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Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

Background: Preterm birth is one of the most important contributors to neonatal mortality and morbidity. Experiencing stress during pregnancy may increase the risk of adverse birth outcomes, including preterm birth. This association has been observed in previous studies, but differences in measures used limit comparability. Objective: The objective of the study was to investigate the association between two measures of maternal stress during pregnancy, life stress and emotional distress, and gestation duration. Methods: Women recruited in the Danish National Birth Cohort from 1996 to 2002, who provided information on their stress level during pregnancy and expecting a singleton baby, were included in the study. We assessed the associations between the level of life stress and emotional distress in quartiles, both collected at 31 weeks of pregnancy on average, and the rate of giving birth using Cox regression within intervals of the gestational period. Results: A total of 80,991 pregnancies were included. Women reporting moderate or high levels of life stress vs no stress had a higher rate of giving birth earlier within all intervals of gestational age (e.g. high level: 27–33 weeks: hazard ratio (HR) 1.38, 95% confidence interval (CI) 1.04, 1.84; 34–36 weeks: 1.10, 95% CI 0.97, 1.25; 37–38 weeks: 1.21, 95% CI 1.15, 1.28). These associations between life stress and preterm birth were mainly driven by pregnancy worries. For emotional distress, a high level of distress was associated with shorter length of gestation in the preterm (27–33 weeks: 1.38, 95% CI 1.02, 1.86; 34–36 weeks: 1.05, 95% CI 0.91, 1.19) and early term (1.11, 95% CI 1.04, 1.17) intervals. Conclusions: Emotional distress and life stress were shown to be associated with gestational age at birth, with pregnancy-related stress being the single stressor driving the association. This suggests that reverse causality may, at least in parts, explain the earlier findings of stress as a risk factor for preter

Objectives: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. Methods: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. Results: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. Conclusions: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata ‘common data elements’ using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR., OBJECTIVES: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. METHODS: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. RESULTS: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. CONCLUSIONS: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.

We previously shared results suggesting that the academic motivation of a sample of French-Canadian adolescents remained stable from few weeks before the first wave to the second wave of the COVID-19 pandemic. We here examine if this pattern persisted using data collected at a third time point.