Bioinformatics and inter-disciplinary work

Bioinformaticians are sometimes seen as multi-disciplinary workers (see the previous post on Results of some bioinformatics polls). If so, then the results of a recent study may be of interest:

Kevin M. Kniffin and Andrew S. Hanks (2013) Boundary spanning in academia: antecedents and near-term consequences of academic entrepreneurialism. Cornell Higher Education Research Institute Working Paper 158.

Kniffin and Hanks used data from the Survey of Earned Doctorates (conducted by the National Science Foundation), based on data from all people who earned PhDs in the U.S.A. between July 1 2009 and June 30 2010 (c. 43,000 people). Apparently, 14,000 people (32.5 %) reported that their doctoral work spanned academic boundaries

Two of their main findings are: (i) individuals who complete an interdisciplinary dissertation display short-term income risk, since they tend to earn nearly $1,700 less in the year after graduation; and (ii) the probability that non-citizens pursue interdisciplinary dissertation work is 4.7% higher when compared with U.S. citizens. Sadly, but not unexpectedly, women tend to earn less compared to men upon completion of the doctorate. Perhaps less expectedly, European American individuals also earn less in their first year after graduation than those in other racial groups.

For us, some of the more interesting data are:

Discipline Agricultural & Life Sciences Biological Sciences Health Sciences Computer Sciences & Mathematics

% of all Research Doctorates 2.3 17.6 4.4 7.0

% Interdisciplinary 44.5 41.1 29.9 22.7

In the regression models, adjusting for all other factors, the "influence of interdisciplinary research upon salary" was positive for Computer Sciences & Mathematics as well as for Health Sciences, but was negative for Biological Sciences. However, the "influence of interdisciplinary research upon employment as postdoctoral researcher" was negative for Computer Sciences & Mathematics as well as for Health Sciences, but was positive for Biological Sciences.

Make of this what you will.

Results of some bioinformatics polls

In 2008, Michael Barton conducted a Bioinformatics Career Survey. Since then, various groups have updated some of that information by conducting polls of their own. Below, I have included some of the more recent results, for your edification.

This first one comes from the Bioinformatics Organization, in response to the question: What is your undergraduate degree in? It is interesting to note that more bioinformaticians are biologists by training, rather than computational people.

The next one is actually an ongoing poll at BioCode's Notes, in response to the question: Which are the best programming languages for a bioinformatician? R is an interesting choice as the most useful language, given the more "traditional" use of Perl and Python.

That leads logically to another of the Bioinformatics Organization's questions: Which computer language are you most interested in learning (next) for bioinformatics R&D? I guess that if you already know R, then either Python or Perl is a useful thing to learn next.

Furthermore, the Bioinformatics Organization also asked: Which math / statistics language / application do you most frequently use? The choice of R here is more obvious, given that it is free, which most of the others are not. I wonder what the answer "none of the above" refers to.