Open science, data sharing and solidarity: who benefits?

Messing with Merton: The intersection between open science practices and Mertonian values

Although adherence to Mertonian values of science (i.e., communism, universalism, organized skepticism, disinterestedness) is desired and promoted in academia, such adherence can cause friction with the normative structures and practices of Open Science. Mertonian values and Open Science practices aim to improve the conduct and communication of research and are promoted by institutional actors. However, Mertonian values remain mostly idealistic and contextualized in local and disciplinary cultures and Open Science practices rely heavily on third-party resources and technology that are not equally accessible to all parties. Furthermore, although still popular, Mertonian values were developed in a different institutional and political context. In this article, we argue that new normative structures for science need to look beyond nostalgia and consider aspirations and outcomes of Open Science practices. To contribute to such a vision, we explore the intersection of several Open Science practices with Mertonian values to flesh out challenges involved in upholding these values. We demonstrate that this intersection becomes complicated when the interests of numerous groups collide and contrast. Acknowledging and exploring such tensions informs our understanding of researchers' behavior and supports efforts that seek to improve researchers' interactions with other normative structures such as research ethics and integrity frameworks.

Cross-border data sharing through the lens of research ethics committee members in sub-Saharan Africa

BACKGROUND

Several factors thwart successful data sharing-ambiguous or fragmented regulatory landscapes, conflicting institutional/researcher interests and varying levels of data science-related expertise are among these. Traditional ethics oversight mechanisms and practices may not be well placed to guarantee adequate research oversight given the unique challenges presented by digital technologies and artificial intelligence (AI). Data-intensive research has raised new, contextual ethics and legal challenges that are particularly relevant in an African research setting. Yet, no empirical research has been conducted to explore these challenges.

MATERIALS AND METHODS

We explored REC members' views and experiences on data sharing by conducting 20 semi-structured interviews online between June 2022 and February 2023. Using purposive sampling and snowballing, we recruited representatives across sub-Saharan Africa (SSA). We transcribed verbatim and thematically analysed the data with Atlas.ti V22.

RESULTS

Three dominant themes were identified: (i) experiences in reviewing data sharing protocols, (ii) perceptions of data transfer tools and (iii) ethical, legal and social challenges of data sharing. Several sub-themes emerged as: (i.a) frequency of and approaches used in reviewing data sharing protocols, (i.b) practical/technical challenges, (i.c) training, (ii.a) ideal structure of data transfer tools, (ii.b) key elements of data transfer tools, (ii.c) implementation level, (ii.d) key stakeholders in developing and reviewing a data transfer agreement (DTA), (iii.a) confidentiality and anonymity, (iii.b) consent, (iii.c) regulatory frameworks, and (iii.d) stigmatisation and discrimination.

CONCLUSIONS

Our results indicated variability in REC members' perceptions, suboptimal awareness of the existence of data protection laws and a unanimously expressed need for REC member training. To promote efficient data sharing within and across SSA, guidelines that incorporate ethical, legal and social elements need to be developed in consultation with relevant stakeholders and field experts, along with the training accreditation of REC members in the review of data-intensive protocols.

A pathway to strengthening open science: comments on the draft South African Ethics in Health Research Guidelines

The recently released draft South African Ethics in Health Research Guidelines: Principles, Processes and Structures (Draft Guidelines) by the National Health Research Ethics Council recognize open data and provide guiding principles for this in the context of health research in South Africa. While its inclusion is a positive development, there is room for improvement. Although the Draft Guidelines leverage the Draft National Policy on Data and Cloud, it lacks incorporation of other relevant government policies, notably the Draft National Open Science Policy, and fails to sufficiently detail the principles of open science and open access. This limited scope and lack of comprehensive definition and detailed guidance present challenges for researchers in conducting ethical and responsible health research in South Africa. It constrains the Draft Guidelines from fully aligning with national imperatives and from fostering African-centric approaches. To address these issues, it is recommended that the Draft Guidelines integrate broader policies and principles, enhance clarity through comprehensive definitions, provide detailed guidance on open access, and promote African-centric approaches. Implementing these solutions will strengthen the Draft Guidelines, aligning them with national visions of open science, and thereby harnessing the full potential of South Africa's diverse scientific community in advancing health research.

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.

Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning

In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The techniques that allow machines to learn and get better at tasks such as recognition and prediction, which form the basis of clinical practice, are referred to as machine learning, which is a subfield of artificial intelligence. The number of artificial intelligence-and machine learnings-related publications in clinical journals has grown exponentially, driven by recent developments in computation and the accessibility of simple tools. However, clinicians are often not included in data science teams, which may limit the clinical relevance, explanability, workflow compatibility, and quality improvement of artificial intelligence solutions. Thus, this results in the language barrier between clinicians and artificial intelligence developers. Healthcare practitioners sometimes lack a basic understanding of artificial intelligence research because the approach is difficult for non-specialists to understand. Furthermore, many editors and reviewers of medical publications might not be familiar with the fundamental ideas behind these technologies, which may prevent journals from publishing high-quality artificial intelligence studies or, worse still, could allow for the publication of low-quality works. In this review, we aim to improve readers’ artificial intelligence literacy and critical thinking. As a result, we concentrated on what we consider the 10 most important qualities of artificial intelligence research: valid scientific purpose, high-quality data set, robust reference standard, robust input, no information leakage, optimal bias-variance tradeoff, proper model evaluation, proven clinical utility, transparent reporting, and open science. Before designing a study, one should have defined a sound scientific purpose. Then, it should be backed by a high-quality data set, robust input, and a solid reference standard. The artificial intelligence development pipeline should prevent information leakage. For the models, optimal bias-variance tradeoff should be achieved, and generalizability assessment must be adequately performed. The clinical value of the final models must also be established. After the study, thought should be given to transparency in publishing the process and results as well as open science for sharing data, code, and models. We hope this work may improve the artificial intelligence literacy and mindset of the readers.

Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledge-related) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity.

New Meanings in the Archive: Privacy, Technological Change and the Status of Sources

This essay reflects on how technological changes in biomedicine can affect what archival sources are available for historical research. Historians and anthropologists have examined the ways in which old biomedical samples can be made to serve novel scientific purposes, such as when decades-old frozen tissue specimens are analyzed using new genomic techniques. Those uses are also affected by shifting ethical regimes, which affect who can do what with old samples, or whether anything can be done with them at all. Archival collections are subject to similar dynamics, as institutional change and shifts in ethical guidelines and privacy laws affect which sources can be accessed and which are closed. I witnessed just such a change during my research into human genetics using archives in the Wellcome Collection. A few years into my project, those archives had their privacy conditions reassessed, and I saw how some sources previously seen as neutral were now understood to contain personal sensitive information. This paper describes the conditions of this shift-including the effects of technological change, new ethical considerations, and changing laws around privacy. I reflect on how these affected my understanding of the history of human genetics, and how I and others might narrate it.

Identifying and addressing data asymmetries so as to enable (better) science

As a society, we need to become more sophisticated in assessing and addressing data asymmetries—and their resulting political and economic power inequalities—particularly in the realm of open science, research, and development. This article seeks to start filling the analytical gap regarding data asymmetries globally, with a specific focus on the asymmetrical availability of privately-held data for open science, and a look at current efforts to address these data asymmetries. It provides a taxonomy of asymmetries, as well as both their societal and institutional impacts. Moreover, this contribution outlines a set of solutions that could provide a toolbox for open science practitioners and data demand-side actors that stand to benefit from increased access to data. The concept of data liquidity (and portability) is explored at length in connection with efforts to generate an ecosystem of responsible data exchanges. We also examine how data holders and demand-side actors are experimenting with new and emerging operational models and governance frameworks for purpose-driven, cross-sector data collaboratives that connect previously siloed datasets. Key solutions discussed include professionalizing and re-imagining data steward roles and functions (i.e., individuals or groups who are tasked with managing data and their ethical and responsible reuse within organizations). We present these solutions through case studies on notable efforts to address science data asymmetries. We examine these cases using a repurposable analytical framework that could inform future research. We conclude with recommended actions that could support the creation of an evidence base on work to address data asymmetries and unlock the public value of greater science data liquidity and responsible reuse.