Validation is a crucial step in the data science lifecycle, encompassing a series of checks and assessments designed to confirm that the outputs of a data science project meet the desired objectives and perform well in real-world scenarios. Without rigorous validation, data science models and insights may lead to erroneous conclusions, suboptimal decisions, and potential financial or reputational damage. Validation helps to ensure the model's predictive performance and generalizability to new, unseen data. It detects and mitigates issues such as overfitting, bias, and variance, confirms the robustness and reliability of data preprocessing steps and feature engineering, and evaluates the impact of assumptions and methodological choices on the final outcomes.

In FAIRiCUBE we have developed a comprehensive and holisitc validation framework that includes the evaluation of performance/impact of the FAIRiCUBE use cases execution, i.e. if the defined research questions are correctly addressed, and the functional aspect of validation, i.e. if technical infrastructure and chosen data processing functions as intended. The validation documentation shall include metadata concepts for data, analysis & processing, and machine learning metadata.