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http://knhi.de/en/network/

Association of physicians, scientists, academics, research institutes and self-help groups that provides and nurtures interdisciplinary cooperation between research and primary, secondary and tertiary health care. Many internationally renowned heart failure researchers and working groups live and work in Germany. Nevertheless, there is insufficient cooperation of the respective working groups and research projects in this area. In order to remain internationally competitive in the heart failure research community, excellent implementation of large scale clinical and genetic trials is indispensable. Further, deficits in the effective presentation and transfer of research findings into clinical practice need to be addressed. An adequate translation of guidelines into practical, tangible instructions can facilitate clinical practice both in primary and tertiary care fundamentally. The need for action to address the research-practice-gap is obvious.

Proper citation: Competence Network Heart Failure (RRID:SCR_004979) Copy   

•   Source: SciCrunch Registry

http://glioblastoma.alleninstitute.org/

Platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels that includes two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: * The open public image database, here, providing in situ hybridization data mapping gene expression across the anatomic structures inherent in glioblastoma, as well as associated histological data suitable for neuropathological examination * A companion database (Ivy GAP Clinical and Genomic Database) offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access. The hope is that researchers all over the world will mine these data and identify trends, correlations, and interesting leads for further studies with significant translational and clinical outcomes. The Ivy Glioblastoma Atlas Project is a collaborative partnership between the Ben and Catherine Ivy Foundation, the Allen Institute for Brain Science and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment.

Proper citation: Ivy Glioblastoma Atlas Project (RRID:SCR_005044) Copy   

•   Source: SciCrunch Registry

http://health.usf.edu/byrd/adrc/index.htm

A statewide consortium dedicated to Alzheimer's disease research to better understand the disease and related memory disorders. It includes Alzheimer's researchers and clinicians from institutions across Florida such as USF Health, Mayo Clinic Jacksonville, and Mount Sinai Medical Center. The purpose of the ADRC is to assist institutions in developing an infrastructure (cores) that can be used for various research projects with the goal of better understanding Alzheimer's disease and related disorders. The Florida ADRC is comprised of six cores, three projects and three pilot projects among other collaborations that utilize these cores.

Proper citation: Florida Alzheimer's Disease Research Center (RRID:SCR_004940) Copy   

•   Source: SciCrunch Registry

https://www.saintluc.be/en/node/2561

An essential reference center in Europe and a leader in French-speaking Belgium that treats all types of adult and childhood cancer. They fight against cancer while giving patients comprehensive and humane care. Their quest for excellence is in three main academic fields: clinical care, research and teaching.

Proper citation: Cliniques Universitaires Saint-Luc Cancer Centre (RRID:SCR_004922) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/gtr/

Central location for voluntary submission of genetic test information by providers including the test''s purpose, methodology, validity, evidence of the test''s usefulness, and laboratory contacts and credentials. GTR aims to advance the public health and research into the genetic basis of health and disease. GTR is accepting registration of clinical tests for Mendelian disorders, complex tests and arrays, and pharmacogenetic tests. These tests may include multiple methods and may include multiple major method categories such as biochemical, cytogenetic, and molecular tests. GTR is not currently accepting registration of tests for somatic disorders, research tests or direct-to-consumer tests.

Proper citation: Genetic Testing Registry (RRID:SCR_005565) Copy   

•   Source: SciCrunch Registry

http://www.bioportfolio.com/

BioPortfolio is a leading news, information and knowledge resource covering the global life science industries impacted on by biotechnology. The site aims to provide the lay person, the researcher and the management executive with a single location to source core information on specific bio-related topics, to collate relevant data associated with each topic and to point the user to relevant knowledge resources. We publish up to the minute news (see biotechnology news categories) and regularly update content across our information databases. BioPortfolio promotes and sells market research and management reports from 30+ publishers. In addition our unique corporate database lists 40,000+ companies and organizations. BioPortfolio aims to bring together high quality information about marketed drugs - medication and relevant clinical trials, research papers and recent news from PubMed, ClinicalTrials.gov, and DailyMed. Additionally, resources include biotech, pharma and medical job listings. When the BioPortfolio site was launched in February 1997 the company aimed to provide a global free-to-use resource with defined aims and mission statement: to meet the increasing demand of consumers, scientists, investors, commerce and government for timely, accurate and commercially useful information and intelligence on biotechnology companies, technologies and products world-wide. Driven by the success of the site we have made major investments and improvements to enhance our content and to apply the latest web technologies to improve functionality and site utility. We believe this unique depth and breadth of content is supporting individuals, organizations and policy-makers to become more aware of the role of biotechnology on the global economy. With 97,000 users visiting the site more than once per month we are confident that we are providing information our users need. We hope you the users find the site of value for both personal and professional reasons. Please enjoy this free resource and email your comments!

Proper citation: BioPortfolio (RRID:SCR_005230) 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://www.curehunter.com/public/showTopPage.do

CureHunter is the only fully integrated scientific search, data retrieval and analysis engine on the web that can read the entire US National Library of Medicine Medline Archive and automatically extract and quantify the evidence for successful clinical outcomes of all known drugs for all known human diseases. * For patients we provide low-cost Summary PDF Reports with all drug evidence for all known cures or symptom improvement * For medical professionals CureHunter on-line access delivers decision support in 10-20 seconds of real clinical time to make an evidence check as SOP as a BP or Temp * For pharma research scientists we offer powerful data export functions that deliver over 1.5 million specific clinical outcome data points to new drug discovery software Use the CureHunter Research Interface: * Discover new potential off-label applications * Export data and apply custom analytics * 1-click drug performance meta-analyses * Keep up-to-date on the latest developments in your field * Optimize formularies with total evidence-based objectivity * RSS Feeds for Tracking Pharma Products

Proper citation: CureHunter (RRID:SCR_005804) Copy   

•   Source: SciCrunch Registry

http://www.utsouthwestern.edu/education/medical-school/departments/neurology/programs/traumatic-brain-injury/index.html

The 16 affiliated Model System centers throughout the United States are responsible for gathering and submitting the core data set to the national database as well as conducting research studies on traumatic brain injury (TBI) both in collaboration with the other centers and within our own site. Through our research we hope to learn more about TBI and about the issues and concerns of people with TBI. Our goals are to improve the outcome and quality of life for people who have had brain injuries and for those who are caring for the person with a TBI. The North Texas Traumatic Brain Injury Model System (NT-TBIMS) pools the efforts and talents of individuals from the Departments of Neurosurgery, Neurology, Physical Medicine and Rehabilitation, Psychiatry (Neuropsychiatry), and Neuroradiology of the two leading medical institutions in the North Texas region. To be a patient involved in the research being conducted by the North Texas Traumatic Brain Injury Model System you must have suffered a TBI, be at least 16 years of age, have received initial treatment for the TBI at either Parkland Health and Hospital System or Baylor University Medical Center and then have received rehabilitative care at either Parkland, University Hospital Zale-Lipshy, or Baylor Institute for Rehabilitation. The patient must also be able to understand and sign an informed consent to participate or, if unable, have a family member or a legal guardian who understands the form sign the informed consent for the patient.

Proper citation: North Texas Traumatic Brain Injury Model System (RRID:SCR_005879) Copy   

•   Source: SciCrunch Registry

http://www.ddduk.org/

The Deciphering Developmental Disorders (DDD) study aims to find out if using new genetic technologies can help doctors understand why patients get developmental disorders. To do this we have brought together doctors in the 23 NHS Regional Genetics Services throughout the UK and scientists at the Wellcome Trust Sanger Institute, a charitably funded research institute which played a world-leading role in sequencing (reading) the human genome. The DDD study involves experts in clinical, molecular and statistical genetics, as well as ethics and social science. It has a Scientific Advisory Board consisting of scientists, doctors, a lawyer and patient representative, and has received National ethical approval in the UK. Over the next few years, we are aiming to collect DNA and clinical information from 12,000 undiagnosed children in the UK with developmental disorders and their parents. The results of the DDD study will provide a unique, online catalogue of genetic changes linked to clinical features that will enable clinicians to diagnose developmental disorders. Furthermore, the study will enable the design of more efficient and cheaper diagnostic assays for relevant genetic testing to be offered to all such patients in the UK and so transform clinical practice for children with developmental disorders. Over time, the work will also improve understanding of how genetic changes cause developmental disorders and why the severity of the disease varies in individuals. The Sanger Institute will contribute to the DDD study by performing genetic analysis of DNA samples from patients with developmental disorders, and their parents, recruited into the study through the Regional Genetics Services. Using microarray technology and the latest DNA sequencing methods, research teams will probe genetic information to identify mutations (DNA errors or rearrangements) and establish if these mutations play a role in the developmental disorders observed in patients. The DDD initiative grew out of the groundbreaking DECIPHER database, a global partnership of clinical genetics centres set up in 2004, which allows researchers and clinicians to share clinical and genomic data from patients worldwide. The DDD study aims to transform the power of DECIPHER as a diagnostic tool for use by clinicians. As well as improving patient care, the DDD team will empower researchers in the field by making the data generated securely available to other research teams around the world. By assembling a solid resource of high-quality, high-resolution and consistent genomic data, the leaders of the DDD study hope to extend the reach of DECIPHER across a broader spectrum of disorders than is currently possible.

Proper citation: Deciphering Developmental Disorders (RRID:SCR_006171) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/tools-catalog/human-imaging-database-hid/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented October 5, 2017.

Database management system developed to handle the increasingly large and diverse datasets collected as part of the MBIRN and FBIRN collaboratories and throughout clinical imaging communities at large. The HID can be extended to contain relevant information concerning experimental subjects, assessments of subjects, the experimental data collected, the experimental protocols, and other metadata normally included with experiments.

Proper citation: Human Imaging Database (RRID:SCR_006126) Copy   

•   Source: SciCrunch Registry

http://www.chictr.org/en/

National clinical trial registry by Ministry of Health of China to join World Health Organization International Clinical Trial Registration Platform (WHO ICTRP Primary Registry), and the approved Primary Registry of WHO ICTRP. It registers both Chinese and global clinical trials, receives data from Partner Registers certified by the WHO ICTRP, and submits data to the WHO ICTRP Central Repository for global search. Moreover, based upon the talent and technical platform, consisting of Chinese Evidence-based Medicine Centre of Ministry of Health of China, Virtual Research Centre of Evidence-Based Medicine of Ministry of Education of China, Chinese Cochrane Centre, UK Cochrane Centre and International Clinical Epidemiology Network Resource and Training Centre in West China Hospital, Sichuan University (INCLEN CERTC), ChiCTR is responsible for providing consultations on trial design, central randomization service, guidance on the writing of clinical trial reports and relevant training. WHO takes the lead in establishing the global clinical trial registration system, which is agreed upon by governments from all over the world. There are both ethical and scientific reasons for clinical trial registration. Trial participants expect that their contributions to biomedical knowledge will be used to improve health care for everyone. Open access to information about ongoing and completed trials meets the ethical duty to trial participants, and promotes greater trust and public confidence in clinical research. Furthermore, trial registration ensures that the results of all trials can be tracked down and should help to reduce unnecessary duplication of research through greater awareness of existing trials and results. The mission of ChiCTR is to Unite clinicians, clinical epidemiologists, biostatisticians, epidemiologists and health care managers both at home and abroad, to manage clinical trials in a strict and scientific manner, and to promote their quality in China, so as to provide reliable evidences from clinical trials for health care workers, consumers and medical policy decision makers, and also to use medical resources more effectively to provide better service for Chinese people and all human beings. Any trial performed in human beings is considered as a clinical trial, and should be registered before its implementation. All the registered clinical trials will be granted a unique registration number by WHO ICTRP.

Proper citation: ChiCTR - Chinese Clinical Trial Registry (RRID:SCR_006037) Copy   

•   Source: SciCrunch Registry

http://coins.mrn.org/

A web-based neuroimaging and neuropsychology software suite that offers versatile, automatable data upload/import/entry options, rapid and secure sharing of data among PIs, querying and export all data, real-time reporting, and HIPAA and IRB compliant study-management tools suitable to large institutions as well as smaller scale neuroscience and neuropsychology researchers. COINS manages over over 400 studies, more than 265,000 clinical neuropsychological assessments, and 26,000 MRI, EEG, and MEG scan sessions collected from 18,000 participants at over ten institutions on topics related to the brain and behavior. As neuroimaging research continues to grow, dynamic neuroinformatics systems are necessary to store, retrieve, mine and share the massive amounts of data. The Collaborative Informatics and Neuroimaging Suite (COINS) has been created to facilitate communication and cultivate a data community. This tool suite offers versatile data upload/import/entry options, rapid and secure sharing of data among PIs, querying of data types and assessments, real-time reporting, and study-management tools suitable to large institutions as well as smaller scale researchers. It manages studies and their data at the Mind Research Network, the Nathan Kline Institute, University of Colorado Boulder, the Olin Neuropsychiatry Research Center (at) Hartford Hospital, and others. COINS is dynamic and evolves as the neuroimaging field grows. COINS consists of the following collaboration-centric tools: * Subject and Study Management: MICIS (Medical Imaging Computer Information System) is a centralized PostgreSQL-based web application that implements best practices for participant enrollment and management. Research site administrators can easily create and manage studies, as well as generate reports useful for reporting to funding agencies. * Scan Data Collection: An automated DICOM receiver collects, archives, and imports imaging data into the file system and COINS, requiring no user intervention. The database also offers scan annotation and behavioral data management, radiology review event reports, and scan time billing. * Assessment Data Collection: Clinical data gathered from interviews, questionnaires, and neuropsychological tests are entered into COINS through the web application called Assessment Manager (ASMT). ASMT's intuitive design allows users to start data collection with little or no training. ASMT offers several options for data collection/entry: dual data entry, for paper assessments, the Participant Portal, an online tool that allows subjects to fill out questionnaires, and Tablet entry, an offline data entry tool. * Data Sharing: De-identified neuroimaging datasets with associated clinical-data, cognitive-data, and associated meta-data are available through the COINS Data Exchange tool. The Data Exchange is an interface that allows investigators to request and share data. It also tracks data requests and keeps an inventory of data that has already been shared between users. Once requests for data have been approved, investigators can download the data directly from COINS.

Proper citation: Mind Research Network - COINS (RRID:SCR_000805) Copy   

•   Source: SciCrunch Registry

http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

•   Source: SciCrunch Registry

http://www.imi-getreal.eu/

Consortium that aims to improve the efficiency of the medicine development process by better incorporating estimates of relative effectiveness into drug development and to enrich decision-making by regulatory authorities and health technology assessment (HTA) bodies through: * Bringing together regulators, HTA bodies, academics, companies, patients and other societal stakeholders; * Assessing existing processes, methodologies, and key research issues; * Proposing innovative (and more pragmatic) trial designs and assessing the value of information; * Proposing and testing innovative analytical and predictive modelling approaches; * Assessing operational, ethical, regulatory issues and proposing and testing solutions; * Creating new decision making frameworks, and building open tools to allow for the evaluation of development programs and use in the assessment of the value of new medicines; * Sharing and discussing deliverables with, among others, Pharmaceutical companies, regulatory authorities, HTA / reimbursement agencies, clinicians and patient organizations; * Developing training activities for researchers, decision makers and societal stakeholders in the public and private sector in order to increase knowledge about various aspects of relative effectiveness. The expected impact is that it will contribute to the knowledge base, particularly to inform clinical decision making and improve the efficiency of the R&D process. GETREAL will help to generate a consensus on best practice in the timing, performance and use of real life clinical studies in regulatory and reimbursement decision-making. It will also help to create a strong platform for the communication of results and for future discussions in this important area.

Proper citation: GetReal (RRID:SCR_003862) Copy   

•   Source: SciCrunch Registry

http://www.asiancancerresearchgroup.org/

An independent, not-for-profit consortium to accelerate research, and improve treatment for patients affected with the most commonly-diagnosed cancers in Asia by generating a genomic data resource for the most prevalent cancers in Asia. ACRG is focusing its initial efforts on Asian liver, gastric and lung cancers. Goals * Generate comprehensive genomics data sets for Asia-prevalent cancers * Conduct all research under good clinical practices and in accordance with local laws * Uncover key mutations and pathways for developing targeted therapies * Discover molecular tumor classifiers for patient stratification * Discover prognostic markers to identify high-risk patients * Freely share resulting raw data with scientific community to empower researchers globally and enable development of new diagnostics and medicines * Publish data analysis results jointly in prominent scientific journals Over the next two years, Lilly, Merck and Pfizer have committed to create an extensive pharmacogenomic cancer database that will be composed of data from approximately 2,000 tissue samples from patients with lung and gastric cancer that will be made publicly available to researchers and, over time, further populated with clinical data from a longitudinal analysis of patients. Comparison of the contrasting genomic signatures of these cancers could inform new approaches to treatment. Lilly has assumed responsibility for ultimately providing the data to the research public through an open-source concept managed by Lilly''''s Singapore research site. Moreover, Lilly, Merck and Pfizer will each provide technical and intellectual expertise. One dataset can be found at http://gigadb.org/dataset/100034

Proper citation: Asian Cancer Research Group (RRID:SCR_004001) Copy   

•   Source: SciCrunch Registry

http://alzheimers.med.umich.edu/

An Alzheimer's disease center which aims to conduct and promote research on Alzheimer's disease and enhance public and professional understanding of dementia through education and outreach efforts. The MADC promotes clinical research on memory and aging which involves the direct use of research volunteers, biomarkers, and other clinical data collected through the University of Michigan Memory and Aging Project.

Proper citation: Michigan Alzheimer's Disease Center (RRID:SCR_008773) Copy   

•   Source: SciCrunch Registry

https://nidagenetics.org/

Site for collection and distribution of clinical data related to genetic analysis of drug abuse phenotypes. Anonymous data on family structure, age, sex, clinical status, and diagnosis, DNA samples and cell line cultures, and data derived from genotyping and other genetic analyses of these clinical data and biomaterials, are distributed to qualified researchers studying genetics of mental disorders and other complex diseases at recognized biomedical research facilities. Phenotypic and Genetic data will be made available to general public on release dates through distribution mechanisms specified on website.

Proper citation: National Institute on Drug Abuse Center for Genetic Studies (RRID:SCR_013061) Copy   

•   Source: SciCrunch Registry

https://kidsfirstdrc.org/portal/portal-features/

Portal for analysis and interpretation of pediatric genomic and clinical data to advance personalized medicine for detection, therapy, and management of childhood cancer and structural birth defects. For patients, researchers, and clinicians to create centralized database of well curated clinical and genetic sequence data from patients with childhood cancer or structural birth defects.

Proper citation: Kids First Data Resource Portal (RRID:SCR_016493) Copy   

•   Source: SciCrunch Registry

http://phenotips.cs.toronto.edu/

A software tool providing a Web interface and a database back-end for collecting clinical symptoms and physical findings observed in patients with genetic disorders. The main goals of this software are * To allow for collecting patient data in standard formats, enabling effortless data exchange and automated search in annotated gene and disease databases, and * To provide advanced functionalities and a friendly user interface that help reduce the clinician''''s workload, permitting seamless use of this application within the clinician''''s routine. PhenoTips uses the Human Phenotype Ontology (HPO) to express clinical phenotypes, and provides a friendly interface with error-tolerant, predictive search of phenotypic descriptions. PhenoTips closely mirrors clinician workflows: observations can be recorded directly during the patient encounter, and the interface is compatible with any device that runs a modern Web browser. The clinician can record demographic information, family history, medical history, various standard measurements, phenotypic abnormalities detected in the patient, pertinent indications that were not observed and that can be helpful for differential diagnosis, relevant images depicting manifestations of the patient''''s disorders, and additional notes for each of these categories. The software automatically plots growth curves, selects phenotypes reflecting abnormal measurements, instantly finds OMIM disorders matching the phenotypic description and suggests other symptoms to investigate in order to reach a more accurate diagnosis.

Proper citation: PhenoTips (RRID:SCR_006340) Copy   

•   Source: SciCrunch Registry