Aiming rapid results sometimes leads to cutting the value of the data obtained and shared. Structural biology offers an enlightening example.
by
“Research is the discovery, the invention of things that others have not done. So, it is by definition a form of competition. It must be assumed,” said Antoine Petit, In Le Monde, on February 14, when he had just been renewed at the head of the CNRS. Except that not everyone agrees, evoking a long list of negative effects of competition, such as the development of individualism, brakes on collaborations, culture of secrecy, time and money spoiled to compete …
“There are always a lot of opinions on the issue, but few informed opinions,” recalls Pierre Azoulay, professor of economics at the Massachusetts Institute of Technology (MIT). Hence the interest of a work under rereading by peers, updated at the start of the year. Carolyn Stein and Ryan Hill, respectively in post-doctorate in Stanford and at Brigham Young University, after their thesis at MIT, demonstrate for the first time a negative effect of this competition: it produces results of less good quality … “We often heard people talking about this association Between competition and negative effects, but it was based on anecdotes, so we wanted to see what data could say, “sums up Carolyn Stein.
Their method is clever. As a competition field, the duo has studied structural biologists who seek to know, thanks to X -rays, the shape of a protein, linked to its function. These data is deposited in a public base, Protein Data Bank (PDB), so that everyone benefits.
The risk of competition
The two researchers describe a model of behavior of this community of biologists. The latter decide to embark on the determination of a protein structure, considering the time they need, the feedback they can expect (in terms of quotes from their article in subsequent work) and resources human to devote to it. But they must take into account the risk of being exceeded by competitors. “It is a simplification of the real world, but specialists in the field told us that it was fairly fair,” says Carolyn Stein. This dilemma, between the time to devote to their work and the fear of being second, is described by a fairly simple mathematical model, which has the good taste of providing predictions, and above all whose variables can be calculated.
Thus, the quality of research is measured by the precision of the three -dimensional structures obtained. The time spent and the number of people involved are available in the database. The number of competitions in competition is also accessible, by studying how many protein structures have been deposited there. Finally, the “potential” of a research is evaluated by the duo, using a tool for predicting the number of times when the article is cited, an indicator certainly disputed, but widely used to estimate the recognition of a work. This prediction is based on the analysis of the type of structures that have been more or less cited in the past.
You have 36.7% of this article to read. The continuation is reserved for subscribers.