Public health data available for research are booming with the expansion of Big Data. This reshapes the data sources for DOHaD enquiries while offering ample opportunities to advance epidemiological modelling within the DOHaD framework. However, Big Data also raises a plethora of methodological challenges related to accurately characterising population health trajectories and biological mechanisms, within heterogeneous and dynamic sociodemographic contexts, and a fast-moving technological landscape. In this chapter, we explore the methodological challenges of research into the causal mechanisms of the transgenerational transfer of disease risks that characterise the DOHaD research landscape and consider these challenges in the light of novel technologies within artificial intelligence (AI) and Big Data. Such technologies could push further the collating of multidimensional data, including electronic health records and tissue banks, to offer new insights. While such methodological and technological innovations may drive clearer and reproducible evidence within DOHaD research, as we argue, many challenges remain, including data quality, interpretability, generalisability, and ethics.

Chapter 8 provides a detailed account of practical steps and advice for doing CODA. While avoiding reintroducing well-known methods of discourse analysis , it highlights important principles and shares practice–based insights that have accumulated over many years of doing CODA. Starting with considerations as to the purpose of individual CODA studies, it discusses relevant procedures step by step, including experimental design aspects, data collection, transcription and data preparation, and practical steps of data analysis such as segmentation, content analysis, tool support, and operationalisation of analysis categories. The final sections of this chapter consider issues pertaining to qualitative and quantitative analysis methods. The practical guidance using only simple and highly accessible tools provided in this chapter will be well received particularly by graduate students (or early-career researchers) with little access to complex technologies or hands–on practical support in their own universities. More experienced researchers will understand the principles quickly and be able to adapt them for their own purposes, using whichever technology they may have access to.