datasetdependency

Dataset dependency refers to the reliance of analyses, models, and data pipelines on the characteristics and availability of one or more datasets. It covers how outputs are shaped by data sources, schemas, labels, and preprocessing steps. It is distinct from software dependencies and emphasizes data-driven constraints and assumptions.

Key dimensions of dataset dependency include distributional properties (covariate and concept shift), data quality (missingness, errors in labeling), provenance and collection methods, coverage or representativeness of the population, and timeliness. Changes in these aspects can alter conclusions, degrade model performance, impede reproducibility, and influence fairness and bias in downstream decisions. Understanding dataset dependency helps identify where results may be sensitive to specific data and where generalization may be limited.

Managing dataset dependency involves practices such as dataset versioning, recording data provenance and lineage, and documenting schemas, labels, and preprocessing steps. Reproducible experiments often require fixed datasets or clearly defined data splits. Evaluating models on multiple datasets or domains, and performing cross-dataset validation, helps assess robustness. It is important to recognize and mitigate data leakage and overfitting to a particular dataset.

Mitigation strategies include monitoring for data drift, applying domain adaptation when appropriate, curating diverse and representative data, and conducting ablation studies to understand the impact of dataset choices. Transparent reporting of dataset dependencies in methodology sections enhances reproducibility and enables others to interpret results within the context of the data used.