Today we launch a series of posts on the politics of data. Big data, small data and data sharing will be critically examined by a range of experts, each exploring the implications of the changing data landscape for research and society. In the first piece, Sabina Leonelli and Louise Bezuidenhout argue the study of data itself is an excellent entry point to reflect on the activities and claims associated to the idea of scientific knowledge. How scientists perceive their research environments, what they recognize as strengths and limitations, and what in these environments pose material or social challenges to data engagement all influence how data travels

Bezuidenhout, Louise

Leonelli, Sabina

LSE Research Online

10/5/2015The Politics of Data: The rising prominence of a data-centricapproach to scientific research.blogs.lse.ac.uk/impactofsocialsciences/2015/10/05/politics-of-data-scientific-data-centrism/Today we launch a series of posts on the politics of data. Big data, small data anddata sharing will be critically examined by a range of experts, each exploring theimplications of the changing data landscape for research and society. In the firstpiece, Sabina Leonelli and Louise Bezuidenhout argue the study of data itself isan excellent entry point to reflect on the activities and claims associated to the ideaof scientific knowledge. How scientists perceive their research environments, whatthey recognize as strengths and limitations, and what in these environments posematerial or social challenges to data engagement all influence how data travels.Scientific research has long involved efforts to generate, analyse and interpretlarge masses of data, as illustrated by the major data collection and curationefforts characterising 17th century astronomy and metereology and 18th centurynatural history. Thus, current big data may well have remarkable volume, velocity,variability, variety and veracity, but they are not a new phenomenon within science, and many disciplines havedeveloped sophisticated strategies to cope with overflows of information. At the same time, contemporarymanifestations of big data have distinctive features that relate to the technologies, institutions and governancestructures of the contemporary scientific world.For instance, this approach is typically associated to the emergence of large-scale, multi-national networks ofscientists; to a strong emphasis on the importance of sharing data and regarding them as valuable research outputsin and of themselves, regardless of whether or not they have yet been used as evidence for a given discovery; theinstitutionalization of procedures and norms for data dissemination through the Open Science and Open Datamovements, and policies such as those recently adopted by Research Councils UK and key research funders suchas the European Research Council, the Wellcome Trust and the Gates Foundation; and the development ofinstruments, building on digital technologies and web services, that facilitate the production and dissemination ofdata with a speed and geographical reach as yet unseen in the history of science.1/4Image credit: W. H. O., Stanley O. Foster M.D., M.P.H, USCDCP (CC0 Public Domain)This peculiar conjuncture of institutional, socio-political, economic and technological developments haveconsiderably increased international debate over processes of data production, dissemination and interpretationwithin science and beyond. This level of reflexivity over data practices is arguably the most novel and interestingaspect of contemporary debates over big data. What we are witnessing is thus not the emergence of a wholly newresearch paradigm dealing with hitherto unseen types of data, but rather the rising prominence of a data-centricapproach to scientific research, where concerns over data sharing and use in the long term take precedence overimmediate attempts to analyze data.Thus conceptualized, data centrism raises fundamental epistemological issues, which are deeply intertwined withthe political challenges posed by big data. What are data, and how are they transformed into meaningfulinformation? What is the status of so-called raw data with respect to other sources of evidence? What constitutesgood, reliable data, and how can this be assessed? Should there be restrictions to data dissemination, particularly incases where the ownership of data is disputed? What role do theory and materials play in data-intensive research,and does it make sense to disseminate big data in the absence of information about data provenance and/or thesamples on which data were originally obtained? What difference do the scale (itself a multifaceted notion),technological sophistication and institutional sanctioning of widespread data dissemination make to discovery andinnovation? Philosophical analysis can help to address these questions in ways that inform both current datapractices and the ways in which have been conceptualized within the social science and humanities, as well as bypolicy bodies and other institutions.Scientific research is often presented as the most systematic set of efforts in the contemporary world aimed tocritically explore and debate what constitutes acceptable and sufficient evidence for any given belief about reality.The very term ‘data’ comes from the Latin ‘given’, and indeed data are meant to document as faithfully andobjectively as possible whatever entities or processes are being investigated. And yet, data collection is alwayssteeped in a specific way of understanding the world and constrained by given material and social conditions, andthe resulting data are therefore marked by the historical circumstances through which they were generated: whatconstitutes trustworthy or sufficient data changes across time and space, making it impossible to ever assemble acomplete and intrinsically reliable dataset. Furthermore, data are valued and used for a variety of reasons, includingas sources of evidence, tokens of exchange and personal identity, signifiers of status and markers of intellectualproperty; and myriads of data types are produced by as many stakeholders, from citizens to industry and2/4governmental agencies, which means that what constitutes data, for whom and for which purposes is constantly atstake.This landscape makes the study of data into an excellent entry point to reflect on the activities and claims associatedto the idea of scientific knowledge, and the implications of existing conceptualisations of various forms of knowledgeproduction and use. This is exemplified by ongoing research at the University of Exeter on data handling practicesamongst scientists in both the developed world and Low and Middle Income Countries (LMICs). As such researchillustrates, what constitutes knowledge, and a ‘scientific contribution’, varies enormously depending not only onaccess to data, but also on what is regarded as relevant data in the first place, and what capabilities any researchgroup has to develop, structure and disseminate their ideas. This research has involved a large empiricalcomponent involving scientists from a number of laboratories in the UK and in African countries, carried out underthe leadership of Professor Brian Rappert and with funding by the Leverhulme Trust.  In interviews, scientists wereasked to discuss their research, their management of data, how they disseminated their data, and what online datathey re-used.  From these interviews it became evident that there were a range of material and social aspects oftheir research environment that played significant roles in their overall data engagement activities.Interviews carried out in sub-saharan Africa yielded particularly significant results in terms of the political andscientific dimensions of big data sharing. In contrast to most current discussions on data, the issues highlighted inthese interviews were often the small, innocuous characteristics of laboratory life in low-resource environments.Thus, issues such as teaching loads, availability of up-to-date computers and software, the age of the researchequipment used to produce the data and similar issues featured significantly in how scientists talked about their ownresearch – how they conducted their own research, how they viewed the resultant products of this research, andwhat they recognized as “data” to share and re-use. Such research clearly demonstrates the importance ofscrutinizing all processes involved in data engagement and to recognize the role that research environments play innot only the creation of data, but also their selection, presentation and dissemination. How scientists perceive theirresearch environments, what they recognize as strengths and limitations, and what in these environments posematerial or social challenges to data engagement all influence what data travels in or out of any research context.Such observations raise interesting questions for data studies, as the limitations of overarching descriptions of dataor data sharing practices become apparent. Indeed, studying how LMIC scientists engage with the data they createand re-use draws considerable attention to both the material and social aspects of data sharing. The types of datashared and valued, the longevity of these data, and the pathways through which they are disseminated and re-usedall have complicated relationships to the research environments in which they are utilized.  In consequence,homogenized perceptions of key issues such as what data are, how raw data differs from processed data, and howdata ownership can be understood reveal their limitations.This is part of a wider series on the Politics of Data. For more on this topic, also see Mark Carrigan’sPhilosophy of Data Science interview series and the Discover Society special issue on the Politics of Data(Science).Image credit top left: opensource.com (CC BY-SA)Note: This article gives the views of the author, and not the position of the Impact of Social Science blog, nor of theLondon School of Economics. Please review our Comments Policy if you have any concerns on posting a commentbelow.About the AuthorsSabina Leonelli is Associate Director of the Exeter Centre for the Study of the Life Sciences ( Egenis), AssociateEditor of the journal History and Philosophy of the Life Sciences, and Principal Investigator of the ERC StartingGrant, [DATA_SCIENCE]: the Epistemology of Data-Intensive Science. Last year she completed a monograph titled“Life in the Digital Age: A Philosophical Study of Data-Centric Biology” in which her general perspective is articulated3/4(under review with Chicago University Press). She can be found on Twitter @sabinaleonelli and the researchgroup @DataScienceFeed.Louise Bezuidenhout (University of Exeter and University of Witwatersrand, South Africa) is an empirical ethicistwhose research focuses on issues relating to responsibility and openness in the life sciences. The majority of herresearch focuses on life science research in developing countries – particularly sub-Saharan Africa. She isfascinated by how and why developing country scientists are represented (or not) in international life science ethicsdiscourse and whether STS research and policy development can be made more sensitive to reflect the pressuresof these research environments.Copyright © The Author (or The Authors) - Unless otherwise stated, this work is licensed under a CreativeCommons Attribution Unported 3.0 License.4/4

The Politics of Data: The rising prominence of a data-centric approach to scientific research.

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