Open and transparent altmetrics for discovery by Peter Kraker.
From the post:
Altmetrics are a hot topic in scientific community right now. Classic citation-based indicators such as the impact factor are amended by alternative metrics generated from online platforms. Usage statistics (downloads, readership) are often employed, but links, likes and shares on the web and in social media are considered as well. The altmetrics promise, as laid out in the excellent manifesto, is that they assess impact quicker and on a broader scale.
The main focus of altmetrics at the moment is evaluation of scientific output. Examples are the article-level metrics in PLOS journals, and the Altmetric donut. ImpactStory has a slightly different focus, as it aims to evaluate the oeuvre of an author rather than an individual paper.
This is all good and well, but in my opinion, altmetrics have a huge potential for discovery that goes beyond rankings of top papers and researchers. A potential that is largely untapped so far.
How so? To answer this question, it is helpful to shed a little light on the history of citation indices.
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Peter observes that co-citation is a measure of subject similarity, without the need to use the same terminology (Science Citation Index). Peter discovered in his PhD research that co-readership is also an indicator of subject similarity.
But more research is needed on co-readership to make it into a reproducible and well understood measure.
Peter is appealing for data sets suitable for this research.
It is subject similarity at the document level but if as useful as co-citation analysis has proven to be, it will be well worth the effort.
Help out if you are able.
I first saw this in a tweet by Jason Priem.