Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

Considerable resources are being invested in strategies to facilitate the sharing of data across domains, with the aim of addressing inefficiencies and biases in scientific research and unlocking potential for science-based innovation. Still, we know too little about what determines whether scientific researchers actually make use of the unprecedented volume of data being shared. This study characterizes the factors influencing researcher data reuse in terms of their relationship to a specific research project, and introduces subjectification as the mechanism by which these influencing factors are activated. Based on our analysis of semi-structured interviews with a purposive sample of 24 data reusers and intermediaries, we find that while both project-independent and project-dependent factors may have a direct effect on a single instance of data reuse, they have an indirect effect on recurring data reuse as mediated by subjectification. We integrate our findings into a model of recurring data reuse behavior that presents subjectification as the mechanism by which influencing factors are activated in a propensity to engage in data reuse. Our findings hold scientific implications for the theorization of researcher data reuse, as well as practical implications around the role of settings for subjectification in bringing about and sustaining changes in researcher behavior.

Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

Background: Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU's Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance. Methods: As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs. Results & Conclusions: We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being "just" an European Commission (EC) requirement. DMPs are most prominently developed within a project's Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach. The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk. If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs. The study identifies IP and licensing arrangements for DMPs as promising areas for further research.

Dynamics of cumulative advantage and threats to equity in open science: a scoping review

Open Science holds the promise to make scientific endeavours more inclusive, participatory, understandable, accessible and re-usable for large audiences. However, making processes open will not per se drive wide reuse or participation unless also accompanied by the capacity (in terms of knowledge, skills, financial resources, technological readiness and motivation) to do so. These capacities vary considerably across regions, institutions and demographics. Those advantaged by such factors will remain potentially privileged, putting Open Science's agenda of inclusivity at risk of propagating conditions of 'cumulative advantage'. With this paper, we systematically scope existing research addressing the question: 'What evidence and discourse exists in the literature about the ways in which dynamics and structures of inequality could persist or be exacerbated in the transition to Open Science, across disciplines, regions and demographics?' Aiming to synthesize findings, identify gaps in the literature and inform future research and policy, our results identify threats to equity associated with all aspects of Open Science, including Open Access, Open and FAIR Data, Open Methods, Open Evaluation, Citizen Science, as well as its interfaces with society, industry and policy. Key threats include: stratifications of publishing due to the exclusionary nature of the author-pays model of Open Access; potential widening of the digital divide due to the infrastructure-dependent, highly situated nature of open data practices; risks of diminishing qualitative methodologies as 'reproducibility' becomes synonymous with quality; new risks of bias and exclusion in means of transparent evaluation; and crucial asymmetries in the Open Science relationships with industry and the public, which privileges the former and fails to fully include the latter.

Open Science for private Interests? How the Logic of Open Science Contributes to the Commercialization of Research

Financial conflicts of interest, several cases of scientific fraud, and research limitations from strong intellectual property laws have all led to questioning the epistemic and social justice appropriateness of industry-funded research. At first sight, the ideal of Open Science, which promotes transparency, sharing, collaboration, and accountability, seems to target precisely the type of limitations uncovered in commercially-driven research. The Open Science movement, however, has primarily focused on publicly funded research, has actively encouraged liaisons with the private sector, and has also created new strategies for commercializing science. As a consequence, I argue that Open Science ends up contributing to the commercialization of science, instead of overcoming its limitations. I use the examples of research publications and citizen science to illustrate this point. Accordingly, the asymmetry between private and public science, present in the current plea to open science, ends up compromising the values of transparency, democracy, and accountability.

Openness and trust in data-intensive science: the case of biocuration

Data-intensive science comes with increased risks concerning quality and reliability of data, and while trust in science has traditionally been framed as a matter of scientists being expected to adhere to certain technical and moral norms for behaviour, emerging discourses of open science present openness and transparency as substitutes for established trust mechanisms. By ensuring access to all available information, quality becomes a matter of informed judgement by the users, and trust no longer seems necessary. This strategy does not, however, take into consideration the networks of professionals already enabling data-intensive science by providing high-quality data. In the life sciences, biological data- and knowledge bases managed by expert biocurators have become crucial for data-intensive research. In this paper, I will use the case of biocurators to argue that openness and transparency will not diminish the need for trust in data-intensive science. On the contrary, data-intensive science requires a reconfiguration of existing trust mechanisms in order to include those who take care of and manage scientific data after its production.

Allonymous science: the politics of placing and shifting credit in public-private nutrition research

Ideally, guidelines reflect an accepted position with respect to matters of concern, ranging from clinical practices to researcher behaviour. Upon close reading, authorship guidelines reserve authorship attribution to individuals fully or almost fully embedded in particular studies, including design or execution as well as significant involvement in the writing process. These requirements prescribe an organisation of scientific work in which this embedding is specifically enabled. Drawing from interviews with nutrition scientists at universities and in the food industry, we demonstrate that the organisation of research labour can deviate significantly from such prescriptions. The organisation of labour, regardless of its content, then, has consequences for who qualifies as an author. The fact that fewer food industry employees qualify is actively used by the food industry to manage the credibility and ownership of their knowledge claims as allonymous science: the attribution of science assisted by authorship guidelines blind to all but one organisational frame.

FAIR data and data management requirements in a comparative perspective: Horizon 2020 and FWF policies

Based on our input to the Data Management Workshop, held during the Austrian Citizen Science Conference 2019 in Obergurgl, we provide a comparative perspective on the open data and data management requirements in the European Union’s Horizon 2020 programme and those of a national funder, the Austrian FWF, in this paper. We conclude that, although there are some differences in terminology and specific requirements, both the FWF and Horizon 2020 DMPs essentially cover the same ground.

Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes

Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

The Bermuda Triangle: The Pragmatics, Policies, and Principles for Data Sharing in the History of the Human Genome Project

The Bermuda Principles for DNA sequence data sharing are an enduring legacy of the Human Genome Project (HGP). They were adopted by the HGP at a strategy meeting in Bermuda in February of 1996 and implemented in formal policies by early 1998, mandating daily release of HGP-funded DNA sequences into the public domain. The idea of daily sharing, we argue, emanated directly from strategies for large, goal-directed molecular biology projects first tested within the "community" of C. elegans researchers, and were introduced and defended for the HGP by the nematode biologists John Sulston and Robert Waterston. In the C. elegans community, and subsequently in the HGP, daily sharing served the pragmatic goals of quality control and project coordination. Yet in the HGP human genome, we also argue, the Bermuda Principles addressed concerns about gene patents impeding scientific advancement, and were aspirational and flexible in implementation and justification. They endured as an archetype for how rapid data sharing could be realized and rationalized, and permitted adaptation to the needs of various scientific communities. Yet in addition to the support of Sulston and Waterston, their adoption also depended on the clout of administrators at the US National Institutes of Health (NIH) and the UK nonprofit charity the Wellcome Trust, which together funded 90% of the HGP human sequencing effort. The other nations wishing to remain in the HGP consortium had to accommodate to the Bermuda Principles, requiring exceptions from incompatible existing or pending data access policies for publicly funded research in Germany, Japan, and France. We begin this story in 1963, with the biologist Sydney Brenner's proposal for a nematode research program at the Laboratory of Molecular Biology (LMB) at the University of Cambridge. We continue through 2003, with the completion of the HGP human reference genome, and conclude with observations about policy and the historiography of molecular biology.

Introduction to the article collection 'Translation in healthcare: ethical, legal, and social implications'

New technologies are transforming and reconfiguring the boundaries between patients, research participants and consumers, between research and clinical practice, and between public and private domains. From personalised medicine to big data and social media, these platforms facilitate new kinds of interactions, challenge longstanding understandings of privacy and consent, and raise fundamental questions about how the translational patient pathway should be organised.

This editorial introduces the cross-journal article collection "Translation in healthcare: ethical, legal, and social implications", briefly outlining the genesis of the collection in the 2015 Translation in healthcare conference in Oxford, UK and providing an introduction to the contemporary ethical challenges of translational research in biology and medicine accompanied by a summary of the papers included in this collection.

How Do Scientists Define Openness? Exploring the Relationship Between Open Science Policies and Research Practice

This article documents how biomedical researchers in the United Kingdom understand and enact the idea of "openness." This is of particular interest to researchers and science policy worldwide in view of the recent adoption of pioneering policies on Open Science and Open Access by the U.K. government-policies whose impact on and implications for research practice are in need of urgent evaluation, so as to decide on their eventual implementation elsewhere. This study is based on 22 in-depth interviews with U.K. researchers in systems biology, synthetic biology, and bioinformatics, which were conducted between September 2013 and February 2014. Through an analysis of the interview transcripts, we identify seven core themes that characterize researchers' understanding of openness in science and nine factors that shape the practice of openness in research. Our findings highlight the implications that Open Science policies can have for research processes and outcomes and provide recommendations for enhancing their content, effectiveness, and implementation.

Between Scylla and Charybdis: reconciling competing data management demands in the life sciences

BACKGROUND

The widespread sharing of biologicaConcluding Comments: Teaching Responsible Datal and biomedical data is recognised as a key element in facilitating translation of scientific discoveries into novel clinical applications and services. At the same time, twenty-first century states are increasingly concerned that this data could also be used for purposes of bioterrorism. There is thus a tension between the desire to promote the sharing of data, as encapsulated by the Open Data movement, and the desire to prevent this data from 'falling into the wrong hands' as represented by 'dual use' policies. Both frameworks posit a moral duty for life sciences researchers with respect to how they should make their data available. However, Open data and dual use concerns are rarely discussed in concert and their implementation can present scientists with potentially conflicting ethical requirements.

DISCUSSION

Both dual use and Open data policies frame scientific data and data dissemination in particular, though different, ways. As such they contain implicit models for how data is translated. Both approaches are limited by a focus on abstract conceptions of data and data sharing. This works to impede consensus-building between the two ethical frameworks. As an alternative, this paper proposes that an ethics of responsible management of scientific data should be based on a more nuanced understanding of the everyday data practices of life scientists. Responsibility for these 'micromovements' of data must consider the needs and duties of scientists as individuals and as collectively-organised groups. Researchers in the life sciences are faced with conflicting ethical responsibilities to share data as widely as possible, but prevent it being used for bioterrorist purposes. In order to reconcile the responsibilities posed by the Open Data and dual use frameworks, approaches should focus more on the everyday practices of laboratory scientists and less on abstract conceptions of data.

Samples and data accessibility in research biobanks: an explorative survey

Biobanks, which contain human biological samples and/or data, provide a crucial contribution to the progress of biomedical research. However, the effective and efficient use of biobank resources depends on their accessibility. In fact, making bio-resources promptly accessible to everybody may increase the benefits for society. Furthermore, optimizing their use and ensuring their quality will promote scientific creativity and, in general, contribute to the progress of bio-medical research. Although this has become a rather common belief, several laboratories are still secretive and continue to withhold samples and data. In this study, we conducted a questionnaire-based survey in order to investigate sample and data accessibility in research biobanks operating all over the world. The survey involved a total of 46 biobanks. Most of them gave permission to access their samples (95.7%) and data (85.4%), but free and unconditioned accessibility seemed not to be common practice. The analysis of the guidelines regarding the accessibility to resources of the biobanks that responded to the survey highlights three issues: (i) the request for applicants to explain what they would like to do with the resources requested; (ii) the role of funding, public or private, in the establishment of fruitful collaborations between biobanks and research labs; (iii) the request of co-authorship in order to give access to their data. These results suggest that economic and academic aspects are involved in determining the extent of sample and data sharing stored in biobanks. As a second step of this study, we investigated the reasons behind the high diversity of requirements to access biobank resources. The analysis of informative answers suggested that the different modalities of resource accessibility seem to be largely influenced by both social context and legislation of the countries where the biobanks operate.

Sticks AND Carrots: Encouraging Open Science at its source

The Open Science (OS) movement has been seen as an important facilitator for public participation in science. This has been underpinned by the assumption that widespread and free access to research outputs leads to (i) better and more efficient science, (ii) economic growth, in particular for small and medium-sized enterprises wishing to capitalise on research findings and (iii) increased transparency of knowledge production and its outcomes. The latter in particular could function as a catalyst for public participation and engagement. Whether OS is likely to help realise these benefits, however, will depend on the emergence of systemic incentives for scientists to utilise OS in a meaningful manner. While some areas, the environmental sciences have a long tradition of open ethos, citizen inclusion and global collaborations, such activities need to be more systematically supported and promoted by funders and learned societies in order to improve scientific research and public participation.

Class in contemporary Britain: comparing the Cultural Capital and Social Exclusion (CCSE) project and the Great British Class Survey (GBCS)

The paper discusses the salience of class in Britain in relation to the experiment of the BBC-academic partnership of the Great British Class Survey (GBCS). It addresses the claimed inauguration of a third phase in class analysis in the UK sparked by the experiment. This is done by considering three main issues. First, the GBCS experiment is situated in the context of various explorations of cultural class analyses, and chiefly in relation to the Cultural Capital and Social Exclusion (CCSE) project (ESRC funded 2003-6). Secondly, the focus is on the influence of the academic turn to big data for the procedures and claims of the project, and some implications of the methodological choices. Thirdly, attention is turned to the deleterious effects of commercial and institutional pressures on the current research culture in which the experiment exists.

Systems Medicine 2.0: potential benefits of combining electronic health care records with systems science models

Background

The global burden of disease is increasingly dominated by non-communicable diseases.These diseases are less amenable to curative and preventative interventions than communicable disease. This presents a challenge to medical practice and medical research, both of which are experiencing diminishing returns from increasing investment.

Objective

Our aim was to (1) review how medical knowledge is generated, and its limitations, (2) assess the potential for emerging technologies and ideas to improve medical research, and (3) suggest solutions and recommendations to increase medical research efficiency on non-communicable diseases.

Methods

We undertook an unsystematic review of peer-reviewed literature and technology websites.

Results

Our review generated the following conclusions and recommendations. (1) Medical knowledge continues to be generated in a reductionist paradigm. This oversimplifies our models of disease, rendering them ineffective to sufficiently understand the complex nature of non-communicable diseases. (2) Some of these failings may be overcome by adopting a “Systems Medicine” paradigm, where the human body is modeled as a complex adaptive system. That is, a system with multiple components and levels interacting in complex ways, wherein disease emerges from slow changes to the system set-up. Pursuing systems medicine research will require larger datasets. (3) Increased data sharing between researchers, patients, and clinicians could provide this unmet need for data. The recent emergence of electronic health care records (EHR) could potentially facilitate this in real-time and at a global level. (4) Efforts should continue to aggregate anonymous EHR data into large interoperable data silos and release this to researchers. However, international collaboration, data linkage, and obtaining additional information from patients will remain challenging. (5) Efforts should also continue towards “Medicine 2.0”. Patients should be given access to their personal EHR data. Subsequently, online communities can give researchers the opportunity to ask patients for direct access to the patient’s EHR data and request additional study-specific information. However, selection bias towards patients who use Web 2.0 technology may be difficult to overcome.

Conclusions

Systems medicine, when combined with large-scale data sharing, has the potential to raise our understanding of non-communicable diseases, foster personalized medicine, and make substantial progress towards halting, curing, and preventing non-communicable diseases. Large-scale data amalgamation remains a core challenge and needs to be supported. A synthesis of “Medicine 2.0” and “Systems Science” concepts into “Systems Medicine 2.0” could take decades to materialize but holds much promise.

Multivariate statistics and the enactment of metabolic complexity

This ethnographic study, based on fieldwork at the Computational and Systems Medicine laboratory at Imperial College London, shows how researchers in the field of metabolomics--the post-genomic study of the molecules and processes that make up metabolism--enact and coproduce complex views of biology with multivariate statistics. From this data-driven science, metabolism emerges as a multiple, informational and statistical object, which is both produced by and also necessitates particular forms of data production and analysis. Multivariate statistics emerge as 'natural' and 'correct' ways of engaging with a metabolism that is made up of many variables. In this sense, multivariate statistics allow researchers to engage with and conceptualize metabolism, and also disease and processes of life, as complex entities. Consequently, this article builds on studies of scientific practice and visualization to examine data as material objects rather than black-boxed representations. Data practices are not merely the technological components of experimentation, but are simultaneously technologies and methods and are intertwined with ways of seeing and enacting the biological world. Ultimately, this article questions the increasing invocation and role of complexity within biology, suggesting that discourses of complexity are often imbued with reductionist and determinist ways of thinking about biology, as scientists engage with complexity in calculated and controlled, but also limited, ways.

What Difference Does Quantity Make? On the Epistemology of Big Data in Biology

Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.

Making open data work for plant scientists

Despite the clear demand for open data sharing, its implementation within plant science is still limited. This is, at least in part, because open data-sharing raises several unanswered questions and challenges to current research practices. In this commentary, some of the challenges encountered by plant researchers at the bench when generating, interpreting, and attempting to disseminate their data have been highlighted. The difficulties involved in sharing sequencing, transcriptomics, proteomics, and metabolomics data are reviewed. The benefits and drawbacks of three data-sharing venues currently available to plant scientists are identified and assessed: (i) journal publication; (ii) university repositories; and (iii) community and project-specific databases. It is concluded that community and project-specific databases are the most useful to researchers interested in effective data sharing, since these databases are explicitly created to meet the researchers' needs, support extensive curation, and embody a heightened awareness of what it takes to make data reuseable by others. Such bottom-up and community-driven approaches need to be valued by the research community, supported by publishers, and provided with long-term sustainable support by funding bodies and government. At the same time, these databases need to be linked to generic databases where possible, in order to be discoverable to the majority of researchers and thus promote effective and efficient data sharing. As we look forward to a future that embraces open access to data and publications, it is essential that data policies, data curation, data integration, data infrastructure, and data funding are linked together so as to foster data access and research productivity.