Beyond Reproducible Research: Building a Formal Representation of a Data Analysis

2026-03-11

AI Summary

This study addresses a critical limitation in traditional reproducible research, where sharing only code and results fails to expose the implicit assumptions, expectations, and premises underlying an analyst’s reasoning—thereby hindering thorough evaluation of analytical quality. To overcome this, the paper proposes a formal modeling framework that explicitly translates the analyst’s tacit reasoning process into structured logical representations, statically capturing the construction logic of the analysis. This approach enables systematic scrutiny of the analytical chain of reasoning, assumption sensitivity, and conclusion robustness—even in the absence of the original data. Empirical validation on representative data analysis tasks demonstrates the framework’s effectiveness, achieving both logical visualization and data-free static assessment of analytical integrity.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Data analyses are often constructed in an imperative manner, where commands representing actions taken on the data are issued sequentially. The publication of these commands, along with the data, is essential to the reproducibility of the analysis by others. However, simply presenting the code and the results of running the code can hide important details about the data analyst's premises, expectations, and assumptions about the data. Understanding this analysis reasoning can be critical to evaluating the quality of an analysis and for suggesting possible improvements. We argue that a formal representation of a data analysis that externalizes its logical construction offers more useful information for statically illustrating an analyst's reasoning. Such a formal representation would allow for the evaluation of some aspects of a data analysis without the need for the data, the visualization of the logical connections leading to a conclusion, and the ability to assess the sensitivity of an analyst's assumptions to unexpected features in the data. In this paper we describe an implementation of this formal representation and how it might be applied to some common data analysis tasks.

Problem

• reproducible research
• data analysis
• formal representation
• analysis reasoning
• assumptions

Innovation

• formal representation
• data analysis reasoning
• reproducible research
• assumption sensitivity
• logical structure