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The problem: Impact Assessment of Journals Impact Factor is widely used (& often abused) to assess impact of journals (& individuals) No single metric.

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Presentation on theme: "The problem: Impact Assessment of Journals Impact Factor is widely used (& often abused) to assess impact of journals (& individuals) No single metric."— Presentation transcript:

1 The problem: Impact Assessment of Journals Impact Factor is widely used (& often abused) to assess impact of journals (& individuals) No single metric can ever capture the full picture of research impact Impact Factor measures average citation performance – but what about top performance? Also: Large journals cannot have high impact factors – see Physical Review Letters 102, 060001 (2009) We need metrics not to replace but to complement the Impact Factor We propose the S-index: a time- sensitive H-index-like metric for journals The IF is the number of citations over a 2-year window, averaged over the whole journal. Not all papers are created equal!

2 Introduce a new metric for the highly cited papers in a journal: S-index today121110090807 For a set of papers H-index: full publication window, full citation window S-index (for 2011):2009-2010 publication window 2011 citation window H-index S-index 2011 S index = maximum number S of papers, published in 2009-2010, cited more than S times in 2011

3 Manolis Antonoyiannakis I am a Senior Assistant Editor in Physical Review Letters, a journal published by the American Physical Society. I am also an Adjunct Research Associate Scientist at Columbia University. From 2008-2010 I was the Scientific Advisor to Prof. Fotis Kafatos, the founding President of the European Research Council in London/Brussels. I devote most of my time handling the peer review of manuscripts, part of my time in bibliostatistics. I am interested in the peer review process from a statistical and systemic perspective and in metrics that quantify the impact of research. More specifically, I am interested in: – understanding and enhancing peer review using data analysis and statistical inference (improving the review workflow process; detecting bias or conflict of interest; creating & improving referee selection protocols and tools; producing & analyzing feedback on editorial decisions; qualitative & quantitative citation analysis; recognizing citation impact patterns; etc.) – metrics - and their limitations - that quantify the impact of scientific research (impact statistics of journals, individuals and groups) – sociological effects influencing the impact of scientific work – networks of referees and authors – behavioral studies of scientists For more info, please visit www.bibliostatistics.orgwww.bibliostatistics.org


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