Scientific research requires that results can be accessed, tested, and replicated by other researchers. In general, performing reproducible research is not simple and it can be even impossible (for example, in the case of astrophysics replicating singular events if even out of the control of the researcher). However, in computational sciences there is no special hindrance to reproduce, repeat, and compare results. In 2009 the Image Processing On Line (IPOL) journal was founded as a modest contribution to implement reproducible research in the Image Processing field, and then expanded to more general signal-processing algorithms, such as video or physiological signal processing, among others. We re-defined the concept of publication, which is no longer just the article, but the combination of the article, its source code, and any associated data needed to reproduce the results, everything as an indivisible whole. In this article, we present the concept of IPOL, our approach to reproducible research, the challenges we had to address and solve, and some applications other than image processing in IPOL (biomedical, educational). We also present other IPOL inspired initiatives before discussing the opportunity of performing reproducible research in these fields, as well as the advantages and difficulties.