The time has come for funding bodies and research institutions to give multidisciplinary researchers due credit when applying for grants, jobs, and promotions. That’s the conclusion of an opinion paper released in June by the Life, Environmental and Geo Sciences Committee of Science Europe, a Brussels-based association of more than 50 funding bodies and research institutions from across Europe that aims to promote the development of a stronger, more inclusive, and more open European research system.
“Young scientists entering multidisciplinary research need to be aware of various challenges that lie ahead in terms of career development. They have to recognize the importance of ‘visibility,’ from early on in their careers,” writesDirk Inzé, in a statement on behalf of the Science Europe Life, Environmental and Geo Sciences Committee. Inzé is the scientific director of the Department of Plant Systems Biology at the Flanders Institute for Biotechnology (VIB) at Ghent University in Belgium.
Research in the life, environmental, and geosciences has come to rely on high-throughput technologies and computational techniques, the Science Europe committee writes in the opinion paper, and such technological advances have ushered in an era where data-intensive science takes center stage and where researchers must increasingly cross disciplinary boundaries to work on large collaborative projects.
Unfortunately, the committee writes, necessary changes in the way researchers are evaluated are lagging. The paper denounces “the lack of clear evaluation metrics for scientists working in multidisciplinary teams,” noting that “[t]he absence of such metrics already has a negative impact on career paths, as many scientists hesitate to participate in multidisciplinary research.”
For those who do enter multidisciplinary research, current authorship standards—which, in the life sciences for example, identify the first author as the researcher who did the most work and the last author as the supervisor of the project—inadequately capture the contributions of different team members, which often are equally valuable. The problem is especially acute for those “contributors from ‘supporting’ disciplines—for example those who deal with data analysis and integration and are not authors of the original hypothesis and/or grant holders,” writes Inzé, on behalf of the committee. Those authors “tend to be placed in the middle of the author list, with no further explanation about their specific input.” To make recognition yet more difficult, different disciplines often use different publishing standards, the opinion paper notes.
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IN MAY last year, a supercomputer in San Jose, California, read 100,000 research papers in 2 hours. It found completely new biology hidden in the data. Called KnIT, the computer is one of a handful of systems pushing back the frontiers of knowledge without human help.
KnIT didn’t read the papers like a scientist – that would have taken a lifetime. Instead, it scanned for information on a protein called p53, and a class of enzymes that can interact with it, called kinases. Also known as “the guardian of the genome”, p53 suppresses tumours in humans. KnIT trawled the literature searching for links that imply undiscovered p53 kinases, which could provide routes to new cancer drugs.
Having analysed papers up until 2003, KnIT identified seven of the nine kinases discovered over the subsequent 10 years. More importantly, it also found what appeared to be two p53 kinases unknown to science. Initial lab tests confirmed the findings, although the team wants to repeat the experiment to be sure.
KnIT is a collaboration between IBM and Baylor College of Medicine in Houston, Texas. It is the latest step into a weird world where autonomous machines make discoveries that are beyond scientists, simply by rifling more thoroughly through what we already know, and faster than any human can.
Read on: @New Scientist