发布来自雅芳父母与儿童纵向研究(ALSPAC)的综合数据:指南和应用实例。

Q1 Medicine Wellcome Open Research Pub Date : 2024-12-24 eCollection Date: 2024-01-01 DOI:10.12688/wellcomeopenres.20530.2
Daniel Major-Smith, Alex S F Kwong, Nicholas J Timpson, Jon Heron, Kate Northstone
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引用次数: 0

摘要

雅芳父母与儿童纵向研究(ALSPAC)是一项前瞻性出生队列研究。自20世纪90年代初开始以来,该研究收集了大约1.5万名母亲、她们的伴侣和她们的后代的30多年数据,迄今为止得出了超过10万个表型变量。维护数据安全、参与者匿名性和保密性是研究的关键原则,这意味着数据访问仅限于真正的研究人员,他们必须申请使用数据,然后在每个项目的基础上共享数据。尽管有这些限制数据访问的正当理由,但这确实与鼓励公开数据以促进透明和可重复研究的新兴最佳科学实践背道而驰。鉴于资源的丰富性质,ALSPAC数据也是一种有价值的教育工具,用于教授各种方法,例如纵向建模和对缺失数据建模的方法。为了支持这些努力并克服研究数据共享政策的限制,我们讨论了生成和公开提供合成ALSPAC数据集的方法;这些综合数据集以原始的ALSPAC数据为模型,从而尽可能地保持变量分布和变量之间的关系(包括缺失数据),同时保持参与者的匿名性和保密性。我们讨论了如何使用R统计编程语言中的“synthpop”包来合成ALSPAC数据(包括一个应用示例),为希望发布此类合成ALSPAC数据的研究人员提供了一系列指导方针,并演示了如何将这种方法用作说明纵向建模方法的教育工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Releasing synthetic data from the Avon Longitudinal Study of Parents and Children (ALSPAC): Guidelines and applied examples.

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort. Since its inception in the early 1990s, the study has collected over thirty years of data on approximately 15,000 mothers, their partners, and their offspring, resulting in over 100,000 phenotype variables to date. Maintaining data security and participant anonymity and confidentiality are key principles for the study, meaning that data access is restricted to bona fide researchers who must apply to use data, which is then shared on a project-by-project basis. Despite these legitimate reasons for restricting data access, this does run counter to emerging best scientific practices encouraging making data openly available to facilitate transparent and reproducible research. Given the rich nature of the resource, ALSPAC data are also a valuable educational tool, used for teaching a variety of methods, such as longitudinal modelling and approaches to modelling missing data. To support these efforts and to overcome the restrictions in place with the study's data sharing policy, we discuss methods for generating and making openly available synthesised ALSPAC datasets; these synthesised datasets are modelled on the original ALSPAC data, thus maintaining variable distributions and relations among variables (including missing data) as closely as possible, while at the same time preserving participant anonymity and confidentiality. We discuss how ALSPAC data can be synthesised using the 'synthpop' package in the R statistical programming language (including an applied example), present a list of guidelines for researchers wishing to release such synthesised ALSPAC data to follow, and demonstrate how this approach can be used as an educational tool to illustrate longitudinal modelling methods.

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来源期刊
Wellcome Open Research
Wellcome Open Research Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
5.50
自引率
0.00%
发文量
426
审稿时长
1 weeks
期刊介绍: Wellcome Open Research publishes scholarly articles reporting any basic scientific, translational and clinical research that has been funded (or co-funded) by Wellcome. Each publication must have at least one author who has been, or still is, a recipient of a Wellcome grant. Articles must be original (not duplications). All research, including clinical trials, systematic reviews, software tools, method articles, and many others, is welcome and will be published irrespective of the perceived level of interest or novelty; confirmatory and negative results, as well as null studies are all suitable. See the full list of article types here. All articles are published using a fully transparent, author-driven model: the authors are solely responsible for the content of their article. Invited peer review takes place openly after publication, and the authors play a crucial role in ensuring that the article is peer-reviewed by independent experts in a timely manner. Articles that pass peer review will be indexed in PubMed and elsewhere. Wellcome Open Research is an Open Research platform: all articles are published open access; the publishing and peer-review processes are fully transparent; and authors are asked to include detailed descriptions of methods and to provide full and easy access to source data underlying the results to improve reproducibility.
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