Skills Data Science Achieving Scientific Reproducibility in Data Systems

Achieving Scientific Reproducibility in Data Systems

v20260724
icde-reproducibility
This guide outlines comprehensive standards for scientific reproducibility in data engineering. It details best practices for pinning hardware, software, datasets, and variance protocols, ensuring that all performance claims are verifiable and re-runnable by peers, making reproducibility a scored component of academic submission.
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Overview

ICDE Reproducibility

Use this before submission and again before camera-ready. ICDE authors are expected to submit supplemental material, and its availability is weighed in the evaluation — so reproducibility is not optional polish, it is scored evidence.

Evidence map

  • Map each performance claim to a verifiable location: a figure regenerated from logged runs, a workload script, or a documented measurement in the supplement.
  • Pin the environment: CPU, memory, storage device (the NVMe/SSD/HDD distinction changes results), OS and kernel, database/library versions, compiler flags, and any cluster topology.
  • Pin the data: dataset provenance, construction steps, scale factors, and for synthetic data the generator with its seeds — a workload nobody can regenerate is not reproducible.
  • Pin the variance protocol: how many runs, warm-up handling, how outliers are treated, and whether reported bars are standard deviations, confidence intervals, or percentiles.
  • Document baseline tuning: the configuration and tuning budget given to each competitor. Reproducibility here means a reader can re-run the fair comparison, not just your system.
  • Trace figures to raw data: emit tables and plots from logged results so the PDF numbers and the supplement cannot drift apart.

Systems-reproducibility audit table

Dimension Weak answer ICDE-ready answer
Hardware "a modern server" Exact CPU, RAM, storage device model, and topology
Data "a large dataset" Named dataset or a seeded generator with scale factors
Variance one median number N runs with declared spread and warm-up policy
Baselines "we compared to X" X's config and tuning budget, re-runnable
Figures hand-entered numbers Plots emitted from logged runs by a script

Degrees of reproducibility

  • Turnkey: run_all.sh regenerates every figure from logged seeds on a documented machine; run_small.sh gives a fast subset for a reviewer with limited hardware.
  • Scripted: scripts exist but need documented manual steps or restricted-data access.
  • Descriptive: prose detailed enough that a competent engineer could rebuild the pipeline.

For ICDE, aim for turnkey on the synthetic experiments — a reviewer will re-run a generator far sooner than they will provision a cluster — and scripted for large real-data or proprietary-hardware runs, with deviations documented. State the level you actually achieved; overpromising turnkey behavior that fails on a clean machine is worse than an honest "scripted."

Vignette: a throughput-plus-latency paper

A submission claims higher ingestion at bounded read-latency cost. Its reproducibility spine: the storage device and queue-depth settings, the workload generator with append-to-scan parameters and seeds, the run count and percentile policy for the latency tails, the baseline LSM's compaction configuration, and a run_small.sh that reproduces the headline crossover on a single machine in minutes — plus one honest sentence on any result that needs the full cluster.

Single-blind note

  • ICDE supplemental material need not be anonymized — names may stay on the repository and in commit history. Spend the saved effort on making the package actually run, not on scrubbing identity a double-blind venue would demand.

Output format

[Claim inventory] <claim -> evidence location>
[Environment pinned] complete / partial / missing <what>
[Variance protocol] <runs / spread type / warm-up>
[Baseline fairness] <tuning budgets documented? y/n>
[Reproduction level] turnkey / scripted / descriptive
[Fixes before submission] <ordered list>
Info
Category Data Science
Name icde-reproducibility
Version v20260724
Size 4.02KB
Updated At 2026-07-28
Language