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DR2013 Data Science Panel Introduction


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"Data Science" panel intro slides at Digital Research 2013, St Anne's, Oxford, September 2013 hosted by e-Research South and Oxford e-Research Centre - see

"Data Science" panel intro slides at Digital Research 2013, St Anne's, Oxford, September 2013 hosted by e-Research South and Oxford e-Research Centre - see

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  • 1. Data Science Panel – Challenges and Curriculum Chair Dave De Roure Panelists Yuri Kalnishkan (Royal Holloway) Jeremy Frey (University of Southampton) Sarah Quinton (Oxford Brookes) Eric Meyer (Oxford Internet Institute)
  • 2. ChristineBorgman
  • 3. What is e-Research? • Research in every domain is increasingly data- and computationally-intensive, carried out collaboratively over distributed infrastructures • e-Research is the continuous technological and methodological innovation in digital methods to achieve new research outcomes – using new forms of data and emerging infrastructural capabilities • The Oxford e-Research Centre is a digital methods incubator with around 30 early-adopter researchers working in and across all disciplines
  • 4. More people Moremachines Big Data and Computation Conventional Computation Crowd & Cloud Social Networking Cyberinfrastructure e-infrastructure Science 2.0 Citizen Science e-Research David De Roure
  • 5. Economic and Social Research Council Shaping Society • Digital Social Research Program • Administrative Data Research Network • Business Datasafe • Big Data Network • Centre for International Social Media Analytics
  • 6. F i r s t BioEssays,,26(1):99–105,January2004
  • 7. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT.  The Problem signal understanding Ich Fujinaga
  • 8. The challenge is to foster the co-constituted socio-technical system on the right i.e. a computationally-enabled sense-making network of expertise, data, models and narratives. Big data elephant versus sense-making network? Iain Buchan
  • 9. data method
  • 10. 1. What is your own "story" is as a data scientist? 2. What are the three most important skills we need to teach data scientists and why? 3. How will these skills be different in 10 years? 4. Are we providing adequate training to meet needs from your point of view? 5. Are we teaching people how to deal with data but not how to really understand it? 6. Is there sufficient critical thinking in data science? Questions