Rankings can also be affected depending on how the identification of the university in the affiliation is done. Automated matches are often lower for languages

Rankings can also be affected depending on how the identification of the university in the affiliation is done. Automated matches are often lower for languages that have different ways of writing things (often when there are different characters, or it's written in different languages), changes to location/name/structure, and how long it's been around. Based on a quick look at wikipedia: For Szeged, the university is kind of old but also only appeared in it's current form in 2000. It's also called Szegedi Tudományegyetem. It has multiple colleges inside it, so it may depend on how well those are represented in the data. Authors using acronyms more makes identification less likely, each of the colleges have acronyms. Finally, where authors publish makes a big difference - not all publishers open their data up and even those that allow access to some raw data it can be less reliable to extract. So if you publish in places where the publisher does not make the affiliations easily machine readable your work may be counted less in many of these automated rankings. Pubmed (not a publisher but a key way a lot of people get some of this data) didn't have affiliations for anyone but the first author until some fairly late date. Either automated mappings are used for a lot of work (since there are tens of millions of unique affiliation strings), or the source material is heavily curated (so for example only the "top" N journals are looked at). source: worked on affiliation mapping at Dimensions in a previous life