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Reflections on a (slightly unusual) multi-disciplinary academic career

Talk given at the School of Computer Science, The University of Manchester, UK Postgraduate Research Symposium 2019

the Carole Goble Doctoral Paper award was given for the first time

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Reflections on a (slightly unusual) multi-disciplinary academic career

  1. 1. Reflections on a (slightly unusual) multi-disciplinary academic career Professor Carole Goble CBE FREng FBCS CITP The University of Manchester, UK
  2. 2. 40 years this coming October…. …. At Manchester
  3. 3. I didn’t intend to stay at the university… 1980s Ian Cottam Behind many a successful professor is a super supportive partner Access Grid
  4. 4. …I didn’t intend to be an academic… 1980s Teaching lectureship Women in Computing 1st CoI on a grant: 1988 1st Publication: 1990 1st Conf attended: 1991 1st Conf presentation: 1992 1st Intl Conf presentation: 1993 1st PI on a grant: 1994 1st Keynote: 2000 Full Professor: 2000
  5. 5. … I became one anyway… 102funding awards 114journal articles 149refereed conference articles Ncollaborators (Linked-In 1496 connections) 60+ keynotes 60+Research Fellows Software and Resources beyond Proof of Concept
  6. 6. … some PhD stuff… oppositions supervisions Mr Cottamand one for me we can walk different paths
  7. 7. What happened?
  8. 8. 1. Mentors don’t worry about gender, location or discipline… Apprenticeship Career Mentorship Career Enabling
  9. 9. 1. Mentors don’t worry about gender, location or discipline… Collaborators / Influencers
  10. 10. 2. Empirical Research • ". . . the fundamental difference between a theoretician and an engineer is that an engineer is away and doing something once he's [sic] grasped that there's a way of doing something, and the theoretician is strangely enough most of the time lagging behind, cleaning-up -- that's the best word I can use . . ." Tom Kilburn 1992 Tom Kilburn
  11. 11. 2. Empirical Research Theory Computer Science Application Computational Science Practice Software Engineering PRODUCT PRINCIPLE USE CASE fundamental applied
  12. 12. 2. Empirical Research (e-)Science 1. Empirical describing natural phenomena 2. Theoretical using models, generalizations 3. Computational simulating phenomena 4. Data intensive unify theory, experiment, and simulation New research outputs Open, reproducible science
  13. 13. 2. Empirical Research Team Based Researchers Computational Biologists Research Software Engineers Data management specialists Mix of complementary skills I’m a Co-ordinator and Resource Investigator*… *Belbin Team Role Theories
  14. 14. 2. Empirical Research Team Based A PhD in a team • Freedom to innovate • Support & stimulation • Fast track take up Watch for • Setting boundaries • Sucked into project delivery • On-boarding frustration Researchers Computational Biologists Research Software Engineers Data management specialists PhD students
  15. 15. 2. Empirical Research Team Based Practice - Software and Resources beyond Proof of Concept VIEWER Practice - Standards COHSE Researchers Computational Biologists Research Software Engineers Data management specialists JERM Ontology myGrid Ontology
  16. 16. Research Software Matters 2010 - 2024 A national facility for cultivating and improving research software to support world-class research. 2006-2010
  17. 17. 3. Multi-disciplinary Collaboration people from different disciplines working together, each drawing on their disciplinary knowledge*. * Life Sciences Plant and Animal health Drug Discovery Systems & Synthetic Biology ‘Omics Industrial Biotechnology Biodiversity Digital Libraries HelioPhysics Astronomy Rare Disease,Virtual Physiology Health Informatics, Precision Medicine Museum Collections Social Science
  18. 18. 3. Multi-disciplinary Collaboration Biodiversity EU Life Science Data Infrastructure Industrial Biotechnology Extreme matrix management is another talk….
  19. 19. Go to where your collaborators are
  20. 20. Computer Scientist Software Engineer People Wrangler Semantics wrangler e-Science computing wrangler e-Science infrastructure wrangler
  21. 21. What multi-disciplinary research?
  22. 22. KnowledgeTurning in Science [Figures: Josh Sommer, Chordoma Foundation] GobleC., De Roure D., Bechhofer S. (2013) AcceleratingScientists’ KnowledgeTurns, doi:10.1007/978-3-642-37186-8_1 Research Commons Access to resources, Flow of Information Coordination & Collaboration Automated processing Mobilisation of the community Scale and Change The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3 (2016) doi:10.1038/sdata.2016.18
  23. 23. Data in Science in a Nutshell Wonderfully messy.Wonderfully human.The spreadsheet is king. A world of possibilities. Cool Machine Learning Uncool Stewardship Incompatibility * Metadata Method Service ethic Interoperability
  24. 24. Social Studies of Science Scientific Instrumentation Scientific Information Technology Scientific Community Steven Shapin Pump and Circumstance: Robert Boyle's LiteraryTechnology (1984)
  25. 25. Computer Science & Software Engineering Distributed Computing Knowledge Computing Social Computing Computational Workflows Open Science Cloud based services Web services Portability Containers Sharing & Collaboration Provenance TransparencyInteroperability Reproducibility FAIR e-Labs & collaboratories Interop SemanticWeb Ontologies Linked Data Reasoning nanopublications
  26. 26. E.g: Computational Workflows automate data and processing tasks • Functional programming, lambda calculus … • Visual programming … • Service-oriented and Cloud computing, repair, preservation, packaging … • Semantic description: validation, planning, optimisation, discovery, automated annotation … • Provenance tracking, credit propagation … • Sharable method • Social workflow collaboratory • Workflow research objectsLots of e-Infrastructure, tools and support
  27. 27. A systematic strategy for large-scale analysis of genotype– phenotype correlations: identification of candidate genes involved in African trypanosomiasis 2007, Vol. 35, No. 16 5625-5633 2007 2017
  28. 28. 2018
  29. 29. Impact -> time, staying power, trust 2003 2007 2008 EU Workflow collaboratory 2018-2023 Projects using the infrastructure Other people’s projects using the infrastructure 2010-2014
  30. 30. Multi-Disciplinary Research… Juggle, Balance, Communicate Respect the ego-system Be Patient BuildTrust & Respect Be Useful Be Reliable Return favours Avoid servitude Learn the culture • Embed • Bigger picture • Rewards • Work practices, language Align and revisit • Problem • Expectations • Timings • Availability • Resources Complement
  31. 31.
  32. 32. • Plan with everyone • Manage expectations • Define boundaries • Be pragmatic • Ask “stupid” questions • Build a network (mentors) • Exploit your unique skillset • Avoid servitude • Swim against the flow • Publish for your career • Adjust to your audience • Have fun
  33. 33. Policy Sharing Semantics Workflows Interdiscp. Software
  34. 34. Scholarly Communication
  35. 35. Write for the different disciplines Value impact & influence through all methods BOSC 2007 Multi-Disciplinary Scholarly Communication 84,371 views
  36. 36. Scholarly Communication Take more seriously in Computer Science • Paper -> Reproducible Research Objects • “Executable papers” • Publish -> Release
  37. 37. TL;DR summary • slightly unusual – we can walk different paths & mentors matter. • multi-disciplinary academic career – empirical research smoothed the way for me – multi-disciplinary is rich with possibilities, but… …requires mindfulness. …the social and technical are hard to separate. • think beyond the PDF!
  38. 38. Acknowledgements • The eScience group • The Information Management Group • All my collaborators and mentors • All my students • All my funders • Simon Harper for this great honour • And of course Ian Cottam…otherwise I wouldn’t have been here.