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Abstract
Changes in access to data are leading to rapid ‘data wealth’ in some scientific fields, even as others remain ‘data‐poor’. Furthermore, the current attention towards developing computer‐based infrastructures and digital access to common data sets—the basics of scientific ‘cyberinfrastructures’—are too‐focused on fields of study characterized by data wealth. To better understand the implications of this twin pursuit of data wealth and cyberinfrastructure, I articulate how data‐poor scholarly fields differ from data‐rich fields. I then suggest four actions that scholars in data‐poor fields can take to improve their work’s value to science and society in lieu of being data‐rich and propose three design considerations for cyberinfrastructures that can better support data‐poor scholarly endeavors.
Notes
1. Earlier versions of this essay were presented to the Information Systems Department at Case Western Reserve University’s Weatherhead School of Management in March 2007; the 2007 Annual Meeting of the Society for the Social Studies of Science in October 2007; and as part of a panel at the International Conference of Information Systems in December 2007. Comments from conference program members and members of these audiences have helped improve this essay. This essay has benefited from discussions with Sandeep Purao, Madhu Reddy, John Jordan, Michel Avital, Kalle Lyytinen, Noriko Hara, Wayne Lutters, Eric Meyer, Carsten Osterlund, Howard Rosenbaum and Ben Light. Comments from the Prometheus reviewers have further improved the current version of this essay.
2. See E. Meyer, ‘Moving from small science to big science: social and organizational impediments to large scale data sharing’, paper presented at the Third International Conference on e‐Social Science, Ann Arbor, MI, 7–9 October 2007 and available online at: http://ess.si.umich.edu/papers/paper218.pdf; M. Nedeva and R. Boden, ‘Changing science: the advent of neoliberalism’, Prometheus, 24, 3, 2006, pp. 269–81; R. Schroeder, Rethinking Science, Technology and Social Change, Stanford University Press, Stanford, CA, 2007; T. Hey and A. Trefethen, ‘The data deluge: an e‐science perspective’, in G. Fox and T. Hey (eds), Grid Computing—Making the Global Infrastructure a Reality, Wiley, London, 2003, pp. 804–24.
3. The vibrant debates on what data are, how to measure, and how to use data are beyond the scope of this essay. For more on this see the work of L. Floridi, ‘What is the philosophy of information?’, Metaphilosophy, 1&&2, 2002, pp. 123–45; I. Cornelius, ‘Theorizing information for information science’, in B. Cronin (ed.), Annual Review of Information Science and Technology, 36, Information Today, Medford, NJ, 2002, pp. 393–425.
4. See www.cyberinfrastructure.us. Moreover, the rapid growth in data collection in these fields stems in large part from using ICT to collect data in more sustained and larger‐scale ways.
5. See C. Anderson, ‘The end of theory: the data deluge makes the scientific method obsolete’, Wired, 23 June 2008 and available online at: www.http://www.wired.com/science/discoveries/magazine/16‐07/pb_theory.
6. For more on this field, see www.aisnet.org and www.isworld.org. A rough estimate is that there are about 5,000 people, with about half being research‐active, worldwide members of this research community.
7. The forum for this debate, however, is found in discussions of what is known in the United States as Cyberinfrastructure (which is called E‐Science in Europe and perhaps the rest of the world).
8. See C. Burton, Places of Inquiry: Research and Advanced Education in Modern Universities, University of California Press, Berkeley, 1995.
9. Nedeva and Boden, op. cit.; H. Rose and S. Rose, Science and Society, Neguin Books, New York, 1971.
10. See S. Jackson, P. Edwards, G. Bowker and C. Knobel, ‘Understanding infrastructure: history, heuristics, and cyberinfrastructure policy’, First Monday, 12 June 2007 and available online at: http://firstmonday.org/issues/issue12_6/jackson/index.html.
11. D. Atkins et al., Revolutionizing Science and Engineering through Cyberinfrastructure, Report of the National Science Foundation Blue‐Ribbon Advisory Panel on Cyberinfrastructure, Directorate for Computer and Information Science and Engineering, National Science Foundation, Arlington, VA, July 2003.
12. Much of the section draws from the presentation by Stuart Feldman (of IBM) at the 2007 NSF/OED Workshop on Cyberinfrastructure. For more on this workshop, please visit: http://www.cyberinfrastructure.us/.
13. Layer‐cake models of technology assemblages are notorious over‐simplifications of a much more complex and complicated set of inter‐relationships, dependencies and paths. That said, there is utility in this simplification, particularly since this essay is focusing on the issues with the layers and not their relationships.
14. Current examples of this include: S. Adolphs et al., ‘Integrating cyberinfrastructure into existing e‐social science research’, Proceedings of the 2007 E‐Social Science Conference, 10–13 October 2007, Ann Arbor, MI and available online at: http://ess.si.umich.edu/; W. Dutton, ‘Reconfiguring access to information and expertise in the social sciences: the social shaping and implications of cyberinfrastructure’, Proceedings of the 2007 E‐Social Science Conference, 10–13 October 2007, Ann Arbor, MI and available online at: http://ess.si.umich.edu/; J. Ure, R. Procter and Y. Lin, ‘Aligning technical and human infrastructures in the semantic web: a socio‐technical perspective’, Proceedings of the 2007 E‐Social Science Conference, 10–13 October 2007, Ann Arbor, MI and available online at: http://ess.si.umich.edu/.
15. See W. Turner, G. Bowker, L. Gasser and M. Zacklad, ‘Information infrastructures for distributed collective practices’, Computer Supported Cooperative Work, 15, 2–3, 2006, pp. 93–110 for an overview.
16. See NSF/OECD, Social and Economic Factors Shaping the Future of the Internet, Workshop Proceedings, 31 January 2007, NSF, Washington, DC; NSF, Cyberinfrastructure Vision for 21st Century Discovery, Version 5, January 2006, NSF, Washington, DC; T. Hey and A. Trefethen, ‘Cyberinfrastructure for e‐science’, Science, 308, 5723, 2005, pp. 817–21. Additional technical reports from the UK’s E‐Science projects can be found at: http://www.nesc.ac.uk/technical_papers/uk.html.
17. See D. Rhoten, ‘A multi‐method analysis of the social and technical conditions for interdisciplinary collaboration’, Final Report for award BCS‐0129573, National Science Foundation, NSF Press, Arlington, VA, 2003.
18. See P. Edwards, S. Jackson, G. Bowker and C. Knobel, Understanding Infrastructure: Dynamics, Tensions, and Design, DeepBlue, Ann Arbor, 2007 and available online at: http://hdl.handle.net/2027.42/49353; Jackson et al., op. cit.; C. Mackie, ‘Cyberinfrastructure, institutions and sustainability’, First Monday, 12, 6, 2007, and available at: http://www.firstmonday.org/issues/issue12_6/mackie/index.html; T. Finholt and G. Olson, ‘From laboratories to collaboratories: a new organizational form for scientific collaboration’, Psychological Science, 9, 1, 1997, pp. 28–36; S. Scott and W. Venters, ‘The practice of e‐science and e‐social science: method, theory, and matter’, in K. Crowston, S. Sieber and E. Wynn (eds), Virtuality and Virtualization, Springer, London, 2007, pp. 267–79; and C. Lee, P. Dourish and G. Mark, ‘The human infrastructure of cyberinfrastructure’, Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work, ACM Press, New York, pp. 483–92.
19. See S. L. Star and K. Ruhleder, ‘Steps toward an ecology of infrastructure: design and access for large information spaces’, Information Systems Research, 7, 1, 1996, pp. 111–34; Finholt and Olson, op. cit.
20. A petabyte is 250 or 1015. For more on this see the series of essays in Anderson, op. cit.
21. Another example would be the National Institutes of Health and the National Library of Medicine. The library has always been a curator for some data sets and plays a pivotal role in medicine and medical research.
22. Of course, given the issue raised above regarding limited variations in the types of data, it may be argued that the data needed to test rival theories are just not being collected.
23. For example, the information systems research community has developed extensive online resources on methods, theories and literature. See http://www.isworld.org (and note that there are no common data sets).
24. See: K. Knorr‐Cetina, ‘The disunity of two leading sciences’, in P. Galison and D. Stump (eds), The Disunity of Science, Boundaries, Context, and Power, Stanford University Press, Stanford, CA, 1994a; and K. Knorr‐Cetina, Epistemic Cultures: How Scientists Make Sense, Indiana University Press, Bloomington, IN, 1994b.
25. For the recent book by that name: S. Leavit and S. Dubner, Freakonomics, Harper Collins, New York, 2005 that uses small‐scale empirical work to call into question deeply‐held positions in contemporary neo‐classical economics.
26. For more on this see the issue of Information Research dedicated to key papers from the 6th conference on Information Seeking in Context, available at: http://informationr.net/ir/11‐4/infres114.html.
27. For example, see J. Abowd and J. Lane, ‘New approaches to confidentiality protection: synthetic data, remote access and research data centers’, in J. Domingo‐Ferrer and V. Torra (eds), Privacy in Statistical Databases, Springer‐Verlag, Berlin, 2004, pp. 282–9.
28. See R. Buzzell and B. Gale, The PIMS Principles: Linking Strategy to Performance, Free Press, New York, 1987; G. Tellis and P. Golder, ‘First to market, first to fail: the real causes of enduring market leadership’, Sloan Management Review, 37, 2, 1996, pp. 11–9.
29. For more on NASA SEL, see https://www.thedacs.com/databases/sled/sel.php; B. Boehm, Software Engineering Economics, Prentice‐Hall, New York, 1981.
30. For F/LOSS scholars there is www.sourceforge.org: a publicly‐accessible data repository on open source software projects.
31. See Meyer, op. cit.
32. See S. Sawyer and H. Huang, ‘Conceptualizing information, technology and people: comparing information science and information systems literatures’, Journal of the American Society of Information Science and Technology, 58, 10, 2007, pp. 1436–47.
33. See D. Vaughan, ‘Theory elaboration: the heuristics of case analysis’, in C. Ragin and H. Becker (eds), What is a Case? Exploring the Foundations of Social Inquiry, Cambridge University Press, Cambridge, MA, 1992, pp. 173–202.
34. See W. Orlikowski, W. and S. Iacono, ‘Desperately seeking the “IT” in IT research: a call to theorizing the IT artifact’, Information Systems Research, 12, 2, 2001, pp. 121–4.
35. See R. Williams and D. Edge, ‘The social shaping of technology’, Research Policy, 25, 1996, pp. 865–99.
36. See National Academies of Science, Facilitating Interdisciplinary Research, NAS Press, Washington, DC, 2005.
37. Mackie, op. cit.; Turner et al., op. cit.
38. See W. Dutton, ‘The web of technology and people: challenges for economic and social research’, Prometheus, 17, 1, 1999, pp. 5–20.
39. There is also strong reason to encourage more collaboration, as Dutton, 1999, op. cit. argues.
40. See L. Haddon, ‘The contribution of domestication research to in‐home computing and media consumption’, The Information Society, 22, 4, 2006, pp. 3–19.