Skill Needs for Early Career Researchers—A Text Mining Approach

Abstract

Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on the main skills and demands that characterize first stage research positions in Europe. Results show that data handling and processing skills are essential for early career researchers, irrespective of their research field. Also, as many analyzed first stage research positions are connected to universities, they include teaching activities to a great extent. Management of time, risks, projects, and resources plays an important part in the job requirements included in the analyzed advertisements. Such information is relevant not only for early career researchers who perform job selection taking into account the match of possessed skills with the required ones, but also for educational institutions that are responsible for skills development of the future R&D professionals.

Toward a Knowledge Discovery Framework for Data Science Job Market in the United States

“…In [8], the author used a demand skill index which is derived by normalization of words' term frequencies. In another research, they used term frequency -inverse document frequency to rank the top terms [16]. A problem with this approach is that terms are not necessarily equivalent to the skills and there would be a need for post-processing of the result to remove other noises.…”

Research Competencies Mediated by Technologies: A Systematic Mapping of the Literature

“…In recent years, the strengthening of DRC can be observed, since it is understood that the handling of data processing skills is essential to young academic researchers, regardless of their disciplinary field (Maer, Mocanu, Zamfir, & Georgescu, 2019). Works like the one produced by Kammerer, Brand, and Jaroska (2018) state that technological developments such as mobile devices and digital assistants are changing how people search for information, acquire knowledge, learn, and create research agendas.…”

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

“…job boards, newspapers). Subsequently, they apply content analysis techniques such as counting the number of skills occurrence and skills co-occurrence in order to provide insights about skills in the investigated job area (Verma et al, 2019;Gardiner et al, 2018;Maer-Matei et al, 2019). Although, these methods are successful when finding and identifying required skills in a given job area, in most cases, they cannot scale.…”