The NIH Common Data Elements (CDE) Repository has been designed to provide access to structured human and machine-readable definitions of data elements that have been recommended or required by NIH Institutes and Centers and other organizations for use in research and for other purposes. Visit the NIH CDE Resource Portal for contextual information about the repository.

Common Data Elements to use in medical research

In 2020, the Neurocritical Care Society’s Curing Coma Campaign launched an international initiative to create common data elements (CDEs) for disorders of consciousness (DoC). This CDE initiative is motivated by the recognition that ongoing progress in our field depends on the development of harmonized and uniform data elements. We formed multidisciplinary Work Groups with expertise in 1) Behavioral Phenotyping; 2) Hospital Course/Confounders/Medications; 3) Neuroimaging; 4) Electrophysiology; 5) Biospecimens; 6) Physiologic Data/Big Data; 7) Therapeutic Interventions; 8) Outcomes/Endpoints; and 9) Goals of Care/Family Data. Here, we disseminate the initial recommendations of this CDE development process and version 1.0 of the case report forms (CRFs) with CDEs that can be used in DoC studies. We aim for these CDEs to support progress in the field of DoC research and to facilitate multi-institutional collaboration.

We welcome feedback and are committed to revising the CDEs and CRFs to ensure that they reflect developments in our field. To provide feedback about the current CDEs and CRFs, and to make recommendations about updates for future versions, please email cde.curingcoma@gmail.com.

This dataset tracks the updates made on the dataset "NIH Common Data Elements Repository" as a repository for previous versions of the data and metadata.

The purpose of the NINDS Common Data Elements (CDEs) Project is to standardize the collection of investigational data in order to facilitate comparison of results across studies and more effectively aggregate information into significant metadata results. The goal of the National Institute of Neurological Disorders and Stroke (NINDS) CDE Project specifically is to develop data standards for clinical research within the neurological community. Central to this Project is the creation of common definitions and data sets so that information (data) is consistently captured and recorded across studies. To harmonize data collected from clinical studies, the NINDS Office of Clinical Research is spearheading the effort to develop CDEs in neuroscience. This Web site outlines these data standards and provides accompanying tools to help investigators and research teams collect and record standardized clinical data. The Institute still encourages creativity and uniqueness by allowing investigators to independently identify and add their own critical variables. The CDEs have been identified through review of the documentation of numerous studies funded by NINDS, review of the literature and regulatory requirements, and review of other Institute''s common data efforts. Other data standards such as those of the Clinical Data Interchange Standards Consortium (CDISC), the Clinical Data Acquisition Standards Harmonization (CDASH) Initiative, ClinicalTrials.gov, the NINDS Genetics Repository, and the NIH Roadmap efforts have also been followed to ensure that the NINDS CDEs are comprehensive and as compatible as possible with those standards. CDEs now available: * General (CDEs that cross diseases) Updated Feb. 2011! * Congenital Muscular Dystrophy * Epilepsy (Updated Sept 2011) * Friedreich''s Ataxia * Parkinson''s Disease * Spinal Cord Injury * Stroke * Traumatic Brain Injury CDEs in development: * Amyotrophic Lateral Sclerosis (Public review Sept 15 through Nov 15) * Frontotemporal Dementia * Headache * Huntington''s Disease * Multiple Sclerosis * Neuromuscular Diseases ** Adult and pediatric working groups are being finalized and these groups will focus on: Duchenne Muscular Dystrophy, Facioscapulohumeral Muscular Dystrophy, Myasthenia Gravis, Myotonic Dystrophy, and Spinal Muscular Atrophy The following tools are available through this portal: * CDE Catalog - includes the universe of all CDEs. Users are able to search the full universe to isolate a subset of the CDEs (e.g., all stroke-specific CDEs, all pediatric epilepsy CDEs, etc.) and download details about those CDEs. * CRF Library - (a.k.a., Library of Case Report Form Modules and Guidelines) contains all the CRF Modules that have been created through the NINDS CDE Project as well as various guideline documents. Users are able to search the library to find CRF Modules and Guidelines of interest. * Form Builder - enables users to start the process of assembling a CRF or form by allowing them to choose the CDEs they would like to include on the form. This tool is intended to assist data managers and database developers to create data dictionaries for their study forms.

Title of data: CTE & SAE Data Inventory. Description of data: List of common data elements in clinical trials with domain, availability/completeness, occurrence in trials, semantic codes and definition. (XLSX 34 kb)

The Commonly Used Data Element Standard aims to update and consolidate FEMA's existing data element standards, ensuring a consistent and high-quality approach to managing commonly used data elements. Incorrect data ingestion can lead to decision-making errors and challenge analysts with limited resources to correct these errors. Ensuring data quality begins with identifying and structuring key data elements that are vital to business operations. By standardizing the management of these elements, we can eliminate inconsistencies, reduce errors, and foster a culture of data excellence. This document provides comprehensive guidelines for adopting these data elements across systems and analytical reports to enhance data accuracy and support our strategic objectives. While compliance to these standards is not mandatory, it is highly recommended to achieve the full benefits of standardized, high-quality data practices within FEMA. By offering these standards as flexible recommendations rather than strict requirements, we allow programs and systems the flexibility to adapt while guiding FEMA towards exemplary data practices. Implementing these standards is crucial for improving our data management strategy, resulting in higher data quality and better alignment with our strategic goals.

Very common form names and the number and percentage of studies their used in.

This FAIRsharing record describes: Semantic data model of the set of common data elements for rare disease registration. To make rare disease registry data Interoperable (the 'I' in FAIR). Version 2.0. License CC0. Here, we present a semantic data model of the set of common data elements for rare diseases registration recommended by the European commission joint research centre. There are 16 data elements: ‘Pseudonym’, ‘Date of Birth’, ‘Sex’, ‘Patient’s status’, ‘Date of death’, ‘First contact with specialised centre’, ‘Age at onset’, Age at diagnosis’, ‘Diagnosis of the rare disease’, ‘Genetic diagnosis’, ‘Undiagnosed case’, ‘Agreement to be contacted for research purposes’, ‘Consent to the reuse of data’, ’Biological sample’, ‘Link to a biobank’, ‘Classification of functioning/disability’. The semantic data model is presented in 11 modules describing the different 16 data elements. Central to each module is the 'person'. Each module has in addition different characteristics assigned to the person.

Additional file 1. Summary of categorized data elements identified by environmental scan mapped to common data elements of Value Set Authority Center.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, this introduces many challenges, especially when managing confidential clinical data. The aim of this 1 hr virtual workshop is to provide participants with knowledge about what synthetic data is, methods to create synthetic data, and the 2023 Pediatric Sepsis Data Challenge. Workshop Agenda: 1. Introduction - Speaker: Mark Ansermino, Director, Centre for International Child Health 2. "Leveraging Synthetic Data for an International Data Challenge" - Speaker: Charly Huxford, Research Assistant, Centre for International Child Health 3. "Methods in Synthetic Data Generation." - Speaker: Vuong Nguyen, Biostatistician, Centre for International Child Health and The HIPpy Lab This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Charly Huxford: Leveraging Synthetic Data for an International Data Challenge presentation and accompanying PowerPoint slides. Vuong Nguyen: Methods in Synthetic Data Generation presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada., NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Data repository of de-identified patient data, aggregated in a standardized manner, to enable analyses across many rare diseases and to facilitate various research projects, clinical studies, and clinical trials. The aim is to facilitate drug and therapeutics development, and to improve the quality of life for the many millions of people who are suffering from rare diseases. The goal of GRDR is to enable analyses of data across many rare diseases and to facilitate clinical trials and other studies. During the two-year pilot program, a web-based template will be developed to allow any patient organization to establish a rare disease patient registry. At the conclusion of the program, guidance will be available to patient groups to establish a registry and to contribute de-identified patient data to the GRDR repository. A Request for Information (RFI) was released on February 10, 2012 requesting information from patient groups about their interest in participating in a GRDR pilot project. ORDR selected 30 patient organizations to participate in this pilot program to test the different functionalities of the GRDR. Fifteen (15) organizations with established registries and 15 organizations that do not have patient registry. The 15 patient groups, each without a registry, were selected to assist in testing the implementation of the ORDR Common Data Elements (CDEs) in the newly developed registry infrastructure. These organizations will participate in the development and promotion of a new patient registry for their rare disease. The GRDR program will fund the development and hosting of the registry during the pilot program. Thereafter, the patient registry is expected to be self-sustaining.The 15 established patient registries were selected to integrate their de-identified data into the GRDR to evaluate the data mapping and data import/export processes. The GRDR team will assist these organizations in mapping their existing registry data to the CDEs. Participating registries must have a means to export their de-identified registry data into a specified data format that will facilitate loading the data into the GRDR repository on a regular basis. The GRDR will also develop the capability to link patients'''' data and medical information to donated biospecimens by using a Voluntary Global Unique Patient Identifier (GUID). The identifier will enable the creation of an interface between the patient registries that are linked to biorepositories and the Rare Disease Human Biospecimens/Biorepositories (RD-HUB) http://biospecimens.ordr.info.nih.gov/.

List of 18 HIV trials in the final set analyzed for data elements.

List of acquired studies using CDISC.

Background: The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course. Methods: We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested. Findings: Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42–2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%). Conclusion: A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

The Common Entity Data Standards (CEDS) Domain Entity Schema (DES) provides a hierarchy of domains, entities, categories, and elements. It is intended for use primarily by people as an index to search, map, and organize elements in a logical way. [from homepage]

Example data elements (for trial NCT00099359: ‘Trial of Three Neonatal Antiretroviral Regimens for Prevention of Intrapartum HIV Transmission’).

The Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) are a set of hospital databases that contain the universe of hospital inpatient discharge abstracts from data organizations in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SID are based on data from short term, acute care, nonfederal hospitals. Some States include discharges from specialty facilities, such as acute psychiatric hospitals. The SID include all patients, regardless of payer and contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. The SID contain clinical and resource-use information that is included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SID, some include State-specific data elements. The SID exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and county-level data from the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

Platform for Traumatic Brain Injury relevant data. System was developed to share data across entire TBI research field and to facilitate collaboration between laboratories and interconnectivity between informatics platforms. FITBIR implements interagency Common Data Elements for TBI research and provides tools and resources to extend data dictionary. Established submission strategy to ensure high quality and to provide maximum benefit to investigators. Qualified researchers can request access to data stored in FITBIR and/or data stored at federated repositories.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing. The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data. Specifically, participants will learn: 1. Primary challenges and successes when sharing quantitative and qualitative clinical research data. 2. Platforms available for open data sharing. 3. Ways to troubleshoot data sharing and publish from open data. Workshop Agenda: 1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital 2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides. Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada., NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."