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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.

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On page 173 showing 3441 ~ 3460 out of 27,138 results
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  • RRID:SCR_005109

    This resource has 100+ mentions.

https://github.com/Illumina/strelka/

Software for somatic single nucleotide variant (SNV) and small indel detection from sequencing data of matched tumor-normal samples. Strelka2 germline and somatic small variant caller.

Proper citation: Strelka2 (RRID:SCR_005109) Copy   


http://www.uottawa.ca/

University of Ottawa often referred to as uOttawa or U of O, is a bilingual public research university in Ottawa, Ontario, Canada.

Proper citation: University of Ottawa; Ontario; Canada (RRID:SCR_006319) Copy   


http://www.usc.es/en/index.html

Proper citation: University of Santiago de Compostela; Santiago de Compostela; Spain (RRID:SCR_007888) Copy   


http://www.remedyinformatics.com/

Software to harmonize the data that you have in different Excel files, databases, repositories, biospecimen applications, etc. and maps it to one common registry. Remedy Informatics' platform aggregates data from multiple sources, harmonizes the data via Ontology, and provides data visualization and pattern recognition and querying tools.

Proper citation: Registry Builder Data Harmonization and Aggregation Tool (RRID:SCR_006559) Copy   


http://nrnb.org/index.html

Biomedical technology research center that develops new algorithms, visualizations and conceptual frameworks to study biological networks at multiple levels and scales, from protein-protein and genetic interactions to cell-cell communication and vast social networks. They are developing freely available, open-source suite of software technology that broadly enables network-based visualization, analysis, and biomedical discovery for NIH-funded researchers. This software is enabling researchers to assemble large-scale biological data into models of networks and pathways and to use these networks to better understand how biological systems operate under normal conditions and how they fail in disease. The National Resource for Network Biology is organized around the following key components: Technology Research and Development, Driving Biomedical Projects, Outreach, Training and Dissemination of Tools. The NRNB supports several types of training events, including both virtual and live workshops; tutorials sessions for clinicians, biologists and bioinformaticians; presentations and demonstrations at conferences; online tutorials and webcasts; and annual symposium.

Proper citation: National Resource for Network Biology (RRID:SCR_004259) Copy   


  • RRID:SCR_005585

    This resource has 1+ mentions.

http://www.dnabaser.com/download/chromatogram-explorer/

A Windows Explorer clone dedicated to DNA sequence analysis and manipulation. View, edit, and convert chromatograms. Trim low quality ends automatically. The Lite version of Chromatogram Explorer is freeware.

Proper citation: DNA Chromatogram Explorer (RRID:SCR_005585) Copy   


http://www.medschool.pitt.edu/

Proper citation: University of Pittsburgh School of Medicine; Pennsylvania; USA (RRID:SCR_006674) Copy   


  • RRID:SCR_003169

    This resource has 10+ mentions.

http://www.broad.mit.edu/annotation/fungi/fgi/

Produces and analyzes sequence data from fungal organisms that are important to medicine, agriculture and industry. The FGI is a partnership between the Broad Institute and the wider fungal research community, with the selection of target genomes governed by a steering committee of fungal scientists. Organisms are selected for sequencing as part of a cohesive strategy that considers the value of data from each organism, given their role in basic research, health, agriculture and industry, as well as their value in comparative genomics.

Proper citation: Fungal Genome Initiative (RRID:SCR_003169) Copy   


http://www.uni-lj.si/en/

Proper citation: University of Ljubljana; Ljubljana; Slovenia (RRID:SCR_004498) Copy   


  • RRID:SCR_004961

    This resource has 50+ mentions.

https://reich.hms.harvard.edu/software

XP-CLR (Chen et al. 2010) uses allele frequency differentiation at linked loci to detect selective sweeps. Source code and documentation are available.

Proper citation: XP-CLR (RRID:SCR_004961) Copy   


http://www4.wiwiss.fu-berlin.de/bizer/d2r-server/

D2R Server is a tool for publishing relational databases on the Semantic Web. It enables RDF and HTML browsers to navigate the content of the database, and allows applications to query the database using the SPARQL query language. Data on the Semantic Web is modeled and represented in RDF. D2R Server uses a customizable D2RQ mapping to map database content into this format, and allows the RDF data to be browsed and searched the two main access paradigms to the Semantic Web. D2R Server''s Linked Data interface makes RDF descriptions of individual resources available over the HTTP protocol. An RDF description can be retrieved simply by accessing the resource''s URI over the Web. Using a Semantic Web browser like Tabulator (slides) or Disco, you can follow links from one resource to the next, surfing the Web of Data. The SPARQL interface enables applications to search and query the database using the SPARQL query language over the SPARQL protocol. Requests from the Web are rewritten into SQL queries via the mapping. This on-the-fly translation allows publishing of RDF from large live databases and eliminates the need for replicating the data into a dedicated RDF triple store. The latest source code is available from the project''s CVS repository and can be browsed online.

Proper citation: D2R Server - Publishing Relational Databases on the Semantic Web (RRID:SCR_004963) Copy   


http://www.icpsr.umich.edu/icpsrweb/NACDA/studies/02744/version/1

Data set of a follow-up study (one of four Established Populations for Epidemiologic Studies of the Elderly - EPESE) that obtains information on four primary outcome variables (cognitive status, depression, functional status, and mortality) and four primary independent variables (social support, social class, social location, and chronic illness); and examines the relationships between social factors and chronic disease on the one hand and health outcomes on the other. This data set complements the other three sites providing a population which is both urban and rural and contains approximately equal numbers of black and white participants across a broad socioeconomic base. The Duke site was originally funded by the NIA Epidemiology, Demography and Biometry Program (EDBP) to complete seven waves of data collection (three in-person and four telephone interviews) in order to examine the health of a sample of 4,162 persons aged 65+, and factors that influence their health and use of health services. The cohort was originally interviewed in 1986/87 and followed annually for 6 years thereafter. The study design consisted of a random stratified household sample with an over-sampling of blacks. Questionnaire topics include the following: Demographics, Alcohol Use, Independence, Health condition, Cognition, Personal mastery, Health Service Utilization, Activity of daily living, Social Support, Hearing and Vision, Incontinence, Social Interaction, Weight and Height, Smoking, Religion, Nutrition, Life Satisfaction, Self Esteem, Sleep, Medications, Economic Status, Depression, Life Changes, Blood pressure. National Death Index files have been searched and death certificates obtained for the members of this study. Sample members have been matched with Medicare Part A files to obtain information on hospitalizations, and will be matched on Medicare Part B (outpatient) files. Data from the first wave of the survey is in the public domain and can be obtained from NACDA or from the National Archives, Center for Electronic Records in Washington, DC. * Dates of Study: 1996-1997 * Study Features: Longitudinal, Oversampling * Sample Size: 1986-1988: 4,162 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02744 * National Archives: http://www.archives.gov/research/electronic-records/

Proper citation: Piedmont Health Survey of the Elderly (RRID:SCR_006349) Copy   


http://www.zv.uni-leipzig.de/en/

Proper citation: University of Leipzig; Saxony; Germany (RRID:SCR_004960) Copy   


http://clip.med.yale.edu/SHM

A targeting model that defines where mutations occur (by specifying the relative rates at which DNA motifs in the Ig sequence are mutated), and a nucleotide substitution model that defines the resulting mutation (by specifying the probability of each base mutating to each of the other three possibilities as a function of the surrounding bases).

Proper citation: Models of SHM Targeting and Substitution (RRID:SCR_005250) Copy   


http://www.unisi.it/internet/home.html

Proper citation: University of Siena; Tuscany; Italy (RRID:SCR_008080) Copy   


http://www.umanitoba.ca/

Proper citation: University of Manitoba; Manitoba; Canada (RRID:SCR_003867) Copy   


  • RRID:SCR_003502

    This resource has 1+ mentions.

http://fcon_1000.projects.nitrc.org/indi/pro/BeijingShortTR.html

Dataset of resting state fMRI scans obtained using two different TR's in healthy college-aged volunteers. Specifically, for each participant, data is being obtained with a short TR (0.4 seconds) and a long TR (2.0 seconds). In addition this dataset contains a 64-direction DTI scan for every participant. The following data are released for every participant: * 8-minute resting-state fMRI scan (TR = 2 seconds, # repetitions = 240) * 8-minute resting-state fMRI scans (TR = 0.4 seconds, # repetitions = 1200) * MPRAGE anatomical scan, defaced to protect patient confidentiality * 64-direction diffusion tensor imaging scan (2mm isotropic) * Demographic information

Proper citation: Beijing: Short TR Study (RRID:SCR_003502) Copy   


  • RRID:SCR_003509

http://rp-www.cs.usyd.edu.au/~yangpy/software/MFGE.html

A hybrid software system for feature selection and sample classification of high-dimensional datasets. It is designed for microarray but can be applied to any other high-dimensional datasets. It uses multiple filters to produce a normalized score for each feature. The score is an indication of the usefulness of each feature. It is then translated into a frequency map with more useful features receive a higher frequency in the map.

Proper citation: MF-GE (RRID:SCR_003509) Copy   


http://www.meduniwien.ac.at/homepage/1/

Proper citation: Medical University of Vienna; Vienna; Austria (RRID:SCR_005007) Copy   


http://medicine.yale.edu/index.aspx

Proper citation: Yale School of Medicine; Connecticut; USA (RRID:SCR_006339) Copy   



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