Data sharing at scale: A heuristic for affirming data cultures

The concept of data sharing and open science have been touted more over the past decade, often in the face of claims of lack of reproducibility and the need for more collaboration across scientific disciplines. At times researchers will point to a specific “culture” evident in their organization that helps or hinder the move towards data sharing. But the concept of aligning data cultures—particularly through the lens of identifying and solving the inherent differences between disciplines—isn’t the way to look at data sharing, argue Poirier and Costelloe-Kuehn. Instead, we must ” showcase and affirm the diversity of traditions and modes of analysis that have shaped how data gets collected, organized, and interpreted in diverse settings,” they say. In this essay, the authors present their heuristic (a problem solving and self-discovery method) for sharing data at scale, from the meta level down to the nano level, giving researchers the tools to “affirm and respect the diversity of cultures that guide global and interdisciplinary research practice.”

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