The Beamplots, a new performance metric form the Web of Science, is claimed to provide researchers and evaluators with contextual insights around individual researcher performance.
Beamplots are a new visualization tool that showcases the range of a researcher’s publication and citation impact in a single data exhibit. It uses a field-normalized citation metric and does not unduly penalize researchers with gaps in their publication record, or disadvantage those who work in fields with distinctly different publication activity.
In contrast to the h-index, which tends to favour senior researchers that work in the physical sciences, a beamplot shows the volume and citation impact of an individual’s publication portfolio through time. Each paper’s citation count is normalized (i.e., benchmarked against other similar publications from the same discipline) and measured as a percentile. It is also not necessarily biased against individuals who have taken a career break or published less at any given time.
In your opinion, how effective will the new Beamplots be in addressing many criticisms of the h-index relating to comparability across disciplines and biases to portfolio size and continuity?
This metric normalises each paper’s citation count to benchmarked against other similar publications from the same discipline. For example, the h-index often favours very basic sciences which are highly cited compared to some subject disciplines which are not cited much. For instance, comparing an h-index of a biologist with the h-index of an academic in management won't be fair. It also tries to remove the bias against individuals who have taken a career break or published fewer papers at any given time than those who are very senior in the field or have published more.
I do like the idea that scientists who worked in different fields can have a fair comparison. Yet, I do not like the concept of removing the "bad" effect of some years of inactivity. This is because if for any reason a scientist was not active for a certain amount of years, this must affect the metrics in my opinion. An index that I like is the M-index, which is simply the H-index divided by the number of years that a scientist has been active. The M-index enables a fair comparison between young and senior scientists. Otherwise the H-index will always favour the older academics.
Thanks Alberto - that's very insightful.
M-index is certainly do a better job but since it also relies on h-index therefore it still has that one size fit all subject areas approach. I can't say that beamplots are better at the moment (still need to see some more examples of beam plots of various researchers across subject areas and length of career) but if we look at the y-axis it does differentiate between a young and senior scientist. What do you think?
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