Work

Growing skills from code via SciESCO: A tri-phasic bibliometric-driven framework of scientific software development

Year: 2026

Type: article

Abstract: Scientific Software Development (SSD) plays a pivotal role in accelerating research and solving real-world problems across disciplines. Yet, the field remains fragmented, lacking a unified framework for analyzing the skills embedded in open-source scientific software. To address this gap, we introduce SciESCO , a tri-phasic framework that combines bibliometric analysis, skill extraction and predictive modeling to provide actionable insights into evolving competencies. In the first phase, focusing on 2239 publications from 2015 to 2025 in SoftwareX and Software Impacts, we analyze scientific software contributions, as these are the only peer-reviewed journals that publish software tools accompanied by complete and openly available metadata, including source code and related documentation. In the second phase, skills are extracted using ESCOX, an open-source tool developed under the SKILLAB EU project that leverages the European Skills/Competences, Qualifications, and Occupations (ESCO) taxonomy. These extracted skills represent the supply side of the labour market and are subsequently classified into eight scientific domains using a large language model (LLM). Finally, a Graph Neural Network (GNN) predicts links between skills and domains. Our results highlight critical competencies such as machine learning, authoring software, UI design, deep learning and data visualization. The SciESCO framework offers a skill-oriented view of SSD, supporting HR professionals, researchers and institutions in aligning talent with scientific needs. This study also identifies gaps in mapping competencies to domains, opening avenues for future research in skill analytics and offering a forward-looking view of the future SSD researcher. Finally, SciESCO provides an open-source tool designed for implementation by stakeholders, practitioners and researchers.

Source: Journal of Systems and Software

DOI: 10.1016/j.jss.2026.112864

Authors: Dimitrios Christos Kavargyris, Nikolaos Mittas, Lefteris Angelis

Institutions: Aristotle University of Thessaloniki, Democritus University of Thrace

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $3,820