Data Generation in the Discovery Sciences—Learning from the Practices in an Advanced Research Laboratory

Abstract

General scientific literacy includes understanding the grounds on which scientific claims are based. The measurements scientists make and the data that they produce from them generally constitute these grounds. However, the nature of data generation has received relatively little attention from those interested in teaching science through inquiry. To inform curriculum designers about the process of data generation and its relation to the understanding of patterns as these may arise from graphs, this 5-year ethnographic study in one advanced research laboratory was designed to investigate how natural scientists make decisions about the inclusion/exclusion of certain measurements in/from their data sources. The study shows that scientists exclude measurements from their data sources even before attempting to mathematize and interpret the data. The excluded measurements therefore never even enter the ground from and against which the scientific phenomenon emerges and therefore remain invisible to it. I conclude by encouraging science educators to squarely address this aspect of the discovery sciences in their teaching, which has both methodological and ethical implications.