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Nanotechnology data sharing: A sociological perspective Sharon Ku Office of History, NIH.

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Presentation on theme: "Nanotechnology data sharing: A sociological perspective Sharon Ku Office of History, NIH."— Presentation transcript:

1 Nanotechnology data sharing: A sociological perspective Sharon Ku Office of History, NIH

2 Outline Historicizing data collection and data sharing in life science Data economies in nanotechnology Social and cultural barriers of sharing The role of curator Interactional expertise and boundary work of data sharing

3 Two paradigms in life science: collecting vs. experimenting The history of GenBank: ‘There was resistance from NIH among staff who feel that molecular geneticists really do not need such database as it did not “inspire excitement”. “Doing’ for a scientist, implies doing experiments.” Collecting and comparing were common ways of producing knowledge in natural history but were often regarded as archaic by experimental biologists, even if these practices involved sophisticated computers.’ (Strasser, 2011) Tensions: Who curates?

4 NIH “data” sharing policy NIH Data Sharing Policy and Implementation Guidance2003: “In NIH's view, all data should be considered for data sharing. Data should be made as widely and freely available as possible while safeguarding the privacy of participants, and protecting confidential and proprietary data.” Only applies to the sharing of final research data for research purposes: “Recorded factual material commonly accepted in the scientific community as necessary to document and support research findings…do not include laboratory notebooks, partial datasets, preliminary analyses, drafts of scientific papers, plans for future research, peer review reports, communications with colleagues, or physical objects, such as gels or laboratory specimens.” Time of sharing to be no later than the acceptance for publication of the main findings from the final dataset. Under the Small Business Act, SBIR grantees may withhold their data for 4 years after the end of the award.

5 Two data economies Main challenge of research data sharing: Negotiating authorship, ownership, credit attribution when mobilizing publication-oriented data economy to database- oriented data economy.

6 Social & cultural barriers of sharing Increase of scientific secrecy due to industrial funding and scientific competition (Hong & Walsh 2009) Increasing reliance on market take-up to adjudicate intellectual disputes Tacit knowledge and context –dependent information required to make data retrievable and reusable. (Bowker 2000) Lack of incentive, expertise, division of labor to change research practice and norms in scientific culture. (Campbell 2002) Lack of trust ; hostile data reuse(Zimmerman 2008; Leonelli 2010)

7 NSI-NKI Thrust 1: Build a diverse collaborative community: (a) experimentalists, computational scientists, and theoreticians to develop and advance the science; (b) engineers and other researchers to apply the science (c) well-trained technicians to employ nanotechnology in practice. NANOTECHNOLOGY KNOWLEDGE INFRASTRUCTURE

8 The role of curator Packaging data to ensure that it can travel well Socio-Technical skills of data packaging: 1) Balance of de-contextualization and re-contextualization 2) Knowing where to find data and sensitive about reliable data 3) Navigator of diverse disciplinary cultures in data generation and usage. Ability to align divergence. 4) Need to be trusted. 5) Information ethics (ethical, legal, ownership, privacy issues in handling data) Current invisibility

9 Training nano-curators: Interactional expertise Different types of expertise(Collins 2010) : 1)Contributory expertise: the ability to practice science 2)Interactional expertise: the ability to converse expertly about a practical skill or expertise, but without being able to practice it, learned through linguistic socialisation among the practitioners. Interactional expertise, as well as contributory expertise, is essential for data sharing. Curators are interactional experts.

10 CONCLUSION Conclusions: Data sharing not ground work but boundary work at the techno-cultural-social interfaces among different knowledge systems: New tools: Nanoinformatics Change of moral economy and research norms New infrastructure New profession

11 References Campbell,, E. et. al (2002) “Data Withholding in Academic Genetics Evidence From a National Survey”, JAMA,287(4): 473 Collin, Harry (2004). “Interactional expertise as a third kind of knowledge”, Phenomenology and the Cognitive Sciences 3: 125–143. Hilgartner, S. (1995) Biomolecular databases: New communication regimes for biology? Science Communication 17, pp. 240–63. Hong & Walsh (2009). “ For Money or Glory? Commercialization, Competition, and Secrecy in the Entrepreneurial University”, The Sociological Quarterly, Vol. 50 (1): 145-171 Leonelli, Sabina (2010). “Packaging Small Facts for Re-Use: Databases in Model Organism Biology”, in P. Howlett &M. Morgan eds. How Well Do Facts Travel? The Dissemination of Reliable Knowledge. Cambridge University Press:325-348. --(2012) “When humans are the exception: cross-species databases at the interface of biological and clinical research” Social Studies of Science 42 (2):214-236. NIH Data Sharing Policy and Implementation Guidance (2003) Strasser, Bruno J. (2011) “The Experimenter’s Museum GenBank, Natural History, and the Moral Economies of Biomedicine”, Isis 102(1): 60-96 Zimmerman, Ann S. (2008). “New Knowledge from Old Data: The Role of Standards in the Sharing and Reuse of Ecological Data”, STHV 33 (5): 631-652

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