References
- Abell, D., & Becker, K. (2021). Enhancing university employer attractiveness for the next generation of academics. Journal of Higher Education Policy & Management, 43(5), 457–473. https://doi.org/10.1080/1360080X.2020.1847241
- Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716
- Adams, K., Ripper, M., Zander, A., & Mullins, G. (2010). The valuing of creativity in the workplace roles of engineering research graduates. 3rd International Symposium for Engineering Education, University College Cork, Ireland.
- Atalay, E., Phongthiengtham, P., Sotelo, S., & Tannenbaum, D. (2018). New technologies and the labor market. Journal of Monetary Economics, 97, 48–67. https://doi.org/10.1016/j.jmoneco.2018.05.008
- Autor, D.H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3
- Berger, T., & Frey, C. B. (2016). Did the computer revolution shift the fortunes of U.S. cities? Technology shocks and the geography of new jobs. Regional Science and Urban Economics, 57, 38–45. https://doi.org/10.1016/j.regsciurbeco.2015.11.003
- Boselli, R., Cesarini, M., Mercorio, F., & Mezzanzanica, M. (2017). Using machine learning for labour market intelligence. In Y. Altun, K. Das, T. Mielikäinen, D. Malerba, J. Stefanowski, J. Read, M. Žitnik, M. Ceci, & S. Džeroski (Eds.), Machine learning and knowledge discovery in databases (pp. 330–342). Springer International Publishing. https://doi.org/10.1007/978-3-319-71273-4_27
- Bourne, H.R. (2013). A fair deal for PhD students and postdocs. eLife, 2, e01139. https://doi.org/10.7554/eLife.01139
- Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at Work (WorkinG PAPer 31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161
- Burning Glass Technologies. (2017). The quant crunch: How the demand for data science skills is disrupting the job market. https://www.burning-glass.com/wp-content/uploads/The_Quant_Crunch.pdf
- Chen, L., Mewburn, I., & Suonimen, H. (2020). A machine-learning based model to identify PhD-level skills in job ads. In M. Kim, D. Beck, & M. Mistica (Eds.), Proceedings of the 18th annual workshop of the Australasian language technology association (pp. 72–80). Australasian Language Technology Association. https://aclanthology.org/2020.alta-1.8
- Deming, D., & Kahn, L. B. (2018). Skill requirements across firms and labor markets: Evidence from job postings for professionals. Journal of Labor Economics, 36(S1), S337–S369. https://doi.org/10.1086/694106
- Durette, B., Fournier, M., & Lafon, M. (2016). The core competencies of PhDs. Studies in Higher Education, 41(8), 1355–1370. https://doi.org/10.1080/03075079.2014.968540
- Eisenstein, M. (2020). Active machine learning helps drug hunters tackle biology. Nature Biotechnology, 38(5), 512–514. Article 5. https://doi.org/10.1038/s41587-020-0521-4
- Gofman, M., & Jin, Z. (2024). Artificial intelligence, education, and entrepreneurship. The Journal of Finance, 79(1), 631–667. https://doi.org/10.1111/jofi.13302
- Gorsky, D., & MacLeod, A. (2016). Shifting norms and expectations for medical school leaders: A textual analysis of career advertisements 2000–2004 cf. 2010–2014. Journal of Higher Education Policy & Management, 38(1), 5–18. https://doi.org/10.1080/1360080X.2015.1126893
- Grüger, J., & Schneider, G. (2019). Automated analysis of job requirements for computer scientists in online job advertisements. Proceedings of the 15th International Conference on Web Information Systems and Technologies, 226–233. https://doi.org/10.5220/0008068202260233
- Hanwell, M.D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R. (2012). Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 17. https://doi.org/10.1186/1758-2946-4-17
- Harper, R. (2012). The collection and analysis of job advertisements: A review of research methodology. Library and Information Research, 36(112), 29–54. https://doi.org/10.29173/lirg499
- Hirel, P. (2015). Atomsk: A tool for manipulating and converting atomic data files. Computer Physics Communications, 197, 212–219. https://doi.org/10.1016/j.cpc.2015.07.012
- Im, Z.J., Mayer, N., Palier, B., & Rovny, J. (2019). The “losers of automation”: A reservoir of votes for the radical right? Research & Politics, 6(1), 2053168018822395. https://doi.org/10.1177/2053168018822395
- Jia, P., Xie, W., Zhang, G., & Wang, X. (2023). Do reviewers get their deserved acknowledgments from the authors of manuscripts? Scientometrics, 128(10), 5687–5703. https://doi.org/10.1007/s11192-023-04790-7
- Jurowetzki, R., Hain, D., Mateos-Garcia, J., & Stathoulopoulos, K. (2021). The privatization of AI research(-ers): Causes and potential consequences – from university-industry interaction to public research brain-drain? (arXiv: 2102.01648). arXiv. https://doi.org/10.48550/arXiv.2102.01648
- Li, G., Yuan, C., Kamarthi, S., Moghaddam, M., & Jin, X. (2021). Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis. Journal of Manufacturing Systems, 60, 692–706. https://doi.org/10.1016/j.jmsy.2021.07.007
- Lukauskas, M., Šarkauskaitė, V., Pilinkienė, V., Stundžienė, A., Grybauskas, A., & Bruneckienė, J. (2023). Enhancing skills demand understanding through job ad segmentation using NLP and clustering techniques. Applied Sciences, 13(10), 6119. Article 10. https://doi.org/10.3390/app13106119
- Meyer, M.A. (2019). Healthcare data scientist qualifications, skills, and job focus: A content analysis of job postings. Journal of the American Medical Informatics Association, 26(5), 383–391. https://doi.org/10.1093/jamia/ocy181
- Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., THE GOOGLE BOOKS TEAM, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M. A., & Aiden, E. L. (2011). Quantitative analysis of culture using millions of digitized books. Science, 331(6014), 176–182. https://doi.org/10.1126/science.1199644
- Muurlink, O., Chen, L., Boorman, R., Pearson, D., & Cohen, G. (2023). Stakeholder perceptions of what industry wants from doctoral students: A systematic literature review. Higher Education Research & Development, 43(4), 1–14. https://doi.org/10.1080/07294360.2023.2269871
- NSB. (2020). Science and engineering indicators: The state of US science and engineering. https://ncses.nsf.gov/pubs/nsb20201
- Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
- Pitt, R., & Mewburn, I. (2016). Academic superheroes? A critical analysis of academic job descriptions. Journal of Higher Education Policy & Management, 38(1), 88–101. https://doi.org/10.1080/1360080X.2015.1126896
- Pucchio, A., Eisenhauer, E. A., & Moraes, F. Y. (2021). Medical students need artificial intelligence and machine learning training. Nature Biotechnology, 39(3), 388–389. Article 3. https://doi.org/10.1038/s41587-021-00846-2
- Ribeiro, B., Meckin, R., Balmer, A., & Shapira, P. (2023). The digitalisation paradox of everyday scientific labour: How mundane knowledge work is amplified and diversified in the biosciences. Research Policy, 52(1), 104607. https://doi.org/10.1016/j.respol.2022.104607
- Schwanghart, W., & Scherler, D. (2014). Short communication: TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences. Earth Surface Dynamics, 2(1), 1–7. https://doi.org/10.5194/esurf-2-1-2014
- Shahbazi, R., & Hedayati, A. (2016). Identifying digital librarian competencies according to the analysis of newly emerging IT-based LIS Jobs in 2013. The Journal of Academic Librarianship, 42(5), 542–550. https://doi.org/10.1016/j.acalib.2016.06.014
- Sheriff, N., & Sevukan, R. (2023). Discovering research data management trends from job advertisements using a text-mining approach. Journal of Information Science, 01655515231193845. https://doi.org/10.1177/01655515231193845
- Sproles, C., & Clemons, A. (2019). The migration of government documents duties to public services: An analysis of recent trends in government documents librarian job advertisements. The Reference Librarian, 60(2), 83–92. https://doi.org/10.1080/02763877.2019.1570419
- Squazzoni, F., Bravo, G., Farjam, M., Marusic, A., Mehmani, B., Willis, M., Birukou, A., Dondio, P., & Grimaldo, F. (2021). Peer review and gender bias: A study on 145 scholarly journals. Science Advances, 7(2), eabd0299. https://doi.org/10.1126/sciadv.abd0299
- U.S. Bureau of Labor Statistics. (2023, September 6). Data scientists: Occupational outlook handbook. https://www.bls.gov/ooh/math/data-scientists.htm#tab-6
- van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
- van Eck, N.J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7
- Vitae. (2023, June 14). Researcher professional development [Homepage]. Vitae. http://www.vitae.ac.uk
- Wright, D.J., & Wang, S. (2011). The emergence of spatial cyberinfrastructure. Proceedings of the National Academy of Sciences, 108(14), 5488–5491. https://doi.org/10.1073/pnas.1103051108
- Zhang, G., Geng, Y., Wang, L., & Wang, X. (2022). What do academic employers want from candidates? A multi-disciplinary research. From Global Indicators to Local Applications. 26th International Conference on Science, Technology and Innovation Indicators (STI 2022), Granada, Spain. https://doi.org/10.5281/zenodo.6966543
- Zhang, G., Shang, F., Wang, L., Xie, W., Jia, P., Jiang, C., & Wang, X. (2023). Is peer review duration shorter for attractive manuscripts? Journal of Information Science, 016555152311743. https://doi.org/10.1177/01655515231174382
- Zhang, G., Wang, L., Xie, W., Shang, F., Xia, X., Jiang, C., & Wang, X. (2022). “This article is interesting, however”: Exploring the language use in the peer review comment of articles published in the BMJ. Aslib Journal of Information Management, 74(3), 399–416. https://doi.org/10.1108/AJIM-06-2021-0172