3/1/2017

4S Annual Meeting 2017

David Ribes

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59:51

Society for Social Studies ofScience
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Session Title

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The Domains of Data Science: Science, Industry & State

Abstract

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Data science has received a remarkably rapid uptake across the organizations of industry, the halls of the academy and theinstitutions of nation states. More than a "siloed" uptake, a characteristic of data science appears to be an indifference oragnosticism to traditional boundaries between those domains; even actively seeking to transcend those boundaries through"domain independent" analytic tools or by fostering new data sharing pathways. This panel draws together scholars examining
the rollout of data science in European, American and Asian contexts with a dual focus on commonalities and divergences
national and transnational policies, and computational technologies and capacities.

Area of STS Scholarship

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Information, Computing and Media Technology
Engineering and Infrastructure
Governance and Public Policy
Keywords

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data science, infrastructure, policy

Additional Information

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Should not be scheduled at the same time as the 'critical data studies' open panel session, please!

Participants

Edit ParticipantsDavid Ribes, dribes@gmail.com; Universty of Washington (Session Organizer)
Geoffrey C. Bowker, gbowker@uci.edu; University of California, Irvine (Chair)

The logic of "domains" in data science
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The logic of "domains" in data science

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David Ribes, dribes@gmail.com; Universty of Washington (Presenter)

Abstract

Edit Abstract"Domain" is an actor's category, long in use within computer and information sciences, and now data science.
The term is used to demarcate disciplinary differences so as to to enact their overcoming. This presentation will
characterize the logic of domain thinking.
Domain boundaries can be enacted at any scale: fields, disciplines, subfields, labs and even individuals. Industry,science and state too are cast as domains. Domain difference can be characterized across the sociotechnicalspectrum: domains may be said have different file formats or data collection methods, but also, epistemologies,
languages or cultures. Ultimately, domain boundaries are identified to be superceded: whether cultural,epistemological, or technical, the work of enacting boundaries is coupled with a vast emerging armamentarium oftools and techniques to engineer domain crossings.
Put briefly, identifying and overcoming domain boundaries is a core operating logic for data science, a distinct
reformulation of the classic 'problem of multidisciplinarity.'

Individual Submission
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4S Annual Meeting 2017

Building the core
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Building the core

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Robin Williams, r.williams@ed.ac.uk; The University of Edinburgh (Presenter)

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The informatisation of research calls for close interaction between scholars from different disciplinary roots.
However tensions may arise where the research goals of one discipline threatens to become subordinated,
reduced to a service role, to another (Barry 2008).[1] This paper explores the dilemmas surrounding the UK’s
national institute for data science, The Alan Turing Institute (ATI) formed in 2015 by the top 5 universities in thisfield. Though its goals included translational research that would advance data science through its application, incollaboration with external stakeholders, the ATI distinguished its role from sector­based institutes by prioritisingfoundational research advances in core areas such as statistics, modelling and machine learning. Though thefuture trajectory and achievements of the ATI are still yet to be established, discipline­based boundaries andknowledge hierarchies threaten to resurface. [1] Barry, A et al (2008) 'Logics of interdisciplinarity', Economy andSociety, 37: 1, 20 — 49

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The enduring spell of tacit knowing? : Data science and its discontents in the managerial andpolicy landscape of Japan
Title (Paper Abstract)
The enduring spell of tacit knowing? : Data science and its discontents in the managerial and policy
landscape of JapanAuthor

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Masato Fukushima, maxiomjp@yahoo.co.jp; University of Tokyo (Presenter)

Abstract

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Abstract

Since scientific practice is understood as being deeply influenced by its specific genius loci (Livingstone 2003),
the recent global development of data science may be an intriguing case of understanding how such a seemingly
universal flow of data is related to a particular locality with a specific understanding of data, or even knowledge. A
part of Japan’s rather poor development of data science—currently a hot topic within academia, industry, and evenpolicy circles—may have resulted from our favored understanding of knowledge that is fundamentally rooted inour body. This view has been
popularized extensively by “The Knowledge­Creating Company” of Nonaka & Taeuchi (1995), with its so­called
SECI model of innovation process based on the spiral interface between tacit and explicit knowledge. This bookhas not only made the term anmoku­chi (tacit knowledge) almost coterminus with innovation itself but also
endorsed the creed that mono­tsukuri—roughly translated as craftsmanship/manufacturing—is our nation’sindustrial identity.
However, the rapid rise of data science, where data largely refer to digital records of any type, has been achallenge to such a widely shared assumption. Drawing on the observation of a couple of different settings wheresuch conflicting views of knowledge actually matter, I discuss the meaning of location vis–à–vis the current
development of data science in the dual sense of the word, that is, the very socio­technical location that matters inthis development and the very understanding of knowledge in relation to its locality and universality.

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The Institution of Data Science in BDHubs: Distributing and Relocating Knowledge Production
Between Industry, Government and the Academy
Title (Paper Abstract)
The Institution of Data Science in BDHubs: Distributing and Relocating Knowledge Production Between
Industry, Government and the Academy

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AuthorStephen Slota, steveslota@gmail.com; (Presenter)

Abstract

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Abstract

The Big Data Innovation Hubs and Spokes (BDHubs) project is an NSF­funded organization with ambitions tobecome an ‘umbrella organization’ supporting cross­sectoral collaboration, capacity­building and the sharing ofexpertise between academic, industry and governmental stakeholders in the data sciences. Unlike in other areasof scientific inquiry, data scientists often have little control over the metadata, software format and even thecontent of their data sets. This results in a mode of scientific inquiry where particular scientific, social and policy
outcomes of research are malleable and responsive to emerging understandings both of how particular large data
sets might be interpreted and the advancement of the tools and methods available to the data scientists andprofessionals working on them. The nature of data scientific work is often intentionally neutral with respect tospecific scientific outcomes compared to the goal of extracting knowledge (and understanding what can potentiallybe learned) from very large, heterogeneous data sets. Big data is a data first endeavor, and the advancement andacceleration of data science under BDHubs revolves closely around expanding data interoperability, sharing tools,resources and methods between data scientists and professionals, and furthering a general understanding of thehttps://convention2.allacademic.com/one/ssss/4s17/index.php?click_key=16&PHPSESSID=3st1db2jk9j7akpkq0trp4ms10

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capacity of those tools, resources and methods to make sense of very large data sets. Novel data science raises
questions of epistemology, reconsiders core scientific norms like reproducibility and interoperability, and presents
new and emergent objects of study in the algorithms, tools and models that mediate between data and thescientists working to interpret it.

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Data Science Institutes in the Netherlands
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Data Science Institutes in the Netherlands

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Sally Wyatt, sally.wyatt@huygens.knaw.nl; Royal Netherlands Academy (Presenter)

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Dutch universities have a long history of collaborating together even though this tradition has been underminedsince the turn of the century, for example via increased competition for research funding and the legal removal ofdemocratic governance (Halffman & Radder, 2015). The recent emergence of data science can be seen as areturn to inter­university collaboration. In this presentation I will examine two Dutch initiatives – Amsterdam Data
Science and Jheronimus Academy of Data Science – that bring together different universities with their local
industry and government partners to offer higher education and research. Drawing on analysis of publicly
available material and interviews with key actors, and on insights from STS scholarship about the ‘triple helix’(Leydesdorff&Etzkowitz, 1996) and knowledge infrastructures (Edwards et al, 2013) in this presentation I willaddress the following questions: How are data and data science conceptualized? What promises are made about
the value of data, data science and data science institutes for students, and the local economy? How do data
science institutes reconfigure power and resources within and between universities? How do the institutes handle
the tension between local (industry and government) needs with the Dutch research policy emphasis on
international excellence? How are local and national knowledge infrastructures mobilized to support the plans ofsuch institutes?

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