data

Three avenues to support open approaches to science - the cases of funding, data acquisition and knowledge curation

Today, I received an email from the Open Society Institute's Information Initiative:

We'd like to ask you to think about two to three emerging opportunities for--or threats to--open society institutions and values that you are aware of which are not receiving sufficient attention and where a funder like OSI could usefully intervene. We encourage you to suggest issues that are still very much on the horizon; there need not be an obvious solution to the points you raise.

I know that the OSI had and has many interesting projects running (also in regions and cultures normally off the radar, including some of those dear to me) but I have often (not just jokingly) taken its abbreviation to stand for "Open Science Institute", and so I take the liberty here to shrink the space of possible replies by concentrating on openness in science, anyway the most prominent topic in my blog.

My intuitive response would be that several inefficiencies in our current knowledge creation and curation systems cry for a test run of open approaches. Not sure whether I can distill this down to three issues, but let's get started by listing some of the ideas, and I hope that you can then help me structure and adapt them appropriately. To facilitate the discussion, I will resort to Cameron's depiction of the research cycle:

Herding effects in science

When I was notified recently that a new article on vocal learning had appeared in PLoS ONE, I took a brief look and found the study relevant but not interesting enough to actually read it now. However, I accidentally came across the phrase "Large-Scale Assessment of the Effect of Popularity on the Reliability of Research" - the title of a paper published the same day whose abstract and discussion actually got me interested, since they centred around the relationship between the popularity of research topics and the reliability of the corresponding results. This is related to the issues of (i) multiple testing, familiar to anyone working with statistics, and (ii) measuring research impact - a common subject here as well as on other blogs.