Multidataset

Multidataset is a term used in data science to describe the use of two or more datasets in a combined analysis. The goal is to leverage information from diverse sources to improve statistical power, generalizability, and robustness, while accounting for differences in study design, measurement, and population.

Multidataset work encompasses more than simply stacking data. It may involve formal data integration, joint modeling, or cross-dataset validation, and is closely related to meta-analysis, data fusion, and transfer learning. The exact approach depends on the research question and the compatibility of datasets.

Common approaches include data harmonization to align variables, normalization to reduce technical variation, and batch effect correction. Analysts may perform pooled analyses, meta-analytic synthesis, or hierarchical models that borrow strength across datasets. In machine learning, multidataset setups can use domain adaptation, transfer learning, or multi-view methods when feature spaces differ.

Applications span genomics and epidemiology, social sciences, ecology, and computer vision, especially when multiple cohorts, sites, or sources are available. For example, meta-analyses across genomic cohorts or joint analyses of clinical trial data can increase power and improve external validity.

Key challenges include heterogeneity of data collection and quality, missing features, label inconsistency, privacy and licensing constraints, and the risk of biased conclusions if study inclusion is not carefully managed. Transparent reporting and data provenance are essential.

Related concepts include meta-analysis, data integration, data fusion, transfer learning, and multi-task learning.