The field of blockchain research is continuing to evolve, with a growing focus on analyzing and extracting insights from the data stored on blockchain networks. Recent studies have employed Natural Language Processing techniques to detect patterns and extract sentiment from blockchain transactional data, with applications in forecasting cryptocurrency price movements. Additionally, there is a growing recognition of the importance of field normalization in scientometrics, with researchers revisiting and improving upon existing methods to ensure fair comparisons across different disciplines. Furthermore, the impact of transformative agreements on hybrid open access publishing is being studied, with findings indicating substantial growth in open access due to these agreements. Noteworthy papers in this area include:

• A bibliometric analysis of scientific publications on blockchain research, which provides a comprehensive overview of the field and identifies emerging research areas.
• A study on sentiment analysis of blockchain transactional data, which introduces a novel framework for enhancing financial predictions in cryptocurrency markets.