I became aware of a couple of interesting opinion pieces in the academic literature this week, both via Twitter.
The first one’s titled Benchmarking Open Access Science Against Good Science (“Commentary”) by Lindenmayer and Likens, published at the Bulletin of the Ecological Society of America, ref. via Sean Ulm. The second is apparently (no author given) an editorial at The Economist, titled “Trouble at the lab” (open access). I consider both to be well worth reading, and more or less right on the money. I’ll summarize the first one briefly here for those without access.
The authors’ principal point is that scientists who use publicly available data sets in their studies need to be very careful with their analyses to avoid coming to wrong conclusions. The basic reason for this: there are often details and subtleties to such data that need to be thoroughly understood, but which are often not. They state:
Our extensive experience from a combined 80 years of collecting empirical data is that large data sets are often nuanced and complex, and appropriate analysis of them requires intimate knowledge of their context and substance to avoid making serious mistakes in interpretation. We therefore suggest that it is essential that those intending to use large, composite open-access data sets must work in close collaboration with those responsible for gathering those data sets.
Then they really unload on a certain class of scientists:
There is also the emerging issue of a generation of what we term here as “parasitic” scientists who will never be motivated to go and gather data because it takes real effort and time and it is simply easier to use data gathered by others. The pressure to publish and extraordinary levels of competition at universities and other institutions (Lindenmayer and Likens 2011) will continue to positively select for such parasitic scientists. This approach to science again has the potential to lead to context-free, junk science. More importantly, it may create massive disincentives for others to spend the considerable time and effort required to collect new data.
It’s not every day you see such harsh things said in academic journals, and they could have avoided use of “parasitic”, but their point is well founded.