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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

http://ncit.nci.nih.gov/

A reference terminology and core biomedical ontology for NCI that covers approximately 100,000 key biomedical concepts with terms, codes, definitions, and more than 200,000 inter-concept relationships. It is the reference terminology for NCI, NCI Metathesaurus and NCI informatics infrastructure covering vocabulary for clinical care, translational and basic research, and public information and administrative activities. It includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities; anatomy; agents, drugs and chemicals; genes and gene products and so on. In certain areas, like cancer diseases and combination chemotherapies, it provides the most granular and consistent terminology available. It combines terminology from numerous cancer research related domains, and provides a way to integrate or link these kinds of information together through semantic relationships. NCIt features: * Stable, unique codes for biomedical concepts; * Preferred terms, synonyms, definitions, research codes, external source codes, and other information; * Links to NCI Metathesaurus and other information sources; * Over 200,000 cross-links between concepts, providing formal logic-based definition of many concepts; * Extensive content integrated from NCI and other partners, much available as separate NCIt subsets * Updated frequently by a team of subject matter experts. NCIt is a widely recognized standard for biomedical coding and reference, used by a broad variety of public and private partners both nationally and internationally including the Clinical Data Interchange Standards Consortium Terminology (CDISC), the U.S. Food and Drug Administration (FDA), the Federal Medication Terminologies (FMT), and the National Council for Prescription Drug Programs (NCPDP).

Proper citation: NCI Thesaurus (RRID:SCR_003563) Copy   

•   Source: SciCrunch Registry

http://www.themmrf.org/research-programs/commpass-study/

A personalized medicine initiative to discover biomarkers that can better define the biological basis of multiple myeloma to help stratify patients. This effort hopes to obtain samples from approximately 1,000 multiple myeloma patients and follow them over time to identify how a patient's genetic profile is related to clinical progression and treatment response. As a partnership between 17 academic centers, 5 pharmaceuticals and the Department of Veterans Affairs, the goal of this eight year study is to create a database that can accelerate future clinical trials and personalized treatment strategies. MMRF's CoMMpass Study has the following goals: * Create a guide to which treatments work best for specific patient subgroups. * Share data with researchers to accelerate drug development for specific subtypes of multiple myeloma patients. In order to facilitate discoveries and development related to targeted therapies, the comprehensive data from CoMMpass is placed in an open-access research portal. The data will be part of the Multiple Myeloma Research Foundation's (MMRF) Personalized Medicine Platform combines CoMMpass data with those collected from MMRF's Genomics Initiative. It is hoped that the longitudinal data, combined with the annotated bio-specimens will help provide insights that can accelerate personalized therapies.

Proper citation: MMRF CoMMpass Study (RRID:SCR_003721) Copy   

•   Source: SciCrunch Registry

http://www.curealzfund.org/

Cure Alzheimer's Fund is a 501(c)(3) public charity. At Cure Alzheimer's Fund, our mission is to fund research with the highest probability of slowing, stopping or reversing Alzheimer's disease. This topical portal has a lot of information including news and blog. Cure Alzheimer's Fund is governed by a board of directors; administered by a small, full-time staff; and guided scientifically by a Research Consortium. A Scientific Advisory Board audits the research program to make sure it is consistent with the objectives of the foundation. Cure Alzheimer's Fund is a doing business as name for the Alzheimer's Disease Research Foundation, federal tax ID # 52-2396428.

Proper citation: Cure Alzheimers Fund (RRID:SCR_003564) Copy   

•   Source: SciCrunch Registry

http://www.research.philips.com/

A global organization that helps Philips introduce innovations that improve people's lives by providing technology options for innovations in the area of health and well-being, targeted at both developed and emerging markets. Positioned at the front-end of the innovation process, they work on everything from spotting trends and ideation to proof of concept and - where needed - first-of-a-kind product development.

Proper citation: Philips Research (RRID:SCR_003871) Copy   

•   Source: SciCrunch Registry

http://www.mip-dili.eu/

Consortium that brings together Europe's top industrial and academic experts to develop new tests that will help researchers detect potential liver toxicity issues much earlier in drug development, saving many patients from the trauma of liver failure. The team aims to deepen the understanding of the science behind drug-induced liver injury, and use that knowledge to overcome the many drawbacks of the tests currently used. A major focus will be on a systematic and evidence-based evaluation of both currently available and new laboratory test systems, including cultures of liver cells in one-dimensional and three dimensional configurations. The project will also develop models that take into account the natural differences between patients. This is important because factors such as certain genes, the liver's immune response, and viral infections have all been associated with an increased risk of DILI. The project will seek to address the current lack of human liver cells available to researchers by using induced pluripotent stem cells (iPSCs) generated from patients who are particularly sensitive to DILI. Another strand of the project will develop computer models to unravel the complex, often inter-related mechanisms behind DILI. Finally, the team will assess how accurate the results of laboratory tests are at predicting actual outcomes in patients.

Proper citation: MIP-DILI (RRID:SCR_003870) Copy   

•   Source: SciCrunch Registry

http://www.newmeds-europe.com/

Consortium that will develop new models and methods to enable novel treatments for schizophrenia and depression including three important missing tools that will facilitate the translation of scientific findings into benefits for patients. The project will focus on developing new animal models which use brain recording and behavioral tests to identify innovative and effective drugs for schizophrenia. The project will develop standardized paradigms, acquisition and analysis techniques to apply brain imaging, especially fMRI and PET imaging to drug development. It will examine how new genetic findings (duplication and deletion or changes in genes) influence the response to various drugs and whether this information can be used to choose the right drug for the right patient. And finally, it will try and develop new approaches for shorter and more efficient trials of new medication - trials that may require fewer patients and give faster results.

Proper citation: NEWMEDS (RRID:SCR_003872) Copy   

•   Source: SciCrunch Registry

http://www.k4dd.eu/

Project whose goal is to improve understanding of how potential drugs bind with their target, and develop methods and tools to allow researchers to study drug-target interactions with greater ease. These tools would help researchers to determine whether a drug candidate is likely to be safe and effective much earlier in the drug development process. The first goal of the team is to enhance understanding of binding kinetics; exactly how do small molecules interact with their targets? Ultimately, the project aims to develop a range of robust techniques, methods and models that could be easily incorporated into the drug development pathway and enable scientists and drug designers worldwide to reliably predict a molecule's kinetic properties (its "kinotype"). This information will allow drug developers to more easily determine the safety and efficacy of a molecule and will weed out ineffective or unsafe molecules earlier in the drug development process. Eventually, the project also hopes to raise awareness of the importance of considering the kinetic aspects of drug-target interactions throughout drug development.

Proper citation: Kinetics for Drug Discovery (RRID:SCR_003868) Copy   

•   Source: SciCrunch Registry

http://www.investigatordatabank.org/

Consortium between several pharmaceutical companies to develop a database that shares clinical trial investigator information that each company has on file to reduce administrative burden for investigators and to increase visibility of qualified investigators to research sponsors. Hosted by a 3rd party, DrugDev, it is the one place where pharmaceutical companies can share investigator and site information and investigators can view, edit, and comment on their own information. Industry members will be able share information from their clinical trial management systems including investigator / site contact details, GCP training records, past trial participation, and recruitment history. Upon investigator opt-in, this information is used by each participating company to identify sites for upcoming studies; to help set recruitment targets and timelines; and to share start-up documents such as CV, GCP, and site profile forms. The Databank has grown to include nearly 180,000 investigators (May 2014) and is already bringing benefits to its member companies in identifying qualified investigators for their studies. It also reported the inclusion of 7,335 protocols and 50K sites, representing a patient population around 1.9M.

Proper citation: Investigator Databank (RRID:SCR_003866) Copy   

•   Source: SciCrunch Registry

http://www.asn-online.org/khi/

Consortium that brings together the kidney community (patient advocacy groups, industry, government agencies, and professional organizations) to overcome existing challenges, including regulatory and nonregulatory barriers, and optimize the development and safety of products that impact kidney health including drugs, devices, biologics, and food products. The goals of the consortium are to: * Facilitate dialogue and research that informs regulatory processes with regard to the kidney health of patients being treated for kidney-related as well as other diseases. * Assess current medical therapies and diagnostics to identify areas in need of greater innovation and/or better defined regulatory pathways. * Develop innovative and efficient trial designs appropriate to answer the most important questions related to kidney health. * Establish expert consensus around common terminology and key definitions related to kidney health. * Develop approaches to the systematic collection of retrospective or prospective data, such as registries and/or global databases, and establishment of data standards. * Coordinate think tanks, public forums, educational exchanges, and other events to promote discussion and updates on topics in kidney health pertaining to drug, device, biologics, and food product development and evaluation. * Create transparent infrastructure and processes that facilitate collaboration and communication among the greater nephrology community and the FDA, including: * Seek input from all stakeholders (including nephrologists and other health professionals, patient groups, industry, the National Institutes of Health, the Centers for Medicare and Medicaid Services, the Health Resources and Services Administration, and other federal agencies). * Leverage previously conducted and ongoing clinical studies, research infrastructure, and databases. * Create an open and efficient mechanism for encouraging and objectively evaluating potential projects submitted to KHI. * Involve consortium members in the selection and execution of projects. * Establish systems to optimize post-market surveillance of products that affect kidney health, either intentionally or via adverse drug reactions. * Author journal articles and white papers regarding key issues, describing opportunities and challenges and proposing solutions, as well as promoting execution of these solutions.

Proper citation: Kidney Health Initiative (RRID:SCR_003869) Copy   

•   Source: SciCrunch Registry