  Extensive efforts to gather materials data have largely overlooked potential
data redundancy. In this study, we present evidence of a significant degree of
redundancy across multiple large datasets for various material properties, by
revealing that up to 95 % of data can be safely removed from machine learning
training with little impact on in-distribution prediction performance. The
redundant data is related to over-represented material types and does not
mitigate the severe performance degradation on out-of-distribution samples. In
addition, we show that uncertainty-based active learning algorithms can
construct much smaller but equally informative datasets. We discuss the
effectiveness of informative data in improving prediction performance and
robustness and provide insights into efficient data acquisition and machine
learning training. This work challenges the "bigger is better" mentality and
calls for attention to the information richness of materials data rather than a
narrow emphasis on data volume.
