Altmetrics for a department

Posted on March 9, 2016

Suppose you want to measure the performance of individual researchers of a university department. Which variables can you get hold on and how relevant would they be to measure academic performance?

Here is my take on it:

1. Google Scholar citations number. Google Scholar records total number of citations, h-index and i10-index as well as the numbers for a fixed period.
2. Scopus citation numbers.
3. Twitter. The number of tweets and the number of followers would be relevant.
   One issue here is that the number of tweets may not be relevant to the academic performance and it is also susceptible to manipulation. Interestingly there has been a comparison between Twitter numbers and standard citation counts with a coefficient between the two numbers named the Kardashian index.
4. Wikidata and Wikipedia presence. Whether Wikidata has a item of the researcher, the number of articles of the researchers, the number of bytes they span, the number of articles recorded in Wikidata. There is an API to get these numbers, and – interestingly – Wikidata can record a range of other identifiers for Google Scholar, Scopus, Twitter, etc. which would make it a convenient open database for keeping track of researcher identifiers across sites of scientometric relevance.
   The number of citations in Wikipedia to the work of a researcher would be interesting to have, but is somewhat more difficult to automatically obtain.
   The numbers of Wikipedia and Wikidata are a bit manipulable.
5. Stackoverflow/Stackexchange points in relevant areas. The question/answering sites under the Stackexchange umbrella have a range of cites that are of academic interest. In my area, e.g., Stackoverflow and Cross Validated.
6. GitHub repositories and stars.
7. Publication download counts. For instance, my department has a repository with papers and the backend keeps track of statistics. The most downloaded papers tend to be introductory or material and overviews.
8. ResearchGate numbers: Publications, reads, citations and impact points.
9. ResearcherID (Thomson Reuters) numbers: total articles in publication list, articles with citation data, sum of the time cited, average citations per article, h-index.
10. Microsoft Academic Search numbers.
11. Count in the dblp computer science bibliography (the Trier database).
12. Count of listings in ArXiv.
13. Counts in Semantic Scholar.
14. ACM digital library counts.