Use this before submission and again before camera-ready. EDBT's community has a reproducibility-forward culture, and the published record is open access on OpenProceedings — so an inspectable, re-runnable package raises a paper's standing and, for an Experiments & Analysis paper, is the contribution. The goal is that a competent reader could rebuild your measurements and reach your conclusions.
| Claim in the paper | Weak availability answer | EDBT-ready answer |
|---|---|---|
| "We evaluate on workload W" | "Data available on request" | Archived workload/query-log derivation + the extracted data or a documented access path |
| "Our operator lowers latency" | "Code will be released" | Runnable system/prototype with a build, a demo run, and the config |
| "We compare N systems" (E&A) | Numbers with no harness | The full comparison harness that regenerates every table |
| "On a 128-node cluster" | Nothing about environment | Hardware/cluster spec, engine build/commit, and how to size a smaller reproduction |
"Available on request" is treated as not available; convert every such line into a concrete package or an explicit, justified exception (licensing, confidentiality).
[Data] pin dataset versions and sources; archive the derived workload/query-log, not just a
description; document filtering and sampling
[System] record the engine/prototype build or commit; ship a build recipe or container
[Environment] state hardware, memory, network, and node counts; note what a smaller reproduction changes
[Harness] the measurement scripts that produce each table/figure, with fixed configuration
[Randomness] log seeds for any stochastic step; say what is and is not deterministic
For EDBT, aim turnkey for anything a reviewer might re-run quickly (a demo run on a small workload, a plot from logged results); large-cluster or licensed-data experiments may stay scripted with the environment and access clearly documented. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine.
Consider an operator evaluated on a cluster. Its reproducibility spine: a container or build recipe for the engine plus the operator; the workload-derivation scripts with pinned dataset versions; the measurement harness that runs the operator and the tuned baseline across node counts; the logged raw results; and the analysis notebooks that turn them into the paper's tables — plus one honest sentence about the parts (the full 128-node run, a licensed dataset) that a reader reproduces at reduced scale and why.
edbt-artifact-evaluation, edbt-camera-ready).[Claim inventory] <claim -> evidence location>
[Availability] concrete / vague / missing
[Provenance gaps] <dataset versions / engine build / environment / seeds>
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Artifact fixes] <additions before upload>