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Research-ready data: the C-Surv data model
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,...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825071/ https://www.ncbi.nlm.nih.gov/pubmed/36609896 http://dx.doi.org/10.1007/s10654-022-00916-y |
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author | Bauermeister, Sarah Bauermeister, Joshua R Bridgman, Ruth Felici, Caterina Newbury, Mark North, Laura Orton, Christopher Squires, Emma Thompson, Simon Young, Simon Gallacher, John E |
author_facet | Bauermeister, Sarah Bauermeister, Joshua R Bridgman, Ruth Felici, Caterina Newbury, Mark North, Laura Orton, Christopher Squires, Emma Thompson, Simon Young, Simon Gallacher, John E |
author_sort | Bauermeister, Sarah |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-022-00916-y. |
format | Online Article Text |
id | pubmed-9825071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-98250712023-01-09 Research-ready data: the C-Surv data model Bauermeister, Sarah Bauermeister, Joshua R Bridgman, Ruth Felici, Caterina Newbury, Mark North, Laura Orton, Christopher Squires, Emma Thompson, Simon Young, Simon Gallacher, John E Eur J Epidemiol Methods 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-022-00916-y. Springer Netherlands 2023-01-07 2023 /pmc/articles/PMC9825071/ /pubmed/36609896 http://dx.doi.org/10.1007/s10654-022-00916-y Text en © Crown 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Methods Bauermeister, Sarah Bauermeister, Joshua R Bridgman, Ruth Felici, Caterina Newbury, Mark North, Laura Orton, Christopher Squires, Emma Thompson, Simon Young, Simon Gallacher, John E Research-ready data: the C-Surv data model |
title | Research-ready data: the C-Surv data model |
title_full | Research-ready data: the C-Surv data model |
title_fullStr | Research-ready data: the C-Surv data model |
title_full_unstemmed | Research-ready data: the C-Surv data model |
title_short | Research-ready data: the C-Surv data model |
title_sort | research-ready data: the c-surv data model |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825071/ https://www.ncbi.nlm.nih.gov/pubmed/36609896 http://dx.doi.org/10.1007/s10654-022-00916-y |
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