Dataset Construction Checklist
A dataset construction checklist turns a modeling idea into an auditable dataset. It should be written before interpreting metrics, because most evaluation failures start as data-definition failures.
A compact construction pipeline is:
where is the source record set, is the preprocessing contract, and is the split rule.
Checklist
- Define the example unit.
- Define the split unit.
- Write the dataset split contract.
- Write the data schema.
- State label semantics and missing-label policy.
- Record missing data, censored label, and weak label handling.
- Record source, license, collection process, and metadata provenance.
- Record data lineage from sources to final splits.
- Write the preprocessing contract.
- Decide the sampling strategy.
- Check duplicates, near-duplicates, and grouped leakage.
- Build a dataset card.
- Attach an evaluation protocol.
Molecular Modeling Checks
- Molecules: standardization, tautomer/protonation/stereo policy, scaffold grouping.
- Proteins: sequence identity, family grouping, structure source, confidence, missing regions.
- Protein-ligand complexes: receptor preparation, ligand preparation, pocket definition, pose source.
- Assays: target context, endpoint, threshold, units, censoring, and harmonization.
- Structure-based tasks: template leakage, ligand-defined pocket leakage, and deployment-available context.
Public Writing Boundary
For a public blog, describe dataset construction as a general method unless the dataset is public and the metadata is verified. Do not publish private dataset names, collaborator details, internal paths, unpublished results, or non-public task identifiers.