Despite this evidence, the scientific community is driven by competitive processes, which sometimes lead to secrecy and unwillingness to freely discuss future work. In particular, since exploiting a dataset is a key asset to get papers accepted, the competitive process may conduct researchers to keep a valuable dataset (or a valuable software) to themselves in the fear that other may exploit it better and faster. This (natural) behavior makes the science progress slower than if a collaborative process was in place.
The “open dataset and open software track” is a tentative to fix this issue in the ACM MMSys conference. The track aims at favoring and rewarding researchers who are willing to share. It aims at making science progress faster, still in the competitive process (we accepted only a subset of the submitted datasets for presentation), but with the collaboration in mind.
The movement for the promotion of reproducible research is ongoing and we are very glad to see that the number of submitted open artifacts has increased since 2011 (the first open dataset track in MMSys history). Previous datasets for MMSys can be found here. This year, we accepted ten papers, which describe dataset and software.
To get one step further, we have embraced the new initiative launched by ACM Digital Library related to reproducibility badges. In very short, the authors of an accepted paper that let other researchers check the artifact they used can be rewarded by obtaining a badge for their paper. We have implemented badges in two tracks in 2017 ACM MMSys.
Badges for Dataset TrackIn the Open Dataset Track, we have selected the badges "Artifacts Evaluated – Functional", which means that the dataset (and the code) has been tested by reviewers, who had no problem executing, testing, and playing with it, and "Artifacts Available", which means that the authors decided to publicly release their dataset and their code.
During the selection process, we have acted as usual in academic conference. We have invited a dozen of researchers (who I know are committed to a more reproducible research) to join the committee. Then, we have associated three reviewers to each paper, a paper being a description of a dataset, which is available in a public url. Reviewing an artifact is not the same experience as reviewing an academic paper. To capture a bit more the experience of engaging into the artifact, we have added some unusual questions in the reviewing form, typically:
Relevance and reusability (from 1. Artifact on a niche subject to 4. A key enabler on a hot topic)
Quality of the documentation (from 1. The documentation is below expectations to 3. Crystal-clear)
Artifact behavior (from 1. Bad experience to 3. Everything works well)
Then, as usual in academic conferences, we selected the dataset papers that got the best appreciation from the reviewers. This year, four of them are related to 360° images and video, the currently hottest topic in multimedia community. Such datasets have been cruelly missing so far, so we are very happy to fill this gap. Two artifacts are related to health, two to transport systems and two to increasingly popular human activity.
Badges for Papers in Research Track
In parallel, the organizers of the MMSys conferences have accepted to badge some of the papers that have been accepted in the main "Research Track" of the conference. In this case, the process has been different. First, we waited to know which papers have been accepted. Then, and only then, we have contacted the authors of these accepted papers and proposed them a deal: if you want a badge, you have to first release the artifact in a public website and also to write a more detailed documentation on how to use this artifact. But since we know that this latter instruction could prevent authors to apply for the badge, we authorize those who applied to get extra-pages as Appendix in their papers.
The authors gave us access to a pre-version of the camera-ready version of their papers, then, I contacted another member of the program committee and we both tested the artifact. In that case, we do not have to consider whether the dataset matters for the community or whether it is an enabler. Since the paper has already been accepted, our only mission is to test the dataset and to check if the documentation is enough for any scientist to play with it.
Three papers have followed the process until the end and we are proud to offer them the badges.