Rapid increase in the implementation of electronic health records (EHRs) has led to an unprecedented expansion in the availability of dense longitudinal cohort datasets for clinical studies. However, there is a growing need to ensure data traceability, validity, and reproducibility for EHR-based clinical research. Applying common data models that standardize EHR data elements could reduce research discrepancies and improve research reproducibility. As a pilot study, we utilized the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) developed by the Observational Health Data Sciences and Informatics (OHDSI) community to annotate cohort data elements from the local Rochester Epidemiology Project (REP). We evaluated the data coverage of the OMOP CDM by manually annotating the cohorts from 92 REP publications. Next, we examined cohort similarities between different publications using OMOP elements. Evaluation results showed that the OMOP CDM covers 99.8% of the content that is associated with cohort attributes. It demonstrated that the OMOP CDM can be used for data element standardization when extracting information from EHR and clinical registries. The OMOP CDM also shows its potential to be used as a tool for retrospective examination of cohort definition consistencies and epidemiology model similarities.