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

Savvas Papagiannidis; Maureen Meadows; Panos  Panagiotopoulos

doi:10.1109/TTS.2023.3243395

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

Savvas Papagiannidis, Maureen Meadows, Panos Panagiotopoulos

Research Centre for Business in Society

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	241-247
Number of pages	7
Journal	IEEE Transactions on Technology and Society
Volume	4
Issue number	3
Early online date	7 Feb 2023
DOIs	https://doi.org/10.1109/TTS.2023.3243395
Publication status	Published - Sept 2023

Bibliographical note

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

computational data science
doctoral training
publishing
academic careers

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Cite this

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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.",

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

Abstract

Bibliographical note

Keywords

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