Accelerating agriculture: Data-intensive plant breeding and the use of genetic gain as an indicator for agricultural research and development.

Accelerating the rate of genetic gain has in recent years become a key objective in plant breeding for the Global South, building on the availability of new data technologies and bridging biological interest in crop improvement with economic interest in enhancing the cost efficiency of breeding programs. This paper explains the concept of genetic gain, the conditions for its emerging status as an indicator of agricultural development and the broader implications of this move, with particular emphasis on the changing knowledge-control regimes of plant breeding, the social and political consequences for smallholder farmers and climate-adaptive agriculture. We analyse how prioritising the variables used to derive the indicator when deciding on agricultural policies affects the relationship between development goals and practice. We conclude that genetic gain should not be considered as a primary indicator of agricultural development in the absence of information on other key areas (including agrobiodiversity, seed systems and the differential impact of climate change on soil, crops and communities), as well as tools to evaluate the pros and cons of the acceleration in seed selection, management and evaluation fostered by the adoption of genetic gain as a key indicator.

Reframing the environment in data-intensive health sciences.

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research.

Process epistemology in the COVID-19 era: rethinking the research process to avoid dangerous forms of reification.

Whether we live in a world of autonomous things, or a world of interconnected processes in constant flux, is an ancient philosophical debate. Modern biology provides decisive reasons for embracing the latter view. How does one understand the practices and outputs of science in such a dynamic, ever-changing world - and particularly in an emergency situation such as the COVID-19 pandemic, where scientific knowledge has been regarded as bedrock for decisive social interventions? We argue that key to answering this question is to consider the role of the activity of reification within the research process. Reification consists in the identification of more or less stable features of the flux, and treating these as constituting stable things. As we illustrate with reference to biological and biomedical research on COVID-19, reification is a necessary component of any process of inquiry and comes in at least two forms: (1) means reification (phenomena-to-object), when researchers create objects meant to capture features of the world, or phenomena, in order to be able to study them; and (2) target reification (object-to-phenomena), when researchers infer an understanding of phenomena from an investigation of the epistemic objects created to study them. We note that both objects and phenomena are dynamic processes and argue that have no reason to assume that changes in objects and phenomena track one another. We conclude that failure to acknowledge these forms of reification and their epistemic role in scientific inquiry can have dire consequences for how the resulting knowledge is interpreted and used.

Where health and environment meet: the use of invariant parameters in big data analysis.

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on "data mash-ups"-that is the linking of data from epidemiology, biomedicine, climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power.

Open science, data sharing and solidarity: who benefits?

Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the "open science" movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel "Open science, data sharing and solidarity: who benefits?" held at the 2021 Biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB), and hosted by Cold Spring Harbor Laboratory (CSHL).

Experimenting with co-development: A qualitative study of gene drive research for malaria control in Mali.

We investigate how technology 'co-development' (between researchers, stakeholders and local communities) is framed in practice by those developing gene drive mosquitos for malaria eradication. Our case study focuses on UK and Mali-based researchers planning to undertake the first field trials in Mali of gene drive mosquitos for malaria control. While they and the wider gene drive research community are explicitly committed to the principle of co-development, how this is framed and practiced is not clear. Through qualitative analysis of 34 interviews complemented by observation and documentary research conducted in 2018, we identify and compare ten framings of co-development mobilised by UK and Malian researchers and stakeholders. For Malians, co-development reflected Mali's broader socio-political context and a desire for African scientific independence and leadership. It was mobilised to secure community and stakeholder support for gene drive mosquito field trials, through outreach, building local scientific capacity and developing those institutions (e.g. regulatory) necessary for field trials to go ahead. For UK participants, co-development was also concerned with scientific capacity-building, knowledge exchange between researchers, and stakeholder and community outreach to secure consent for field trials. Overall, our findings suggest co-development is opening up previously expert-dominated spaces as researchers attempt to take responsibility for the societal implications of their work. However, its main function is as a project management tool to enable and instrumentally support technological development, field trials and eventual deployment. This function extends into areas which are traditionally the responsibility of the state, such as regulatory development, facilitated by Mali's fragile political and economic situation. Paradoxically, co-development simultaneously depoliticises gene drive, masking power relations and closing down substantive debate and agency. Characterised by extreme poverty, conflict and weak institutions, Mali may become a site for technological experimentation where there is little interrogation of gene drive or its governance.

Actionable data for precision oncology: Framing trustworthy evidence for exploratory research and clinical diagnostics.

Huge amounts of genomic data produced by researchers around the world undermine data-centred discovery and therapeutic development. This paper considers how researchers make decisions about the actionability of specific datasets and the conditions that allow such data to be trusted. We discuss the case of COSMIC, a leading cancer genomics database which aggregates a large amount of sources. We research what the actionability of cancer data means in different situations of use, contrasting exploratory and diagnostics research. They highlight different questions and concerns upon genomic data use in medical research. At the same time, strategies and justifications pursued to evaluate and re-use can also share important similarities. To explain differences and similarities, we argue for an understanding of actionability and trust in data that depends on the goals and resources within the situation of inquiry, and the social epistemology of standards.

Data management challenges for artificial intelligence in plant and agricultural research.

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems.

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

How to build an effective research network: lessons from two decades of the GARNet plant science community.

Successful collaborative research is dependent on excellent ideas and innovative experimental approaches, as well as the provision of appropriate support networks. Collaboration requires venues, infrastructures, training facilities, and, perhaps most importantly, a sustained commitment to work together as a community. These activities do not occur without significant effort, yet can be facilitated and overseen by the leadership of a research network that has a clearly defined role to help build resources for their community. Over the past 20 years, this is a role that the UKRI-BBSRC-funded GARNet network has played in the support of the UK curiosity-driven, discovery-led plant science research community. This article reviews the lessons learnt by GARNet in the hope that they can inform the practical implementation of current and future research networks.

How to choose your research organism.

Despite August Krogh's famous admonition that a 'convenient' organism exists for every biological problem, we argue that appeals to 'convenience' are not sufficient to capture reasoning about organism choice. Instead, we offer a detailed analysis based on empirical data and philosophical arguments for a working set of twenty criteria that interact with each other in the highly contextualized judgements that biologists make about organism choice. We propose to think of these decisions as a form of 'differential analysis' where researchers weigh multiple criteria for organismal choice against each other, and often utilize multidimensional refinement processes to finalize their choices. The specific details of any one case make it difficult to draw generalizations or to abstract away from specific research situations. However, this analysis of criteria for organismal choice and how these are related in practice allows us to reflect more generally on what makes a particular organism useful or 'good.'

The challenges of big data biology.

The availability of big data has the potential to transform many areas of the life sciences and usher in new ways of doing research. Here, I argue that big data biology also raises fundamental questions in the philosophy of science: for example, what is a good dataset, and how can reliable knowledge be extracted from big data? Collaborations between biologists, data scientists and philosophers of science will help us to answer these and other questions.

What distinguishes data from models?

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes' characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes' attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs.

'Extreme' organisms and the problem of generalization: interpreting the Krogh principle.

Many biologists appeal to the so-called Krogh principle when justifying their choice of experimental organisms. The principle states that "for a large number of problems there will be some animal of choice, or a few such animals, on which it can be most conveniently studied". Despite its popularity, the principle is often critiqued for implying unwarranted generalizations from optimal models. We argue that the Krogh principle should be interpreted in relation to the historical and scientific contexts in which it has been developed and used. We interpret the Krogh Principle as a heuristic, i.e., as a recommendation to approach biological problems through organisms where a specific trait or physiological mechanism is expected to be most distinctively displayed or most experimentally accessible. We designate these organisms "Krogh organisms". We clarify the differences between uses of model organisms and non-standard Krogh organisms. Among these is the use of Krogh organisms as "negative models" in biomedical research, where organisms are chosen for their dissimilarity to human physiology. Importantly, the representational scope of Krogh organisms and the generalizability of their characteristics are not fixed or assumed but explored through experimental studies. Research on Krogh organisms is steeped in the comparative method characteristic of zoology and comparative physiology, in which studies of biological variation produce insights into general physiological constraints. Accordingly, we conclude that the Krogh principle exemplifies the advantages of studying biological variation as a strategy to produce generalizable insights.

Concealment and discovery: The role of information security in biomedical data re-use.

This paper analyses the role of information security (IS) in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in material, social and regulatory landscapes of research. We consider data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage, a Welsh databank that facilitates appropriate re-use of health data derived from research and routine medical practice in the region, and the Medical and Environmental Data Mash-up Infrastructure, a project bringing together researchers to link and analyse complex meteorological, environmental and epidemiological data. Through an in-depth analysis of how data are sourced, processed and analysed in these two cases, we show that IS takes two distinct forms: epistemic IS, focused on protecting the reliability and reusability of data as they move across platforms and research contexts, and infrastructural IS, concerned with protecting data from external attacks, mishandling and use disruption. These two dimensions are intertwined and mutually constitutive, and yet are often perceived by researchers as being in tension with each other. We discuss how such tensions emerge when the two dimensions of IS are operationalized in ways that put them at cross purpose with each other, thus exemplifying the vulnerability of data management strategies to broader governance and technological regimes. We also show that whenever biomedical researchers manage to overcome the conflict, the interplay between epistemic and infrastructural IS prompts critical questions concerning data sources, formats, metadata and potential uses, resulting in an improved understanding of the wider context of research and the development of relevant resources. This informs and significantly improves the reusability of biomedical data, while encouraging exploratory analyses of secondary data sources.