Scientists write and maintain software artifacts to construct, validate, and apply scientific theories. Despite the centrality of software in their work, their practices differ significantly from those of professional software engineers. We sought to understand what makes scientists effective at their work and how software engineering practices and tools can be adapted to fit their workflows. We interviewed 25 scientists and support staff to understand their work. Then, we constructed a theory that relates six factors that contribute to their efficacy in creating and maintaining software systems. We present the theory in the form of a cycle of scientific computing efficacy and identify opportunities for improvement based on the six contributing factors.

Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in children may lead to blindness and may be a sign of life-threatening conditions, such as brain tumors. Robust and accurate clinical diagnosis of this syndrome can be facilitated by automated analysis of fundus images using deep learning, especially in the presence of challenges posed by pseudopapilledema that has similar fundus appearance but distinct clinical implications. We present a deep learning-based algorithm for the automatic detection of pediatric papilledema. Our approach is based on optic disc localization and detection of explainable papilledema indicators through data augmentation. Experiments on real-world clinical data demonstrate that our proposed method is effective with a diagnostic accuracy comparable to expert ophthalmologists1.