J. W. Lockhart

C. Biliotti, M. Riccaboni, J. W. Lockhart et al.

Women and men pursue different but complementary forms of scientific innovation. Analyzing 261,452 solo-authored papers by U.S. scholars, with patterns confirmed by millions of multi-authored articles, we show that women more often bridge distant disciplines through novel reference combinations, while men more often recombine concepts within fields. Women's interdisciplinary innovations prove more disruptive and more prescient, yet science penalizes them for it. For equally innovative work, women's papers land in lower-prestige journals and tend to receive less downstream citation credit, though their disruptive impact is greater. These gaps narrow only at extreme levels of novelty, suggesting women must produce exceptionally surprising work to achieve parity. Men's within-field concept innovations, by contrast, attract recognition from disciplinary gatekeepers who control careers. The asymmetry reveals not a deficit in women's contributions but a reward structure that systematically undervalues the boundary-crossing work most likely to transform fields.

A. R. Azari, J. W. Lockhart, M. W. Liemohn et al.

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of black box or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.