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http://www.broadinstitute.org/annotation/genome/magnaporthe_comparative/MultiHome.html

The Magnaporthe comparative genomics database provides accesses to multiple fungal genomes from the Magnaporthaceae family to facilitate the comparative analysis. As part of the Broad Fungal Genome Initiative, the Magnaporthe comparative project includes the finished M. oryzae (formerly M. grisea) genome, as well as the draft assemblies of Gaeumannomyces graminis var. tritici and M. poae. It provides users the tools to BLAST search, browse genome regions (to retrieve DNA, find clones, and graphically view sequence regions), and provides gene indexes and genome statistics. We were funded to attempt 7x sequence coverage comprising paired end reads from plasmids, Fosmids and BACs. Our strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated and reassembled. Our specific aims are as follows: 1. Generate and assemble sequence reads yielding 7X coverage of the Magnaporthe oryzae genome through whole genome shotgun sequencing. 2. Generate and incorporate BAC and Fosmid end sequences into the genome assembly to provide a paired-end of average every 2 kb. 3. Integrate the genome sequence with existing physical and genetic map information. 4. Perform automated annotation of the sequence assembly. 5. Distribute the sequence assembly and results of our annotation and analysis through a freely accessible, public web server and by deposition of the sequence assembly in GenBank.

Proper citation: Magnaporthe comparative Database (RRID:SCR_003079) Copy   

•   Source: SciCrunch Registry

https://github.com/dattalab/moseq2-app

Software application as starting point to MoSeq2 package suite. Unsupervised machine learning method which takes inputs from depth cameras in 3D and transforms them into different behavioral motifs which called syllables. Used to extract mouse pose from depth video and model how pose evolves over time.

Proper citation: moseq2-app (RRID:SCR_025031) Copy   

•   Source: SciCrunch Registry

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   

•   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   

•   Source: SciCrunch Registry

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.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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.mybiosoftware.com/population-genetics/332

A tool for SNP Search and downloading with local management. It also offers flanking sequence downloading and automatic SNP filtering. It requires Windows and .NET Framework.

Proper citation: SNPHunter (RRID:SCR_002968) Copy   

•   Source: SciCrunch Registry

http://altbibl.io/dst4l/

An experimental course and blog offered by the Harvard-Smithsonian Center for Astrophysics John G. Wolbach Library and the Harvard Library to train librarians to respond to the growing data needs of their communities. Data science techniques are becoming increasingly important to all fields of scholarship. In the hands-on course, librarians learn the latest tools for extracting, wrangling, storing, analyzing, and visualizing data. By experiencing the research data lifecycle themselves, librarians develop the data savvy skills that can help transform the services they offer. Material will be made available via the DST4L website as it progresses.

Proper citation: Data Scientist Training for Librarians (RRID:SCR_004124) Copy   

•   Source: SciCrunch Registry

http://dash.harvard.edu/

Harvard University''s central service for sharing and preserving work. In addition to the scholarly journal articles targeted by Harvard''s several open access resolutions, DASH maybe used to self-archive manuscripts and materials. DASH supports a variety of file formats, and users are encouraged to deposit related materials with manuscripts (including data, images, audio and video files, etc.) When users deposit their work in DASH, it becomes visible to colleagues around the world by virtue of metadata harvesting, Google Scholar, and other indexing services. Higher visibility leads to higher rates of citation and impact. When users post early versions of their work, before publication, they establish intellectual priority sooner. Users act in their own best interests by taking part in the University''s mission to share and preserve the knowledge produced there. Because Harvard now has a prior, non-exclusive license to faculty journal articles in schools with open access policies, those faculty members are required to act accordingly when publishing journal articles, either by attaching an addendum to their publication agreement or obtaining a waiver. They then must deposit the publication in DASH.

Proper citation: Digital Access to Scholarship at Harvard (RRID:SCR_004122) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://cardiogenomica.altervista.org/CARDIOGENOMICS/CardioGenomics%20Homepage.htm

The primary goal of the CardioGenomics PGA is to begin to link genes to structure, function, dysfunction and structural abnormalities of the cardiovascular system caused by clinically relevant genetic and environmental stimuli. The principal biological theme to be pursued is how the transcriptional network of the cardiovascular system responds to genetic and environmental stresses to maintain normal function and structure, and how this network is altered in disease. This PGA will generate a high quality, comprehensive data set for the functional genomics of structural and functional adaptation of the cardiovascular system by integrating expression data from animal models and human tissue samples, mutation screening of candidate genes in patients, and DNA polymorphisms in a well characterized general population. Such a data set will serve as a benchmark for future basic, clinical, and pharmacogenomic studies. Training and education are also a key focus of the CardioGenomics PGA. In addition to ongoing journal clubs and seminars, the PGA will be sponsoring symposia at major conferences, and developing workshops related to the areas of focus of this PGA. Information regarding upcoming events can be found in the Events section of this site, and information about training and education opportunities sponsored by CardioGenomics can be found on the Teaching and Education page. The CardioGenomics project came to a close in 2005. This server, cardiogenomics.med.harvard.edu, remains online in order to continue to distribute data that was generated by investigators under the auspices of the CardioGenomics Program for Genomic Applications (PGA). :Sponsors: This resource is supported by The National Heart, Lung and Blood Institute (NHLBI) of the NIH., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CardioGenomics (RRID:SCR_007248) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://harvard.eagle-i.net/i/00000139-928e-36d0-f016-703c80000000

The Biostatistics Core serves the needs of the HIV/AIDS researchers within the Ragon Institute and its affiliates. In particular, members of the Biostatistics Core provide expertise in the planning, conduct and analysis of research with the goal of enhancing the scientific quality of HIV-related research at the institute. The primary objective of the core is to ensure that studies are well designed, correctly analyzed, clearly presented, and correctly interpreted.

Proper citation: Ragon Institute Biostatistics Core (RRID:SCR_010055) Copy   

•   Source: SciCrunch Registry

http://harvard.eagle-i.net/i/0000012e-5eed-e0fd-55da-381e80000000

Core facility that provides the following services: Coulter XL flow analysis, Cytomation MoFlo cell sorting, LaserScan Cytometry, BD Biosciences LSR II flow cytometry analysis, Flow cytometry data analysis. The Flow Cytometry Facility is a core facility of Schepens Eye Research Institute that provides fluorescent-based cell analysis and sorting to Boston area biomedical researchers.

Proper citation: SERI Flow Cytometry Core Facility (RRID:SCR_010059) Copy   

•   Source: SciCrunch Registry