data analysis

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.

This study investigates the misalignment between data workers’ implicit cognitive models of complex hierarchical data (e.g., nested tables) and the explicit data models encoded in analysis code, and how such misalignment negatively impacts analytical efficiency and accuracy. Method: Through semi-structured interviews, cognitive sketching, and reflexive thematic coding with 10 collaborative data practitioners, we systematically identify divergent, coexisting cognitive models within teams. Contribution/Results: We introduce the novel concept of “parallel risk”—a form of collaborative breakdown arising from persistent cognitive misalignment between data model designers and end users. All participants exhibited internal representations inconsistent with the true data structure, leading to systematic reasoning errors. Based on these findings, we derive human-centered design principles and intervention strategies for analytical tools that promote cognitive alignment. This work establishes a theoretical foundation and practical framework for improving usability in data engineering and visualization systems.

This paper systematically examines the structural role and evolutionary trajectory of simulation methods across the statistical lifecycle. Addressing the current fragmentation and conceptual ambiguity in simulation practice, the study introduces, for the first time, a comprehensive functional taxonomy—spanning model specification, diagnostic checking, validation, and inference—and proposes a “simulation-driven” paradigm for statistical practice, prioritizing computational scalability. Methodologically, it integrates Monte Carlo simulation, approximate Bayesian computation (ABC), simulation-based calibration, and posterior predictive checking, implemented via high-performance computing frameworks to enable large-scale empirical analysis. Key contributions are: (1) establishing simulation as foundational statistical infrastructure; (2) providing an actionable roadmap for algorithm design, statistical software development, and pedagogical reform; and (3) advancing a paradigm shift in statistical practice—from model-centric to simulation-augmented inference.

Long-standing deficiencies in standardized, high-quality criteria for data science simulation studies have led to inconsistent design practices, poor reproducibility, and limited external validity. To address this, we propose MERITS—a simulation quality framework for trustworthy data science—systematically defining six core dimensions: Modularity, Efficiency, Realism, Stability, Intuitiveness, and Transparency. MERITS is the first to operationalize the PCS (Predictability-Computability-Stability) theory into concrete design principles and innovatively introduces a “cooking metaphor” to structure simulations as executable “recipes.” The framework includes 13 actionable design guidelines and is validated through empirical reconstruction of existing studies. Designed for cross-disciplinary applicability, MERITS has been successfully applied to diagnostic reconstructions of prior work, yielding substantial improvements in interpretability, reproducibility, and external validity.

Communication barriers between data scientists and domain experts arise from oversimplified, accuracy-centric model performance reporting, hindering shared understanding of model limitations and contextual applicability. Method: We propose a visualization-mediated model explanation framework grounded in human-computer interaction principles, participatory design, and visual narrative techniques. This yields the first domain-expert-oriented model communication guideline—emphasizing risk, trade-offs, and situational appropriateness rather than isolated metrics like accuracy. An iterative empirical study was conducted using regression models, incorporating structured expert feedback for evaluation. Contribution/Results: The framework significantly improves domain experts’ ability to identify model limitations, recognize inherent trade-offs, and proactively make context-driven adoption decisions. Its core innovation lies in repositioning visualization as an interdisciplinary consensus-building medium—shifting the paradigm from “metric reporting” to “collaborative understanding.”

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.