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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.
The lack of a rigorous account of biological information as a proximal causal factor in biological systems is a striking gap in the scientific worldview. In this chapter we outline a proposal to fill that gap by grounding the idea of biological information in a contemporary philosophical account of causation. Biological information is a certain kind of causal relationship between components of living systems. Many accounts of information in the philosophy of biology have set out to vindicate the common assumption that nucleic acids are distinctively informational molecules. Here we take a more unprejudiced approach, developing an account of biological information and then seeing how widely it applies.
In the first section, ‘Information in Biology’, we begin with the most prominent informational idea in modern biology – the coding relation between nucleic acid and protein. A deeper look at the background to Francis Crick's Central Dogma, and a comparison with the distinction in developmental biology between permissive and instructive interactions, reveals that ‘information’ is a way to talk about specificity. The idea of specificity has a long history in biology, and a closely related idea is a key part of a widely supported contemporary account of causation in philosophy that grounds causal relationships in ideas about manipulability and control. In the second section, ‘Causal Specificity: An Information-Theoretic Approach’, we describe the idea of ‘causal specificity’ and an information-theoretic measure of the degree of specificity of a cause for its effect. Biological specificity, we suggest, is simply causal specificity in biological systems. Since we have already argued that ‘information’ is a way to talk about biological specificity, we conclude that causal relationships are ‘informational’ simply when they are highly specific. The third section, ‘Arbitrariness, Information, and Regulation’, defends this identification against the claim that only causal relationships in which the relation between cause and effect is ‘arbitrary’ should count as informational. Arbitrariness has an important role, however, in understanding the regulation of gene expression via gene regulatory networks. Having defended our identification of information with specificity, we show in the final section, ‘Distributed Specificity’, that information is more widely distributed in biological systems than is often supposed. Coding sequences of DNA are only one source of biological specificity, and hence only one locus of biological information.
Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the specificity of coding DNA and other factors in a simple model of the production of mRNA.
A survey of the Milky Way disk and the Magellanic System at the wavelengths of the 21-cm atomic hydrogen (H i) line and three 18-cm lines of the OH molecule will be carried out with the Australian Square Kilometre Array Pathfinder telescope. The survey will study the distribution of H i emission and absorption with unprecedented angular and velocity resolution, as well as molecular line thermal emission, absorption, and maser lines. The area to be covered includes the Galactic plane (|b| < 10°) at all declinations south of δ = +40°, spanning longitudes 167° through 360°to 79° at b = 0°, plus the entire area of the Magellanic Stream and Clouds, a total of 13 020 deg2. The brightness temperature sensitivity will be very good, typically σT≃ 1 K at resolution 30 arcsec and 1 km s−1. The survey has a wide spectrum of scientific goals, from studies of galaxy evolution to star formation, with particular contributions to understanding stellar wind kinematics, the thermal phases of the interstellar medium, the interaction between gas in the disk and halo, and the dynamical and thermal states of gas at various positions along the Magellanic Stream.