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Scientometrics

, Volume 97, Issue 2, pp 383–395 | Cite as

Assessing non-standard article impact using F1000 labels

  • Ehsan Mohammadi
  • Mike Thelwall
Article
First Online:

Abstract

Faculty of 1000 (F1000) is a post-publishing peer review web site where experts evaluate and rate biomedical publications. F1000 reviewers also assign labels to each paper from a standard list or article types. This research examines the relationship between article types, citation counts and F1000 article factors (FFa). For this purpose, a random sample of F1000 medical articles from the years 2007 and 2008 were studied. In seven out of the nine cases, there were no significant differences between the article types in terms of citation counts and FFa scores. Nevertheless, citation counts and FFa scores were significantly different for two article types: “New finding” and “Changes clinical practice”: FFa scores value the appropriateness of medical research for clinical practice and “New finding” articles are more highly cited. It seems that highlighting key features of medical articles alongside ratings by Faculty members of F1000 could help to reveal the hidden value of some medical papers.

Keywords

Faculty of F1000 Altmetrics Beyond impact Research assessment Post-publishing peer review 
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References

  1. Aksnes, D. W., & Taxt, R. E. (2004). Peer reviews and bibliometric indicators: A comparative study at a Norwegian university. Research Evaluation, 13(1), 33–41.CrossRefGoogle Scholar
  2. Allen, L., Jones, C., Dolby, K., Lynn, D., & Walport, M. (2009). Looking for landmarks: the role of expert review and bibliometric analysis in evaluating scientific publication outputs. PLoS ONE, 4(6), e5910.CrossRefGoogle Scholar
  3. Archambault, E., Campbell, D., Gingras, Y., & Lariviere, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology, 60(7), 1320–1326.CrossRefGoogle Scholar
  4. Banzi, R., Moja, L., Pistotti, V., Facchini, A., & Liberati, A. (2011). Conceptual frameworks and empirical approaches used to assess the impact of health research: An overview of reviews. Health research policy and systems/BioMed Central, 9, 26. doi: 10.1186/1478-4505-9-26.CrossRefGoogle Scholar
  5. Bornmann, L., & Daniel, H.-D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.CrossRefGoogle Scholar
  6. Bornmann, L., & Leydesdorff, L. (2012). The validation of (advanced) bibliometric indicators through peer assessments: A comparative study using data from InCites and F1000. Digital Libraries; Applications. http://arxiv.org/abs/1211.1154.
  7. Camacho-Miñano, M–. M., & Núñez-Nickel, Manuel. (2009). The multilayered nature of reference selection. Journal of the American Society for Information Science and Technology, 60(4), 754–777. doi: 10.1002/asi.21018.CrossRefGoogle Scholar
  8. Chalmers, I., & Glasziou, P. (2009). Avoidable waste in the production and reporting of research evidence. Lancet, 374(9683), 86–89. doi: 10.1016/S0140-6736(09)60329-9.CrossRefGoogle Scholar
  9. Cole, S., Cole, J. R., & Simon, G. A. (1981). Chance and consensus in peer review. Science, 214(4523), 881–886.CrossRefGoogle Scholar
  10. Cronin, B. (1984). The citation process. The role and significance of citations in scientific communication. London: Taylor Graham.Google Scholar
  11. F1000. (2012a). About F1000. http://f1000.com/prime/about/whatis.
  12. F1000. (2012b). F1000 Faculty. http://f1000.com/prime/thefaculty.
  13. Falagas, M. E., Kouranos, V. D., Arencibia-Jorge, R., & Karageorgopoulos, D. E. (2008). Comparison of SCImago journal rank indicator with journal impact factor. FASEB journal: official publication of the Federation of American Societies for Experimental Biology, 22(8), 2623–2628. doi: 10.1096/fj.08-107938.CrossRefGoogle Scholar
  14. Field, A. (2009). Discovering statistics using SPSS. Thousand Oaks: SAGE.Google Scholar
  15. Fienberg, S. E., & Martin, M. E. (1985). Sharing research data. Washington: Natl Academy.Google Scholar
  16. Franceschet, M., & Costantini, A. (2011). The first Italian research assessment exercise: A bibliometric perspective. Journal of Informetrics, 5(2), 275–291.CrossRefGoogle Scholar
  17. Hanney, S., Frame, I., Grant, J., Buxton, M., Young, T., & Lewison, G. (2005). Using categorisations of citations when assessing the outcomes from health research. Scientometrics, 65(3), 357–379. doi: 10.1007/s11192-005-0279-y.CrossRefGoogle Scholar
  18. Harnad, S. (1985). Rational disagreement in peer review. Science, Technology and Human Values, 10(3), 55–62.MathSciNetCrossRefGoogle Scholar
  19. Huggett, S. (2012). F1000 Journal Rankings: An alternative way to evaluate the scientific impact of scholarly communications. Research Trends, 26, 7–11.Google Scholar
  20. Jones, T. H., Donovan, C., & Hanney, S. (2012). Tracing the wider impacts of biomedical research: A literature search to develop a novel citation categorisation technique. Scientometrics, 93(1), 125–134.CrossRefGoogle Scholar
  21. Koenig, M. E. D. (1982). Determinants of expert judgement of research performance. Scientometrics, 4(5), 361–378. doi: 10.1007/BF02135122.CrossRefGoogle Scholar
  22. Kostoff, R. N. (1998). The use and misuse of citation analysis in research evaluation. Scientometrics, 43(1), 27–43. doi: 10.1007/BF02458392.CrossRefGoogle Scholar
  23. Kostoff, R. N. (2007). The difference between highly and poorly cited medical articles in the journal Lancet. Scientometrics, 72(3), 513–520. doi: 10.1007/s11192-007-1573-7.CrossRefGoogle Scholar
  24. Kousha, K., & Thelwall, M. (2008). Assessing the impact of disciplinary research on teaching: An automatic analysis of online syllabuses. Journal of the American Society for Information Science and Technology, 59(13), 2060–2069.CrossRefGoogle Scholar
  25. Kousha, K., Thelwall, M., & Rezaie, S. (2011). Assessing the citation impact of books: The role of Google Books, Google Scholar, and Scopus. Journal of the American Society for Information Science and Technology, 62(11), 2147–2164. doi: 10.1002/asi.21608.CrossRefGoogle Scholar
  26. Kuruvilla, S., Mays, N., Pleasant, A., & Walt, G. (2006). Describing the impact of health research: A Research Impact Framework. BMC Health Services Research, 6(1), 134.CrossRefGoogle Scholar
  27. Lewison, G. (2005). Citations to papers from other documents. Handbook of Quantitative Science and Technology. http://www.springerlink.com/index/T2H0245570526217.pdf.
  28. Lewison, T., & Sullivan, R. (2008). How do the media report cancer research? A study of the UK’s BBC website. British Journal of Cancer, 99(4), 569–576. doi: 10.1038/sj.bjc.6604531.CrossRefGoogle Scholar
  29. Lewison, G., & Sullivan, R. (2008). The impact of cancer research: how publications influence UK cancer clinical guidelines. British Journal of Cancer, 98(12), 1944–1950. doi: 10.1038/sj.bjc.6604405.CrossRefGoogle Scholar
  30. Li, & Thelwall, M. (2012). F1000, Mendeley and traditional bibliometric indicators. 17th International Conference on Science and Technology Indicators (Vol. 3, pp. 1–11).Google Scholar
  31. MacRoberts, M. H., & MacRoberts, B. R. (1996). Problems of citation analysis. Scientometrics, 36(3), 435–444.CrossRefGoogle Scholar
  32. MacRoberts, M. H., & MacRoberts, B. R. (2010). Problems of citation analysis: A study of uncited and seldom-cited influences. Journal of the American Society for Information Science and Technology, 61(1), 1–12.CrossRefGoogle Scholar
  33. Mahdi, S., D’Este, P., & Neely, A. D. (2008). Citation counts: Are they good predictors of RAE scores?: A bibliometric analysis of RAE 2001. London: AIM Research.Google Scholar
  34. Maier, G. (2006). Impact factors and peer judgment: The case of regional science journals. Scientometrics, 69(3), 651–667.CrossRefGoogle Scholar
  35. Moed, H. F. (2005). Citation analysis in research evaluation (Vol. 9). Norwell: Kluwer Academic.Google Scholar
  36. Nederhof, A. J., & Van Raan, A. F. J. (1993). A bibliometric analysis of six economics research groups: A comparison with peer review. Research Policy, 22(4), 353–368.CrossRefGoogle Scholar
  37. Niederkrotenthaler, T., Dorner, T. E., & Maier, M. (2011). Development of a practical tool to measure the impact of publications on the society based on focus group discussions with scientists. BMC Public Health, 11, 588. doi: 10.1186/1471-2458-11-588.CrossRefGoogle Scholar
  38. Norris, M., & Oppenheim, C. (2003). Citation counts and the Research Assessment Exercise V: Archaeology and the 2001 RAE. Journal of Documentation, 59(6), 709–730.CrossRefGoogle Scholar
  39. Oppenheim, C. (1995). The correlation between citation counts and the 1992 Research Assessment Exercise Ratings for British library and information science university departments. Journal of Documentation, 51(1), 18–27.MathSciNetCrossRefGoogle Scholar
  40. Oppenheim, C., & Summers, M. A. C. (2008). Citation counts and the Research Assessment Exercise, part VI: Unit of assessment 67 (music). Information Research, 13(2), 3.Google Scholar
  41. Opthof, T., & Leydesdorff, L. (2011). A comment to the paper by Waltman et al., Scientometrics, 87, 467–481, 2011. Scientometrics, 88(3), 1011–1016.CrossRefGoogle Scholar
  42. Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007). Sharing detailed research data is associated with increased citation rate. PLoS ONE, 2(3), e308. doi: 10.1371/journal.pone.0000308.CrossRefGoogle Scholar
  43. Price, & Simon, (2009). Patient education and the impact of new medical research. Journal of Health Economics, 28(6), 1166–1174. doi: 10.1016/j.jhealeco.2009.08.005.CrossRefGoogle Scholar
  44. Priem, & Hemminger, B. M. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First Monday, 15(7), http://frodo.lib.uic.edu/ojsjournals/index.php/fm/. Retrieved from http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2874.
  45. Priem, Taraborelli, D., Groth, P., & Neylon, C. (2011). Altmetrics: A manifesto. http://altmetrics.org/manifesto.
  46. Reale, E., Barbara, A., & Costantini, A. (2007). Peer review for the evaluation of academic research: lessons from the Italian experience. Research Evaluation, 16(3), 216–228.CrossRefGoogle Scholar
  47. Sarli, C. C., Dubinsky, E. K., & Holmes, K. L. (2010). Beyond citation analysis: A model for assessment of research impact. Journal of the Medical Library Association: JMLA, 98(1), 17–23.CrossRefGoogle Scholar
  48. Sarli, C. C., & Holmes, K. L. (2012). The becker medical library model for assessment of research impact. St Louis: Bernard Becker Medical Library, Washington University School of Medicine.Google Scholar
  49. Seglen, P. O. (1997). Citations and journal impact factors: questionable indicators of research quality. Allergy, 52(11), 1050–1056.CrossRefGoogle Scholar
  50. Seng, L. B., & Willett, P. (1995). The citedness of publications by United Kingdom library schools. Journal of Information Science, 21(1), 68–71.CrossRefGoogle Scholar
  51. Small, H. (2004). On the shoulders of Robert Merton: Towards a normative theory of citation. Scientometrics, 60(1), 71–79. http://www.springerlink.com/index/X6VTVM1209131570.pdf.Google Scholar
  52. Smith, A. T., & Eysenck, M. (2002). The correlation between RAE ratings and citation counts in psychology. London.Google Scholar
  53. Stern, R. E. (1990). Uncitedness in the biomedical literature. Journal of the American society for information science, 41(3), 193–196.CrossRefGoogle Scholar
  54. Tomlinson, S. (2000). The research assessment exercise and medical research. British Medical Journal, 320(7235), 636–639.CrossRefGoogle Scholar
  55. Van Raan, A. F. J. (2006). Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups. Scientometrics, 67(3), 491–502.Google Scholar
  56. Vaughan, L., & Shaw, D. (2005). Web citation data for impact assessment: A comparison of four science disciplines. Journal of the American Society for Information Science and Technology, 56(10), 1075–1087.CrossRefGoogle Scholar
  57. Waltman, L., van Eck, N. J., van Leeuwen, T. N., Visser, M. S., & van Raan, A. F. J. (2011). On the correlation between bibliometric indicators and peer review: Reply to Opthof and Leydesdorff. Scientometrics, 3, 1017–1022.CrossRefGoogle Scholar
  58. Wardle, D. A. (2010). Do’Faculty of 1000′(F1000) ratings of ecological publications serve as reasonable predictors of their future impact? Ideas in Ecology and Evolution, 3, 11–15.Google Scholar
  59. Weiss, A. P. (2007). Measuring the impact of medical research: moving from outputs to outcomes. American Journal of Psychiatry, 164(2), 206.CrossRefGoogle Scholar
  60. Wets, K., Weedon, D., & Velterop, J. (2003). Post-publication filtering and evaluation: Faculty of 1000. Learned Publishing, 16(4), 249–258. doi: 10.1087/095315103322421982.CrossRefGoogle Scholar
  61. Zaman, M. uz, & Britain, G. (2004). Review of the academic evidence on the relationship between teaching and research in higher education. https://www.education.gov.uk/publications/eOrderingDownload/RR506.pdf.
  62. Zuccala, A. (2010). The mathematical review system: does reviewer status play a role in the citation process? Scientometrics, 84(1), 221–235.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2013

Authors and Affiliations

  1. 1.Statistical Cybermetrics Research Group, School of TechnologyUniversity of WolverhamptonWolverhamptonUK

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