Focusing on the top panel respondent evaluations of number of citations with

Focusing on the top panel respondent evaluations of

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Focusing on the top panel (respondent evaluations of number of citations with actual cita- tions), it is apparent that there are fluctuations in both the peer judgement of citation counts and actual citations. That is bound to be expected as there are definitely variations in citation rate, even within journals. Stuart Cantrill, one of the authors on this paper and chief editor of NatureChemistry , has commented on this phenomenon in the context of that journal’s JIF, noting the contribution of a small handful of highly cited papers to their overall impact factor, with the majority of articles receiving fewer citations than the impact factor would predict [ 16 ]. This phenomenon also led to a published proposal by several leading scientific publishers for the adoption of citation distributions as a replacement for JIF [ 17 ]. We see the same kind of fluctuations in the evaluations of our respondents. What is inter- esting to us are the differences in what the respondents expect to be well-cited papers versus Table 1. Respondent demographics. N = 363 Respondent Demographics Subdiscipline Analytical Biochemistry Inorganic Organic Medicinal Physical Polymers Other Decline Respondents 14 52 44 206 60 28 21 15 2 Papers Classified by Subdiscipline (63) 1 14 33 37 3 26 12 19 Employment Graduate Student Postdoc Faculty Member Professional within Chemical Industry Trained Chemist in a Non-Research Field Decline Respondents 93 64 55 117 28 6 Age 20–30 30–40 40–50 50–60 60–70 Above 70 Decline Respondents 140 141 54 23 2 0 3 Table 2. Correlations between answers given for each question. N = 363 Cited Significant Share: Chemists Share: All Cited 1 0.9 0.73 0.7 Significant 1 0.82 0.71 Share: Chemists 1 0.64 Share: All 1 Perception of the importance of chemistry research papers and comparison to citation rates PLOS ONE | March 28, 2018 5 / 14
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which papers are actually well cited. Fig 2 is a more direct way to view the correlation between respondent choices and actual citation counts. To put these graphs in perspective, we calculated the correlation between the aggregate responses for individual survey questions and the actual citations from both 2013 (the year the survey was taken) and 2016. As shown in Figs 1 and 2 , there were a number of papers that were selected disproportionately more than these same papers were actually cited. We were curious if that might be because the respondents were viewing the ‘leading edge’ of chemistry. That is, if these articles were very cutting edge, it might take their citation counts a longer time to build up, like the noted Sleeping Beauty papers [ 18 ]. The data doesn’t support this point of view as the correlations, generally, decrease from the 2013 citations to the 2016 citations. Fig 1. Respondent evaluations and citations (2013) by paper. The top panel shows the composite selections of our respondents for the question asking which papers they thought had the most citations (blue) and the actual number of citations in 2013 (gray). The other panels also include the number of citations (gray) along with:
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