Towards a Computational History of Science: Limitations and Perspectives of an Emerging Research Approach

Data science has recently emerged as a multi-disciplinary field of research where statistics, data analysis, machine learning and their related techniques are combined in a systematic way to support understanding of actual phenomena concerned with data. The growing power of storage infrastructures and the consequent availability of large amount of data opened up unprecedented opportunities to support the specification of ad-hoc data-driven approaches and tools for a number of application fields, such as biology, medicine, economy, politics, and history. In historical studies of science and knowledge, the use of data-science solutions is gaining more and more attention and the scientific debate is more topical than ever.