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Louise Bezuidenhout - OpenCon Oxford, 1st Dec 2017

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Louise Bezuidenhout, Institute for Science, Innovation and Society, Oxford:

Projects such as the CODATA-RDA School for Research Data Science highlight the need for building capacity in research data skills around the world. Indeed, without these key skills it is likely that many disciplines and communities will continue to miss out on the benefits of a growing pool of open data resources online. Educating researchers in data skills is thus fundamental in maximizing the benefits of Open Science, but it is also an opportunity to shape the future by educating for responsible data science.

This talk will examine the ethics/Open Science component of the CODATA-RDA school and highlight how the commitment to responsible research underpins all areas of instruction. It will also discuss some of the difficulties of educating for data ethics and responsible practice in a field that is multi-disciplinary and multi-national. Finally, the talk will cover the practice-oriented, modular approach to ethics that has been developed in the CODATA-RDA school to specifically address these challenges.

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Louise Bezuidenhout - OpenCon Oxford, 1st Dec 2017

  1. 1. Embedding Openness in Practice Lessons from the CODATA/RDA School for Research Data Science LOUISE BEZUIDENHOUT INSTITUTE FOR SCIENCE, INNOVATION AND SOCIETY, UNIVERSITY OF OXF ORD
  2. 2. Educating for Responsible Data Scientists • Evolution of data-centric science needs specialist data scientists • Competence in discipline (type of data) and meta-discipline (tools for data usage) • Key understandings of tools and structures supporting data-centric sciences • Responsible data scientists thus: • Understand ethical issues relating to their discipline • Scrutinize the development of data infrastructures • Highlight ethical issues with the application of data tools Key for the future of the Open Science movement Monitor potential injustice in the evolution of an open science landscape
  3. 3. The Challenge … 1. Deciding on a content • Little consensus on what an “ethics of data science” is • Need to make content relevant to scientists from a wide range of disciplinary backgrounds 2. The challenges of teaching ethics • Aiming for awareness, consensus, or internalization • Translating ethics teaching into in situ daily research practices 3. CODATA/RDA SRDS-specific • Multidisciplinary – different ethical concerns • Many data types and sources • Different cultural and legal backgrounds
  4. 4. The Challenge … 4. Attitudes to ethics: • Ethics happens once during an REC review • I don’t need ethics – I don’t work with humans/animals • I didn’t collect the data, so ethics is not my problem • I’m not once of the bad guys … • Ethics is what other people worry about •Making transition from “it’s a nice idea” to “I can see how it works in practice”
  5. 5. The Aim … • To make ethics an integral and value-adding component of the CODATA/RDA SRDS • To make students aware of the key concepts driving Open Data and Responsible Research and Innovation (RRI) movements • To initiate discussion about responsibilities • To enable students to make the transition from openness in theory to openness in practice • To encourage students to integrate openness into all aspects of their research
  6. 6. Stopping the Compartmentalization of Openness and RRI Data management Online presence Responsible/ethical research • Hands on – practical • Bottom-up ethics • Avoid ”stand alone” courses
  7. 7. Teaching an Ethics of Openness Practice-Oriented Data Ethics Lecture on Open Science Lecture on using the RRI toolkit Exercises associating ethics with learnt tools • Evolution of OS movement • Benefits of OS • Key ethical concepts: • justice, responsibility, beneficence • How does ethics fit into broader scheme of RRI • Ethics, gender equality, governance, OA, public engagement, science education • How does an RRI research programme look • Introduce What ethical conundrums come up that are relevant to ALL data scientists?
  8. 8. Exploiting Modular Teaching …
  9. 9. R question 2 The Association for Computing Machinery Code of Conduct details a number of ethical duties that professionals with regards to the public. Please choose the three you think are most important.
  10. 10. Lessons From CODATA/RDA SRDS • Modular works well • Ethics ”prompts” associated with modular skill teaching integrated ethics into daily research activities • Important to follow up theoretical ethics lectures with practical tasks: students need to see how key concepts of openness translate into ALL aspects of daily research • Transitioning from theory to practice is scary • RRI toolkit enabled students to think beyond “retro-fitting” openness to projects • Need to assist students to see how ethics, regulations, and expectations impact on daily research practices • Eliminating the “it’s not me” in ethics discussions • Stop students from thinking that ethics doesn’t apply to them (didn’t create data, not human data, no animal work etc) • Expand horizons: drill down to the ethical implications of the “nitty gritty” of daily research • Ethical research is something anyone can do • Highlight flexibility, contextuality, diversity: ethics is not something that is “set in stone” • Foster enthusiasm: students are more receptive when they feel they can contribute • As data experts, students need to recognize they are in the best position to safeguard science
  11. 11. Thank you Special thanks to: • Sarah Jones (HATII) and Gail Clement (Caltech) • Hugh Shanahan and Rob Quick • CODATA and RDA