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Communitywide Database Designs for Tracking Innovation Impact: Comets, Stars and Nanobank

Author

Listed:
  • Lynne G. Zucker
  • Michael R. Darby
  • Jason Fong

Abstract

Data availability is arguably the greatest impediment to advancing the science of science and innovation policy and practice (SciSIPP). This paper describes the contents, methodology and use of the public online COMETS (Connecting Outcome Measures in Entrepreneurship Technology and Science) database spanning all sciences, technologies, and high-tech industries; its parent COMETSandSTARS database which adds more data at organization and individual scientist-inventor-entrepreneur level restricted by vendor licenses to onsite use at NBER and/or UCLA; and their prototype Nanobank covering only nano-scale sciences and technologies. Some or all of these databases include or will include: US patents (granted and applications); NIH, NSF, SBIR, STTR Grants; Thomson Reuters Web of Knowledge; ISI Highly Cited; US doctoral dissertations; IPEDS/HEGIS universities; all firms and other organizations which ever publish in ISI listed journals beginning in 1981, are assigned US patents (from 1975), or are listed on a covered grant; additional nanotechnology firms based on web search. Ticker/CUSIP codes enable linking public firms to the major databases covering them. A major matching/disambiguation effort assigns unique identifiers for an organization or individual so that their appearances are linked within and across the constituent legacy databases. Extensive geographic coding enables analysis at country, region, state, county, or city levels. The databases provide very flexible sources of data for serious research on many issues in the study of organizations in innovation systems in the development and spread of knowledge, and the economics of science. Enabling the study of these topics, among others, COMETS contributes substantially to the science of science and technology.

Suggested Citation

  • Lynne G. Zucker & Michael R. Darby & Jason Fong, 2014. "Communitywide Database Designs for Tracking Innovation Impact: Comets, Stars and Nanobank," Annals of Economics and Statistics, GENES, issue 115-116, pages 277-311.
    • Handle: RePEc:adr:anecst:y:2014:i:115-116:p:277-311
    DOI: 10.15609/annaeconstat2009.115-116.277
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    2. Enrico Moretti & Daniel J. Wilson, 2017. "The Effect of State Taxes on the Geographical Location of Top Earners: Evidence from Star Scientists," American Economic Review, American Economic Association, vol. 107(7), pages 1858-1903, July.
    3. Leila Tahmooresnejad & Catherine Beaudry & Andrea Schiffauerova, 2015. "The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 753-787, January.
    4. Jade Yu-Chieh Lo & Mark Thomas Kennedy, 2015. "Approval in Nanotechnology Patents: Micro and Macro Factors That Affect Reactions to Category Blending," Organization Science, INFORMS, vol. 26(1), pages 119-139, February.
    5. Enrico Moretti, 2021. "The Effect of High-Tech Clusters on the Productivity of Top Inventors," American Economic Review, American Economic Association, vol. 111(10), pages 3328-3375, October.
    6. Ventura, Samuel L. & Nugent, Rebecca & Fuchs, Erica R.H., 2015. "Seeing the non-stars: (Some) sources of bias in past disambiguation approaches and a new public tool leveraging labeled records," Research Policy, Elsevier, vol. 44(9), pages 1672-1701.
    7. Yizhou Zhang & Geoffrey J. D. Hewings, 2019. "Nonlinear tax-induced migration: an overlooked tale," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 425-438, June.
    8. Leila Tahmooresnejad & Catherine Beaudry, 2019. "Collaboration or funding: lessons from a study of nanotechnology patenting in Canada and the United States," The Journal of Technology Transfer, Springer, vol. 44(3), pages 741-777, June.
    9. Munari, Federico & Toschi, Laura, 2014. "Running ahead in the nanotechnology gold rush. Strategic patenting in emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 194-207.

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • J44 - Labor and Demographic Economics - - Particular Labor Markets - - - Professional Labor Markets and Occupations
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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