Crowdsourcing and curation: perspectives from biology and natural language processing

Area of Science:

• Biocuration
• Crowdsourcing
• Natural Language Processing

Background:

• Crowdsourcing is increasingly used in natural language processing and biocuration.
• However, high-level discussions on crowdsourcing methodology for biocuration are less common.

Purpose of the Study:

• To explore the application of crowdsourcing in biocuration.
• To examine case studies highlighting different ways to leverage crowdsourcing.
• To address issues of expertise, participant motivation, feasibility, cost, and quality in crowdsourced biocuration.

Main Methods:

• Exploration of crowdsourcing for biocuration through case studies.
• Discussion of panel session talks covering expertise, training, task decomposition, and community editing.
• Analysis of biological data and metadata capture via community editing.

Main Results:

• Case studies demonstrate diverse applications of crowdsourcing in biocuration.
• Key considerations for successful crowdsourcing include required expertise, participant motivation, and task design.
• Training can potentially improve crowd performance.

Conclusions:

• Crowdsourcing presents a viable approach for biocuration tasks.
• Careful planning regarding expertise, motivation, and task decomposition is crucial for effective implementation.
• Community editing can facilitate biological data and metadata capture.