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http://www.psidev.info/index.php?q=node/257

Markup language for encoding raw spectrometer output. From 2005-2008 there has existed two separate XML formats for encoding raw spectrometer output: mzData developed by the PSI and mzXML developed at the Seattle Proteome Center at the Institute for Systems Biology. It was recognized that the existence of two separate formats for essentially the same thing generated confusion and required extra programming effort. Therefore the PSI, with full participation by ISB, has developed a new format by taking the best aspects of each of the precursor formats to form a single one. It is intended to replace the previous two formats. This new format was originally given a working name of dataXML. The final name is mzML.

Proper citation: mzML (RRID:SCR_003763) Copy   

•   Source: SciCrunch Registry

http://www.tipharma.com/

A not-for-profit organization that initiates and manages drug development consortia that involve the pharmaceutical industry, academia, and the Dutch Government. The aim of all their consortia is to conduct pre-competitive research while strengthening the Netherlands' international reputation for drug development. The consortia are focused on addressing therapeutic areas that are listed as priority areas by the World Health Organization including: autoimmune diseases, cardiovascular diseases, cancer, infectious diseases, and diseases of the brain. The consortia are also focused on enabling: therapeutic target discovery, validation, and animal models; lead selection and in-silico modeling; predictive drug disposition and toxicology; biomarkers and biosensors; drug formulation, delivery, and targeting; and, production technologies. TI Pharma consortia aim to improve the efficiency of the drug-development process, with a focus on advancing regulatory science. In addition to research, TI Pharma also has objectives to train and educate scientists in the Netherlands on the drug discovery and development processes, as well as on entrepreneurship. Participants include: all Dutch universities and academic medical centers, more than 30 industrial partners, small-to-medium sized companies, and representatives from the Dutch Medicines Evaluation Board (Netherlands regulatory body for drugs).

Proper citation: TI Pharma (RRID:SCR_003758) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.bc.edu/marthlab/wiki/index.php/PyroBayes

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. A base caller for pyrosequences from the 454 Life Sciences sequencing machines.

Proper citation: PyroBayes (RRID:SCR_003757) Copy   

•   Source: SciCrunch Registry

https://www.nanoporetech.com/

Commercial organization developing a disruptive, proprietary technology platform for the direct, electronic analysis of single molecules. The instruments GridION and MinION are adaptable for the analysis of DNA, RNA, proteins, small molecules and other types of molecule. Consequently, the platform has a broad range of potential applications, including scientific research, personalized medicine, crop science and security / defence.

Proper citation: Oxford Nanopore Technologies (RRID:SCR_003756) Copy   

•   Source: SciCrunch Registry

http://aging.ucsd.edu/

Portal dedicated to the development and application of the latest advances in biomedical and behavioral science knowledge to issues of successful, healthy aging and the prevention and reduction of the burden of disability and disease in late life. Additionally, they provide numerous grants to junior faculty, as well as education programs for doctors and researchers through monthly Grand Rounds. From studying memory to identifying genes with important roles in aging, Stein Institute scientists are continuously pushing the boundaries of knowledge. One of their most promising ongoing projects is the Successful AGing Evaluation (SAGE) Study. SAGE is the only large-scale study on successful aging that considers the impact of positive psychological traits, such as resilience and wisdom, in addition to biological factors, providing a much more complete picture of older adults. Their monthly public lectures presented by renowned physicians and scientists are broadcast on UCSD-TV and have been viewed more than one billion times. This year they partnered with the Clinical and Translational Research Institute and the Osher Lifelong Learning Institute to organize Making Sense of Science, a course for older adults interested in science and health. In addition, They distribute a free monthly newsletter and work extensively with the community, participating in numerous events and conferences.

Proper citation: Stein Institute for Research on Aging (RRID:SCR_003759) Copy   

•   Source: SciCrunch Registry

http://neuroscienceblueprint.nih.gov/

Collaborative framework that includes the NIH Office of the Director and the 14 NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, the Blueprint identifies cross-cutting areas of research, and confronts challenges too large for any single Institute or Center. The Blueprint makes collaboration a day-to-day part of how the NIH does business in neuroscience, complementing the basic missions of Blueprint partners. During each fiscal year, the partners contribute a small percentage of their funds to a common pool. Since the Blueprint's inception in 2004, this pool has comprised less than 1 percent of the total neuroscience research budget of the partners. In 2009, the Blueprint Grand Challenges were launched to catalyze research with the potential to transform our basic understanding of the brain and our approaches to treating brain disorders. * The Human Connectome Project is an effort to map the connections within the healthy brain. It is expected to help answer questions about how genes influence brain connectivity, and how this in turn relates to mood, personality and behavior. The investigators will collect brain imaging data, plus genetic and behavioral data from 1,200 adults. They are working to optimize brain imaging techniques to see the brain's wiring in unprecedented detail. * The Grand Challenge on Pain supports research to understand the changes in the nervous system that cause acute, temporary pain to become chronic. The initiative is supporting multi-investigator projects to partner researchers in the pain field with researchers in the neuroplasticity field. * The Blueprint Neurotherapeutics Network is helping small labs develop new drugs for nervous system disorders. The Network provides research funding, plus access to millions of dollars worth of services and expertise to assist in every step of the drug development process, from laboratory studies to preparation for clinical trials. Project teams across the U.S. have received funding to pursue drugs for conditions from vision loss to neurodegenerative disease to depression. Since its inception in 2004, the Blueprint has supported the development of new resources, tools and opportunities for neuroscientists. For example, the Blueprint supports several training programs to help students pursue interdisciplinary areas of neuroscience, and to bring students from underrepresented groups into the neurosciences. The Blueprint also funds efforts to develop new approaches to teaching neuroscience through K-12 instruction, museum exhibits and web-based platforms. From fiscal years 2007 to 2009, the Blueprint focused on three major themes of neuroscience - neurodegeneration, neurodevelopment, and neuroplasticity. These efforts enabled unique funding opportunities and training programs, and helped establish new resources including the Blueprint Non-Human Primate Brain Atlas.

Proper citation: NIH Blueprint for Neuroscience Research (RRID:SCR_003670) Copy   

•   Source: SciCrunch Registry

http://c-path.org/programs/pkd/

Consortium to develop evidence supporting the use of imaging Total Kidney Volume (TKV) as a prognostic biomarker that predicts the progression of Autosomal Dominant Polycystic Kidney Disease (ADPKD) to select patients likely to respond to therapy into clinical trials. It aims to replace the currently used measurement of glomerular filtration rate (GFR). Scientists will use the data collected to develop a disease progression model that will evaluate the relationship between TKV and the known complications of ADPKD, including rate of loss of kidney function, hypertension, gross hematuria, kidney stones, urinary tract infections, development of end-stage renal disease, and mortality. These analyses will be used to support the regulatory qualification of TKV as an accepted measure for assessing the progression of ADPKD in clinical trials in which new therapies are tested. PKDOC has the following goals: # Develop standard clinical data elements and definitions that are specific to ADPKD # Create a database of aggregated data from existing multiple, longitudinal, and well-characterized research registries maintained over decades by the leading institutions in ADPKD clinical investigation # Advance and harmonize the missions of regulatory agencies by creating tools that help with the evaluation of new pharmaceutical compounds # Develop a quantitative disease progression model to examine the linkage between TKV and disease outcomes

Proper citation: Polycystic Kidney Disease Outcomes Consortium (RRID:SCR_003674) Copy   

•   Source: SciCrunch Registry

http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/UCM382536.pdf

Urinary kidney biomarkers (KIM-1, albumin, total protein, 2-microglobulin, cystatin C, clusterin and trefoil factor-3) that are considered acceptable biomarkers for the detection of acute drug-induced nephrotoxicity in rats and can be included along with traditional clinical chemistry markers and histopathology in toxicology studies. These biomarkers may be used voluntarily as additional evidence of nephrotoxicity in nonclinical safety assessment studies to complement the standard data (BUN and sCr). In ROC analyses, some of these biomarkers showed better sensitivity and specificity than BUN and sCr relative to histopathological alterations considered to be the gold standard when tested with a limited number of nephrotoxicant and control compounds.

Proper citation: PSTC Nephrotoxicity Biomarkers (RRID:SCR_003709) Copy   

•   Source: SciCrunch Registry

http://www.brandeis.edu/

Private research university located in the Boston suburb of Waltham, Massachusetts. Founded in 1948 as a non-sectarian, coeducational institution sponsored by the Jewish community, Brandeis was established on the site of the former Middlesex University.

Proper citation: Brandeis University; Massachusetts; USA (RRID:SCR_003669) Copy   

•   Source: SciCrunch Registry

http://www.addconsortium.org/

A collaborative network among university-led drug discovery centers and programs to allow scientists to exchange technical expertise on drug discovery and development strategies as well as form partnerships with each other, biopharma companies, and drug discovery-focused contract service organizations and consultants. The website will also serve as a repository for drug discovery events, educational material, job postings, and partnership opportunities. Through active member participation this website will become a valuable tool for every scientist working in the drug discovery arena. In addition, involvement of members will enable them to effectively advocate to the NIH and other funding agencies to increase the awareness of the growing number of academic drug discovery scientists and their success as well as their needs.

Proper citation: Academic Drug Discovery Consortium (RRID:SCR_003706) Copy   

•   Source: SciCrunch Registry

http://www.ebioscience.com/

An Antibody supplier

Proper citation: eBioscience (RRID:SCR_003660) Copy   

•   Source: SciCrunch Registry

http://www.sabbiotech.com/

An Antibody supplier

Proper citation: Signalway (RRID:SCR_003659) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/efo/

An application focused ontology modelling the experimental factors in ArrayExpress and Gene Expression Atlas. It has been developed to increase the richness of the annotations that are currently made in the ArrayExpress repository, to promote consistent annotation, to facilitate automatic annotation and to integrate external data. The ontology describes cross-product classes from reference ontologies in area such as disease, cell line, cell type and anatomy. The methodology employed in the development of EFO involves construction of mappings to multiple existing domain specific ontologies, such as the Disease Ontology and Cell Type Ontology. This is achieved using a combination of automated and manual curation steps and the use of a phonetic matching algorithm. The ontology is evaluated with use cases from the ArrayExpress repository and ArrayExpress Atlas. You may also browse the EFO in the NCBO Bioportal. Term submissions are welcome.

Proper citation: Experimental Factor Ontology (RRID:SCR_003574) Copy   

•   Source: SciCrunch Registry

http://genome.sph.umich.edu/wiki/RAREMETAL

A software program that facilitates the meta-analysis of rare variants from genotype arrays or sequencing.

Proper citation: RAREMETAL (RRID:SCR_003573) Copy   

•   Source: SciCrunch Registry

http://wiki.healthgrid.org/Main_Page

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 6, 2016. HealthGrid is a wiki dedicated to grids for health. It is maintained as a dynamic knowledge resource for the healthgrid community. The HealthGrid community (a world-wide initiative) gathers individuals from the public and private domain world-wide who are actively exploring the beneficial impact of healthgrid technology on healthcare provision and research.

Proper citation: HealthGrid Wiki (RRID:SCR_003572) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/adverse-event-reporting-ontology/

An ontology aimed at supporting clinicians at the time of data entry, increasing quality and accuracy of reported adverse events.

Proper citation: Adverse Event Reporting Ontology (RRID:SCR_003571) Copy   

•   Source: SciCrunch Registry

http://www.isi.edu/integration/karma/

An information integration software tool that enables users to integrate data from a variety of data sources including databases, spreadsheets, delimited text files, XML, JSON, KML and Web APIs. Users integrate information by modeling it according to an ontology of their choice using a graphical user interface that automates much of the process. Karma learns to recognize the mapping of data to ontology classes and then uses the ontology to propose a model that ties together these classes. Users then interact with the system to adjust the automatically generated model. During this process, users can transform the data as needed to normalize data expressed in different formats and to restructure it. Once the model is complete, users can publish the integrated data as RDF or store it in a database.

Proper citation: Karma (RRID:SCR_003732) Copy   

•   Source: SciCrunch Registry

http://www.asf.alaska.edu/

Satellite facility that downlinks, processes, archives, and distributes remote-sensing data to scientific users around the world. Three major components: * Satellite Tracking Ground Station: Part of NASA?s Near Earth Network system of ground stations around the world. * Synthetic Aperture Radar Distributed Active Archive Center (SAR DAAC): ASF maintains the NASA archive of SAR data from a variety of satellites and aircraft, and provides these data and associated specialty support services to U.S. Government-approved researchers in support of NASA?s Earth Science Data and Information System project. * ASF Enterprise Center (ASFE): In support of UAF?s mission to be a student-centered research university, the ASF-E focuses on applications of remote-sensing data, specifically for UAF research. The ASF-E includes the GeoData Center (GDC), which provides data management and archive services for UAF principal investigators and maintains a variety of geophysical data collections in support of scientific research.

Proper citation: Alaska Satellite Facility (RRID:SCR_003610) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ccsegthickness

An end-to-end pipeline for corpus callosum processing that provides automated midsagittal alignment, CC segmentation with a quality control tool, and thickness profile generation. Groupwise analysis is facilitated by permutation testing with FWER and FDR multiple comparison correction. Results display is facilitated by a display script that shows p-values on a 3D pipe representation of a CC. This pipeline is implemented in MATLAB and requires the Image Processing Toolbox. There are plans to implement it completely in Python.

Proper citation: Corpus Callosum Thickness Profile Analysis Pipeline (RRID:SCR_003575) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/MDCDRG

Ontology of Medical Diagnostic Categories-Diagnosis Related Groups

Proper citation: Medical Diagnostic Categories - Diagnosis Related Groups (RRID:SCR_003725) Copy   

•   Source: SciCrunch Registry