Article age- and field-normalized tools to evaluate scientific impact and momentum
Scientometrics, ISSN: 1588-2861, Vol: 126, Issue: 4, Page: 2865-2883
2021
- 19Citations
- 37Captures
- 1Mentions
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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In search of more refined metrics
An article published in February in the journal Scientometrics discussed a computer tool that can be used to assess the scientific production of researchers. It
Article Description
The Field Weighted Citation Index (FWCI) is an article age- and field-normalized metric to evaluate scientific visibility and impact. The Topic Prominence Percentile (TPP) is another parameter that allegedly measures an article’s “momentum.” Both are available at SciVal and are thought-provoking but have been scarcely used by the community, partially because it is very time-consuming to collect these parameters, paper by paper. In this article, we created and tested a computer code that can efficiently harvest the FWCI and TPP of articles of any chosen researcher, research group, or institution from the Scopus database. After collecting the desired data, our algorithm computes the sum, mean and standard deviation, mode, and median. It also calculates an alternative metric, proposed here, i.e., a normalized parameter that divides each FWCI by the number of authors of that article and then produces similar metrics. We first used the new algorithm to collect an article dataset from a selected researcher, used as an example, who has published 226 articles since 2000. The automated data collection task took 35 min versus 4 h manually. To demonstrate the power of this approach, we present the most relevant results. For instance, 20% of this researcher’s papers have achieved very high visibility, an FWCI ≥ 2. Surprisingly, however, his articles of the highest FWCI are not the most cited. His 20 oldest papers have a similar FWCI to the 20 newest, showing that his scientific output reached a steady-state long ago. Moreover, we discovered that the papers of the highest FWCI have a higher share (65%) of international collaborators than the articles of the lowest FWCI (< 40%). These results corroborate the well-known trend that international collaboration increases scientific visibility. To generalize these findings, we also successfully compared the FWCI statistics of several senior researchers and young investigators who work in diverse fields, revealing significant differences. This way, we demonstrated that the proposed computer code and resulting metrics provide a new scientometric tool. However, a drawback is that a significant fraction of the “topics” defined by SciVal does not perfectly fit the article’s field, which leads to errors in the computation of the FWCI. Therefore, while the FWCI is a handy parameter to evaluate and compare the scientific visibility and impact of researchers of any age and science field, reliable analyses will only be possible using an improved choice of topics.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85101786529&origin=inward; http://dx.doi.org/10.1007/s11192-021-03877-3; https://link.springer.com/10.1007/s11192-021-03877-3; https://link.springer.com/content/pdf/10.1007/s11192-021-03877-3.pdf; https://link.springer.com/article/10.1007/s11192-021-03877-3/fulltext.html; https://dx.doi.org/10.1007/s11192-021-03877-3; https://link.springer.com/article/10.1007/s11192-021-03877-3
Springer Science and Business Media LLC
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