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Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution

The current project of the Phenoscape team is Enabling Machine-actionable Semantics for Comparative Analysis of Trait Evolution (SCATE).

The objective is to create infrastructure that will provide comparative trait analysis tools easy access to algorithms powered by machine reasoning with the semantics of trait descriptions. Similar to how Google, IBM Watson, and others have enabled developers of smartphone apps to incorporate, with only a few lines of code, complex machine-learning and artificial intelligence capabilities such as sentiment analysis, we aim to demonstrate how easy access to knowledge computing opens up new opportunities for analysis, tools, and research in comparative trait analysis.

As driving biological research questions, we focus on addressing three long-standing limitations in comparative studies of trait evolution: recombining trait data, modeling trait evolution, and generating testable hypotheses for the drivers of trait adaptation.

SCATE is funded by NSF collaborative grants DBI-1661456 (Duke University), DBI-1661529 (Virginia Tech), DBI-1661516 (University of South Dakota), and DBI-1661356 (UNC Chapel Hill and RENCI) from Sep 1, 2017 to Aug 31, 2020.

The grant proposal text with references is publicly available: W. Dahdul, J.P. Balhoff, H. Lapp, P. M. Mabee, J. Uyeda, & T.J. Vision. (2017). Enabling machine-actionable semantics for comparative analyses of trait evolution. Zenodo. http://doi.org/10.5281/zenodo.885538.

See the project website for more information. For a little more background on the how and why of incorporating ontologies into comparative analysis, see ComparativeAnalysis.

Previous Phenoscape projects and Acknowledgements

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