About the Journal
Aims and Scope. Journal of Data-Centric AI Systems provides a focused venue for research on dataset curation, labeling quality, data augmentation, data drift, dataset bias. The journal prioritizes technically substantive, internationally relevant work in which the principal novelty lies inside the stated computing scope rather than only in the application domain.
Core topics in scope include:
- dataset curation
- labeling quality
- data augmentation
- data drift
- dataset bias
- training-data diagnostics
- data documentation
- data-centric AI
- dataset engineering
- data quality for ML
Evidence and methodological expectations. Data-centric work should document provenance, preprocessing, data quality, schema or representation choices, scalability, reproducibility, and statistically or operationally meaningful evaluation. Comparative studies should use representative datasets or workloads and report limitations.
Normally outside scope. Dashboard-only studies, descriptive statistics without computing novelty, undocumented proprietary data demonstrations, and routine database implementations without new data-management or analytical insight are normally outside scope.
Research integrity and reproducibility. Authors should disclose datasets, software, model or system configurations, experimental protocols, statistical procedures, ethical approvals where applicable, competing interests, funding, and any material use of generative AI. Data and code should be shared when legally and ethically possible, or the restriction must be explained.
