Te Pūnaha Matatini Whānau

A more social network

A more social network

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:

Formula: revealed comparative preference (RCP) of an institution

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.


Author

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.

The (my) future and other predictions with greater than 5% error

The (my) future and other predictions with greater than 5% error

What are you going to do after you finish your PhD? Where do you want to go? Are you going to become a lecturer? These are all questions that I field on a regular basis. Rather than going with my instinctive response of “What the hell? I don’t even know what my PhD is about yet!”, I usually say something like “I don’t know, but hopefully something in conservation or consulting”. Apparently this puts me in the minority of PhD students in that I do not desire to go into academia.

This was a topic discussed at the New Zealand Association of Scientists conference I attended on the 26th April; you can also read about it in my previous blog post. One of the speakers referenced the Royal Society report where it stated that while about half of PhD students continue on with research, becoming early career researchers, most end up leaving academia for work in industry. This is despite most PhD candidates desiring a job in academia at the outset of the project. The question asked at the conference is how can we, as the scientific community, support PhDs and Post Docs so that if an academic career does not pan out they can successfully and relatively painlessly transition into industry? As one of the members of the emerging researchers panel said, when she was faced with the current situation, it is not unusual to feel like the best option is just to “give up”.

I am lucky in that I have a great team of supervisors (I have 4 ± 1 supervisors) who want my PhD to be more about preparing me for future work rather than me just churning out papers. They have suggested that I take opportunities to learn skills that will be useful in industry and that I take time to build connections inside and outside of academia. However, not everyone is as lucky in having such excellent supervisors. I have heard horror stories about supervisors who refuse to meet with their students and those who take no role in preparing the student for the future. What can we do for these students without supervisor support?

This is a place where student-led organisations can step in. The Te Pūnaha Matatini Whānau committee is well aware of these trends and are currently working on a number of projects to address this. The Whānau has connected with industry partners such as data analytic companies. The intention is for TPM Whānau members to be eligible to undertake internships at the companies. This will teach the members new skills and give experience that will be valuable in industry. We are also organising a data debate on the issues of data privacy between industry members and Te Pūnaha Matatini.

Ultimately however, no matter how supportive the supervisor is, it is up to the student to make sure that they obtain the experience and skills they need. As one of my supervisors said, “if you are smart enough to get to PhD level you are smart enough to look after yourself”.

With that I will sign off and go look after myself.

Jonathan


About

Jonathan Goodman is a Te Pūnaha Matatini Whānau committee member.

Jonathan is a PhD student in Statistics at the University of Canterbury. His current research is looking at pest control in the Greater Wellington Region taking into account procedural, geographic and socio-economic measures. Jonathan is excited about applying statistics to real world problems and facilitating positive social impacts.
My First Conference(s)

My First Conference(s)

By Jonathan Goodman

Never do things by halves, jump in the deep end, give it a go, eat your vegetables, trust your supervisors. This is all good advice and I now realise I must have taken it, having presented at the first conference I have ever attended, then attending another conference three days later run by an organisation I had never heard of before. I have also joined the Te Pūnaha Matatini Whānau committee based solely on my supervisor’s advice. Before I go on, I must admit that all of these actions have proved to be worthwhile and rewarding.

The first conference was the Te Pūnaha Matatini cross-theme hui. This was the first Te Pūnaha Matatini gathering I have attended since joining the Centre of Research Excellence as a PhD student at the start of the year. The hui consisted of a series of short talks, including my first at a conference, interspersed with four rounds of the “Research Knockout” – a game designed by Alex James. The game started with the creation of teams of 3-5 researchers from Te Pūnaha Matatini’s three research themes. Each team then generated a potential research project. Each round of the knockout consisted of pairing up the groups and amalgamating their ideas into an enhanced version. This continued until there were just two groups remaining. In the grand finale, there was a final presentation followed by a vote. The winning research topic was ‘Measuring the impact of the communication of science’.

The question of science outreach also came up at the conference run by the New Zealand Association of Scientists (NZAS). The conference was held at Te Papa in Wellington and celebrated the 75th anniversary of the Association. The conference had a selection of engaging speakers looking at the role of scientists in the past, the present, and into the future. A number of speakers talked about science communication.

One of the presenters, Simon Nathan, spoke about James Hector and how he effectively pushed the cause of New Zealand science, through his role of Chief Government Scientist, by constantly reminding politicians about the value of science. Rebecca Priestley talked about how science outreach was different back in the days of the Department of Scientific and Industrial Research (DSIR). Instead of scientists engaging in outreach programs, interested journalists and citizens would phone and be able to speak directly with the scientist who was in the best position to answer their queries. Te Pūnaha Matatini’s own Shaun Hendy presented on how social media is currently the only way scientists are able to directly communicate with the population without the risk of their message being obscured. His three guidelines for public engagement were very apt.

Researchers should:

1) Not be d!@#s

2) Get on social media

3) See rule number 1.

The other major theme of the conference was the structure of the pathways inside and outside academia for emerging researchers. I will touch on this in another blog post on the Te Pūnaha Matatini Whānau page.

Having had a rewarding weekend forming connections with talented scientists, and with the science community as a whole, I will sign off hoping that I have followed Shaun’s rules.

Jonathan Goodman

The First Post

By Ben Curran

It’s an interesting thing, writing the first post. It’s an interesting thing writing the first line.

Whether it’s the first line of a paper, a chapter, a grant application or a blog post, I always find the first line … daunting. It’s only now, having finished the thesis and other things need to be written, that I recall how awful that first line is. Even if there is a specific goal behind the writing, an idea that you set out to communicate, what words do you choose for the first sentence? Who are you talking to? What sort of tone are you after? These are things that paralyse the first line.

And then there’s the times when you’re forcing yourself to write when there is no specific goal other than to practice writing. I liken this situation to the one I encountered all too often in one of my previous incarnations as a bartender – there’s always a customer who comes in at some point and says “surprise me”. Most often they got a glass of water.

Like it or not, writing is a large part of what we do. Sure, the thinking, the testing, the figuring out what’s going on are important, but in the end they mean nothing if we can’t communicate the results. And for larger audiences, writing is the primary means of communication.

Writing has to start somewhere though. Writing the first line, whether it’s a good sentence or not, is always awful. It is almost certainly going to be at least edited, if not entirely removed. Which makes it, in the greater scheme of things, not particularly important. This, to a certain extent, can be extended to the entire first draft of pretty much any work. One of my PhD supervisors, in an effort to get me writing, used to stress that whatever I wrote for the first draft was going to come back with red ink all over it. I was told to just write something, anything, a foundation upon which the story you are trying to tell could be built.

If you’re not used to working with wood, there is often a feeling of trepidation in making the first cut. Making the first bend in a piece of metal, applying the soldering iron for the first time to a circuit board. All of these things impart a sense of beginning and often the thought that runs through your head is “what if I screw it up”. It’s the same thing with writing. Measure twice, cut once, Dad said. The first draft is only the first measurement. The first sentence is only the first line on the plans, drawn with pencil.

So if you have a specific idea to communicate, start writing. After a while that feeling of trepidation is replaced by familiarity. Knowing the first draft is only the rough plan of your work means that, eventually, writing the first sentence becomes … an odd thing. Just odd. And yet familiar, interesting even.

And as a scientist, when I see something interesting, I usually want to stop and take a serious look. Turn it over, see how it works. This is where it can be good if you don’t have a specific idea to communicate, put that first, odd sentence down and see where it takes you. Possibly somewhere very much like here.