EDBT Reproducibility

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.

Evidence map

• Map each claim, mechanism, and reported number to a verifiable location — a paper section, a table generated from a logged run, or a script in the artifact.
• For a mechanism, give enough of the algorithm, data structures, parameters, and system integration that a reader could reimplement or rebuild it.
• For an evaluation, report workloads and their derivation, dataset versions and sources, the measurement harness, metrics, and the analysis scripts.
• Keep the availability statement truthful and specific: what is shared, the workloads and data, the hardware assumptions, and — if something cannot be shared — exactly why.
• Keep the paper and the artifact consistent: a number in the PDF that no script produces is the contradiction reviewers read as carelessness.

Availability statement audit

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).

Provenance pinning (database-systems flavor)

[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

Degrees of reproducibility (state the one you achieved)

• Turnkey: one documented command (or container) regenerates each table/figure from a run or from logged results.
• Scripted: scripts exist but require documented manual steps, a specific cluster, or external data access.
• Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.

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.

Vignette: a distributed-operator study

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.

Consistency and camera-ready pass

• Before submission: every reported number traces to the artifact; the availability statement matches reality; if the cycle is double-blind, the artifact carries no identity strings.
• Before camera-ready: deposit the package in a DOI-issuing archive (Zenodo, figshare, Software Heritage) with an OSI-approved license, replace any anonymized links with the permanent ones, and make the statement consistent with the open-access OpenProceedings record (edbt-artifact-evaluation, edbt-camera-ready).

Output format

[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>