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Open Science: One Term, Five Schools of Thought

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Fecher, Benedikt, Friesike, Sascha·2014·Opening Science

Abstract

Open Science is an umbrella term encompassing a multitude of assumptions about the future of knowledge creation and dissemination. Based on a literature review, this chapter aims at structuring the overall discourse by proposing five Open Science schools of thought: The infrastructure school (which is concerned with the technological architecture), the public school (which is concerned with the accessibility of knowledge creation), the measurement school (which is concerned with alternative impact measurement), the democratic school (which is concerned with access to knowledge) and the pragmatic school (which is concerned with collaborative research). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Research works printed but not linked: 43

  1. Altunay, M., et al. (2010). A science driven production Cyberinfrastructure—the Open Science grid. Journal of Grid Computing, 9(2), 201–218. doi: 10.1007/s10723-010-9176-6 . · DOI
  2. Antelmann, K. (2004). Do Open-Access articles have a greater research impact? College & Research Libraries, 65(5), 372–382. · DOI · Paper page
  3. Arazy, O., Morgan, W., & Patterson, R. (2006). Wisdom of the Crowds: Decentralized Knowledge Construction in Wikipedia. SSRN Electronic Journal. doi: 10.2139/ssrn.1025624 . · DOI
  4. Auer, S., et al. (2007). DBpedia: a nucleus for a web of open data. In K. Aberer et al., (Eds.), The Semantic Web (pp. 722–735). Berlin, Heidelberg: Springer Berlin Heidelberg. Available at: http://www.springerlink.com/index/10.1007/978-3-540-76298-0_52 . · DOI · Paper page
  5. Boulton, G., et al. (2011). Science as a public enterprise: the case for open data. The Lancet, 377(9778), 1633–1635. doi: 10.1016/S0140-6736(11)60647-8 . · DOI
  6. Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: implications for scientific and technical human capital. Research Policy, 33(4), 599–616. doi: 10.1016/j.respol.2004.01.008 . · DOI · Paper page
  7. Campbell, E. G., et al. (2002). Data Withholding in Academic Genetics: evidence from a national survey. JAMA, 287(4), 473. doi: 10.1001/jama.287.4.473 . · DOI · Paper page
  8. Carroll, M. W. (2011). Why full Open Access matters. PLoS Biology, 9(11), p.e1001210. doi: 10.1371/journal.pbio.1001210 . · DOI
  9. Catlin-Groves, C. L. (2012). The citizen science landscape: from volunteers to citizen sensors and beyond. International Journal of Zoology, 2012, 1–14. doi: 10.1155/2012/349630 . · DOI
  10. Cribb, J., & Sari, T. (2010). Open Science: sharing knowledge in the global century. Collingwood: CSIRO Publishing. · DOI
  11. De Roure, D., et al. (2008). myExperiment: defining the social virtual research environment. In IEEE (pp. 182–189). Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4736756 .
  12. De Roure, D., & Goble, C. (2009). Software design for empowering scientists. IEEE Software, 26(1), 88–95. doi: 10.1109/MS.2009.22 . · DOI · Paper page
  13. De Roure, D., Jennings, N.R., Shadbolt, N. R. (2003). The semantic grid: a future e-science infrastructure. In F. Berman, G. Fox, & T. Hey (Eds.), Wiley Series in communications networking & Distributed Systems (pp. 437–470). Chichester: John Wiley & Sons, Ltd.
  14. Ebner, M., & Maurer, H. (2009). Can Weblogs and Microblogs change traditional scientific writing? Future Internet, 1(1), 47–58. doi: 10.3390/fi1010047 . · DOI
  15. Foster, I. (2002). The grid: a new infrastructure for 21st century science. Physics Today, 55(2), 42. doi: 10.1063/1.1461327 . · DOI
  16. Gowers, T., & Nielsen, M. (2009). Massively collaborative mathematics. Nature, 461(7266), 879–881. doi: 10.1038/461879a . · DOI
  17. Grand, A., et al. (2012). Open Science: a new “Trust Technology”? Science Communication, 34(5), 679–689. doi: 10.1177/1075547012443021 . · DOI
  18. Haeussler, C. (2011). Information-sharing in academia and the industry: A comparative study. Research Policy, 40(1), 105–122. doi: 10.1016/j.respol.2010.08.007 . · DOI
  19. Hand, E. (2010). Citizen science: people power. Nature, 466(7307), 685–687. doi: 10.1038/466685a . · DOI · Paper page
  20. Harnad, S., et al. (2004). The access/impact problem and the green and gold roads to Open Access. Serials Review, 30(4), 310–314. doi: 10.1016/j.serrev.2004.09.013 . · DOI · Paper page
  21. Hey, T. (2005). Cyberinfrastructure for e-Science. Science, 308(5723), 817–821. doi: 10.1126/science.1110410 . · DOI · Paper page
  22. Hunter, L., & Leahey, E. (2008). Collaborative research in sociology: trends and contributing factors. The American Sociologist, 39(4), 290–306. · DOI
  23. Irwin, A. (1995). Citizen science: a study of people, expertise, and sustainable development, London. New York: Routledge.
  24. Irwin, A. (2006). The politics of talk: coming to terms with the “New” scientific governance. Social Studies of Science, 36(2), 299–320. doi: 10.1177/0306312706053350 . · DOI · Paper page
  25. Löh, A., & Hinze, R. (2006). Open data types and open functions (p. 133.). In ACM Press, doi: 10.1145/1140335.1140352 . · DOI
  26. Miller, P., Styles, R., Heath, T. (2008). Open data commons, a license for open data. In Proceedings of the WWW 2008 Workshop on Linked Data on the Web.
  27. Molloy, J. C. (2011). The open knowledge foundation: open data means better science. PLoS Biology, 9(12), p.e1001195. doi: 10.1371/journal.pbio.1001195 . · DOI · Paper page
  28. Morris, T., & Mietchen, D. (2010). Collaborative structuring of knowledge by experts and the public. In Proceedings of the 5th Open Knowledge Conference (pp. 29–41). London, UK.
  29. Murray-Rust, P. (2008). Open data in science. Serials Review, 34(1), 52–64. doi: 10.1016/j.serrev.2008.01.001 . · DOI
  30. Nentwich, M. (2003). Cyberscience: research in the age of the Internet. Vienna: Austrian Academy of Sciences Press.
  31. Neylon, C., & Wu, S. (2009). Open Science: tools, approaches, and implications. In Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (pp. 540–544).
  32. Nielsen, M. A. (2012). Reinventing discovery: the new era of networked science. Princeton: Princeton University Press.
  33. Phelps, L., Fox, B. A., & Marincola, F. M. (2012). Supporting the advancement of science: Open Access publishing and the role of mandates. Journal of Translational Medicine, 10, 13. doi: 10.1186/1479-5876-10-13 . · DOI
  34. Powell, M. C., & Colin, M. (2009). Participatory paradoxes: facilitating citizen engagement in science and technology from the top-down? Bulletin of Science, Technology & Society, 29(4), 325–342. doi: 10.1177/0270467609336308 . · DOI
  35. Priem, J., et al. (2011). Uncovering impacts: CitedIn and total-impact, two new tools for gathering altmetrics (pp. 9–11). In iConference 2012. Available at: http://jasonpriem.org/self-archived/two-altmetrics-tools.pdf .
  36. Priem, J., & Costello, K. L. (2010). How and why scholars cite on Twitter. Proceedings of the American Society for Information Science and Technology, 47(1), 1–4. doi: 10.1002/meet.14504701201 . · DOI · Paper page
  37. Priem, J., & Hemminger, B. M. (2012). Decoupling the scholarly journal. Frontiers in Computational Neuroscience, 6. doi: 10.3389/fncom.2012.00019 . · DOI
  38. Priem, J., Piwowar, H., Hemminger, B. M. (2012). Altmetrics in the wild: using social media to explore scholarly impact. In ACM Web Science Conference 2012 Workshop. Evanston, IL, USA. Available at: http://altmetrics.org/altmetrics12/priem/ .
  39. Tacke, O. (2010). Open Science 2.0: how research and education can benefit from open innovation and Web 2.0. In T. J. Bastiaens, U. Baumöl, & B. J. Krämer (Eds.), On collective intelligence (pp. 37–48). Berlin: Springer Berlin Heidelberg. · DOI
  40. Vision, T. J. (2010). Open data and the social contract of scientific publishing. BioScience, 60(5), 330–331. doi: 10.1525/bio.2010.60.5.2 . · DOI
  41. Weiss, A. (2005). The power of collective intelligence. NetWorker, 9(3), 16–23. doi: 10.1145/1086762.1086763 . · DOI
  42. Weller, K., & Puschmann, C. (2011). Twitter for scientific communication: how can citations/references be identified and measured? In Proceedings of the ACM WebSci’11 (pp. 1–4). Koblenz: ACM Publishers.
  43. Yeong, C. H., & Abdullah, B. J. J. (2012). Altmetrics: the right step forward. Biomedical Imaging and Intervention Journal, 8(3), 1–2.

Cited by

Metadata

Authors
Fecher, B., & Friesike, S.
Venue
Opening Science
Published
2013-12-17
Pages
17-47
OpenAlex
W1522019205
Cited by
654
References
0
FWCI
48.84
Type
book chapter
Language
en
Open access
hybrid

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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