This article outlines the state of digital perspectives in historical research, some of the methods and tools in use by digital historians, and the possible or even necessary steps in the future development of the digital approach. We begin by describing three main computational approaches: digital databases and repositories, network analysis, and Machine Learning. We also address data models and ontologies in the larger context of the demand for sustainability and linked research data. The section is followed by a discussion of the (much needed) standards and policies concerning data quality and transparency. We conclude with a consideration of future scenarios and challenges for computational research.
The Sphere project stands at the intersection of the humanities and information sciences. The project aims to better understand the evolution of knowledge in the early modern period by studying a collection of 359 textbook editions published between 1472 and 1650 which were used to teach geocentric cosmology and astronomy at European universities. The relatively large size of the corpus at hand presents a challenge for traditional historical approaches, but provides a great opportunity to explore such a large collection of historical data using computational approaches. In this paper, we present a review of the different computational approaches, used in this project over the period of the last three years, that led to a better understanding of the dynamics of knowledge transfer and transformation in the early modern period.
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