Recently, there has been growing awareness of the so-called ‘reproducibility crisis’ which refers to the failure to replicate the findings of many scientific studies. This may arise from the employment of questionable research practices, such as ‘p-hacking’ (conducting many statistical tests, and only reporting significant results), HARKING (hypothesising after the results are known), and outcome switching (promoting secondary outcomes to primary outcomes to fit unexpected results). Open Science practices, which encourage open methodology (including pre-registration of hypotheses and outcomes), open data (in a publicly accessible repository), and open access to publication (including pre-prints), are vital to combatting these. This chapter sets out how Open Science practices can be applied to psychiatric research, including consideration of challenges which can arise, such as how to share data safely and appropriately. The chapter includes an explanation of key principles and constructs, and explains how Open Science practises can be embedded throughout the life-cycle of a project, with practical how-to guides and sign-posting to further resources.

This chapter examines the implications arising from the book’s content, and draws some general conclusions. Section 10.2 considers implications on a chapter-by-chapter basis, covering the small effect of breastfeeding on cognition, the nature of energy metabolism in the brain, dietary components versus patterns, the gut–brain axis, caffeine and sugar, immorality and hunger, lifespan extension and depression as a common consequence of nutritional deficiency. Section 10.3 presents conclusions and future directions, organised under three headings. First, the necessity to improve dietary recording methods, examining biomarkers for individual foods, active and passive food image collection, and monitoring eating. Second, nutraceutical and nutrigenomics, with the former’s failure so far to deliver concrete benefits – and why – and the latter’s potential to explain this through understanding individual differences. Third, the translation and reproducibility crises in biomedicine, and some consideration of their solutions as they apply here.

Here we discuss how the use of artificial intelligence will change the way science is done. Deep learning algorithms can now surpass the performance of human experts, a fact that has major implications for the future of our discipline. Successful uses of AI technology all possess two ingredients for deep learning: copious training data and a clear way to classify it. When these two conditions are met, researchers working in tandem with AI technologies can organize information and solve scientific problems with impressive efficiency. The future of science will increasingly rely on human–machine partnerships, where people and computers work together, revolutionizing the scientific process. We provide an example of what this may look like. Hoping to remedy a present-day challenge in science known as the “reproducibility crisis,” researchers used deep learning to uncover patterns in papers that signal strong and weak scientific findings. By combining the insights of machines and humans, the new AI model acheives the highest predictive accuracy.

Applying the BIC in practice is far from straightforward and fraught with difficulties because it requires the regularization of space-time infinities by implementing some cosmic “measure.” Furthermore, a suitable physical quantity must be chosen as proxy for the number of reference class observers in some given space-time region. Unfortunately, the choices made in this procedure are prone to being exploited – often unintentionally – by the researchers as so-called researcher degrees of freedom (a term from the social science literature) to yield those results that would best conform to their theoretical preferences. In the light of this difficulty, the prospects for obtaining compelling evidence in favor of any specific multiverse theory by testing whether our observations are those that typical multiverse inhabitants would make do look bad. As it turns out, the multiverse theories that have the best chances of being successfully tested empirically are those that do not behave as typical multiverse theories in important respects – i.e., those multiverse theories according to which all universes in the multiverse are similar or identical in a significant number of ways.