title: Metrics and the effective computational scientist: process, quality and communication.
abstract: Recent treatments of computational knowledge worker productivity have focused upon the value the discipline brings to drug discovery using positive anecdotes. While this big picture approach provides important validation of the contributions of these knowledge workers, the impact accounts do not provide the granular detail that can help individuals and teams perform better. I suggest balancing the impact-focus with quantitative measures that can inform the development of scientists. Measuring the quality of work, analyzing and improving processes, and the critical evaluation of communication can provide immediate performance feedback. The introduction of quantitative measures can complement the longer term reporting of impacts on drug discovery. These metric data can document effectiveness trends and can provide a stronger foundation for the impact dialogue.
authors.affiliation: Natural Discovery LLC, P.O. Box 96, Princeton, NJ 08542, USA. crystalvisionetb@gmail.com
authors.forename: Eric T
authors.initials: ET
authors.lastname: Baldwin
mesh_terms: D017076:Computer-Aided Design; D015195:Drug Design; D004345:Drug Industry / Q000379:methods*; D006801:Humans