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.

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.

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.

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.

Ethics review of big data research: What should stay and what should be reformed?

BACKGROUND: Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts. MAIN TEXT: In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC's scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC's way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science. CONCLUSIONS: We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.

Defining ethical standards for the application of digital tools to population health research.

There is growing interest in population health research, which uses methods based on artificial intelligence. Such research draws on a range of clinical and non-clinical data to make predictions about health risks, such as identifying epidemics and monitoring disease spread. Much of this research uses data from social media in the public domain or anonymous secondary health data and is therefore exempt from ethics committee scrutiny. While the ethical use and regulation of digital-based research has been discussed, little attention has been given to the ethics governance of such research in higher education institutions in the field of population health. Such governance is essential to how scholars make ethical decisions and provides assurance to the public that researchers are acting ethically. We propose a process of ethics governance for population health research in higher education institutions. The approach takes the form of review after the research has been completed, with particular focus on the role artificial intelligence algorithms play in augmenting decision-making. The first layer of review could be national, open-science repositories for open-source algorithms and affiliated data or information which are developed during research. The second layer would be a sector-specific validation of the research processes and algorithms by a committee of academics and stakeholders with a wide range of expertise across disciplines. The committee could be created as an off-shoot of an already functioning national oversight body or health technology assessment organization. We use case studies of good practice to explore how this process might operate.

La recherche sur la santé de la population à l'aide de méthodes fondées sur l'intelligence artificielle suscite un intérêt croissant. Ce type de recherche s'appuie sur une série de données cliniques et non cliniques pour prédire les risques sanitaires, par exemple en détectant les épidémies et en surveillant la propagation des maladies. Une part importante de cette recherche emploie des données issues des réseaux sociaux, appartenant au domaine public, ou des données secondaires anonymes relatives à la santé. Par conséquent, elles ne sont soumises à aucun contrôle de la part d'un comité d'éthique. L'usage et les règles déontologiques ont certes fait l'objet de discussions, mais l'attention portée à la gouvernance de l'éthique dans le cadre des recherches que des établissements d'enseignement supérieur ont menées sur la santé des populations reste minime. Pourtant, une telle gouvernance est essentielle pour que les spécialistes puissent prendre des décisions éthiques et garantir au public que les chercheurs agissent dans le respect de la déontologie. Nous proposons un processus de gouvernance éthique pour la recherche sur la santé de la population dans les établissements d'enseignement supérieur. Notre approche consiste à établir un rapport à la fin de la recherche, qui se concentre sur le rôle joué par les algorithmes d'intelligence artificielle dans l'accroissement de la prise de décisions. Le premier niveau de rapport pourrait comporter des registres nationaux accessibles selon le principe de science ouverte pour les algorithmes open-source ainsi que les données ou informations connexes développés durant la recherche. Le second niveau serait composé d'une validation sectorielle des algorithmes et processus de recherche par un comité d'universitaires et d'intervenants possédant une large gamme de compétences dans diverses disciplines. Ce comité pourrait être créé en tant que ramification d'un organisme national de surveillance déjà à l'œuvre, ou d'un organisme d'évaluation des technologies de la santé. Nous utilisons des études de cas pour identifier les bonnes pratiques et découvrir comment ce processus pourrait être appliqué.

Existe un interés creciente en la investigación sanitaria poblacional, que utiliza métodos basados en la inteligencia artificial. Dicha investigación se basa en una serie de datos clínicos y no clínicos para hacer predicciones sobre los riesgos sanitarios, como la identificación de epidemias y el seguimiento de la propagación de enfermedades. Gran parte de esta investigación utiliza los datos de las redes sociales de dominio público o los datos sanitarios secundarios anónimos y, por lo tanto, está exenta del escrutinio del comité de ética. Si bien se ha debatido sobre el uso y la regulación éticos de la investigación basada en tecnología digital, se ha prestado poca atención a la gobernanza ética de dicha investigación en las instituciones de enseñanza superior relacionadas con la salud poblacional. Esa gobernanza es esencial sobre cómo los académicos toman decisiones éticas y ofrece garantías al público de que los investigadores actúan de manera ética. Se propone un proceso de gobernanza ética para la investigación sanitaria poblacional en las instituciones de educación superior. El enfoque adopta la forma de una revisión una vez que la investigación ha sido completada, con especial atención a la función que los algoritmos de inteligencia artificial desempeñan en el aumento de la toma de decisiones. La primera fase de revisión podría consistir en la creación de repositorios nacionales de ciencia abierta para los algoritmos de código abierto y los datos o información afiliados que se desarrollen durante la investigación. La segunda fase consistiría en la validación de los procesos y algoritmos de investigación en un sector específico por parte de un comité de académicos y partes interesadas con una amplia gama de conocimientos especializados en todas las disciplinas. El comité podría crearse como una rama de un organismo nacional de supervisión ya en funcionamiento o de una organización de evaluación sobre tecnologías de la salud. Se utilizan estudios de casos de buenas prácticas para explorar cómo podría funcionar este proceso.

Can digital data diagnose mental health problems? A sociological exploration of 'digital phenotyping'.

This paper critically explores the research and development of 'digital phenotyping', which broadly refers to the idea that digital data can measure and predict people's mental health as well as their potential risk for mental ill health. Despite increasing research and efforts to digitally track and predict ill mental health, there has been little sociological and critical engagement with this field. This paper aims to fill this gap by introducing digital phenotyping to the social sciences. We explore the origins of digital phenotyping, the concept of the digital phenotype and its potential for benefit, linking these to broader developments within the field of 'mental health sensing'. We then critically discuss the technology, offering three critiques. First, that there may be assumptions of normality and bias present in the use of algorithms; second, we critique the idea that digital data can act as a proxy for social life; and third that the often biological language employed in this field risks reifying mental health problems. Our aim is not to discredit the scientific work in this area, but rather to call for scientists to remain reflexive in their work, and for more social science engagement in this area.

Towards a national genomics medicine service: the challenges facing clinical-research hybrid practices and the case of the 100 000 genomes project.

Clinical practice and research are governed by distinct rules and regulations and have different approaches to, for example, consent and providing results. However, genomics is an example of where research and clinical practice have become codependent. The 100 000 genomes project (100kGP) is a hybrid venture where a person can obtain a clinical investigation only if he or she agrees to also participate in ongoing research-including research by industry and commercial companies. In this paper, which draws on 20 interviews with professional stakeholders involved in 100kGP, we investigate the ethical issues raised by this project's hybrid nature. While some interviewees thought the hybrid nature of 100kGP was its vanguard, interviewees identified several tensions around hybrid practice: how to decide who should be able to participate; how to determine whether offering results might unduly influence participation into wide-ranging but often as yet unknown research and how to ensure that patients/families do not develop false expectations about receiving results. These areas require further debate as 100kGP moves into routine healthcare in the form of the national genomic medicine service. To address the tensions identified, we explore the appropriateness of Faden et al.'s framework of ethical obligations for when research and clinical care are completely integrated. We also argue that enabling ongoing transparent and trustworthy communication between patients/families and professionals around the kinds of research that should be permitted in 100kGP will help to understand and ensure that expectations remain realistic. Our paper aims to encourage a focused discussion about these issues and to inform a new 'social contract' for research and clinical care in the health service.

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.

Public trust and 'ethics review' as a commodity: the case of Genomics England Limited and the UK's 100,000 genomes project.

The UK Chief Medical Officer's 2016 Annual Report, Generation Genome, focused on a vision to fully integrate genomics into all aspects of the UK's National Health Service (NHS). This process of integration, which has now already begun, raises a wide range of social and ethical concerns, many of which were discussed in the final Chapter of the report. This paper explores how the UK's 100,000 Genomes Project (100 kGP)-the catalyst for Generation Genome, and for bringing genomics into the NHS-is negotiating these ethical concerns. The UK's 100 kGP, promoted and delivered by Genomics England Limited (GEL), is an innovative venture aiming to sequence 100,000 genomes from NHS patients who have a rare disease, cancer, or an infectious disease. GEL has emphasised the importance of ethical governance and decision-making. However, some sociological critique argues that biomedical/technological organisations presenting themselves as 'ethical' entities do not necessarily reflect a space within which moral thinking occurs. Rather, the 'ethical work' conducted (and displayed) by organisations is more strategic, relating to the politics of the organisation and the need to build public confidence. We set out to explore whether GEL's ethical framework was reflective of this critique, and what this tells us more broadly about how genomics is being integrated into the NHS in response to the ethical and social concerns raised in Generation Genome. We do this by drawing on a series of 20 interviews with individuals associated with or working at GEL.

Healthcare professionals' and patients' perspectives on consent to clinical genetic testing: moving towards a more relational approach.

BACKGROUND: This paper proposes a refocusing of consent for clinical genetic testing, moving away from an emphasis on autonomy and information provision, towards an emphasis on the virtues of healthcare professionals seeking consent, and the relationships they construct with their patients. METHODS: We draw on focus groups with UK healthcare professionals working in the field of clinical genetics, as well as in-depth interviews with patients who have sought genetic testing in the UK's National Health Service (data collected 2013-2015). We explore two aspects of consent: first, how healthcare professionals consider the act of 'consenting' patients; and second how these professional accounts, along with the accounts of patients, deepen our understanding of the consent process. RESULTS: Our findings suggest that while healthcare professionals working in genetic medicine put much effort into ensuring patients' understanding about their impending genetic test, they acknowledge, and we show, that patients can still leave genetic consultations relatively uninformed. Moreover, we show how placing emphasis on the informational aspect of genetic testing is not always reflective of, or valuable to, patients' decision-making. Rather, decision-making is socially contextualised - also based on factors outside of information provision. CONCLUSIONS: A more collaborative on-going consent process, grounded in virtue ethics and values of honesty, openness and trustworthiness, is proposed.

Sociology of Low Expectations: Recalibration as Innovation Work in Biomedicine.

Social scientists have drawn attention to the role of hype and optimistic visions of the future in providing momentum to biomedical innovation projects by encouraging innovation alliances. In this article, we show how less optimistic, uncertain, and modest visions of the future can also provide innovation projects with momentum. Scholars have highlighted the need for clinicians to carefully manage the expectations of their prospective patients. Using the example of a pioneering clinical team providing deep brain stimulation to children and young people with movement disorders, we show how clinicians confront this requirement by drawing on their professional knowledge and clinical expertise to construct visions of the future with their prospective patients; visions which are personalized, modest, and tainted with uncertainty. We refer to this vision-constructing work as recalibration, and we argue that recalibration enables clinicians to manage the tension between the highly optimistic and hyped visions of the future that surround novel biomedical interventions, and the exigencies of delivering those interventions in a clinical setting. Drawing on work from science and technology studies, we suggest that recalibration enrolls patients in an innovation alliance by creating a shared understanding of how the "effectiveness" of an innovation shall be judged.

Knowledge, beliefs, and decisions of pregnant Australian women concerning donation and storage of umbilical cord blood: a population-based survey.

BACKGROUND: Many women giving birth in Australian hospitals can choose to donate their child's umbilical cord blood to a public cord blood bank or pay to store it privately. We conducted a survey to determine the proportion and characteristics of pregnant women who are aware of umbilical cord blood (UCB) banking and who have considered and decided about this option. The survey also sought to ascertain information sources, knowledge, and beliefs about UCB banking, and the effect of basic information about UCB on decisions. METHODS: Researchers and hospital maternity staff distributed a survey with basic information about UCB banking to 1,873 women of at least 24 weeks' gestation who were attending antenatal classes and hospital clinics in 14 public and private maternity hospitals in New South Wales. RESULTS: Most respondents (70.7%) were aware of UCB banking. Their main information sources were leaflets from hospital clinics, print media, antenatal classes, TV, radio, friends, and relatives. Knowledge about UCB banking was patchy, and respondents overestimated the likelihood their child would need or benefit from UCB. Women who were undecided about UCB banking were younger, less educated, or from ethnic or rural backgrounds. After providing basic information about UCB banking, the proportion of respondents who indicated they had decided whether or not to donate or store UCB more than doubled from 30.0 to 67.7 percent. CONCLUSIONS: Basic information for parents about UCB banking can affect planned decisions about UCB banking. Information should be accurate and balanced, should counter misconceptions, and should target specific groups.

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.

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.