Data-driven and mechanism-driven research should not be seen as opposites. Diverse datasets—whether from observations, reanalyses, or machine-learning products—can reveal patterns and anomalies that point scientists toward important questions. Yet the crucial step lies beyond simply recognizing patterns: it is the choice of methods to examine them, the framing of hypotheses, and the sustained effort to pursue the fundamental “why.”

Research that remains focused on safe, tool-oriented answers may address immediate needs but contributes little to lasting conceptual growth. Real progress comes when data are used not only for description, but also to refine, challenge, and deepen our mechanistic understanding.