Journal of Nanoparticle Research

, Volume 8, Issue 3–4, pp 301–321 | Cite as

The structure and infrastructure of the global nanotechnology literature

  • Ronald N. Kostoff
  • Jesse A. Stump
  • Dustin Johnson
  • James S. Murday
  • Clifford G.Y. Lau
  • William M. Tolles


Text mining is the extraction of useful information from large volumes of text. A text mining analysis of the global open nanotechnology literature was performed. Records from the Science Citation Index (SCI)/Social SCI were analyzed to provide the infrastructure of the global nanotechnology literature (prolific authors/journals/institutions/countries, most cited authors/papers/journals) and the thematic structure (taxonomy) of the global nanotechnology literature, from a science perspective. Records from the Engineering Compendex (EC) were analyzed to provide a taxonomy from a technology perspective.

  • The Far Eastern countries have expanded nanotechnology publication output dramatically in the past decade.

  • The Peoples Republic of China ranks second to the USA (2004 results) in nanotechnology papers published in the SCI, and has increased its nanotechnology publication output by a factor of 21 in a decade.

  • Of the six most prolific (publications) nanotechnology countries, the three from the Western group (USA, Germany, France) have about eight percent more nanotechnology publications (for 2004) than the three from the Far Eastern group (China, Japan, South Korea).

  • While most of the high nanotechnology publication-producing countries are also high nanotechnology patent producers in the US Patent Office (as of 2003), China is a major exception. China ranks 20th as a nanotechnology patent-producing country in the US Patent Office.


nanotechnology nanoscience nanomaterials nanoparticles nanotubes nanostructures nanocomposites nanowires nanocrystals nanofabrication nanolithography quantum dots self-assembly text mining computational linguistics bibliometrics 


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  1. Bhushan B., 2004. Springer Handbook of Nanotechnology. SpringerGoogle Scholar
  2. Colton R.J. (2004). Nanoscale measurements and manipulation. Journal of Vacuum Science and Technology B 22(4):1609–1635CrossRefGoogle Scholar
  3. Davidse R.J., Van Raan A.F.J. (1997). Out of particles: impact of CERN, DESY, and SLAC research to fields other than physics. Scientometrics 40(2):171–193CrossRefGoogle Scholar
  4. Dowling A. et al., 2004. Nanoscience and Nanotechnologies: Opportunities and Uncertainties. The Royal Society and the Royal Academy of Engineering. 29 JulyGoogle Scholar
  5. Freitas R.A., 1999. Nanomedicine, Vol. 1: Basic Capabilities. Landes BioscienceGoogle Scholar
  6. Freitas R.A., 2003. Nanomedicine, Vol. 2: Biocompatibility. Landes BioscienceGoogle Scholar
  7. Garfield E. (1985). History of citation indexes for chemistry - a brief review. JCICS 25(3):170–174Google Scholar
  8. Goddard W.A., D.W. Brenner, S.E. Lyshevski & G.J. Iafrate, 2002. Handbook of Nanoscience, Engineering, and Technology. CRC PressGoogle Scholar
  9. Goldman J.A., Chu W.W., Parker D.S., Goldman R.M. (1999). Term domain distribution analysis: a data mining tool for text databases. Methods of Information in Medicine 38: 96–101Google Scholar
  10. Gordon M.D., Dumais S. (1998). Using latent semantic indexing for literature based discovery. Journal of the American Society for Information Science 49(8):674–685Google Scholar
  11. Greengrass E., 1997. Information Retrieval: An Overview. National Security Agency. TR-R52–02–96Google Scholar
  12. Hearst M.A., 1999. Untangling text data mining. Proceedings of ACL 99, the 37th Annual Meeting of the Association for Computational Linguistics, University of Maryland, June 20–26Google Scholar
  13. Huang Z., Chen H., Chen Z.K., and Roco M.C. (2004). International Nanotechnology Development in 2003; Country, Institution, and Technology Field Analysis based on USPTO Patent Database. Journal of Nanoparticle Research 6:325–354CrossRefGoogle Scholar
  14. Karypis G., 2005. CLUTO – A Clustering Toolkit.∼ ∼cluto
  15. Kostoff R.N. (1998). The use and misuse of citation analysis in research evaluation. Scientometrics 43(1):27–43CrossRefGoogle Scholar
  16. Kostoff R.N. (2003a). Text mining for global technology watch. In: Drake M. (ed) Encyclopedia of Library and Information Science, Second Edition. Marcel Dekker, Inc., New York, NY, Vol. 4. pp. 2789–2799Google Scholar
  17. Kostoff R.N., 2003b Stimulating innovation. In: Larisa V. Shavinina (ed.). International Handbook of Innovation. Elsevier Social and Behavioral Sciences, Oxford, U.K., pp. 388–400Google Scholar
  18. Kostoff R.N. (2003c). Bilateral asymmetry prediction. Medical Hypotheses 61(2): 265–266CrossRefGoogle Scholar
  19. Kostoff R.N. (2005a). Systematic acceleration of radical discovery and innovation in science and technology. DTIC Technical Report Number ADA430720 ( Defense Technical Information Center, Fort Belvoir, VAGoogle Scholar
  20. Kostoff R.N., Del Rio J.A., García E.O., Ramírez A.M., Humenik J.A. (2001). Citation mining: integrating text mining and bibliometrics for research user profiling. Journal of the American Society for Information Science and Technology 52(13):1148–1156CrossRefGoogle Scholar
  21. Kostoff R.N., Eberhart H.J., Toothman D.R. (1997). Database Tomography for information retrieval. Journal of Information Science 23(4):301–311CrossRefGoogle Scholar
  22. Kostoff R.N., Green K.A., Toothman D.R. and Humenik J.A. (2000). Database Tomography applied to an aircraft science and technology investment strategy. Journal of Aircraft 37(4):727–730CrossRefGoogle Scholar
  23. Kostoff R.N., J.S. Murday, C.G.Y. Lau & W.M. Tolles, 2005a. The Seminal Literature of Nanotechnology Research. DTIC Technical Report Number ADA435986 ( Defense Technical Information Center. Fort Belvoir, VA. Also, an abridged version is published in this issue
  24. Kostoff R.N., Shlesinger M., Malpohl G. (2004b). Fractals roadmaps using bibliometrics and database tomography. Fractals 12(1): 1–16Google Scholar
  25. Kostoff R.N., Shlesinger M., Tshiteya R. (2004a). Nonlinear dynamics roadmaps using bibliometrics and Database Tomography. International Journal of Bifurcation and Chaos 14(1):61–92CrossRefGoogle Scholar
  26. Kostoff R.N., Stump J.A., Johnson D., Murday J.S., Lau C.G.Y., Tolles WM. (2005e). The structure and infrastructure of the global nanotechnology literature. DTIC Technical Report Number ADA435984 ( Defense Technical Information Center, Fort Belvoir, VAGoogle Scholar
  27. Kricka L.J. and Fortina P. (2002). Nanotechnology and applications: An all-language literature survey including books and patents. Clinical Chemistry 48(4):662–665PubMedGoogle Scholar
  28. Losiewicz P., Oard D., Kostoff R.N. (2000). Textual data mining to support science and technology management. Journal of Intelligent Information Systems 15: 99–119CrossRefGoogle Scholar
  29. MacRoberts M., MacRoberts B. (1996). Problems of citation analysis. Scientometrics 36(3):435–444CrossRefGoogle Scholar
  30. Narin F., 1976. Evaluative bibliometrics: the use of publication and citation analysis in the evaluation of scientific activity (monograph). NSF C-637. National Science Foundation. Contract NSF C-627. NTIS Accession No. PB252339/ASGoogle Scholar
  31. Narin F., Olivastro D., Stevens K.A. (1994). Bibliometrics theory, practice and problems. Evaluation Review 18(1):65–76Google Scholar
  32. Schubert A., Glanzel W., Braun T. (1987). Subject field characteristic citation scores and scales for assessing research performance. Scientometrics 12(5–6):267–291CrossRefGoogle Scholar
  33. SCI (2005) Science Citation Index. Institute for Scientific Information, Phila., PAGoogle Scholar
  34. Simon J. (2005). Micro- and nanotechnologies: dullish electrons and smart molecules. Comptes Rendus Chimie 8(5):893–902CrossRefGoogle Scholar
  35. Swanson D.R. (1986) Fish Oil, Raynauds Syndrome, and undiscovered public knowledge. Perspect Biol Med. 30(1):7–18PubMedGoogle Scholar
  36. Swanson D.R., Smalheiser N.R. (1997). An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif Intell 91(2):183–203CrossRefGoogle Scholar
  37. TREC (Text Retrieval Conference), 2004. Home Page,
  38. Viator J.A., Pestorius F.M. (2001). Investigating trends in acoustics research from 1970–1999. Journal of the Acoustical Society of America 109(5):1779–1783 Part 1CrossRefPubMedGoogle Scholar
  39. Winkmann G., Schlutius S., Schweim H.G. (2002). Citation rates of medical German-language journals in English-language papers - do they correlate with the Impact Factor, and who cites?. Klinische Monatsblatter fur Augenheilkunde 219(1–2):72–78CrossRefGoogle Scholar
  40. Zhao Y., Karypis G. (2004). Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning 55(3): 311–331CrossRefGoogle Scholar
  41. Zhu D.H. & A.L. Porter, 2002. Automated extraction and visualization of information for technological intelligence and forecasting. Technological Forecasting and Social Change. 69 (5):495–506.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Ronald N. Kostoff
    • 1
  • Jesse A. Stump
    • 1
  • Dustin Johnson
    • 1
    • 3
  • James S. Murday
    • 1
    • 4
  • Clifford G.Y. Lau
    • 1
    • 5
  • William M. Tolles
    • 2
  1. 1.Office of Naval ResearchArlingtonUSA
  2. 2.AlexandriaUSA
  3. 3.Northrop Grumman TASCFairfaxUSA
  4. 4.Chemistry Division, Code 6100Naval Research LaboratoryWashingtonUSA
  5. 5.Institute for Defense AnalysesAlexandriaUSA

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