{"id":22800,"date":"2023-10-23T11:59:07","date_gmt":"2023-10-23T09:59:07","guid":{"rendered":"https:\/\/www.wjst.de\/blog\/?p=22800"},"modified":"2023-10-23T12:15:49","modified_gmt":"2023-10-23T10:15:49","slug":"google-scholar-ranking-of-my-co-authors-is-completely-useless","status":"publish","type":"post","link":"https:\/\/www.wjst.de\/blog\/sciencesurf\/2023\/10\/google-scholar-ranking-of-my-co-authors-is-completely-useless\/","title":{"rendered":"Google Scholar ranking of my co-authors is completely useless"},"content":{"rendered":"

The title says it already while a new r-blogger post<\/a> helped tremendously to analyze my own scholar account<\/a> for the first time.<\/p>\n

\"\"<\/a><\/p>\n

I always wondered how Google Scholar ranked my 474 earlier co-authors. <\/p>\n

The ranking above shows not the most relevant ones, in particular as there was a lot of name dropping of institute heads in the past. There are even people that I never met before and there are some\u00a0 co-authors that I don’t want to be associated with<\/a>. But well, there are so many excellent people that I highly appreciate.<\/p>\n

The published R script<\/a> is very helpful for finding out more details. I had to adjust only two lines where I dropped the new column for interpolated rank. Also the dataset was not unique<\/p>\n

\r\n# all_df$interrank <- na.approx(all_df$interrank)\r\nall_df <- distinct(all_df, coauthors, .keep_all = TRUE) %>%\r\n  mutate(cites_corr = cites\/n)\r\n<\/pre>\n
Dividing citations to the number of papers reversed the picture where suddenly the best co-authors moved up in the ranking.<\/p>\n
BTW the original Google algorithm remains in the dark while the blog post finds<\/p>\n
So the total co-citations and the number of co-authored papers is used to compute the rank of co-authors. Not every co-author is a Scholar co-author. Some don\u2019t have accounts for example.<\/p><\/blockquote>\n
\"\"<\/a>
a revised co-author list<\/figcaption><\/figure>\n\n
 <\/p>\n
\n  CC-BY-NC Science Surf , accessed 26.04.2026<\/span>\n <\/div>","protected":false},"excerpt":{"rendered":"
The title says it already while a new r-blogger post helped tremendously to analyze my own scholar account for the first time. I always wondered how Google Scholar ranked my 474 earlier co-authors.   CC-BY-NC Science Surf , accessed 26.04.2026<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8,2,6,9],"tags":[],"class_list":["post-22800","post","type-post","status-publish","format-standard","hentry","category-asthma-allergy","category-genetics-biology","category-joke-fun","category-computer-software"],"_links":{"self":[{"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/posts\/22800","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/comments?post=22800"}],"version-history":[{"count":6,"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/posts\/22800\/revisions"}],"predecessor-version":[{"id":22808,"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/posts\/22800\/revisions\/22808"}],"wp:attachment":[{"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/media?parent=22800"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/categories?post=22800"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wjst.de\/blog\/wp-json\/wp\/v2\/tags?post=22800"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}