

A web application which allows users to find reference papers and cite them during the authoring process. Paperpile utilizes Google and Chrome apps in order to directly download PDFs from various databases to Google Drive. If writing in Google Docs, users can search for certain reference papers and insert them into their draft as citations using the Google Drive Paperpile tool. Users can also collaborate on papers and add different citations.

Proper citation: Paperpile (RRID:SCR_014002) Copy   

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http://www.nitrc.org/projects/ruby-nifti/

A library for handling NIfTI data in the Ruby programming language. Ruby NIfTI supports basic read and write access to NIfTI files, including basic and extended header information and image information. It doesn't attempt to touch the image data but it does provide access to qform and sform orientation matrices. It also provides a nice interface to get at NIfTI info from within Ruby.

Proper citation: Ruby NIfTI (RRID:SCR_014164) Copy   

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https://hddc.hms.harvard.edu

Community of scientists focused on the study of epithelial cell function and mucosal biology including inflammation and host defense of the gastrointestinal tract. It focuses on the intestinal and inflammatory bowel diseases; gut microbiology; and stem cell and developmental biology of the intestine and liver in organ physiology, regenerative medicine, and metabolism.

Proper citation: Harvard Digestive Disease Center (RRID:SCR_015587) Copy   

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https://bitbucket.org/nsegata/graphlan/wiki/Home

Software tool for producing high-quality circular representations of taxonomic and phylogenetic trees. Used for concise, integrative, informative, and publication-ready representations of phylogenetically- and taxonomically-driven investigation as a high-resolution microbial tree of life with taxonomic annotations., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GraPhlAn (RRID:SCR_016130) Copy   

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https://www.hsph.harvard.edu/hmac/

Core assists with consultation for microbiome project development, provides validated meta omic analysis of microbial community data, and supports fully collaborative grant funded investigations.

Proper citation: Harvard School of Public Health Microbiome Analysis Core Facility (RRID:SCR_017187) Copy   

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https://nar.oxfordjournals.org/content/35/suppl_1/D322.full-text-lowres.pdf

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The GO Partition Database was designed to feature ontology partitions with GO terms of similar specificity. The GO partitions comprise varying numbers of nodes and present relevant information theoretic statistics, so researchers can choose to analyze datasets at arbitrary levels of specificity. The GO Partition Database, featuring GO partition sets for functional analysis of genes from human and ten other commonly-studied organisms with a total of 131,972 genes.

Proper citation: Gene Ontology Partition Database (RRID:SCR_007693) Copy   

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http://www.strokedatabase.org/pages/software.html

Diffusion tensor imaging (DTI) tractography: An automated system for etiologic classification of ischemic stroke -- Causative Classification System for Ischemic Stroke DTI Task Card for Siemens systems, DTI Visualization platform independent tool kit, PWI analysis tools for bolus-tracking data

Proper citation: International Stroke Database/Software (RRID:SCR_007348) Copy   

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http://HIVBrainSeqDB.org

The HIV Brain Sequence Database (HIVBrainSeqDB) is a public database of HIV envelope sequences, directly sequenced from brain and other tissues from the same patients. For inclusion in the database, sequences must: (i) be deposited in Genbank; (ii) include some portion of the HIV env region; (iii) be clonal, amplified directly from tissue; and (iv) be sampled from the brain, or sampled from a patient for which the database already contains brain sequence. Sequences are annotated with clinical data including viral load, CD4 count, antiretroviral status, neurocognitive impairment, and neuropathological diagnosis, all curated from the original publication. Tissue source is coded using an anatomical ontology, the Foundational Model of Anatomy, to capture the maximum level of detail available, while maintaining ontological relationships between tissues and their subparts. 44 tissue types are represented within the database, grouped into 4 categories: (i) brain, brainstem, and spinal cord; (ii) meninges, choroid plexus, and CSF; (iii) blood and lymphoid; and (iv) other (bone marrow, colon, lung, liver, etc). Currently, the database contains 2517 envelope sequences from 90 patients, obtained from 22 published studies. 1272 sequences are from brain; the remaining 1245 are from blood, lymph node, spleen, bone marrow, colon, lung and other non-brain tissues. The database interface utilizes a faceted interface, allowing real-time combination of multiple search parameters to assemble a meta-dataset, which can be downloaded for further analysis. This online resource will greatly facilitate analysis of the genetic aspects of HIV macrophage tropism, HIV compartmentalization and evolution within the brain and other tissue reservoirs, and the relationship of these findings to HIV-associated neurological disorders and other clinical consequences of HIV infection.

Proper citation: HIV Brain Sequence Database (RRID:SCR_008819) Copy   

•   Source: SciCrunch Registry
  

https://atgu.mgh.harvard.edu/plinkseq/

An open-source C/C++ library for working with human genetic variation data. The specific focus is to provide a platform for analytic tool development for variation data from large-scale resequencing projects, particularly whole-exome and whole-genome studies. However, the library could in principle be applied to other types of genetic studies, including whole-genome association studies of common SNPs. (entry from Genetic Analysis Software)

Proper citation: PLINK/SEQ (RRID:SCR_013193) Copy   

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https://ccsp.hms.harvard.edu/

Center includes studies for responsiveness and resistance to anti cancer drugs. Committed to training students and postdocs, promoting junior faculty and ensuring that data and software are reproducible, reliable and publicly accessible. Member of National Cancer Institute’s Cancer Systems Biology Consortium.

Proper citation: Harvard Medical School Center for Cancer Systems Pharmacology (RRID:SCR_022831) Copy   

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https://biofactoid.org

Web tool for creating digital profile of scientific discoveries in article and connecting them to related research. Authors describe molecular interactions supported by their results, letting researchers explore first hand account of article findings and connect to related articles and knowledge. Web based system for scientists to compose structured representation of networks of interactions between genes, their products, and chemical compounds, represented using power of formal ontology.

Proper citation: Biofactoid (RRID:SCR_021011) Copy   

•   Source: SciCrunch Registry
  

https://www.tissue-atlas.org/

Gathers together imaging and omic datasets into molecular maps of normal and diseased tissue from human and animal models, with emphasis on cancer. Used to access datasets, educational curriculum and talks, and recommended methods and software.

Proper citation: Harvard Tissue Atlas (RRID:SCR_022829) Copy   

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http://www.people.fas.harvard.edu/~junliu/em/em.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A haplotype inference program.

Proper citation: EM-DECODER (RRID:SCR_000023) Copy   

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https://www.dfhcc.harvard.edu/research/core-facilities/tumor-imaging-metrics

Core provides centralized, standardized, accurate, consistent, and timely longitudinal, multimodality anatomic, volumetric and functional tumor metrics including CT, MR, PET/CT and other nuclear medicine imaging studies to evaluate therapeutic response for patients enrolled in DF/HCC oncologic clinical trials. TIMC supports more than 30 tumor response assessment criteria including but not limited to RECIST 1.1, iRECIST, Lugano, LYRIC, IWCLL and RANO. Results of tumor metric analyses are offered on password-protected secure web-based report.

Proper citation: Harvard Tumor Imaging Metrics Core Facility (RRID:SCR_012298) Copy   

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http://www.schepens.harvard.edu/graphics

Core facility that provides the following services: Web and graphic services, Web design and content management service, Photography service. Peter Mallen is a designer and illustrator who manages a full-service web and graphics studio in a moderately sized scientific research facility, Schepens Eye Research Institute. He provides a full range of graphic design, scientific illustration, web design and development, content management, and general design and production services. His products are utilized by: faculty and scientific staff (books, scientific publications, slide shows, grant applications); professional staff (Administration, Development & Public Affairs; brochures, books, pamphlets); the general public (Institute websites) as well as clients of our 250-seat state-of-the-art conference center, Starr Center for Scientific Communications.

Proper citation: Harvard SERI Graphic Services Core (RRID:SCR_012309) Copy   

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https://hddc.hms.harvard.edu/gnotobiotics-microbiology-and-metagenomics

Core facility that assists investigators evaluating host microbiota and its role in normal physiology and disease. It includes a number of resources for groups studying the role of the microbiota in human health and disease.

Proper citation: Harvard Digestive Diseases Center Biomedical CORE D: Gnotobiotic Mice, Microbiology and Metagenomics (RRID:SCR_012319) Copy   

•   Source: SciCrunch Registry