Artificial Intelligence

Capturing Context in Scientific Experiments: Towards Computer-Driven Science

Monday, April 02, 2018, 11:00am - 12:00pm PDTiCal
6th Floor Large Conference Room
This event is open to the public.
AI Seminar - Interview talk
Daniel Garijo, ISI

Scientists publish computational experiments in ways that do not facilitate reproducibility or reuse. Significant domain expertise, time and effort are required to understand scientific experiments and their research outputs. In order to improve this situation, mechanisms are needed to capture the exact details and the context of computational experiments. Only then, Intelligent Systems would be able to help researchers understand, discover, link and reuse products of existing research.

In this presentation, I will introduce my work and vision towards enabling scientists to share, link, curate and reuse their computational experiments and results. In the first part of the talk, I will present my work for capturing and sharing the context of scientific experiments by using scientific workflows and machine-readable representations. Thanks to this approach, experiment results are described in an unambiguous manner, have a clear trace of their creative process and include a pointer to the sources used for their generation. In the second part of the talk, I will describe examples of how the context of scientific experiments may be exploited to browse, explore and inspect research results. I will end the talk by presenting new ideas for improving and benefiting from the capture of the context of scientific experiments and how to involve scientists in the process of curating and creating abstractions on available research metadata. 

Short bio: Daniel Garijo has been a postdoctoral researcher at the Information Sciences Institute since September 2016. Before joining ISI, he worked at the Ontology Engineering Group at Universidad Politécnica de Madrid, where he obtained his Ph.D. in 2015. His research activities focus on e-Science and the Semantic Web, specifically on how to increase the accessibility and understandability of scientific workflows using their context (execution provenance, metadata, software, etc.) and publishing them as Linked Data.

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