Training the next generation of doctoral researchers in data science: The impact on publications and beyond

Savvas Papagiannidis, Maureen Meadows, Panos Panagiotopoulos

Research output: Contribution to journalArticlepeer-review

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Abstract

Developments in data science methods have changed how we design, review and publish social science research. The impact on academic development has been multifaceted: new research opportunities have come with additional demands on training researchers to develop advanced data skills and apply them to research outputs. For early career researchers, meeting existing work demands and investing time in data skills can be a difficult proposition. In this paper, we consider the challenges that doctoral and early career researchers face when it comes to short- and long-term career goals and discuss how to collectively overcome them. Recommendations are organised around the key areas identified by the Learning, Leading, Linking framework. We emphasise that doctoral researchers in social sciences should be supported to develop their skills and pursue meaningful collaborations with other disciplines and external stakeholders as domain specialists.
Original languageEnglish
Pages (from-to)241-247
Number of pages7
JournalIEEE Transactions on Technology and Society
Volume4
Issue number3
Early online date7 Feb 2023
DOIs
Publication statusPublished - Sept 2023

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Keywords

  • computational data science
  • doctoral training
  • publishing
  • academic careers

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