Carbon Accounting in the Digital Industry: The Need to Move towards Decision Making in Uncertainty.

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the following research question: how do practitioners researching, working, or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that these calls for more data obscure the fact that there will always be uncertainty, and that we must learn to govern from within it.

Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.

Computationally expensive data processing in neuroimaging research places demands on energy consumption-and the resulting carbon emissions contribute to the climate crisis. We measured the carbon footprint of the functional magnetic resonance imaging (fMRI) preprocessing tool fMRIPrep, testing the effect of varying parameters on estimated carbon emissions and preprocessing performance. Performance was quantified using (a) statistical individual-level task activation in regions of interest and (b) mean smoothness of preprocessed data. Eight variants of fMRIPrep were run with 257 participants who had completed an fMRI stop signal task (the same data also used in the original validation of fMRIPrep). Some variants led to substantial reductions in carbon emissions without sacrificing data quality: for instance, disabling FreeSurfer surface reconstruction reduced carbon emissions by 48%. We provide six recommendations for minimising emissions without compromising performance. By varying parameters and computational resources, neuroimagers can substantially reduce the carbon footprint of their preprocessing. This is one aspect of our research carbon footprint over which neuroimagers have control and agency to act upon.

Roadmap for low-carbon ultra-low temperature storage in biobanking.

Biobanks have become an integral part of health and bioscience research. However, the ultra-low temperature (ULT) storage methods that biobanks employ [ULT freezers and liquid nitrogen (LN2)] are associated with carbon emissions that contribute to anthropogenic climate change. This paper aims to provide a 'Roadmap' for reducing carbon emissions associated with ULT storage in biobanking. The Roadmap offers recommendations associated with nine areas of ULT storage practice: four relating to ULT freezers, three associated with LN2 storage, and two generalised discussions regarding biosample management and centralisation. For each practice, we describe (a) the best approaches to mitigate carbon emissions, (b) explore barriers associated with hindering their implementation, and (c) make a series of recommendations that can help biobank stakeholders overcome these barriers. The recommendations were the output of a one year, UK-based, multidisciplinary research project that involved a quantitative Carbon Footprinting Assessment of the emissions associated with 1 year of ULT storage (for both freezers and LN2) at four different case study sites; as well as two follow up stakeholder workshops to qualitatively explore UK biobank stakeholder perceptions, views, and experiences on how to consider such assessments within the broader social, political, financial, technical, and cultural contexts of biobanking.

Responsibility for the Environmental Impact of Data-Intensive Research: An Exploration of UK Health Researchers.

Concerns about research's environmental impacts have been articulated in the research arena, but questions remain about what types of role responsibilities are appropriate to place on researchers, if any. The research question of this paper is: what are the views of UK health researchers who use data-intensive methods on their responsibilities to consider the environmental impacts of their research? Twenty-six interviews were conducted with UK health researchers using data-intensive methods. Participants expressed a desire to take responsibility for the environmental impacts of their research, however, they were unable to consolidate this because there were often obstacles that prevented them from taking such role responsibilities. They suggested strategies to address this, predominantly related to the need for regulation to monitor their own behaviour. This paper discusses the implications of adopting such a regulatory approach as a mechanism to promote researchers' role responsibilities using a neo-liberal critique.

The ethical challenges of diversifying genomic data: A qualitative evidence synthesis.

This article aims to explore the ethical issues arising from attempts to diversify genomic data and include individuals from underserved groups in studies exploring the relationship between genomics and health. We employed a qualitative synthesis design, combining data from three sources: 1) a rapid review of empirical articles published between 2000 and 2022 with a primary or secondary focus on diversifying genomic data, or the inclusion of underserved groups and ethical issues arising from this, 2) an expert workshop and 3) a narrative review. Using these three sources we found that ethical issues are interconnected across structural factors and research practices. Structural issues include failing to engage with the politics of knowledge production, existing inequities, and their effects on how harms and benefits of genomics are distributed. Issues related to research practices include a lack of reflexivity, exploitative dynamics and the failure to prioritise meaningful co-production. Ethical issues arise from both the structure and the practice of research, which can inhibit researcher and participant opportunities to diversify data in an ethical way. Diverse data are not ethical in and of themselves, and without being attentive to the social, historical and political contexts that shape the lives of potential participants, endeavours to diversify genomic data run the risk of worsening existing inequities. Efforts to construct more representative genomic datasets need to develop ethical approaches that are situated within wider attempts to make the enterprise of genomics more equitable.

Clinical Research Risks, Climate Change, and Human Health.

This Viewpoint discusses ways in which clinical research directly and indirectly contributes to anthropogenic climate change, highlights the need for increased scientific soundness and value in research, and calls for the development of tools for calculating the environmental impact of studies and a strategy for ensuring that research does not compromise the rights and welfare of nonparticipants and future generations.

UK health researchers' considerations of the environmental impacts of their data-intensive practices and its relevance to health inequities.

BACKGROUND: The health sector aims to improve health outcomes and access to healthcare. At the same time, the sector relies on unsustainable environmental practices that are increasingly recognised to be catastrophic threats to human health and health inequities. As such, a moral imperative exists for the sector to address these practices. While strides are currently underway to mitigate the environmental impacts of healthcare, less is known about how health researchers are addressing these issues, if at all. METHODS: This paper uses an interview methodology to explore the attitudes of UK health researchers using data-intensive methodologies about the adverse environmental impacts of their practices, and how they view the importance of these considerations within wider health goals. RESULTS: Interviews with 26 researchers showed that participants wanted to address the environmental and related health harms associated with their research and they reflected on how they could do so in alignment with their own research goals. However, when tensions emerged, their own research was prioritised. This was related to their own desires as researchers and driven by the broader socio-political context of their research endeavours. CONCLUSION: To help mitigate the environmental and health harms associated with data-intensive health research, the socio-political context of research culture must be addressed.

Drivers and constraints to environmental sustainability in UK-based biobanking: balancing resource efficiency and future value.

BACKGROUND: Biobanks are a key aspect of healthcare research; they enable access to a wide range of heterogenous samples and data, as well as saving individual researchers time and funds on the collection, storage and/or curation of such resources. However, biobanks are also associated with impacts associated with a depletion of natural resources (energy, water etc.) production of toxic chemicals during manufacturing of laboratory equipment, and effects on biodiversity. We wanted to better understand the biobanking sector in the UK as a first step to assessing the environmental impacts of UK biobanking. METHODS: We explored the sample storage infrastructure and environmental sustainability practices at a number of UK biobanks through a mixed methods quantitative and qualitative approach, including information gathering on an online platform, and eight in-depth interviews. RESULTS: Environmental sustainability was deprioritised behind biobanks' financial sustainability practices. Nevertheless, both often aligned in practice. However, there was a tendency towards underutilisation of stored samples, the avoidance of centralisation, and providing accessibility to biosamples, and this conflicted with valuing sustainability goals. This related to notions of individualised and competitive biobanking culture. Furthermore, the study raised how value attachments to biosamples overshadows needs for both financial and environmental sustainability concerns. CONCLUSIONS: We need to move away from individualised and competitive biobanking cultures towards a realisation that the health of the publics and patients should be first and foremost. We need to ensure the use of biosamples, ahead of their storage ('smart attachments'), align with environmental sustainability goals and participants' donation wishes for biosample use.

Systems thinking and efficiency under emissions constraints: Addressing rebound effects in digital innovation and policy.

Innovations and efficiencies in digital technology have lately been depicted as paramount in the green transition to enable the reduction of greenhouse gas emissions, both in the information and communication technology (ICT) sector and the wider economy. This, however, fails to adequately account for rebound effects that can offset emission savings and, in the worst case, increase emissions. In this perspective, we draw on a transdisciplinary workshop with 19 experts from carbon accounting, digital sustainability research, ethics, sociology, public policy, and sustainable business to expose the challenges of addressing rebound effects in digital innovation processes and associated policy. We utilize a responsible innovation approach to uncover potential ways forward for incorporating rebound effects in these domains, concluding that addressing ICT-related rebound effects ultimately requires a shift from an ICT efficiency-centered perspective to a "systems thinking" model, which aims to understand efficiency as one solution among others that requires constraints on emissions for ICT environmental savings to be realized.

The UK COVID-19 contact tracing app as both an emerging technology and public health intervention: The need to consider promissory discourses.

The UK's National Health Service (NHS) COVID-19 contact tracing app was announced to the British public on 12th April 2020. The UK government endorsed the app as a public health intervention that would improve public health, protect the NHS and 'save lives'. On 5th May 2020 the technology was released for trial on the Isle of Wight. However, the trial was halted in June 2020, reportedly due to technological issues. The app was later remodelled and launched to the public in September 2020. The rapid development, trial and discontinuation of the app over a short period of a few months meant that the mobilisation and effect of the discourses associated with the app could be traced relatively easily. In this paper we aimed to explore how these discourses were constructed in the media, and their effect on actors - in particular, those who developed and those who trialled the app. Promissory discourses were prevalent, the trajectory of which aligned with theories developed in the sociology of expectations. We describe this trajectory, and then interpret its implications in terms of infectious disease public health practices and responsibilities.

Access to Biobanks: Responsibilities Within a Research Ecosystem.

The past few decades have seen rapid increases in the size and scope of biobanks, with large-scale publicly funded ventures supporting health-related research becoming the norm. As these biobanks are increasingly asked to share their data, including for example, genome-wide analyses, questions arise about how such decisions are made, including whether applicants' research aligns with the aims of the biobank. To better understand how biobanks make decisions relating to their data use, we sought the views and experiences of those involved in decision-making relating to data access at 11 large-scale publicly funded health biobanks. We were particularly interested in how potentially contentious applications were approached. Interviewees had some concerns about decisions on applications they felt their governance structures could not reach. We ask broader questions about the responsibility of those involved in biobank access decisions-those working early in the research process-when considering such issues.

Reimagining research ethics to include environmental sustainability: a principled approach, including a case study of data-driven health research.

In this paper we argue the need to reimagine research ethics frameworks to include notions of environmental sustainability. While there have long been calls for healthcare ethics frameworks and decision-making to include aspects of sustainability, less attention has focused on how research ethics frameworks could address this. To do this, we first describe the traditional approach to research ethics, which often relies on individualised notions of risk. We argue that we need to broaden this notion of individual risk to consider issues associated with environmental sustainability. This is because research is associated with carbon emissions and other environmental impacts, both of which cause climate change health hazards. We introduce how bioethics frameworks have considered notions of environmental sustainability and draw on these to help develop a framework suitable for researchers. We provide a case study of data-driven health research to apply our framework.

The environmental impact of data-driven precision medicine initiatives.

Opportunities offered by precision medicine have long been promised in the medical and health literature. However, precision medicine - and the methodologies and approaches it relies on - also has adverse environmental impacts. As research into precision medicine continues to expand, there is a compelling need to consider these environmental impacts and develop means to mitigate them. In this article, we review the adverse environmental impacts associated with precision medicine, with a particular focus on those associated with its underlying need for data-intensive approaches. We illustrate the importance of considering the environmental impacts of precision medicine and describe the adverse health outcomes that are associated with climate change. We follow this with a description of how these environmental impacts are being addressed in both the health and data-driven technology sector. We then describe the (scant) literature on environmental impacts associated with data-driven precision medicine specifically. We finish by highlighting various environmental considerations that precision medicine researchers, and the field more broadly, should take into account.

Understanding Pandemic Solidarity: Mutual Support During the First COVID-19 Lockdown in the United Kingdom.

Throughout the COVID-19 pandemic, the concept of solidarity has been invoked frequently. Much interest has centred around how citizens and communities support one another during times of uncertainty. Yet, empirical research which accounts and understands citizen's views on pandemic solidarity, or their actual practices has remained limited. Drawing upon the analysis of data from 35 qualitative interviews, this article investigates how residents in England and Scotland enacted, understood, or criticised (the lack of) solidarity during the first national lockdown in the United Kingdom in April 2020-at a time when media celebrated solidarity as being at an all-time high. It finds that although solidarity was practiced by some people, the perceived lack of solidarity was just as pronounced. We conclude that despite frequent mobilisations of solidarity by policy makers and other public actors, actual practices of solidarity are poorly understood-despite the importance of solidarity for public health and policy.

Democratic research: Setting up a research commons for a qualitative, comparative, longitudinal interview study during the COVID-19 pandemic.

The sudden and dramatic advent of the COVID-19 pandemic led to urgent demands for timely, relevant, yet rigorous research. This paper discusses the origin, design, and execution of the SolPan research commons, a large-scale, international, comparative, qualitative research project that sought to respond to the need for knowledge among researchers and policymakers in times of crisis. The form of organization as a research commons is characterized by an underlying solidaristic attitude of its members and its intrinsic organizational features in which research data and knowledge in the study is shared and jointly owned. As such, the project is peer-governed, rooted in (idealist) social values of academia, and aims at providing tools and benefits for its members. In this paper, we discuss challenges and solutions for qualitative studies that seek to operate as research commons.

Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems.

PURPOSE OF REVIEW: We review recent developments within digital phenotyping for mental health, a field dedicated to using digital data for diagnosing, predicting, and monitoring mental health problems. We especially focus on recent critiques and challenges to digital phenotyping from within the social sciences. RECENT FINDINGS: Three significant strands of criticism against digital phenotyping for mental health have been developed within the social sciences. This literature problematizes the idea that digital data can be objective, that it can be unbiased, and argues that it has multiple ethical and practical challenges. Digital phenotyping for mental health is a rapidly growing and developing field, but with considerable challenges that are not easily solvable. This includes when, and if, data from digital phenotyping is actionable in practice; the involvement of user and patient perspectives in digital phenotyping research; the possibility of biased data; and challenges to the idea that digital phenotyping can be more objective than other forms of psychiatric assessment.

The environmental sustainability of data-driven health research: A scoping review.

Data-Driven and Artificial Intelligence technologies are rapidly changing the way that health research is conducted, including offering new opportunities. This will inevitably have adverse environmental impacts. These include carbon dioxide emissions linked to the energy required to generate and process large amounts of data; the impact on the material environment (in the form of data centres); the unsustainable extraction of minerals for technological components; and e-waste (discarded electronic appliances) disposal. The growth of Data-Driven and Artificial Intelligence technologies means there is now a compelling need to consider these environmental impacts and develop means to mitigate them. Here, we offer a scoping review of how the environmental impacts of data storage and processing during Data-Driven and Artificial Intelligence health-related research are being discussed in the academic literature. Using the UK as a case study, we also offer a review of policies and initiatives that consider the environmental impacts of data storage and processing during Data-Driven and Artificial Intelligence health-related research in the UK. Our findings suggest little engagement with these issues to date. We discuss the implications of this and suggest ways that the Data-Driven and Artificial Intelligence health research sector needs to move to become more environmentally sustainable.

Public Trust and Trustworthiness in Biobanking: The Need for More Reflexivity.

Low levels of public trust in biobanks are perceived to be a deterrent to participation and a threat to their sustainability. Acting in a "trustworthy" manner is seen to be one approach to ensuring public trust in biobanks. Striving to improve public trust in biobanks and prioritizing the need for institutional trustworthiness are both vital endeavors. However, there has been little discussion in the context of biobanking about the meaning of these two concepts, and the relationship between them. In this article, we argue that it is important to examine this, to ensure clarity around their meaning, as well as their relationship with each other as they apply to biobanking. We conclude by making a series of recommendations for biobanks.