Section: New Results
Research Management: Research Reproducibility and Credit
We are actively promoting better research practices, in particular in term of research reproducibility and contribution recognition. Our contribution this year is threefold
First, we have participated to the writing of a book introducing reproducible research . For a researcher, there is nothing more frustrating than the failure to reproduce major results obtained a few months back. The causes of such disappointments can be multiple and insidious. This phenomenon plays an important role in the so-called "research reproducibility crisis". This book takes a current perspective onto a number of potentially dangerous situations and practices, to examplify and highlight the symptoms of non-reproducibility in research. Each time, it provides efficient solutions ranging from good-practices that are easily and immediately implementable to more technical tools, all of which are free and have been put to the test by the authors themselves. Students and engineers and researchers should find efficient and accessible ways leading them to improve their reproducible research practices.
Second, to allow students and engineers and researchers to receive proper training in reproducible research, we have run the second session of the Mooc "Reproducible research: Methodological principles for a transparent science" on the FUN platform from April, 1 to June, 13 2019. This MOOC allows scientists to learn modern and reliable tools such as Markdown for taking structured notes, Desktop search applications, GitLab for version control and collaborative working, and Computational notebooks (Jupyter, RStudio, and Org-Mode) for efficiently combining the computation, presentation, and analysis of data. More than 2,100 persons registered to this session and we are currently working on a third session which is expected to start in the beginning of the year 2020.
Third, software is a fundamental pillar of modern scientific research, not only in computer science, but actually across all fields and disciplines. However, there is a lack of adequate means to cite and reference software, for many reasons. An obvious first reason is software authorship, which can range from a single developer to a whole team, and can even vary in time. The panorama is even more complex than that, because many roles can be involved in software development: software architect, coder, debugger, tester, team manager, and so on. Arguably, the researchers who have invented the key algorithms underlying the software can also claim a part of the authorship. And there are many other reasons that make this issue complex. We provide in  a contribution to the ongoing efforts to develop proper guidelines and recommendations for software citation, building upon the internal experience of Inria, the French research institute for digital sciences. As a central contribution, we make three key recommendations. (1) We propose a richer taxonomy for software contributions with a qualitative scale. (2) We claim that it is essential to put the human at the heart of the evaluation. And (3) we propose to distinguish citation from reference which is particularly important in the context of reproducible research.