Submissions:2016/Linked data in the hands of biological researchers: A model organism database powered by Wikidata

Title

Linked data in the hands of biological researchers: A model organism database powered by Wikidata

Theme

Health care and science

Academic Peer Review option

y

Type of submission

presentation

Author

Timothy E. Putman

E-mail address

tputman@scripps.edu

Username

Putmantime

Affiliation

The Scripps Research Institute

Abstract

Rapid improvements in sequencing technologies have resulted in tens of thousands of sequenced bacterial genomes. For decades this type of data has been stored in government funded, large scale databases like the National Center for Biotechnology Information (NCBI) and in primary publications in Pubmed. That knowledge is then extracted and documented by professional curators. Major drawbacks to large scale, expert curated databases are the scope of data (rapidly becoming too large to curate professionally) and the expense of maintaining and extending the infrastructure required. Wikidata is an, openly editable, semantic web compatible framework for knowledge representation and its knowledge integration capabilities make it ideally suited to the challenge of representing the exploding body of information about microbial genomics. In addition to its its technical suitability, the diverse nature of its content goes well beyond life sciences creating a much larger user base with vested interest in seeing it endure. We are developing a microbial specific semantic data model in Wikidata modeling bacterial species, genes, proteins, diseases they cause, and drugs that treat them. The unique ability of Wikidata to allow users to enter data directly provides a central place for community curation, the only feasible way to handle the scope of this data. To get the community involved, we are building a web application that brings our data model of linked organisms, genes, proteins, diseases and drugs to the to the community in an intuitive and powerful interface. This community driven aggregation of knowledge represents a new way of curating massive datasets through community distribution of labor, collective contribution and collective gain.

Length of presentation

20

Special schedule requests

Preferred room size

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