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Journal article

Research-ready data: the C-Surv data model

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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s10654-022-00916-y

Authors

More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author
ORCID:
0000-0001-9463-6971
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author


More from this funder
Funder identifier:
https://ror.org/029chgv08
Grant:
216767/Z/19/Z
More from this funder
Funder identifier:
https://ror.org/03x94j517
Grant:
MR/T033371/1
MR/L023784/2
MR/L023784/1


Publisher:
Springer Nature
Journal:
European Journal of Epidemiology More from this journal
Volume:
38
Issue:
2
Pages:
179-187
Place of publication:
Netherlands
Publication date:
2023-01-07
Acceptance date:
2022-09-10
DOI:
EISSN:
1573-7284
ISSN:
0393-2990
Pmid:
36609896


Language:
English
Pubs id:
1319307
Local pid:
pubs:1319307
Deposit date:
2025-06-25
ARK identifier:

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