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International Journal on Digital Libraries

, Volume 16, Issue 3–4, pp 207–227 | Cite as

Knowledge infrastructures in science: data, diversity, and digital libraries

  • Christine L. Borgman
  • Peter T. Darch
  • Ashley E. Sands
  • Irene V. Pasquetto
  • Milena S. Golshan
  • Jillian C. Wallis
  • Sharon Traweek
Article

Abstract

Digital libraries can be deployed at many points throughout the life cycles of scientific research projects from their inception through data collection, analysis, documentation, publication, curation, preservation, and stewardship. Requirements for digital libraries to manage research data vary along many dimensions, including life cycle, scale, research domain, and types and degrees of openness. This article addresses the role of digital libraries in knowledge infrastructures for science, presenting evidence from long-term studies of four research sites. Findings are based on interviews (\(n=208\)), ethnographic fieldwork, document analysis, and historical archival research about scientific data practices, conducted over the course of more than a decade. The Transformation of Knowledge, Culture, and Practice in Data-Driven Science: A Knowledge Infrastructures Perspective project is based on a 2 \(\times \) 2 design, comparing two “big science” astronomy sites with two “little science” sites that span physical sciences, life sciences, and engineering, and on dimensions of project scale and temporal stage of life cycle. The two astronomy sites invested in digital libraries for data management as part of their initial research design, whereas the smaller sites made smaller investments at later stages. Role specialization varies along the same lines, with the larger projects investing in information professionals, and smaller teams carrying out their own activities internally. Sites making the largest investments in digital libraries appear to view their datasets as their primary scientific legacy, while other sites stake their legacy elsewhere. Those investing in digital libraries are more concerned with the release and reuse of data; types and degrees of openness vary accordingly. The need for expertise in digital libraries, data science, and data stewardship is apparent throughout all four sites. Examples are presented of the challenges in designing digital libraries and knowledge infrastructures to manage and steward research data.

Keywords

Digital libraries Scholarly communication Big science Data management Little science Open access Open data 

Notes

Acknowledgments

The research reported in this paper is supported by Alfred P. Sloan Foundation Award #20113194, The Transformation of Knowledge, Culture and Practice in Data-Driven Science: A Knowledge Infrastructures Perspective. We are grateful to our program officer, Joshua Greenberg, and to our external advisory board—Alyssa Goodman, George Djorgovski, and Alex Szalay—for their guidance and support. We also acknowledge the contributions of Laura A. Wynholds and David S. Fearon, Jr. for conducting early interviews; and Elaine Levia for technical, bibliographic, and administrative support.

References

  1. 1.
    Abazajian, K.N., Adelman-McCarthy, J.K., Agüeros, M.A., et al.: The seventh data release of the sloan digital sky survey. Astrophys. J. Suppl. Ser. 182(2), 543–558 (2009)CrossRefGoogle Scholar
  2. 2.
    ADS. The SAO/NASA Astrophysics Data System. (2015). http://www.adsabs.harvard.edu
  3. 3.
    Ahn, C.P., Alexandroff, R., Allende Prieto, C., et al.: The ninth data release of the sloan digital sky survey: first spectroscopic data from the SDSS-III Baryon oscillation spectroscopic survey. Astrophys. J. Suppl. Ser. 203(2), 21 (2012)CrossRefGoogle Scholar
  4. 4.
    Arzberger, P., Schroeder, P., Beaulieu, A., et al.: An international framework to promote access to data. Science 303(5665), 1777–1778 (2004)CrossRefGoogle Scholar
  5. 5.
    Astronomy and Astrophysics Survey Committee: Astronomy and Astrophysics in the New Millennium. National Academy of Sciences, Washington, DC (2001)Google Scholar
  6. 6.
    Bechhofer, S., Ainsworth, J., Bhagat, J., et al.: Why Linked Data is Not Enough for Scientists. 2010 IEEE Sixth International Conference on e-Science (e-Science), pp. 300–307, (2010)Google Scholar
  7. 7.
    Bell, G., Hey, T., Szalay, A.S.: Beyond the data deluge (Computer Science). Science 323(5919), 1297–1298 (2009)CrossRefGoogle Scholar
  8. 8.
    Berman, F., Cerf, V.G.: Who will pay for public access to research data? Science 341(6146), 616–617 (2013)CrossRefGoogle Scholar
  9. 9.
    Bicarregui, J., Gray, N., Henderson, R., Jones, R., Lambert, S., Matthews, B.: Data management and preservation planning for big science. Int. J. Digit. Curation 8(1), 29–41 (2013)CrossRefGoogle Scholar
  10. 10.
    Blocker, A.W., Meng, X.-L.: The potential and perils of preprocessing: building new foundations. Bernoulli 19(4), 1176–1211 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Borgman, C.L.: What are digital libraries? Compet. Vis. Inf. Process. Manag. 35(3), 227–243 (1999)CrossRefGoogle Scholar
  12. 12.
    Borgman, C.L.: Scholarship in the Digital Age: Information, Infrastructure, and the Internet. MIT Press, Cambridge (2007)Google Scholar
  13. 13.
    Borgman, C.L.: Big Data, Little Data, No Data: Scholarship in the Networked World. The MIT Press, Cambridge (2015)Google Scholar
  14. 14.
    Borgman, C.L., Bates, M., Cloonan, M., et al.: Social Aspects of Digital Libraries. Final Report to the National Science Foundation. (1996)Google Scholar
  15. 15.
    Borgman, C.L., Bowker, G.C., Finholt, T.A., and Wallis, J.C.: Towards a virtual organization for data cyberinfrastructure. In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM, pp. 353–356 (2009)Google Scholar
  16. 16.
    Borgman, C.L., Darch, P.T., Sands, A.E., Wallis, J.C., Traweek, S.: The ups and downs of knowledge infrastructures in science: implications for data management. 2014 IEEE/ACM Joint Conference on Digital Libraries (JCDL), IEEE Computer Society, pp. 257–266 (2014)Google Scholar
  17. 17.
    Borgman, C.L., Traweek, S.: The transformation of knowledge, culture, and practice in data-driven science: a knowledge infrastructures perspective. 2012. http://knowledgeinfrastructures.gseis.ucla.edu/?page_id=50
  18. 18.
    Borgman, C.L., Wallis, J.C., Enyedy, N.D.: Building digital libraries for scientific data: an exploratory study of data practices in habitat ecology. In: Proceedings of the 10th European Conference on Research and Advanced Technology for Digital Libraries, Springer Berlin Heidelberg, pp. 170–183 (2006)Google Scholar
  19. 19.
    Borgman, C.L., Wallis, J.C., Enyedy, N.D.: Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries. Int. J. Digital Libr. 7(1–2), 17–30 (2007)CrossRefGoogle Scholar
  20. 20.
    Borgman, C.L., Wallis, J.C., Mayernik, M.S.: Who’s got the data? Interdependencies in science and technology collaborations. Comput. Support. Coop. Work 21(6), 485–523 (2012)CrossRefGoogle Scholar
  21. 21.
    Borgman, C.L., Wallis, J.C., Mayernik, M.S., Pepe, A.: Drowning in data: digital library architecture to support scientific use of embedded sensor networks. Joint Conference on Digital Libraries, Association for Computing Machinery, pp. 269–277 (2007)Google Scholar
  22. 22.
    Borne, K.D.: Planets Stars and Stellar Systems. In: Oswalt, T.D., Bond, H.E. (eds.) Virtual Observatories, Data Mining, and Astroinformatics. Springer, Netherlands (2013)CrossRefGoogle Scholar
  23. 23.
    Boulton, G., Campbell, P., Collins, B., et al.: Science as an Open Enterprise. The Royal Society, London (2012)Google Scholar
  24. 24.
    Bowker, G.C.: Memory Practices in the Sciences. MIT Press, Cambridge (2005)Google Scholar
  25. 25.
    Brunsmann, J., Wilkes, W., Schlageter, G., Hemmje, M.: State-of-the-art of long-term preservation in product lifecycle management. Int. J. Digital Libr. 12(1), 27–39 (2012)CrossRefGoogle Scholar
  26. 26.
    Capshew, J.H., Rader, K.A.: Big science: price to the present. Osiris 7, 2–25 (1992)CrossRefGoogle Scholar
  27. 27.
    Center for Dark Energy Biosphere Investigations. C-DEBI Strategic Implementation Plan, 2010–2015 (2010)Google Scholar
  28. 28.
    Center for Dark Energy Biosphere Investigations. C-DEBI Data Management Philosophy and Policy. 2012Google Scholar
  29. 29.
    Center for Dark Energy Biosphere Investigations. C-DEBI. (2014). http://www.darkenergybiosphere.org/
  30. 30.
    Chompalov, I.: Lessons Learned from the Study of Multi-organizational Collaborations in Science and Implications for the Role of the University in the 21st Century. In: Herbst, M. (ed.) The Institution of Science and the Science of Institutions, pp. 167–184. Springer, Netherlands (2014)CrossRefGoogle Scholar
  31. 31.
    CODATA-ICSTI Task Group on Data Citation Standards and: Practices. Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data. Data Science Journal 12, 1–75 (2013)Google Scholar
  32. 32.
    Collins, H.M.: LIGO becomes big science. Hist. Stud. Phys. Biol. Sci. 33(2), 261–297 (2003)CrossRefGoogle Scholar
  33. 33.
    Committee for a Decadal Survey of Astronomy and Astrophysics; National Research Council. New Worlds, New Horizons in Astronomy and Astrophysics. The National Academies Press, Washington, D.C., (2010)Google Scholar
  34. 34.
    Committee on Ensuring the Utility and Integrity of Research Data in a Digital Age. Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age. National Academy Press, Washington, D.C. (2009)Google Scholar
  35. 35.
    Cragin, M.H., Palmer, C.L., Carlson, J.R., Witt, M.: Data sharing, small science, and institutional repositories. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 368(1926), 4023–4038 (2010)CrossRefGoogle Scholar
  36. 36.
    Darch, P.T.: When Scientists Meet the Public: An Investigation into Citizen Cyberscience. (2011)Google Scholar
  37. 37.
    Darch, P.T., Borgman, C.L.: Ship space to database: motivations to manage research data for the deep subseafloor biosphere. In: Proceedings of the 77th Annual Meeting of the Association for Information Science and Technology (2014)Google Scholar
  38. 38.
    Darch, P.T., Borgman, C.L., Traweek, S., Cummings, R.L., Wallis, J.C., Sands, A.E.: What lies beneath? Knowledge infrastructures in the subseafloor biosphere and beyond. Int. J. Digital Libr. 16(1), 61–77 (2015)Google Scholar
  39. 39.
    Darch, P.T., Sands, A.E.: Beyond big or little science: understanding data lifecycles in astronomy and the deep subseafloor biosphere. (2015)Google Scholar
  40. 40.
    David, P.A.: The economic logic of ‘Open Science’ and the balance between private property rights and the public domain in scientific data and information: A primer. In: The Role of the Public Domain in Scientific Data and Information. National Academy Press, Washington, D.C., 19–34 (2003)Google Scholar
  41. 41.
    Digital Curation Centre. What is digital curation? (2014). http://www.dcc.ac.uk/digital-curation/what-digital-curation
  42. 42.
    Edwards, K.: Center for Dark Energy Biosphere Investigations (C-DEBI): A Center for Resolving the Extent. Function, Dynamics and Implications of the Subseafloor Biosphere (2009)Google Scholar
  43. 43.
    Edwards, P.N.: A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. MIT Press, Cambridge (2010)Google Scholar
  44. 44.
    Edwards, P.N., Jackson, S.J., Chalmers, M.K., et al.: Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. University of Michigan, Ann Arbor (2013)Google Scholar
  45. 45.
    Edwards, P.N., Mayernik, M.S., Batcheller, A.L., Bowker, G.C., Borgman, C.L.: Science friction: data, metadata, and collaboration. Soc. Stud. Sci. 41(5), 667–690 (2011)CrossRefGoogle Scholar
  46. 46.
    European Commission High Level Expert Group on Scientific Data. Riding the wave: How Europe can gain from the rising tide of scientific data. European Union (2010)Google Scholar
  47. 47.
    Exploring Computer Science. Mobilize: Mobilizing for Innovative Computer Science Teaching and Learning. (2014). http://www.exploringcs.org/about/related-grants/mobilize
  48. 48.
    Faniel, I.M., Jacobsen, T.E.: Reusing scientific data: how earthquake engineering researchers assess the reusability of colleagues’ data. J. Comput. Supported Coop. Work 19(3–4), 355–375 (2010)CrossRefGoogle Scholar
  49. 49.
    Fearon Jr., D.S., Borgman, C.L., Traweek, S., Wynholds, L.A.: Curators to the Stars (Poster). Annual Meeting of the American Society for Information Science & Technology (2010)Google Scholar
  50. 50.
    Finkbeiner, A.K.: A Grand and Bold Thing: the Extraordinary New Map of the Universe Ushering in a New Era of Discovery. Free Press, New York (2010)Google Scholar
  51. 51.
    Frieman, J.: Dark energy survey. Bull. Am. Astron. Soc. 43, 20501 (2011)Google Scholar
  52. 52.
    Furner, J.: Little book, big book: before and after little science, big science: a review article, part I. J. Libr. Inf. Sci. 35(2), 115–125 (2003)Google Scholar
  53. 53.
    Furner, J.: Little book, big book: before and after little science, big science: a review article, part II. J. Libr. Inf. Sci. 35(3), 189–201 (2003)Google Scholar
  54. 54.
    Galison, P.: The Collective Author. Scientific authorship: Credit and intellectual property in science pp. 325–355 (2003)Google Scholar
  55. 55.
    Galison, P., Hevly, B.W.: Big Science: The Growth of Large-Scale Research. Stanford University Press, Stanford (1992)Google Scholar
  56. 56.
    Gitelman, L. (ed.): Raw Data. Is an Oxymoron. The MIT Press, Cambridge (2013)Google Scholar
  57. 57.
    Glaser, B.G., Strauss, A.L.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine Pub. Co., Chicago (1967)Google Scholar
  58. 58.
    Goodman, A.A., Pepe, A., Blocker, A.W., et al.: Ten simple rules for the care and feeding of scientific data. PLoS Comput. Biol. 10(4), e1003542 (2014)CrossRefGoogle Scholar
  59. 59.
    Gray, J., Liu, D.T., Nieto-Santisteban, M., Szalay, A.S., DeWitt, D.J., Heber, G.: Scientific data management in the coming decade. SIGMOD Rec. 34(4), 34–41 (2005)CrossRefGoogle Scholar
  60. 60.
    Gray, J., Slutz, D., Szalay, A.S., et al.: Data Mining the SDSS SkyServer Database. (2002)Google Scholar
  61. 61.
    Gray, N., Carozzi, T.D., Woan, G.: Managing research data in big science. (2012). arXiv:1207.3923
  62. 62.
    Greenberg, J.: Theoretical considerations of lifecycle modeling: an analysis of the dryad repository demonstrating automatic metadata propagation, inheritance, and value system adoption. Cat. Classif. Quart. 47(3–4), 380–402 (2009)Google Scholar
  63. 63.
    Heidorn, P.B.: Shedding light on the dark data in the long tail of science. Libr. Trends 57(2), 280–299 (2008)CrossRefGoogle Scholar
  64. 64.
    Hey, T., Trefethen, A.E.: Cyberinfrastructure for e-Science. Science 308(5723), 817–821 (2005)CrossRefGoogle Scholar
  65. 65.
    Higgins, S.: The DCC curation lifecycle model. Int. J. Digit. Curation 3(1), 134–140 (2008)MathSciNetCrossRefGoogle Scholar
  66. 66.
    Higgins, S.: The lifecycle of data management. In: Managing Research Data. Facet Publishing; 1 st edn (January 31, 2012), p. 224 (2012)Google Scholar
  67. 67.
    Humphrey, C.: e-Science and the Life Cycle of Research. (2008)Google Scholar
  68. 68.
    IODP. International Ocean Discovery Program. (2014). http://iodp.org/
  69. 69.
    Ivezic, Z., Tyson, J.A., Abel, B., et al. LSST: from science drivers to reference design and anticipated data products (Version 4.0). (2014). http://arxiv.org/abs/0805.2366
  70. 70.
    Jackson, S.J., Buyuktur, A.: Who killed WATERS? Mess, method, and forensic explanation in the making and unmaking of large-scale science networks. Sci. Technol. Hum. Values 39(2), 285–308 (2014)CrossRefGoogle Scholar
  71. 71.
    Jackson, S.J., Ribes, D., Buyuktur, A., Bowker, G.C.: Collaborative rhythm: temporal dissonance and alignment in collaborative scientific work. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, ACM, pp. 245–254 (2011)Google Scholar
  72. 72.
    Johns Hopkins University. Krieger Astronomer Awarded \(\$9.5\) Million to Create “Virtual Telescope. 2013. http://krieger.jhu.edu/blog/2013/11/04/krieger-astronomer-awarded-9-5-million-to-create-virtual-telescope/
  73. 73.
    Karasti, H., Baker, K.S.: Digital data practices and the long term ecological research program growing global. Int. J. Digit. Curation 3(2), 42–58 (2008)CrossRefGoogle Scholar
  74. 74.
    Karasti, H., Baker, K.S., Halkola, E.: Enriching the notion of data curation in e-science: data managing and information infrastructuring in the long term ecological research (lter) network. J. Comput.-Support. Coop. Work 15(4), 321–358 (2006)CrossRefGoogle Scholar
  75. 75.
    Karasti, H., Baker, K.S., Millerand, F.: Infrastructure time: long-term matters in collaborative development. Comput. Support. Coop. Work (CSCW) 19(3–4), 377–415 (2010)CrossRefGoogle Scholar
  76. 76.
    Knorr-Cetina, K.: Epistemic Cultures: How the Sciences Make Knowledge. Harvard University Press, Cambridge (1999)Google Scholar
  77. 77.
    Lambright, W.H.: Government and science: a troubled, critical relationship and what can be done about it. Public Adm. Rev. 68(1), 5–18 (2008)CrossRefGoogle Scholar
  78. 78.
    Laney, D.: 3D Data Management: Controlling Data Volume. Velocity and Variety. META Group (Gartner) (2001)Google Scholar
  79. 79.
    Latour, B., Woolgar, S.: Laboratory Life: The Construction of Scientific Facts. Princeton University Press, Princeton (1986)Google Scholar
  80. 80.
    Lenoir, T., Hays, M.: The Manhattan project for biomedicine. Controlling Our Destinies. Historical, Philosophical, Ethical, and Theological Perspectives on the Human Genome Project, pp. 29–62 (2000)Google Scholar
  81. 81.
    Liu, X., Wang, Q., Zhou, Z.: IODP in Japan. Adv. Earth Sci. 4, 10 (2004)Google Scholar
  82. 82.
    LSST. Large Synoptic Survey Telescope: Timeline. 2013. http://www.lsst.org/lsst/science/timeline
  83. 83.
    LSST Collaboration. Community Science Input and Participation. Large Synoptic Survey Telescope, 2013. http://www.lsst.org/lsst/science/participate
  84. 84.
    LSST Science Collaboration, Abell, P.A., Allison, J., et al.: LSST Science Book, Version 2.0. (2009)Google Scholar
  85. 85.
    Mandell, R.A.: Researchers’ Attitudes towards Data Discovery: Implications for a UCLA Data Registry. Social Science Research Network, Rochester (2012)Google Scholar
  86. 86.
    Maurer, B.A.: Models of Scientific Inquiry and Statistical Practice: Implications for the structure of scientific knowledge. In: The Nature of Scientific Evidence: Statistical, philosophical, and empirical considerations. The University of Chicago Press, Chicago, pp. 17–50 (2004)Google Scholar
  87. 87.
    Mayernik, M.S.: Metadata Realities for Cyberinfrastructure: Data Authors as Metadata Creators. (2011). doi: 10.2139/ssrn.2042653
  88. 88.
    Mayernik, M.S., Wallis, J.C., Borgman, C.L.: Unearthing the infrastructure: humans and sensors in field-based research. Comput. Support. Coop. Work 22(1), 65–101 (2013)CrossRefGoogle Scholar
  89. 89.
    McCray, W.P.: Giant Telescopes: Astronomical Ambition and the Promise of Technology. Harvard University Press, Cambridge, MA (2004)Google Scholar
  90. 90.
    Meyer, E.T., Schroeder, R.: Knowledge Machines: Digital Transformations of the Sciences and Humanities. MIT Press, Cambridge (2015)Google Scholar
  91. 91.
  92. 92.
    Onsrud, H., Campbell, J.: Big opportunities in access to “Small Science” Data. Data Sci. J. 6, OD58–OD66 (2007)CrossRefGoogle Scholar
  93. 93.
    Orcutt, B.N., LaRowe, D.E., Biddle, J.F., et al.: Microbial activity in the marine deep biosphere: progress and prospects. Extreme Microbiol. 4, 189 (2013)Google Scholar
  94. 94.
    Palmer, C.L., Cragin, M.H., Heidorn, P.B., Smith, L.C.: Data Curation for the Long Tail of Science: The Case of Environmental Sciences (2007)Google Scholar
  95. 95.
    Parsons, M.A., Fox, P.A.: Is data publication the right metaphor? Data Sci. J. 12, WDS32–WDS46 (2013)Google Scholar
  96. 96.
    Pepe, A., Goodman, A., Muench, A., Crosas, M., Erdmann, C.: How do astronomers share data? Reliability and persistence of datasets linked in AAS publications and a qualitative study of data practices among US astronomers. PLoS One 9(8), e104798 (2014)CrossRefGoogle Scholar
  97. 97.
    Pepe, A., Mayernik, M.S., Borgman, C.L., Van de Sompel, H.: From artifacts to aggregations: modeling scientific life cycles on the semantic web. J. Am. Soc. Inf. Sci. Technol. 61(3), 567–582 (2010)Google Scholar
  98. 98.
    Price, D.J. de S.: Little Science, Big Science. Columbia University Press, New York, NY, USA, (1963)Google Scholar
  99. 99.
    Ray, J.M. (ed.): Research Data Management: Practical Strategies for Information Professionals. Purdue University Press, West Lafayette (2014)Google Scholar
  100. 100.
    Renear, A.H., Sacchi, S., Wickett, K.M.: Definitions of dataset in the scientific and technical literature. Proc. Am. Soc. Inf. Sci. Technol. 47(1), 1–4 (2010)CrossRefGoogle Scholar
  101. 101.
    Ribes, D., Jackson, S.J.: Data Bite Man: The Work of Sustaining a Long-Term Study. In: Gitelman, L., ed. “Raw Data” Is an Oxymoron. pp. 147–166. The MIT Press, Cambridge, MA (2013)Google Scholar
  102. 102.
    Sands, A.E., Borgman, C.L., Traweek, S., Wynholds, L.A.: We’re working on it: transferring the sloan digital sky survey from laboratory to library. Int. J. Digit. Curation 9(2), 98–110 (2014)CrossRefGoogle Scholar
  103. 103.
    Schofield, P., Eppig, J., Huala, E., et al.: Sustaining the data and bioresource commons. Science 330, 592–593 (2010)CrossRefGoogle Scholar
  104. 104.
    SDSS. Sloan Digital Sky Survey (2014). http://www.sdss.org/
  105. 105.
    Smith, R.W.: The Biggest Kind of Big Science: Astronomers and the Space Telescope. In: Galison, P., Hevly, B.W. (eds.) Big Science: The Growth of Large-scale Research, pp. 184–211. Stanford University Press, Stanford (1992)Google Scholar
  106. 106.
    Suber, P.: Open Access. MIT Press, Cambridge (2012)Google Scholar
  107. 107.
    Szalay, A.S.: Jim Gray, astronomer. Commun. ACM 51, 59–65 (2008)CrossRefGoogle Scholar
  108. 108.
    Thakar, A.R., Szalay, A.S., Fekete, G., Gray, J.: The catalog archive server database management system. Comput. Sci. Eng. 10(1), 30–37 (2008)Google Scholar
  109. 109.
    Traweek, S.: Beamtimes and Lifetimes: The World of High Energy Physicists. Harvard University Press, Cambridge (1988)Google Scholar
  110. 110.
    Traweek, S.: Big Science as Colonialist Discourse: Regional Differences in Japanese High Energy Physics. In: Galison, P., Hevly, Bruce William (eds.) Big Science: The Growth of Large-scale Research, pp. 100–128. Stanford University Press, Stanford (1992)Google Scholar
  111. 111.
    Traweek, S.: Border Crossings: Narrative Strategies in Science Studies and Among High Energy Physicists at Tsukuba Science City, Japan. In: Science as Practice and Culture. University of Chicago Press, Chicago, pp. 429–465 (1992)Google Scholar
  112. 112.
    Traweek, S.: Generating High Energy Physics in Japan: Moral Imperatives of a Future Pluperfect. In: Kaiser, D. (ed.) Pedagogy and Practice in Physics. MIT Press, Cambridge, MA (2004)Google Scholar
  113. 113.
    Ucla, O.I.P.: Social entrepreneurship: nexleaf takes it to the next level. Invent. Intell. Prop. News 2, 3 (2010)Google Scholar
  114. 114.
    Vermeulen, N.: Supersizing Science: On Building Large-Scale Research Projects in Biology. Universal-Publishers Boca Raton, FL (2010)Google Scholar
  115. 115.
    Wallis, J.C.: The Distribution of Data Management Responsibility within Scientific Research Groups (2012). http://search.proquest.com/docview/1029942726/abstract?accountid=14512
  116. 116.
    Wallis, J.C., Borgman, C.L.: Who is Responsible for Data? An Exploratory Study of Data Authorship, Ownership, and Responsibility. Annual Meeting of the American Society for Information Science & Technology, Information Today, pp. 1–10 (2011)Google Scholar
  117. 117.
    Wallis, J.C., Borgman, C.L., Mayernik, M.S., Pepe, A.: Moving archival practices upstream: an exploration of the life cycle of ecological sensing data in collaborative field research. Int. J. Digit. Curation 3(1), 114–126 (2008)CrossRefGoogle Scholar
  118. 118.
    Wallis, J.C., Borgman, C.L., Mayernik, M.S., Pepe, A., Ramanathan, N., Hansen, M.A.: Know Thy Sensor: Trust, Data Quality, and Data Integrity in Scientific Digital Libraries. In: Proceedings of the 11th European Conference on Research and Advanced Technology for Digital Libraries, Berlin: Springer, pp. 380–391 (2007)Google Scholar
  119. 119.
    Wallis, J.C., Rolando, E., Borgman, C.L.: If We Share Data, Will anyone use them? Data sharing and reuse in the long tail of science and technology. PLoS One 8(7), e67332 (2013)CrossRefGoogle Scholar
  120. 120.
    Wray, K.B.: Scientific authorship in the age of collaborative research. Stud. Hist. Philos. Sci. Part A 37(3), 505–514 (2006)CrossRefGoogle Scholar
  121. 121.
    Wynholds, L.A., Fearon, D.S., Borgman, C.L., Traweek, S.: When Use Cases Are Not Useful: Data Practices, Astronomy, and Digital Libraries. In: Proceedings of the 11th Annual Joint Conference on Digital Libraries, ACM, pp. 383–386 (2011)Google Scholar
  122. 122.
    Wynholds, L.A., Wallis, J.C., Borgman, C.L., Sands, A.E., Traweek, S. Data, Data Use, and Scientific Inquiry: Two Case Studies of Data Practices. In: Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, Association for Computing Machinery, pp. 19–22 (2012)Google Scholar
  123. 123.
    York, D.G., Adelman, J., Anderson, J.E., et al.: The sloan digital sky survey: technical summary. Astron. J. 120, 1579–1587 (2000)CrossRefGoogle Scholar
  124. 124.
    Zooniverse. Galaxy Zoo. 2014. http://www.galaxyzoo.org/

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Christine L. Borgman
    • 1
  • Peter T. Darch
    • 1
  • Ashley E. Sands
    • 1
  • Irene V. Pasquetto
    • 1
  • Milena S. Golshan
    • 1
  • Jillian C. Wallis
    • 1
  • Sharon Traweek
    • 1
  1. 1.Center for Knowledge Infrastructures, Department of Information StudiesUniversity of California, Los AngelesLos AngelesUSA

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