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http://central.xnat.org

Online repository of open access images including MR Sessions, MRI, Freesurfer APARC, Freesurfer ASEGs, Clinical Assessments, Atlas Scaling Factors, and Fast Segmentations data. CENTRAL currently contains 374 Projects, 3808 Subjects, and 5174 Imaging Sessions (June 2014). Central is powered by XNAT (The Extensible Neuroimaging Archive Toolkit), an open source software platform designed to facilitate management and exploration of neuroimaging and related data. XNAT includes a secure database backend and a rich web-based user interface.

Proper citation: XNAT Central (RRID:SCR_006235) Copy   

•   Source: SciCrunch Registry

http://obi-ontology.org/

An ontology for the description of biological and clinical investigations built with international, collaborative effort. The ontology represents the design of an investigation, the protocols and instrumentation used, the material used, the data generated and the type analysis performed on it. This includes a set of universal terms that are applicable across various biological and technological domains, and domain-specific terms relevant only to a given domain. Currently OBI is being built under the Basic Formal Ontology (BFO). This project was formerly titled the Functional Genomics Investigation Ontology (FuGO) project.

Proper citation: Ontology for Biomedical Investigations (RRID:SCR_006266) Copy   

•   Source: SciCrunch Registry

http://openfurther.org/

Data and knowledge management infrastructure for the new Center for Clinical and Translational Science (CCTS) at the University of Utah. This clinical cohort search tool is used to search across the University of Utah clinical data warehouse and the Utah Population Database for people who satisfy various criteria of the researchers. It uses the i2b2 front end but has a set of terminology servers, metadata servers and federated query tool as the back end systems. FURTHeR does on-the-fly translation of search terms and data models across the source systems and returns a count of results by unique individuals. They are extending the set of databases that can be queried.

Proper citation: FURTHeR (RRID:SCR_006383) Copy   

•   Source: SciCrunch Registry

http://www.h-outcomes.com/

Software application that supports the execution of multivariable prediction models with patient-specific characteristics so that personalized estimates of outcomes, often as a function of alternative treatments, can be generated within the routine flow of patient care. This can support evidence-based, shared medical decision-making to improve the safety, outcomes and cost-effectiveness of care. The current application is in the setting of generating individualized informed consent documents for PCI. However, the tool can support that translation of novel biomarkers, genetics and pharmacogenomic interactions into clinical care. The platform gives healthcare providers instantaneous access to the latest clinical prediction models coupled with rich visualization tools. These models may come from national organizations, outcomes researchers or a specific institution. In addition to decision support applications, it can be used to rapidly create personalized educational materials, patient letters, informed consent documents and a broad array of other items that can help elevate the quality of healthcare delivery.

Proper citation: ePRISM (RRID:SCR_006386) Copy   

•   Source: SciCrunch Registry

http://ctakes.apache.org

An open-source natural language processing system for information extraction from electronic medical record clinical free-text. This is a system through which one creates one or more pipelines to process clinical notes and to identify clinical named entities. It processes clinical notes, identifying types of clinical named entities, drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity that is found is given attributes for the text span, the ontology mapping code, the context (family history of, current, unrelated to patient), and negated/not negated. cTAKES is built on the UIMA framework. cTAKES 2.5 does not provide a GUI of its own for installation or processing. The cTAKES documentation shows how to use the GUIs provided by the UIMA framework, and how to run cTAKES from a command line. Before using cTAKES you need to know that cTAKES does not provide any mechanisms of its own to handle patient data securely. It is assumed that cTAKES is installed on a system that can process patient data, or that any data being processed by cTAKES has already been through a deidentification step in order to comply with any applicable laws. The tool has been developed and deployed at Mayo Clinic since early 2000.

Proper citation: cTAKES (RRID:SCR_006379) Copy   

•   Source: SciCrunch Registry

http://www.nkdep.nih.gov/lab-evaluation/gfr/creatinine-standardization.shtml

Standard specification to reduce inter-laboratory variation in creatinine assay calibration and therefore enable more accurate estimates of glomerular filtration rate (eGFR). Created by NKDEP''''s Laboratory Working Group in collaboration with the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and the European Communities Confederation of Clinical Chemistry (now called the European Federation of Clinical Chemistry and Laboratory Medicine), the effort is part of a larger NKDEP initiative to help health care providers better identify and treat chronic kidney disease in order to prevent or delay kidney failure and improve patient outcomes. Recommendations are intended for the USA and other countries or regions that have largely completed standardization of creatinine calibration to be traceable to an isotope dilution mass spectrometry (IDMS) reference measurement procedure. The program''''s focus is to facilitate the sharing of information to assist in vitro diagnostic manufacturers, clinical laboratories, and others in the laboratory community with calibrating their serum creatinine measurement procedures to be traceable to isotope dilution mass spectrometry (IDMS). The program also supports manufacturers'''' efforts to encourage their customers in the laboratory to coordinate use of standardized creatinine methods with implementation of a revised GFR estimating equation appropriate for use with standardized creatinine methods. Communication resources and other information for various segments of the laboratory community are available in the Creatinine Standardization Recommendations section of the website. Also available is a protocol for calibrating creatinine measurements using whole blood devices. The National Institute for Standards and Technology (NIST) released a standard reference material (SRM 967 Creatinine in Frozen Human Serum) for use in establishing calibrations for routine creatinine measurement procedures. SRM 967 was validated to be commutable with native serum samples for many routine creatinine procedures and is useful to establish or verify traceability to an IDMS reference measurement procedure. Establishing calibrations for serum creatinine methods using SRM 967 not only provides a mechanism for ensuring more accurate measurement of serum creatinine, but also enables more accurate estimates of GFR. For clinical laboratories interested in independently checking the calibration supplied by their creatinine reagent suppliers/manufacturers, periodic measurement of NIST SRM 967 should be considered for inclusion in the lab''''s internal quality assurance program. To learn more about SRM 967, including how to purchase it, visit the NIST website, https://www-s.nist.gov/srmors/quickSearch.cfm

Proper citation: Creatinine Standardization Program (RRID:SCR_006441) Copy   

•   Source: SciCrunch Registry

http://www.hopkinsguides.com/

Authoritative, need-to-know information from Johns Hopkins available for mobile devices and the web. Guides provide up to date information and break down details of diagnosis, drug indications, dosing, pharmacokinetics, side effects and interactions, pathogens, management, and vaccines into frequently-updated, quick-read entries. Available for infectious disease (ABX), diabetes, and HIV.

Proper citation: Johns Hopkins Point of Care Guides (RRID:SCR_006314) Copy   

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http://omim.org

Online catalog of human genes and genetic disorders, for clinical features, phenotypes and genes. Collection of human genes and genetic phenotypes, focusing on relationship between phenotype and genotype. Referenced overviews in OMIM contain information on all known mendelian disorders and variety of related genes. It is updated daily, and entries contain copious links to other genetics resources.

Proper citation: OMIM (RRID:SCR_006437) Copy   

•   Source: SciCrunch Registry

http://www.patientcrossroads.com/

A trusted third-party gatekeeper of patient data from participants in a rare disease ecosystem, collecting and managing the information in a scalable, cost-effective manner. Each patient registry provides critical disease knowledge which makes that disease easier to study, increasing the probability a treatment can be developed. PatientCrossroads takes a network approach to patient registry programs. Unlike companies that merely sell registry software, we offer a full range of administration, management, and genetic curation services. What does this consolidated, patient-centric approach to patient registries mean? * Patients can more easily find registries and provide their valuable data (including locations of blood and tissue samples as well as reports of diagnoses, disease symptoms, treatment usage, and lifestyle activities) * Patients can be confident in the privacy of their de-identified data and the knowledge that PatientCrossroads does not sell patient data * Researchers and pharmaceutical companies have a larger, more easily accessible pool of potential patients for research studies and clinical trials targeting specific rare diseases * Pharmaceutical companies can collect post-market surveillance data in a more scalable and cost-effective manner * Rare disease advocacy and research foundations can more easily organize their global patient populations for inclusion in trials and studies

Proper citation: PatientCrossroads (RRID:SCR_006279) Copy   

•   Source: SciCrunch Registry

http://univ.cns.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 19,2021.Designed with the neurosurgeon in mind, this portal contains everything you need to acquire new skills and techniques, including courses, an image database, and the world''s largest neurosurgical wiki reference - NeuroWiki. The new University of Neurosurgery includes: * More than 40 new online courses - in all neurosurgical subspecialties. * Archived webinars. * Lectures from the CNS Annual Meetings. * Neurosurgical image database. We are continuing to add new content - check back often.

Proper citation: Congress of Neurological Surgeons University of Neurosurgery (RRID:SCR_006309) Copy   

•   Source: SciCrunch Registry

http://www.nncc-exam.org/

Organization that established credentialing mechanisms to promote patient safety and to improve the quality of care provided to nephrology patients. There is a diversity of examinations providing the opportunity for certification at various levels of education, experience, and areas of practice within nephrology nursing. All of the certification examinations are endorsed by American Nephrology Nurses'''' Association (ANNA). The Commission recognizes the value of education, administration, research, and clinical practice in fostering personal and professional growth and currently provides six examinations to validate clinical performance: * The Certified Dialysis Nurse examination * The Certified Dialysis LPN/LVN examination * The Certified Nephrology Nurse examination * The Certified Clinical Hemodialysis Technician * The Certified Clinical Hemodialysis Technician - Advanced * The Certified Nephrology Nurse - Nurse Practitioner

Proper citation: Nephrology Nursing Certification Commission (RRID:SCR_003994) Copy   

•   Source: SciCrunch Registry

http://www.egappreviews.org/

Initiative to develop a systematic, evidence-based process for evaluating genetic tests and other applications of genomic technology that are rapidly moving from research to use in clinical practice. A key objective of this process is to provide objective, timely, and credible information that is clearly linked to the scientific evidence on specific applications of genetic and genomic tests. The primary focus of EGAPP activities is an independent, nonfederal expert panel, the EGAPP Working Group. Other components of the EGAPP initiative include a federal interagency, the CDC staff and consultants, and an EGAPP initiative evaluation team.

Proper citation: EGAPP (RRID:SCR_004189) Copy   

•   Source: SciCrunch Registry

http://images.wellcome.ac.uk/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023.Digital collection of images, with themes ranging from medical and social history to contemporary healthcare and biomedical science. The collection contains historical images from the Wellcome Library collections, Tibetan Buddhist paintings, ancient Sanskrit manuscripts written on palm leaves, beautifully illuminated Persian books and much more. The Biomedical Collection holds over 40 000 high-quality images from the clinical and biomedical sciences. Selected from the UK''s leading teaching hospitals and research institutions, it covers disease, surgery, general healthcare, sciences from genetics to neuroscience including the full range of imaging techniques. They are always looking for new high quality biomedical images from scientific researchers, clinical photographers and artists in any field of science or medicine. As a contributor you retain your original material and copyright, and receive commission and full credit each time your images are used. The annual Wellcome Images awards (previously known as Biomedical Images Awards) reward contributors for their outstanding work and winners are chosen by a panel of experts. The resulting public exhibitions are always extremely popular and receive widespread acclaim. All images on the Wellcome Images site are available free for use in: * private study and non-commercial research * examination papers * criticism and review, this applies only where there are no multiple copies made * theses submitted by a student at a higher or further education institution for the purposes of securing a degree * personal use by private individuals

Proper citation: Wellcome Images (RRID:SCR_004181) Copy   

•   Source: SciCrunch Registry

http://www.transformproject.eu/portfolio-item/d6-2-clinical-research-information-model/

A clinical research information model for the integration of clinical research covering randomized clinical trials (RCT), case-control studies and database searches into the TRANSFoRm application development. TRANSFoRm clinical research is based on primary care data, clinical data and genetic data stored in databases and electronic health records and employs the principle of reusing primary care data, adapting data collection by patient reported outcomes (PRO) and eSource based Case Report Forms. CRIM was developed using the TRANSFoRm clinical use cases of GORD and Diabetes. Their use case driven approach consisted of three levels of modelling drawing heavily on the clinical research workflow of the use cases. Different available information models were evaluated for their usefulness to represent TRANSFoRm clinical research, including for example CTOM of caBIG, Primary Care Research Object Model (PRCOM) of ePCRN and BRIDG of CDISC. The PCROM model turned out to be the most suitable and it was possible to extend and modify this model with only 12 new information objects, 3 episode of care related objects and 2 areas to satisfy all requirements of the TRANSFoRm research use cases. Now the information model covers Good Clinical Practice (GCP) compliant research, as well as case control studies and database search studies, including the interaction between patient and GP (family doctor) during patient consultation, appointment, screening, patient recruitment and adverse event reporting.

Proper citation: TRANSFoRm Clinical Research Information Model (RRID:SCR_003889) Copy   

•   Source: SciCrunch Registry

http://www.transformproject.eu/

Project to develop a ''rapid learning healthcare system'' driven by advanced computational infrastructure that can improve both patient safety and the conduct and volume of clinical research in Europe. Three carefully chosen clinical ''use cases'' will drive, evaluate and validate the approach to the ICT (information and communications technology) challenges. The project will build on existing work at international level in clinical trial information models (BRIDG and PCROM), service-based approaches to semantic interoperability and data standards (ISO11179 and controlled vocabulary), data discovery, machine learning and electronic health records based on open standards (openEHR). TRANSFoRm will extend this work to interact with individual eHR systems as well as operate within the consultation itself providing both diagnostic support and support for the identification and follow up of subjects for research. The approach to system design will be modular and standards-based, providing services via a distributed architecture, and will be tightly linked with the user community. Four years of development and testing will end with a fifth year that will be dedicated to summative validation of the project deliverables in the Primary Care setting. In order to support patient safety in both clinical and research settings, significant ICT challenges need to be overcome in the areas of interoperability, common standards for data integration, data presentation, recording, scalability, and security., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: TRANSFoRm (RRID:SCR_003888) Copy   

•   Source: SciCrunch Registry

http://www.alzheimer-europe.org/Research/PharmaCog

Project aiming to tackle bottlenecks in Alzheimer''''s disease research and drug discovery by developing and validating new tools to test candidate drugs for the treatment of symptoms and disease in a faster and more sensitive way. They will provide the tools needed to define more precisely the potential of a drug candidate, reduce the development time of new medicines and thus accelerate the approvals of promising new medicines. By bringing together databases of previously conducted clinical trials and combining the results from blood tests, brain scans and behavioral tests, the scientists will develop a ''''signature'''' that gives more accurate information on the progression of the disease and the effect of candidate drugs than current methods do. The scientists will conduct parallel studies in laboratory models, healthy volunteers and patients in order to better predict good new drugs as early as possible. This will enable them, for instance, to find out how memory loss in Alzheimer''''s disease can be simulated in healthy volunteers, for example with sleep deprivation or drugs that temporarily affect the memory, in order to test the effect of candidate-medicines early in the drug development process.

Proper citation: PharmaCog (RRID:SCR_003878) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/p2tconsortium/

A three-member pharmaceutical industry consortium that aims to provide a new platform to improve access to information about clinical trials for patients and providers. The platform aims to enhance the existing clinicaltrials.gov by providing more detailed and patient-friendly information about available trials and embedding a machine-readable target health profile to improve the ability of healthcare software to match individual health profiles with applicable clinical trials. Using clinicaltrials.gov as its foundation and Eli Lilly''''s Application Programming Interface (API), the consortium is focused on creating an open platform to make this data more amenable to patients and providers, as well as creating an opportunity to integrate a patient''''s electronic health record into the clinical trial matching service. This feature will allow patients to search for trials using their own Blue Button data. The following features are planned add-ons to clinicaltrials.gov: * Target Profile is a machine readable query, that can be executed against an electronic file (or record) with patient health data such as an Electronic Health Record (EHR), an Electronic Medical Record (EMR) or Personally Controlled Health Record (PCHR) * Augmented Content is public, IRB approved information about the study that has not been published on clinicaltrials.gov, and that is shared with / targeted for patients with a matching Target Profile. The following are the incremental goals of the consortium: * Advancement of the Lilly API platform to support read/write interaction and additional data objects and information. * The initial 3 sponsor organizations - Lilly, Pfizer and Novartis - will upload Target Profiles for a select set of clinical trials. A Target Profile is a machine interpretable description of the characteristics of patients who may qualify for that trial i.e. a query that can be executed against a patient''''s electronic health record or personal health record. Additionally, sponsors of clinical research studies will also be able to upload Augmented Content to the Lilly Platform to supplement information on clinicaltrials.gov with additional, patient-focused information about the study, e.g., a study brochure and practical information on how to contact investigational sites. * A matching service, developed by Corengi, will compare Target Profiles to a de-dentified personally controlled health record (PCHR), represented by patient''''s Blue Button Plus CCDA XML document. * Integration into a patient community platform from Avado for providing the patient PCHR and presenting the results of the match service. The patient will be able to explore the respective matching studies for additional information and next steps such as contacting a nearby investigator clinic or hospital. The first demo of the prototype was made available on June 2014, built on a database of anonymized patient health records from different clinical research studies sponsored by Lilly, Novartis, and Pfizer. Other website: http://portal.lillycoi.com/

Proper citation: Patients to Trials Consortium (RRID:SCR_003877) Copy   

•   Source: SciCrunch Registry

https://www.bigtencrc.org/

A consortium that aims to transform cancer research through collaborative oncology trials that leverage the scientific and clinical expertise of the Big Ten universities. The goal is to align the conduct of cancer research through collaborative, hypothesis-driven, highly translational oncology trials that leverage the scientific and clinical expertise. The clinical trials that will be developed will be linked to molecular diagnostics, enabling researchers to understand what drives the cancers to grow and what might be done to stop them from growing. The consortium also leverages geographical locations and existing relationships among the cancer centers. One of the consortium's goals is to harmonize contracts and scientific review processes to expedite clinical trials. The consortium will only focus on phase 0 to II trials because larger trials - even a randomized phase II trial - are difficult to conduct at a single cancer center.

Proper citation: Big Ten Cancer Research Consortium (RRID:SCR_004025) Copy   

•   Source: SciCrunch Registry

http://www.euadr-project.org/

Consortium that created the capability to detect Adverse Drug Response (ADR) signals by creating the infrastructure for large-scale monitoring of drug safety using electronic health records (EHR). The platform leverages EHR''''s comprising demographics, drug use and clinical data of over 30 million patients from several European countries. Special attention was given to patient groups that are not routinely involved in clinical trials, for ethical or practical reasons (e.g. pregnant women, elderly people, people using many drugs simultaneously, and children). This project also studies and compares a number of different techniques that all aim to detect unexpected or disproportional rates of events. The algorithms that they studied originate not only from the field of (pharmaco)epidemiology, but also from fields such as bio-terrorism, machine learning, and classical signal detection. EU-ADR specific objectives are: To detect events, To relate these events to drugs, To develop hypothesis that explain adverse events, To detect adverse events earlier, and To avoid false positives. The web-based platform is available at https://bioinformatics.ua.pt/euadr/ EU-ADR has contributed to the ability to conduct better drug safety studies based on the re-use of healthcare data. By facilitating the early detection of adverse drug reactions, but also providing key information on populations at risk, potential drug interactions, potential underlying mechanisms and intervening pathways in adverse events, etc., the project will allow for improved and more complete information to be available for drug and healthcare delivery, leading to increased patient safety and its associated cost savings. The EU-ADR system can be considered as a complementary tool to already existing pharamcovigilance systems. Should the system be widespread in the long term, it has the potential to contribute to the development of future electronic health record systems, insofar as the expected benefits of these IT tools are only fully attainable when EHRs develop themselves in consistency, richness and formats that allow them to be subject of such tools. In anticipation, EU-ADR has been designed to be modular and scalable, so that different EHR databases (other than those participating in the Consortium) can be progressively enlisted in the future, adopt the software for data extraction and therefore become susceptible of exploitation by the system, for maximum global effect.

Proper citation: EU-ADR (RRID:SCR_004028) Copy   

•   Source: SciCrunch Registry

http://en.ecgpedia.org/wiki/Main_Page

Free online electrocardiography (ECG) course and textbook via a wiki where anyone can contribute and changes are supervised by physicians. Learn from cases and examples. It designed for medical professionals such as cardiac care nurses and physicians. All content is freely accessible. The information on this site should NOT be used as a substitute for the advice of an appropriately qualified and licensed physician or other health care provider. For questions like these we advise you to contact your physician.

Proper citation: ECGpedia (RRID:SCR_004486) Copy   

•   Source: SciCrunch Registry