Congratulations to all the winners of the inaugural Prime Minister’s science prizes. I am particularly pleased to know four of the winners personally.
Dr Jeff Tallon and Dr Bob Buckley, from Industrial Research Ltd, are two of New Zealand’s greatest physical scientists. I discussed some of their work in a blog post last month. Twenty five years ago, the Jeff and Bob took New Zealand to the forefront of research and development in high temperature superconductivity, and have kept us there ever since. Their work has not only had immense scientific impact, but has led to the development of a superconductivity industry in New Zealand. Jeff is a Principal Investigator in the MacDiarmid Institute, and Bob is a member of the Institute’s governance board. Bob and Jeff have both been important mentors in my career.
Elizabeth Connor is the winner of the Science Communicators Prize. I taught Elizabeth at Victoria University of Wellington during her BSc(Hons) in physics. After her honours degree, Elizabeth travelled overseas to pursue further training in science communication, before returning last year. She has since worked with us at the MacDiarmid Institute on several projects, including our Interface newsletter, and for Radio New Zealand. You can read some of her work in our newsletter here. She is one of our up and coming science journalists. I hope that Elizabeth continues to go from strength to strength in her journalism.
John Watt is another winner with MacDiarmid Institute affiliation. We knew about John’s prize in advance as he was the winner of last year’s MacDiarmid Young Scientist of the Year award, which has now been superseded by the Prime Minister’s Emerging Scientist award. John submitted his PhD thesis earlier this year and is awaiting his oral exam at the moment. You can see some of John’s work on palladium nanocrystals here. After he graduates, he is going to work with a Victoria University spin out company. The prize will give him an excellent opportunity to become one of New Zealand’s scientific entrepreneurs.
In a post a few weeks ago, there was a discussion on the value of patents. Sciblogs reader Bruce Hamilton pointed out that the value of an abandoned patent could simply be as an output for a funding agency. Could it be then the requirements of funding agencies for outputs is driving patenting activities? Bruce has put together a selection of statistics from IPONZ looking at patenting activity in some of New Zealand’s research organisations, both public and private. Bruce did not intend the list below to be exhaustive, but he has covered a selection of Universities, CRIs, large private companies and smaller start-ups. It’s very interesting to see who some of our top patenting organisations are, and how many of them have patents in progress.
|
Number |
Aborted (%) |
In Progress (%) |
Completed (%) |
| Fisher & Paykel |
424 |
60 |
2 |
38 |
| Uniservices |
388 |
66 |
14 |
21 |
| Industrial Research Limited |
374 |
66 |
5 |
29 |
| Agresearch |
210 |
55 |
12 |
33 |
| Fletcher |
201 |
41 |
9 |
50 |
| Carter Holt Harvey |
186 |
56 |
9 |
35 |
| Fonterra |
143 |
43 |
17 |
39 |
| Otago University |
100 |
77 |
4 |
19 |
| Gallagher Group |
83 |
43 |
12 |
48 |
| Massey University |
76 |
55 |
8 |
37 |
| Genesis R&D Corp |
45 |
36 |
11 |
56 |
| IGNS |
37 |
58 |
6 |
36 |
| Otago Innovation |
18 |
50 |
17 |
33 |
| Syft Technologies |
15 |
57 |
0 |
43 |
| Blis Technologies |
13 |
64 |
0 |
36 |
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Aborted = Abandoned + Voided Pre-acceptance
In Progress = Filed, Examination, Accepted
Completed = Granted, Expired or Not Renewed.
At least in this data set, it does look like public organisations abort more of their patents than their private counterparts. However, public research organisations are charged with disseminating their research findings through journal articles or presentations at scientific conferences. Once a piece of research has entered the public domain, it can no longer be patented, so public research organisations may chose to protect their IP by filing a provisional patent prior to publishing or presenting their work. This gives them the option of proceeding with a full patent within the next year should they choose to do so, while allowing them to publish their work. Private research organisations, under less pressure to publish, can simply choose to not release their findings while they decide whether a piece of work is worth the expense of filling a full patent.
Thanks to Bruce for taking the time to extract this interesting data.
One of the interesting things we can do with the OECD patent database is look at how those patents are distributed among applicants. The applicant for a patent is often the organisation or company that employs the inventors rather than the inventors themselves. By looking at the distribution of patents among applicants we are looking at the size distribution of patent portfolios. Note that an organisation may apply for patents from multiple addresses – in this case I have treated each address as a separate applicant.
The plot on the right shows the distribution of European Patent Office patents among applicants from the USA, New Zealand, Australia and Finland. The data is shown on log-log axes – remarkably, the data in all four countries fall roughly on straight lines with slopes close to -2. In other words, the proportion of applicants with p patents is inversely proportional to p squared.
This appears to be yet another example of Zipf’s law, which is a frequency distribution that crops up in all sorts of strange places (none stranger than the popularity of opening moves in chess). One way such distributions can arise is through a process called “preferential attachment” (sometimes called a rich get richer process). In our case, such a distribution could be generated if an applicant’s probability of getting a new patent increases with the number of patents the applicant already has. The value of the exponent generated by such a process (close to -2 in the data shown) depends on the rate at which first-time applicants enter the population versus the rate at which new patents arise amongst existing applicants.
What is interesting is that the exponents are quite similar across the four countries, suggesting that the process that generates the distribution is the same in each. The main difference between countries is the absolute scale of the distribution rather than the slope.
What determines this scale? The best correlate I have found is the level of business expenditure on research and development (BERD) in each country. If we instead plot the number of applicants per million dollars of BERD, the distributions almost collapse on to one another. Actually, you can see that with this rescaling New Zealand comes out quite well – we get more patents for the dollars we spend than the other countries shown.
Despite the value for money New Zealand businesses appear to get for their R&D spend, the data show that you largely do get what you pay for. Indeed, the similarity of the exponents between countries also suggest that the innovation process itself does not vary widely – it is the amount of research you do rather than the way you do it that is important. Unfortunately in New Zealand, our focus is all too often on how we innovate, rather than how much we innovate.
In an earlier post I looked at the 2008 CRI co-author network. Now let’s turn to the University network. Using the Thompson Reuters Web of Science again, I found 5116 publications in 2008 with authors from New Zealand universities. In total 13930 authors contributed to these papers. The network is shown on the right.
Again, a remarkably large fraction of authors belong to the giant component. In the 2008 CRI co-author network, 2325 of the of the 4496 authors belonged to the largest connected component. Here, 9771 of the 13930 authors belong to the largest component – that’s a remarkable 70%.
We can make some other comparisons between the CRI and the university networks. In the university network, on average each author has 8.4 collaborators; in the CRI network, each author has 5.1 collaborators. Apparently, university authors are more collaborative.
However, just comparing the average numbers of co-authors is misleading. I’ve graphed the distribution of co-author numbers for the universities and the CRIs on the left i.e. the proportion of authors with certain numbers of co-authors. From the graph it’s apparent that the difference between the university and CRI networks lie in the tails of the distributions. There are a number of university authors that participate in very large collaborations. For instance, there are a dozen or so authors in the network whose only published work in 2008 was one with 343 co-authors. Big science!
It is probably not surprising that university researchers are more likely than those in a CRI to participate in very large overseas collaborations. This skews the average number of co-authors for university researchers relative to CRI researchers, making the mean number of co-authors larger.
