Grand Challenge: Mediating Between Confirmatory and Exploratory Research Cultures in Health Sciences and Visual Analytics

TL;DR

This paper explores the cultural and methodological differences between health sciences and visual analytics, proposing a research agenda to foster better collaboration and integration of confirmatory and exploratory research approaches.

Contribution

It identifies seven key research needs and actions to bridge disciplinary gaps, creating a framework for interdisciplinary collaboration in health sciences and visual analytics.

Findings

Seven research needs for interdisciplinary collaboration identified

A framework organized into culture, standards, and processes proposed

Guidelines for integrating confirmatory and exploratory research developed

Abstract

Collaboration between health science and visual analytics research is often hindered by different, sometimes incompatible approaches to research design. Health science often follows hypothesis-driven protocols, registered in advance, and focuses on reproducibility and risk mitigation. Visual analytics, in contrast, relies on iterative data exploration, prioritizing insight generation and analytic refinement through user interaction. These differences create challenges in interdisciplinary projects, including misaligned terminology, unrealistic expectations about data readiness, divergent validation norms, or conflicting explainability requirements. To address these persistent tensions, we identify seven research needs and actions: (1) guidelines for broader community adoption, (2) agreement on quality and validation benchmarks, (3) frameworks for aligning research tasks, (4) integrated workflows combining confirmatory and exploratory stages, (5) tools for harmonizing terminology across disciplines, (6) dedicated bridging roles for transdisciplinary work, and (7) cultural adaptation and mutual recognition. We organize these needs in a framework with three areas: culture, standards, and processes. They can constitute a research agenda for developing reliable, reproducible, and clinically relevant data-centric methods.