Many research projects necessitate the involvement of inputs from other researchers, including those in a different area. For example:

• Research on schedulers depends on the availability of traces (e.g., from large ML clusters or servers running databases for web servers) that are not readily available to the average computer systems researcher. At the same time, these traces are considered sensitive (because they reveal information about cluster size and machine specification) and are not often posted online.
• Research on verification and testing requires system specifications, which need to be validated by the programmers who built the software but are not experts in verification. Few groups have sufficient expertise to both write specifications for real-world software and use them for verification tasks, and collaboration is thus key to these efforts.
• Research that extends prior work, which constructs data or analysis artifacts, and would benefit from starting from these artifacts.

However, finding other researchers' digital artifacts, and then using (or extending) them is challenging today: we have relatively few mechanisms to discover existing digital artifacts (the ones we have usually require serendipitously reading a research paper or website that describes the artifact); no mechanisms to ensure that contributions from publicly available artifacts are recognized; and nearly no good mechanism to share artifacts among a small set of research groups (each of which might have an evolving set of individual participants).

We will address these challenges by developing systems that make it easier to discover digital artifacts, make it easier to track the contributions of each artifact's provider, and reduce friction for sharing artifacts between research groups.

Investigators: Michael Coblenz