Flexible and transparent data reuse
|Starts:||14:00 3 Apr 2019|
|Ends:||15:00 3 Apr 2019|
|What is it:||Seminar|
|Organiser:||Department of Computer Science|
|Who is it for:||External researchers, Adults, Alumni, Current University students, General public|
Join us for this School research seminar, part of the Data science seminar series and hosted by Professor Carole Goble.
A central challenge in our modern information environment is how to use, integrate and repurpose data that stem from a multitude of diverse sources. Within data science, ~60-70% of the time is spent gathering, preparing, integrating, and munging data. In science, there is, for instance, the need to know which of the thousands of prior experimental records are reliable, applicable and can be reused for an experiment.
In this talk, guest speaker Professor Paul Groth discusses the goal of developing intelligent systems that work with people to combine and reuse data flexibly, reproducibly and transparently. He will give examples from his work on flexible knowledge graph construction and taxonomy creation. He will then discuss interoperable data provenance tracking to provide transparency for these sort of complex data workflows. He will outline a future for using transparency to create more flexible, intelligently supported data integration and reuse environments.
Role: Professor of Algorithmic Data Science
Organisation: University of Amsterdam
Biography: Paul Groth is Professor of Algorithmic Data Science at the University of Amsterdam where he leads the Intelligent Data Engineering Lab (INDElab). His research focuses on intelligent systems for dealing with large amounts of diverse contextualised knowledge with a particular focus on web and science applications. Previously Paul led the design of a number of large scale data integration and knowledge graph construction efforts in the biomedical domain. Paul was co-chair of the W3C Provenance Working Group that created a standard for provenance interchange. He has also contributed to the emergence of community initiatives to build a better scholarly ecosystem including altmetrics and the FAIR data principles.
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