Artifact Review and Badging
The ACM Digital Library implements a new program, which aims at promoting the reproducibility of research and public release of research “artifacts” (code source and dataset). The main idea is to award with a badge the papers that have publicly released the artifacts the authors used in their paper. The badge appears as a highlight associated with the paper in ACM DL. More details about the badging system: https://www.acm.org/publications/policies/artifact-review-badging
MMSys has a long tradition of promoting artifact sharing and was the first ACM SIGMM conference to implement the ACM badging policy.
Since your paper has been accepted to MMSys regular track (congratulations again), we offer you the opportunity to get the “Artifact Evaluated” badge. This offer comes in addition to the acceptance of your paper and is optional.
Note that all the details that enable readers to properly use the artifacts can appear in a special Appendix, which is graciously offered to the authors, with no effect on the final page count of the final manuscript. The artifact will be hosted by ACM DL.
If you are interested by being awarded with a badge, here is the procedure:
- You first have to confirm your interest in obtaining the badge. Please contact Gwendal Simon email@example.com and Lucile Sassatelli firstname.lastname@example.org (reproducibility chairs) before January 21st. Please indicate whether the artifact you accept to publicly share is a dataset, or a code source, or both.
- You will then have to submit your software and/or dataset artifacts by January 31st. Depending on the number of positive replies we get by January 21st, we will tell the exact platform onto which the submission must be uploaded for review. The submission must include:
- The extra-pages describing the artifacts that you would like to add to your accepted paper.
The artifacts. We strongly recommend you to use a format for the artifacts that ensure easy reviewing and reproducibility. Specifically, the authors are encouraged to:
– either make a self-contained docker image or Virtualbox virtual machine as detailed here: http://www.mmsys2019.org/participation/sharing-open-source-software/.
– or use one of the tools that allow direct integration of your artifacts into the ACM DL: these three tools (Collective Knowledge, OCCAM and Code Ocean) are each described with very short (~2min) videos here: https://www.acm.org/publications/dl-pilot-integrations. They cover a wide range of cases and programming languages, and are worth considering in most cases.
After reviewing, you will get notified before the camera-ready deadline. The remarks from the reviewers may help you to improve the description of the dataset for the preparation of the final version of your paper.