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. 2025 Jun 11;54(4):dyaf128.
doi: 10.1093/ije/dyaf128.

Exploring the world for longitudinal datasets: data resources for transformative mental health research

Louise Arseneault  1 , Bridget T Bryan  1 , Thomas Canning  1   2 , Malaika Okundi  1 , Alice Stephens  1   3 , Elena Triantafillopoulou  1 , Daniel Yu  1   4 , Mariana Bolivar  5 , Parisa Mansoori  5 , Lea Milligan  5 , Ed Evans  6 , Grace Gatera  7
Affiliations

Exploring the world for longitudinal datasets: data resources for transformative mental health research

Louise Arseneault et al. Int J Epidemiol. .
No abstract available

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Figures

Figure 1.
Figure 1.
Flowchart presenting the key partners and the stages for landscaping longitudinal datasets worldwide. For datasets in academia, we adopted an active search approach for identifying longitudinal datasets which involved looking through repositories containing information about multiple datasets, such as academic journals and global consortia. Examples of repositories include the Maelstrom Research platform, the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) Data Repository, and the Health Data Research Gateway. We screened academic journals such as the International Journal of Epidemiology and Advances in Life Course Research for cohort profile papers. We also pulled datasets from global constoria, such as the International Household Survey Network, the Asia Pacific Cohort Studies Collaboration, and the International Hundred Thousand Cohorts Consortium. We also adopted a passive search approach which involved receiving information from individuals across the world in the mental health and epidemiology communities regarding datasets and repositories. For datasets outside academia, the team engaged in three complementary approaches. First, approaching industry contacts to identify relevant datasets and discuss barriers to sharing non-academic data. Second, searching open data portals using an automated strategy. Third, using desk research to focus on the most promising types of non-academic datasets previously identified.
Figure 2.
Figure 2.
Flowchart reporting the number of datasets processed during the landscaping stages.
Figure 3.
Figure 3.
Number of participants at inception for all identified longitudinal datasets.
Figure 4.
Figure 4.
Number of participants at inception for longitudinal datasets focused on mental health (N = 358).
See this image and copyright information in PMC

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