Scientometrics of big science: a case study of research in the Sloan Digital Sky Survey
Large-scale scientific projects have become a major impetus of scientific advances. But few studies have specifically analyzed how those projects bolster scientific research. We address this question from a scientometrics perspective. By analyzing the bibliographic records of papers relevant to the Sloan Digital Sky Survey (SDSS), we found that the SDSS helped scientists from many countries further develop their own research; investigators initially formed large research groups to tackle key problems, while later papers involved fewer authors; and the number of research topics increased but the diversity of topics remains stable. Furthermore, the entropy analysis method has proven valuable in terms of analyzing patterns of research topics at a macroscopic level.
KeywordsBibliometric Entropy analysis Publication analysis Sloan Digital Sky Survey Large-scale scientific project
Special thanks go to the two reviewers, Helmut Abt and Virginia Trimble, who offered many great suggestions and insights to this study. This study is supported by the NSF under grant # IIS-0612129. Thanks also to Thomson ISI for providing the bibliometric data. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web Site is http://www.sdss.org/. The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington.
- Gray, J., Liu, D. T., Santisteban, M. N., Szalay, A. S., DeWitt, D., & Heber, G. (2005). Scientific data management in the coming decade. Microsoft Research Technical Report MSR-TR-2005-10. Redmond, WA. http://research.microsoft.com/~Gray/. Accessed 27 May 2007.
- Schilling, G. (2001). The virtual observatory moves closer to reality. Science, 289(5477), 238, July 14.Google Scholar
- Schmidt, M., Glaser, J., Havemann, F., & Hewz, M. (2006). A methodological study for measuring the diversity of science. In Proceedings International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting (pp. 129–137), Nancy, France.Google Scholar
- Szalay, A. S. (2001). The national virtual observatory. In F. R. Harnden, F. A. Primini, Jr., & H. E. Payne (Eds.), Astronomical Data Analysis Software and Systems X ASP Conference Series, 238, pp. 3–12.Google Scholar
- Szalay, A. S., Kunszt, P., Thakar, A., Gray, J., Slutz, D., & Bruner, R. J. (2000). Designing and mining multi-terabyte astronomy archives: The Sloan Digital Sky Survey. In Proceedings of the ACM SIGMOD, Austin, TX, May 2000.Google Scholar
- Toutanova, K., Klein, D., Manning, C. D., & Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT-NAACL) (pp. 252–259), Edmonton, Canada, May 2003.Google Scholar
- Toutanova, K., & Manning, C. D. (2000). Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000) (pp. 63–70), Hong Kong, China, October 2000.Google Scholar