In “Show me the data” Rossner, et al (2007, The Journal of Cell Biology, Vol. 179, No. 6, 1091-1092) wrote:
“Just as scientists would not accept the findings in a scientific paper without seeing the primary data, so should they not rely on Thomson Scientific’s impact factor, which is based on hidden data. As more publication and citation data become available to the public through services like PubMed, PubMed Central, and Google Scholar®, we hope that people will begin to develop their own metrics for assessing scientific quality rather than rely on an ill-defined and manifestly unscientific number.”
Rossner et al are quite right, and the optimal, inevitable solution is at hand:
(2) This will allow scientometric search engines such as Citebase (and others) to harvest their metadata, including their reference lists, and to calculate open, transparent research impact metrics.
The prospect of having Open Research Metrics for analysis and research assessment — along with the prospect of maximizing research usage and impact through OA — will motivate adopting the mandates, closing the autocatalytic circle of benefits from OA.
Brody, T., Carr, L., Gingras, Y., Hajjem, C., Harnad, S. and Swan, A. (2007) Incentivizing the Open Access Research Web: Publication-Archiving, Data-Archiving and Scientometrics. CTWatch Quarterly 3(3).
Harnad, S. (2007) Open Access Scientometrics and the UK Research Assessment Exercise. Proceedings of 11th Annual Meeting of the International Society for Scientometrics and Informetrics 11(1) : 27-33, Madrid, Spain. Torres-Salinas, D. and Moed, H. F., Eds.
Shadbolt, N., Brody, T., Carr, L. and Harnad, S. (2006) The Open Research Web: A Preview of the Optimal and the Inevitable, in Jacobs, N., Eds. Open Access: Key Strategic, Technical and Economic Aspects. Chandos.
American Scientist Open Access Forum