An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.
Welcome on this site about reproducible research. This site is intended to gather a lot of information and useful links about reproducible research. As the authors mainly work in signal/image processing, that is also the main focus of this site. Follow the links in the text or in the menu to navigate through this site.
This site hosts a blog with a wide range of posts related to reproducibility. A description of how we make our research reproducible can be found on the How To page. We try to keep track of a bibliography of reproducible research-related articles. The Links page contains a large set of links about RR, tools, etc. And on the Reproducible Material page, you can find a set of links to code and data for papers in signal and image processing.
After a colleague asked something about a paper you wrote, you spend a considerable amount of time finding back the right program files you used in that paper. Not to talk about the time to get back to the set of parameters used to produce that nice result.
Because this type of situations sounded all too familiar to many of us, we are now trying to make our research reproducible. Most of the ideas about reproducible research come from Jon Claerbout and his research group at Stanford University. We believe reproducible research
can be helpful in many ways:
- It will help us in the first place, to reproduce figures in the revisions of a paper, to create earlier results again in a later stage of our research, etc.
- Other people who want to do research in the field can really start from the current state of the art, instead of spending months trying to figure out what was exactly done in a certain paper. It is much easier to take up someone else’s work if documented code is also available.
- It highly simplifies the task of comparing a new method to existing methods. Results can be compared more easily, and one is also sure that the implementation is the correct one.
This may all sound very trivial, and in discussions with colleagues, there was a general agreement that this is how research should be performed. However, in practice, only a minority of the papers available today provide code and data. Making articles reproducible indeed requires a certain investment in time. However, we think that it is worth the investment. The interest is hard to quantify, but from download statistics and Google rankings, we can see that it really pays off!