In the immortal words of Vanilla Ice – Stop, collaborate and listen. Collaboration is a cornerstone of modern science and with flight tickets cheaper than ever before and the internet effectively eliminating the expense of correspondence, academics and researchers are looking further afield and reaching more contemporaries across the globe. However, different institutions have different facilities and research focuses, not everyone speaks the same language, and so perhaps these researchers may be picky when it comes to who they work with. It raises the question of whether they do have a preference in collaborator based on affiliation and, if so, can this preference be measured and distilled into cold, hard data?
Of course they do, and of course it can be. More to the point, why?
Arguably the most tangible and conveniently quantifiable means in which academic collaboration manifests is in scientific papers and articles, typically with several authors from varying affiliations. A notable drawback in previous studies on research collaboration is that the measures used (such as the fractional count detailed in Nature Index) consider results for each institution, rather than individual academic, and disregard the size of each institution; as a result, smaller and younger institutions may stack up unfavourably compared to those that are more established and larger. For example, take a look at how the eight New Zealand universities compare against each other:
- The nodes representing each university are weighted by their respective output (total number of co-authored papers by academics affiliated with these universities).
- The links connecting universities to each other are weighted by the number of papers co-authored by researchers from both institutions.
- The higher the link weight, the more that the connected universities are attracted to each other.
The skewing effect that university size has on this network is pretty apparent from how Lincoln University has much fewer co-authorships with Victoria University and University of Waikato than with the rest of the network, given its relatively small output. Also of note is that the University of Auckland and AUT have a much lower link weight than one would expect for two universities across the street from each other, yet the University of Auckland and the University of Canterbury have a much stronger link despite being at opposite ends of the country.
First, to address the effect of institution output. We do this using something we call the revealed comparative preference (RCP) of an institution i for collaborating with institution j:
where Xij is the number of co-authorships between i and j, Xi is the total number of papers co-authored by i with other institutions in the data set, and X is the total number of co-authorships between all the institutions in the data set.
Plainly speaking, it’s a measure of whether two institutions are doing more than collaborating than we might expect with each other relative to their tendency to collaborate with the other universities in the data set. If Pij > 1 , then universities i and j share more co-authorships than we expect relative to the other institutions in the data set, so we say they have a comparative preference for collaborating with each other. Conversely, Pij < 1 indicates that the two universities are doing less than we might expect.
Anyway. Here’s the NZ university network revised with the links now weighted by their corresponding RCP values:
Better. Here it’s apparent that AUT has a stronger link with Auckland Uni in addition to Lincoln and Waikato, and it should be pointed out that University of Auckland, AUT and Massey University are also closer to each other in the network, bearing in mind that all three have campuses within Auckland.
Now with a working measure, we move on to a larger sample. Bring on the Australians.
Clearly the Tasman Sea has a solid effect on the way New Zealand based researchers connect with those based in Australia; the links within the NZ cluster of universities have greater RCP weightings than those within the Australian cluster, implying a preference for domestic rather than trans-Tasman co-operation. Another feature to consider is that the Australian universities in the same states are grouped together, which is consistent with the idea that geographical proximity plays a significant part in a researcher’s choice of collaborator.
It would only be natural to wonder how academics interact on a global scale – do we ever grow out of talking almost exclusively to our friends and shun outsiders in some weird, grown up, Mean Girls-esque collection of cliques?
From observing how the Dutch and German institutions are grouped together, we might conclude that the language barrier is a large hurdle to overcome when jointly writing scientific literature – this also seems apparent from the Chinese-Hong Kong cluster, as well as Korean and Japanese institutions as well. But languages also tend to cluster geographically, so it is hard to disentangle the effect of language from distance.
It’s no question that with the constant progress of technology, connecting with people is becoming less costly. However, there are factors remaining that impede the prospect of a totally connected scientific community, some of which have been speculated on here. Of course pictures and hand waving don’t constitute a solid argument, but a thorough analysis of these factors and their effect on university collaboration will be in store for you, dear reader.
In the meantime, perhaps one should learn German, or Mandarin, or Dutch, or even Japanese. It’s not that hard.
About the data visualisations
In order to make the larger graphs efficient enough to be used in browser, the amount of connections a node could have to other nodes was limited to its top four RCP values. This change had no significant effect on the clustering observed when the full connection matrix was used. The change was only implemented for the QS, ANZAC and benchmark data sets.
Bonnie Yu is a research assistant at Te Pūnaha Matatini and a member of Te Pūnaha Matatini’s Whānau group for emerging scientists. Her research projects focus on university collaboration networks.
The data visualisations of this post were prepared by fellow research assistant, Nickolas Morton.
Interesting idea for data visualisation but your presentation is only as good and complete at the data you include, which means a proper interpretation requires a discussion of the data you’ve excluded. For example, as far as I can tell that last graphic only considers a limited subset of institutions from various countries, yet it misleadingly suggests this national level output in the legend. There at 40 universities in Australia, yet in the last graphic you’ve only considered 7 (which isn’t even the full Go8) and in NZ, in the last graphic as far as I can tell only Auckland Uni is considered (unless ‘Auckland’ on the ‘NZ’ dot means NZ institutions located in Auckland, but I suspect not). It would be useful to make the limitations of these visualizations clear in the legend – lest people erroneously use this to draw conclusions from incomplete analysis.
Nice work. Collaboration = coauthorship? And your source is Scopus?
Melanie: the selection of international universities is the QS 100 from 2015. The legend just describes the colour scheme, which is nationality. As described earlier in the posts, nodes are universities, nit national outputs.
David: Yes, collaboration = co-authorship RCP as defined in the equation above. Data is from Scopus