SEARCH

SEARCH BY CITATION

References

  • Ackerman, M.S. (1994). Definitional and contextual issues in organizational and group memories, available from the author's website, http://www.ics.uci.edu/∼ackerman.
  • Arthur, D. J. and Stevens, K. T. (1989). Assessing the Adequacy of Documentation Through Document Quality Indicators*. In Proceedings of the Conference on Software maintenance, Miami, 16–19. Oct. Washington D.C.: IEEE Computer Society Press (pp. 4049)
  • Arzberger, P., Schroeder, P., Beaulieu, A., Bowker, G., Casey, K., Laaksonen, L., Moorman, D., Uhlir, P., Wouters, P. (2004). Promoting Access to Public Research Data for Scientific, Economic, and Social Development. Data science Journal, vol. 3, 29 November 2004. 135152.
  • Atkins, D. E., Droegemeier, K. K., Feldman, S. I., Garcia-Molina, H., Klein, M. L., Messina, P., Messerschmitt, D. G., Ostriker, J. P., and Wright, M. H. (2003). Revolutionalizing science and engineering through cyber-infrastructure: Report of the National Science Foundation Blue-Ribbon Panel on Cyberinfrastructure. National Science Foundation. http://www.nsf.gov/cise/sci/reports/atkins.pdf (accessed September 18, 2006)
  • Birnholtz, J. and Bietz, M. (2000). Data at Work: Supporting Sharing in Science and Engineering. ACM conference.
  • Blommaert, Jan (1997). Workshopping: Notes on Professional Vision in Discourse Analysis. Wilrijk: Antwerp Papers in Linguistics 91.
  • Borgman, C.L., Wallis, J.C., Enyedy, N. (forthcoming). Little science confronts the data deluge: Habitat ecology, embedded sensor networks, and digital libraries. International Journal on Digital Libraries.
  • Borgman, C. L. (2007). Scholarship in the Digital Age: Information, Infrastructure, and the Internet. Cambridge, MA: MIT Press.
  • Boruch, Robert F. (1978, Ed.). Secondary analysis. New Directions for program Evaluation, no. 4. San Francisco: Jossey-Bass.
  • Boruch, R. F., et al. (1991). Sharing Confidential and sensitive data. in Siber, J.E. (Ed.), Sharing social science data: advantages and challenges. SAGE publications.
  • Bowering, D. J. (1984, Ed.). Secondary analysis of available data bases. San Francisco: Jossey-Bass.
  • Breusch, T. and Holloway, S. (2004). Australian social science data archive. The Australian economic review, vol. 37, no. 2, pp. 2229.
  • Carlson, S., and Anderson, B. (2007). What are data? The many kinds of data and their implications for data re-use. Journal of Computer-Mediated Communication, 12 (2), article 15. http://jcmc.indiana.edu/vol12/issue2/carlson.html
  • Clubb, M. J., Erik, W. A., Geda L. C., and Traugott, W. M. Sharing research data in the social sciences. in Fienberg, S. E., Martin, M. E., & Straf, M. L. (Eds.). (1985). Sharing research data. Washington, DC: National Academy Press.
  • Corti, L. (2000, December). Progress and Problems of Preserving and Providing Access to Qualitative Data for Social Research – The International Picture of an Emerging Culture. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research [Online Journal], 1 (3).
  • Corti, L. (2002). Qualilitative data processing guidelines. Qualidata, UK Data Archive, University of Essex, Colchester.
  • Corti, L. (2005) Qualitative Archiving and Data Sharing: Extending the reach and impact of qualitative data, IASSIST Quarterly, 29 (3).
  • Corti, L. & Bishop, L. (2005, February). Strategies in Teaching Secondary Analysis of Qualitative Data [67 paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research [On-line Journal], 6 (1), Art. 47. Available at: http://www.qualitative-research.net/fqs-texte/1-05/05-1-47-e.htm [Date of Access: Jan. 14, 2008].
  • Council on governmental relations. (2006). Access to and retention of research data: rights and responsibilities. http://206.151.87.67/docs/CompleteDRBooklet.htm [Data of Access: Jan. 14, 2008].
  • Dale, A., Arber, S. and Procter, M. (1988), Doing Secondary analysis. London, Unwin Hyman.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319340.
  • Fienberg, S. E., Martin, M. E., & Straf, M. L. (Eds.). (1985). Sharing research data. Washington, DC: National Academy Press.
  • Gutmann, M., K. Schürer, D. Donakowski and Hilary Beedham The selection, appraisal, and retention of digital social science data. Data Science Journal, Volume 3, 30 December 2004.
  • Halstuk, M. E. and Chamberlin, B. F. (2006). The Freedom of Information Act 1966–2006: A retrospective on the rise of privacy protection over the public interest in knowing what the government's up to. Communication law and policy [1081-1680] vol: 11 iss: 4 pg: 511564
  • Hyman, H. H. (1987). Secondary analysis of sample surveys, with a new introduction. Wesleyan University Press. Middlettown, Connecticut.
  • ICPSR (Inter-university Consortium for political and social research). (2005). Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle. http://www.icpsr.umich.edu/access/dataprep.pdf
  • Jacobs, J. A. and Humphrey, C. (2004) Preserving research data. Communications of the ACM. vol. 47, 9: 2729.
  • Lesk, M. (2004). Online Data and Scientific Progress: Content in Cyberinfrastructure. Presentation given as part of the UK Digital Curation Centre's Visitor Programme. Edinburgh: 24 September, 2004. [Available: http://www.dcc.ac.uk/docs/bl-sep04a.ppt.]
  • Markus, M. L. (2001) Toward a theory of knowledge reuse: type of knowledge reuse situations and factors in reuse success. Journal of Management Information Systems. 18 (1), 5793.
  • McCall, R. B. and Applebaum, M. I. (1991). Some issues of conducting secondary analysis. Developmental psychology. vol. 27, No. 6, 911917.
  • Moran, T. P., Chiu, P., Harrison, S., Kurtenbach, G., Minneman, S.; and Melle, W.V. (1996). Evolutionary engagement in an ongoing collaborative work process: A case study. In Proceedings of the ACM 1996 Conference on Computer-Supported Cooperative Work, Cambridge, MA, 150159.
  • Morkes, J. and Nielsen, J. (1998). Applying Writing Guidelines to Web Pages. Sun Microsystems. Internet. <http://www.useit.com/papers/webwriting/rewriting.html>
  • National Research Council (U.S.). (2003). Sharing publication-related data and materials: responsibilities of authorship in the life sciences, Washington, D.C. : National Academies Press.
  • National Institutes of Health (2003). Data Sharing Policy and Implementation Guidance. Available: http://grants2.nih.gov/grants/policy/data_sharing/data_sharing_guidance.htm.
  • Niu, J. and Hedstrom, M. (2007). Incentives and barriers in data sharing —- a survey report. Working paper.
  • Orlikowski, W.J. (1995). Evolving with Notes: Organizational change around groupware technology, CISR WP No. 279, Sloan WP No. 3823, CCS WP No. 186, Center for Information System Research, Sloan School of Management, MIT.
  • Sieber, J. E. (1991). Sharing social science data: advantages and challenges. Newbury Park, Calif: Sage Publications.
  • Strathern, M. (2005, March). Useful knowledge. Lecture presented at The Isaiah Berlin Lecture, Manchester, UK.
  • Tsakonas, G. and Papatheodorou, C. (2006). Analyzing and evaluating usefulness and usability in electronic information services. Journal of Information Science. 2006; 32, 400.
  • Van den Berg, H. (2005, January). Reanalyzing Qualitative Interviews From Different Angles: The Risk of Decontextualization and Other Problems of Sharing Qualitative Data [48 paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research [On-line Journal], 6 (1), Art. 30. Available at: http://www.qualitative-research.net/fqs-texte/1-05/05-1-30-e.htm [Date of Access: Jan. 14, 2008].
  • Wilson, R. E. and Maxwell, C. D. (2006, Oct) NIJ's Data Resources Program and the NACJD Paper presented at the annual meeting of the American Society of Criminology (ASC), Los Angeles Convention Center, Los Angeles, CA 2006-10-05 from http://www.allacademic.com/meta/p143510_index.html
  • Zimmerman, A. (2003). Data Sharing and Secondary Use of Scientific Data: Experiences of Ecologists. Unpublished Dissertation, Information and Library Studies, University of Michigan, Ann Arbor. i Indirect identifiers are variables that will not reveal identify of human subjects when used alone, but may do so when used in combination with other variables. For example, ZIP code may not be troublesome in the univariate case, but when combined with other attributes like race and annual income, a ZIP code may allow unique individuals (extremely wealthy, poor) residents of that ZIP code to become visible. If protecting confidentiality of human subjects diminishes ithe value of data for secondary analysis, data depositors could apply for restricted use.