There has been a major shift among journals towards making data available at the time of publication. The PLoS stable of journals which includes PLoS Medicine, PLoS Biology, and PLoS One, for example, have a uniform publication policy that is quite forthright about the need to share data.
I have mixed feelings about this. I have certainly advocated for data sharing and (with Pascale Allotey) conducted one of the earliest empirical investigations of data sharing in Medicine. I can understand, however, why researchers are reluctant to provide open access to data. The data can represent hundreds, thousands, or tens of thousands of person-hours of collection and curation. The data also represent a form of Intellectual Property in the development of the ideas and methods that lead to the data collection. For many researchers, there may be a sense that others are going to swoop in and collect the glory with none of the work. There have certainly been strong advocates for data sharing where the motivation looked to be potentially exploitative (see our commentary).
I recently stumbled across a slightly different issue in data sharing. It arose in an article in PLoS One by Buttelmann and colleagues. Their study looked at whether great apes (Orangutans, Chimpanzees and Bonobos) could distinguish in a helping task between another’s true and false beliefs. The data set comprised 378 observations from 34 apes in two different studies, and they made their data available … as a jpg file. A small portion of it appears below, and you can download the whole image from PLoS One.
It seems strange to me to share the data as an image file. If you wanted people to use the data, surely you would share it as a text file, CSV, xlsx, etc. If the intention was to satisfy the journal requirements but discourage use, then an image file looks (at first glance) to be a perfect medium. Fortunately, there are some excellent online tools for optical character recognition (OCR), and the one I used made quick work of the image file. I downloaded it as in xlsx format, read it into R, and cleaned up a few typographical errors that were introduced by the OCR. You can download their data in a machine-readable form here. I have included in the download an R script for reading the data in and running a simple mixed effects model to re-analyse their study data. My approach was a little better than theirs, but the results look pretty similar. I am not sure why they did not account for the repeated measurement within ape, but ignoring that seems to be the typical approach taken within the discipline.