Authors: Good BM; Su AI
Abstract: MOTIVATION: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains. RESULTS: Here, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume 'microtasks' and systems for solving high-difficulty 'megatasks'. Within these classes, we discuss system types, including volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions that highlights the positives and negatives of different approaches. CONTACT: bgood@scripps.edu.
Journal: Bioinformatics (Oxford, England) Volume: 29 Issue: 16 Pages: 1925-33 Date: June 21, 2013 PMID: 23782614 |
Good BM, Su AI (2013) Crowdsourcing for bioinformatics. Bioinformatics (Oxford, England) 29: 1925-33.
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