Format

Send to

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2013 Aug 15;29(16):1925-33. doi: 10.1093/bioinformatics/btt333. Epub 2013 Jun 19.

Crowdsourcing for bioinformatics.

Author information

1
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. bgood@scripps.edu

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.

PMID:
23782614
PMCID:
PMC3722523
DOI:
10.1093/bioinformatics/btt333
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems Icon for PubMed Central
    Loading ...
    Support Center