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An Introduction to the Dataverse Network as an Infrastructure for Data Sharing

Author

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  • Gary King

    (Harvard University, Cambridge, Massachusetts, king@harvard.edu.)

Abstract

The author introduces a set of integrated developments in Web application software, networking, data citation standards, and statistical methods designed to increase scholarly recognition for data contributions; to put some of the universe of data and data-sharing practices on firmer ground; and to facilitate the public distribution of persistent, authorized, and verifiable data, with powerful and easy-to-use technology, even when the data are confidential or proprietary. The goal is to solve some of the political and sociological problems of data sharing via technological means, with the result intended to benefit both the scientific community and the sometimes apparently contradictory goals of individual researchers.

Suggested Citation

  • Gary King, 2007. "An Introduction to the Dataverse Network as an Infrastructure for Data Sharing," Sociological Methods & Research, , vol. 36(2), pages 173-199, November.
    • Handle: RePEc:sae:somere:v:36:y:2007:i:2:p:173-199
    DOI: 10.1177/0049124107306660
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    References listed on IDEAS

    as
    1. Richard G. Anderson & William H. Greene & Bruce D. McCullough & Hrishikesh D. Vinod, 2005. "The role of data & program code archives in the future of economic research," Working Papers 2005-014, Federal Reserve Bank of St. Louis.
    2. Dewald, William G & Thursby, Jerry G & Anderson, Richard G, 1986. "Replication in Empirical Economics: The Journal of Money, Credit and Banking Project," American Economic Review, American Economic Association, vol. 76(4), pages 587-603, September.
    3. Arthur M. Diamond Jr., 1986. "What is a Citation Worth?," Journal of Human Resources, University of Wisconsin Press, vol. 21(2), pages 200-215.
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    Cited by:

    1. Dobromir Rahnev & Kobe Desender & Alan L. F. Lee & William T. Adler & David Aguilar-Lleyda & Başak Akdoğan & Polina Arbuzova & Lauren Y. Atlas & Fuat Balcı & Ji Won Bang & Indrit Bègue & Damian P. Bir, 2020. "The Confidence Database," Nature Human Behaviour, Nature, vol. 4(3), pages 317-325, March.
    2. Ana Trisovic & Katherine Mika & Ceilyn Boyd & Sebastian Feger & Mercè Crosas, 2021. "Repository Approaches to Improving the Quality of Shared Data and Code," Data, MDPI, vol. 6(2), pages 1-12, February.
    3. Elizabeth O. Ofili & Daniel Sarpong & Richard Yanagihara & Paul B. Tchounwou & Emma Fernández-Repollet & Mohamad Malouhi & Muhammed Y. Idris & Kimberly Lawson & Nadine H. Spring & Brian M. Rivers, 2021. "The Research Centers in Minority Institutions (RCMI) Consortium: A Blueprint for Inclusive Excellence," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    4. Freire, Danilo, 2021. "How to Improve Data Validation in Five Steps," Working Papers 11017, George Mason University, Mercatus Center.
    5. Glenn Firebaugh, 2007. "Replication Data Sets and Favored-Hypothesis Bias," Sociological Methods & Research, , vol. 36(2), pages 200-209, November.
    6. Mercè Crosas & Gary King & James Honaker & Latanya Sweeney, 2015. "Automating Open Science for Big Data," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 260-273, May.

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