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Eigenfactor
The Eigenfactor™ is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on ResearchAssessment (DORA).
Are Conference Posters Being Cited?
We present a small case study on citations of conference posters using poster collections from both Figshare and Zenodo. The study takes into account the years 2016-2020 according to the dates of publication on the platforms. Citation data was taken from DataCite, Crossref and Dimensions. Primarily, we want to know to what extent scientific posters are being cited and thereby which impact posters potentially have on the scholarly landscape and especially on academic publications. Our data-driven analysis reveals that posters are rarely cited. Citations could only be found for 1% of the posters in our dataset. A limitation in this study however is that the impact of academic posters was not measured empirical but rather descriptive.