AI Article Synopsis

  • Research-ready data enhances the rigor and opportunities in scientific research by creating a unified data environment, particularly valuable for analyses involving multiple cohorts.
  • The C-Surv model was developed through stakeholder feedback and is designed with a structured approach based on 18 relevant data themes, using standard naming conventions for variables in longitudinal studies.
  • Implementing this common data model provides significant advantages, as shown by reduced data preparation times compared to specific cohort models, and aids in the efficient discovery and analysis of research data.

Article Abstract

Research-ready data (data curated to a defined standard) increase scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following stakeholder consultation, a standard data model (C-Surv) optimised for data discovery, was developed using data from 5 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. Data preparation times were compared between cohort specific data models and C-Surv.It was concluded that adopting a common data model as a data standard for the discovery and analysis of research cohort data offers multiple benefits.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825071PMC
http://dx.doi.org/10.1007/s10654-022-00916-yDOI Listing

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