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.

Towards a Computational History of Science: Limitations and Perspectives of an Emerging Research Approach / G. Giannini. - In: PHYSIS, RIVISTA INTERNAZIONALE DI STORIA DELLA SCIENZA. - ISSN 0031-9414. - 57:1(2022), pp. 245-258.

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

G. Giannini
2022

Abstract

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.
Computational history; History of Science; New trends
Settore M-STO/05 - Storia della Scienza e delle Tecniche
H2020_ERC19GGIAN_01 - The Accademia del Cimento in Florence: tracing the roots of the European scientific Enterprise (TACITROOTS) - GIANNINI, GIULIA - H2020_ERC - Horizon 2020_Europern Research Council - 2019
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/937226
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