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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

https://github.com/alyssafrazee/polyester

An R package designed to simulate RNA sequencing experiments with differential transcript expression. Given a set of annotated transcripts, it will simulate the steps of an RNA-seq experiment (fragmentation, reverse-complementing, and sequencing) and produce files containing simulated RNA-seq reads. Simulated reads can be analyzed using a choice of downstream analysis tools. Polyester has a built-in wrapper function to simulate a case/control experiment with differential transcript expression and biological replicates. Users are able to set the levels of differential expression at transcripts of their choosing. This means they know which transcripts are differentially expressed in the simulated dataset, so accuracy of statistical methods for differential expression detection can be analyzed. Polyester offers several unique features: * Built-in functionality to simulate differential expression at the transcript level * Ability to explicitly set differential expression signal strength * Simulation of small datasets, since large RNA-seq datasets can require lots of time and computing resources to analyze * Generation of raw RNA-seq reads, as opposed to alignments or transcript-level abundance estimates * Transparency/open-source code

Proper citation: Polyester (RRID:SCR_003602) Copy   

•   Source: SciCrunch Registry

http://www.myelinrepair.org/

A non-profit foundation that funds basic research and is focused on accelerating the development of myelin repair therapeutics for multiple sclerosis. They have defined a 15-year research plan to develop a drug or drugs and believes its Accelerated Research Collaborative (ARC) model can subsequently be used to accelerate the treatment for all diseases. The ARC framework coordinates and manages the entire therapeutic development continuum from discovery biology to FDA approval. The model works by coordinating multi-disciplinary basic research from academic and government laboratories, systematically validating and derisking potential compounds/targets, and collaborating with pharma partners to increase the probability of successful programs.

Proper citation: Myelin Repair Foundation (RRID:SCR_003723) Copy   

•   Source: SciCrunch Registry

https://www.tocris.com/

An Antibody supplier

Proper citation: Tocris Bioscience (RRID:SCR_003689) Copy   

•   Source: SciCrunch Registry

http://www.transceleratebiopharmainc.com/

Non-profit research organization aiming to accelerate drug development by increasing the quality and efficiency of clinical studies through the development of shared tools, methods, and platforms. Consortium partnerships are limited to pharmaceutical and biotechnology companies with research & development operations, although there are collaborations with external organizations such the Clinical Data Interchange Standards Consortium (CDISC). Its current focus is to collaborate on: * Standardizing risk-based monitoring * Development of methods to qualify and train clinical trial sites * Development of a common investigator web portal * Development of clinical data standards on efficacy, and methods for comparator drug trials It currently has 5 projects: # Standardized Approach for High-Quality, Risk-Based Monitoring program aims to develop an industry-wide standard and approach for risk-based monitoring of clinical trials in order to enhance patient safety and ensure the quality of clinical trial data. # Shared Site Qualification and Training program aims to standardize GCP training and site qualification credentials in order to realize efficiencies and accelerate study start-up timelines. # Common Investigator Site Portal is a platform designed to streamline investigator and site access through harmonized delivery of content and services. # Data Standards project is a partnership with CDISC to develop industry-wide data standards in priority therapeutic areas to support the exchange and submission of clinical research and meta-data, improving patient safety and outcomes. # Comparator Drugs project aims to establish reliable, rapid sourcing of quality products for use in clinical trials through a comparator supply model enabling accelerated trial timelines and enhanced patient safety.

Proper citation: TransCelerate BioPharma (RRID:SCR_003728) Copy   

•   Source: SciCrunch Registry

http://national_databank.mclean.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. A publicly accessible data repository to provide neuroscience investigators with secure access to cohort collections. The Databank collects and disseminates gene expression data from microarray experiments on brain tissue samples, along with diagnostic results from postmortem studies of neurological and psychiatric disorders. All of the data that is derived from studies of the HBTRC collection is being incorporated into the National Brain Databank. This data is available to the general public, although strict precautions are undertaken to maintain the confidentiality of the brain donors and their family members. The system is designed to incorporate MIAME and MAGE-ML based microarray data sharing standards. Data from various types of studies conducted on brain tissue in the HBTRC collection will be available from studies using different technologies, such as gene expression profiling, quantitative RT-PCR, situ hybridization, and immunocytochemistry and will have the potential for providing powerful insights into the subregional and cellular distribution of genes and/or proteins in different brain regions and eventually in specific subregions and cellular subtypes.

Proper citation: National Brain Databank (RRID:SCR_003606) Copy   

•   Source: SciCrunch Registry

https://www.projectdatasphere.org/

Initiative to advance oncology research by enabling collaborative sharing of historical oncology clinical trial data through a universal platform (database). The initiative aims to network all stakeholders in the cancer community researchers, industry, academia, advocacy, and other organizations to share insights and collaborate on issues that could not be solved individually. To do this, they have made efforts to address issues of data privacy, security, intellectual property, resources, and incentives as part of its effort to maximize participation. Data contributions include control arms of clinical trials, and the platform uses data-security precautions and analytics to pool multiple studies associated with the same diagnosis in a manner that seeks to protect the privacy of patients and the security of the data contributed.

Proper citation: Project Data Sphere (RRID:SCR_003726) Copy   

•   Source: SciCrunch Registry