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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
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
Service that extracts and analyzes methodological information from thousands of published studies across the functional neuroimaging literature. It currently offers searching articles by methodological choices, visualization of methods trends over time, and extraction of methods information from text. It currently includes 282,476 methodological details about 21,562 articles, published between 1992 and 2014. (2014) * Search for articles by title, authors, and methods. * Interactive visualizations of methodological parameters over time, by author, and more. * All data are programmatically accessible through a simple application programming interface. It''s simple to re-analyze the data or integrate with your own application.
Proper citation: NeuroTrends (RRID:SCR_003921) Copy
Project that aims to create more appropriate in vitro platforms for target validation and drug discovery for breast, prostate and lung cancers. Laboratory platforms to validate whether target modulation would provide a clinical benefit are usually highly reductionist, often using long-established cell lines growing in 2 dimensions in vitro. These models do not reflect the complexity and heterogeneity of a tumor in situ, where biochemical pathways are wired with connections to the complex tumor environment provided by the stroma. PREDECT has the goal of comparing the pathological and molecular profiles of novel in vitro platforms with those of human tumors. Because obtention of clinical material presents both logistics and quality problems for ongoing and intense studies of target validation, PREDECT aims to use material from genetically engineered mouse models, and some advanced xenografts, whose pathology and molecular profiles closely match cohorts of human tumors. PREDECT hopes to provide more appropriate in vitro platforms both for target validation and subsequent preclinical studies which will replace a current cascade of tests which are poorly predictive of clinical activity. The project is expected to shift paradigms in cell biology as well as in preclinical target validation where it should permit greater predictivity of drug efficacy in patient cohorts.
Proper citation: PREDECT (RRID:SCR_003883) Copy
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
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
http://purl.bioontology.org/ontology/SNOMEDCT
Ontology of SNOMED (Systematized Nomenclature of Medicine) clinical terms.
Proper citation: Systematized Nomenclature of Medicine - Clinical Terms (RRID:SCR_003915) Copy
http://www.rcpsych.ac.uk/pressparliament/podcasts.aspx
Each month our podcast team will be broadcasting the very latest breakthroughs and discoveries in neurosciences, psychiatry and psychology.
Proper citation: Royal College of Psychiatrists Podcasts (RRID:SCR_003913) Copy
An experimental course and blog offered by the Harvard-Smithsonian Center for Astrophysics John G. Wolbach Library and the Harvard Library to train librarians to respond to the growing data needs of their communities. Data science techniques are becoming increasingly important to all fields of scholarship. In the hands-on course, librarians learn the latest tools for extracting, wrangling, storing, analyzing, and visualizing data. By experiencing the research data lifecycle themselves, librarians develop the data savvy skills that can help transform the services they offer. Material will be made available via the DST4L website as it progresses.
Proper citation: Data Scientist Training for Librarians (RRID:SCR_004124) Copy
Repository to host data and tools associated with articles published by GigaScience & GigaByte journals. GigaDB defines a dataset as a group of files (e.g., sequencing data, analyses, imaging files, software programs) that are related to and support an article or study. Through their association with DataCite, each dataset will be assigned a DOI that can be used as a standard citation for future use of these data in other articles by the authors and other researchers. Datasets in GigaDB all require a title that is specific to the dataset, an author list, and an abstract that provides information specific to the data included within the dataset. Detailed information about the dataset is curated by dedicated biocurators in collaboration with the article authors at the time of publication of the associated manuscript to ensure full transparency and reproducibility of all journal articles published in GigaScience and GigaByte journals.
Proper citation: GigaDB (RRID:SCR_004002) Copy
Harvard University''s central service for sharing and preserving work. In addition to the scholarly journal articles targeted by Harvard''s several open access resolutions, DASH maybe used to self-archive manuscripts and materials. DASH supports a variety of file formats, and users are encouraged to deposit related materials with manuscripts (including data, images, audio and video files, etc.) When users deposit their work in DASH, it becomes visible to colleagues around the world by virtue of metadata harvesting, Google Scholar, and other indexing services. Higher visibility leads to higher rates of citation and impact. When users post early versions of their work, before publication, they establish intellectual priority sooner. Users act in their own best interests by taking part in the University''s mission to share and preserve the knowledge produced there. Because Harvard now has a prior, non-exclusive license to faculty journal articles in schools with open access policies, those faculty members are required to act accordingly when publishing journal articles, either by attaching an addendum to their publication agreement or obtaining a waiver. They then must deposit the publication in DASH.
Proper citation: Digital Access to Scholarship at Harvard (RRID:SCR_004122) Copy
https://rarediseases.org/organizations/nihoffice-of-rare-disease-research/
Organization which develops and maintains a centralized database on rare disease clinical research supported by the NIH. It also stimulates rare disease research by supporting scientific workshops and symposia, responds to requests for information on highly technical matters and matters of public policy, provides information to the Office of the Director on matters relating to rare diseases and orphan products, and coordinates and serves as a liaison with Federal and non-Federal national and international organizations.
Proper citation: Office of Rare Diseases Research (RRID:SCR_004121) Copy
http://purl.bioontology.org/ontology/MPATH
A structured controlled vocabulary of mutant and transgenic mouse pathology phenotypes
Proper citation: Mouse Pathology Ontology (RRID:SCR_003950) Copy
http://purl.bioontology.org/ontology/NHSQI2009
Ontology to organise a 2009 set of over 200 NHS quality indicators from different sources. Relationships between indicators, a basic set of inclusion / exclusion criteria, clinical pathway, clinical code and purpose (per 1992 Institute of Medicine, originally intended to categorise clinical guidelines) are identifies and made searchable.
Proper citation: NHS Quality Indicators (RRID:SCR_004005) Copy
A comprehensive, free, and interactive platform to help individuals discover more about clinical trials that may be appropriate for them for a variety of diseases. The platform allows stakeholders within the clinical trials community (investigators, site personnel, sponsors, and disease advocates) to engage with potential enrollees and educate them about specific clinical trials. They have identified some of the most commonly used criteria for the clinical trials in each disease. Using these criteria, they developed a questionnaire for a single disease. Then, looking at just those questions, they can start to get a sense of which clinical trials might be appropriate for a particular person which is a helpful to start to narrow down the list of potentially appropriate trials. All clinical trials that are posted on www.clinicaltrials.gov for the diseases that Corengi covers will be on the website.
Proper citation: Corengi (RRID:SCR_003942) Copy
Portal on the evolution of the Human Forebrain with schematically depicted sequential age levels of cortical evolution: Staggered Dual Parameter Grid, Growth Rings of the Neocortex, Growth Shells of Thalamus, Major Nuclei of the Thalamus, Dual Parameter - Grid, Types of Neocortical Lamination, and Rolf Hasslers Hexa-Partition of Unit Thalamic Inputs. The cytoarchitectonic subdivisions of both the thalamus and the neocortex are topographically defined in terms of the variables of phylogenetic age and input specificity. The cortical and thalamic parcellations of Brodmann, von Economo and Hassler are each quantitatively correlated to a specific Cartesian coordinate value designating discrete levels for both age and input basic parameters. The variable of phylogenetic age is represented in the cortex by the five circumferential growth rings demonstrated by Sanides, plus an additional growth ring detected intermediate to the fifth and sixth age levels and designated as prekoniocortex. The paleocortex and the archaecortex are the two primordial neocortical precursors that form the mammalian neocortex. In contrast to the arrangement in the planar cortex, six phylogenetically distinct growth shells are detected in the three-dimensional thalamus and are designated after the corresponding schematic levels of Rolf Hassler''s paradigm of hexapartition of unit-thalamic inputs. The subthalamus and the epithalamus analogously represent the primordial diencephalic precursors of the mammalian dorsal thalamus, Both the neocortex and the dorsal thalamus evolved in response to the necessity for a more comprehensive blending of inputs from differing neuraxial levels. Unlike the age variable, the parameter of input specificity is most readily apparent in the dorsal thalamus; which is the site of termination for each major forebrain input. Accordingly, the fourteen individual units of the parameter of input specificity are designated after each of the specific input classifications projecting discretely to circumscribed thalamic sectors, An identical complement of input parameter levels also occurs in the cortex by way of thalamic relay across the internal capsule. Furthermore, each thalamic nucleus of specific parameter coordinates directs its main projection to cells of the cortex displaying identical coordinate values, establishing forebrain interconnectivity as an additional function of the dual parameter paradigm.
Proper citation: EVOLUTIONARY FOUNDATIONS FOR THE HUMAN FOREBRAIN (RRID:SCR_004199) Copy
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
http://purl.bioontology.org/ontology/OPB
A reference ontology of classical physics as applied to the dynamics of biological systems. It is designed to encompass the multiple structural scales (multiscale atoms to organisms) and multiple physical domains (multidomain fluid dynamics, chemical kinetics, particle diffusion, etc.) that are encountered in the study and analysis of biological organisms.
Proper citation: Ontology of Physics for Biology (RRID:SCR_004144) Copy
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
http://www.compact-research.org/
Consortium to reduce delivery and targeting bottlenecks for developing novel innovative biopharmaceutical based medicines. The project aims to shed new light on the obstacles biopharmaceuticals (medicines based on biological molecules such as proteins, peptides or nucleic acids) need to overcome to get to where they are needed in the body. The team will then use this information to develop and validate biopharmaceutical formulations to deliver these novel drugs to their targets. By finding more effective ways of administering these biopharmaceutical drugs, and improving their ability to travel through the body to where they are needed, COMPACT will allow more patients to benefit from biopharmaceuticals. Furthermore, designing less invasive administration routes and reducing the dose (and therefore the side effects) and frequency of administration will help to improve patient compliance with treatments.
Proper citation: COMPACT (RRID:SCR_003850) Copy
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