escience2019 has ended
Wednesday, September 25 • 5:03pm - 5:21pm
Love, Money, Fame, Nudge

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Love, Money, Fame, Nudge. These are the four levers we have to encourage scientists to use our e-Infrastructures. They are the same incentives we use for e-Scientists to use and develop e-Infrastructures. We use them to encourage contribution to our FAIR Research [1] and Data Commons [2] and adoption of our software platforms. We use them to encourage researchers to become skilled in FAIR data stewardship and produce reproducible papers. They are key to persuading institutions to professionalise research software engineering [3]. They underpin pretty much everything we are doing and hope to do.

Of all of these, Nudge [4] is the one lever that is the most robust, the most likely to get embedded and the most likely to get sustained. Nudge is getting the job done by using stealth and side effects. Nudge is looking at what we have or what is out there and tweaking it – like bioschemas.org has done in life sciences using schema.org. The e-infrastructures that have really affected research practice include Google (search, docs, scholar), cloud computing, and containerisation.

Nudge is the ramp that takes you the “last mile” [5] (or is that the “first mile?”) to the fancy e-Infrastructure from the not so fancy spreadsheet. Nudge technologies include: Jupyter notebooks, RStudio, Open refine and even Galaxy workflows.

Nudge is also the least likely to lead to a paper in IEEE e-Science. It may not even get funded, being “merely useful”. It’s not likely to win the prize as a vision.

If the future of e-Science is that there is no e-Science, just Science for everyone, the long tail included, then maybe we should ask ourselves “how do we make e-Science invisible?” and that could translate to asking ourselves, in everything we are doing, “what’s the nudge?”.

  1. Wilkinson MD, Dumontier M et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, https://doi.org/10.1038/sdata.2016.18
  2. Grossman RL, Heath A, Murphy M, Patterson M, Wells W, (2016) “A Case for Data Commons: Toward Data Science as a Service,” Computing in Science & Engineering 18(5):10–20.
  3. Society of Research Software Engineering, (2019) https://society-rse.org/
  4. Thaler RH, Sunstein CR (2008) Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press. ISBN 978-0-14-311526-7. OCLC 791403664.
  5. Koureas D, Arvanitidis C (2016) Community engagement: The ‘last mile’ challenge for European research e-infrastructures. Research Ideas and Outcomes 2: e9933. https://doi.org/10.3897/rio.2.e9933


Wednesday September 25, 2019 5:03pm - 5:21pm
Cockatoo Room