A bibliometric analysis of the first decade of the Journal of Big Data

Abstract This study provides a bibliometric overview of the first decade of the Journal of Big Data (JBD) since its launch in 2014. Using bibliographic records for 1,023 articles and reviews indexed in Scopus (2014–2024) and 917 records in Web of Science, the analysis combines performance indicators with science-mapping techniques, including co-citation, bibliographic coupling, and keyword co-occurrence implemented in VOSviewer and bibliometrix. The results document rapid growth in output, a Scopus/SCImago h-index of 91 by 2024, and strong international contributions led by the USA, China, India, and European countries. Citation and topical structures reveal three main thematic cores centred on big data infrastructures (e.g., Hadoop, MapReduce, Apache Spark), machine and deep learning (e.g., convolutional and recurrent neural networks), and application domains such as cybersecurity, healthcare analytics, and sentiment analysis. The findings characterize JBD’s position within the wider data science ecosystem, showing how its publications concentrate influence in a small number of highly cited surveys and frameworks while supporting an increasingly diverse long tail of topics and emerging research fronts.