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Metrics and the effective computational scientist: process, quality and communication.
1Natural Discovery LLC, P.O. Box 96, Princeton, NJ 08542, USA. crystalvisionetb@gmail.com
Drug Discovery Today
|March 13, 2012
Summary
Computational knowledge worker productivity in drug discovery needs more than anecdotes. Quantitative measures can offer immediate feedback for scientist development and improve processes, complementing long-term impact reporting.
Area of Science:
- Computational science
- Drug discovery
- Knowledge management
Background:
- Current focus on anecdotal evidence for computational knowledge worker productivity in drug discovery.
- Anecdotal evidence validates contributions but lacks granular detail for performance improvement.
Purpose of the Study:
- To propose a balanced approach to measuring computational knowledge worker productivity.
- To integrate quantitative measures for immediate performance feedback and scientist development.
- To complement existing impact-focused reporting with data-driven insights.
Main Methods:
- Implementing quantitative measures for work quality.
- Analyzing and optimizing scientific processes.
- Critically evaluating communication effectiveness.
Main Results:
- Quantitative measures provide immediate performance feedback for individuals and teams.
- Data can document effectiveness trends in scientific workflows.
- Enhanced data foundation for discussing the impact of computational roles.
Conclusions:
- Balancing impact-focused narratives with quantitative metrics is crucial for scientist development.
- Quantitative data offers a more granular and actionable understanding of productivity.
- This approach strengthens the dialogue around the value and effectiveness of computational roles in drug discovery.
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