9  data analysis

abstract: data analysis and data visualization with programming (Python, R) and specialized tools.

The most spectacular phase of the work process is data analysis.¹ Here, the researcher translates his or her own research questions into the operations offered by the tools used. Two types of data are used in the analyses: on the one hand, the original or enriched data available after data harmonization (title, place of publication, language, number of pages), and on the other hand, calculated data based on their analysis (including font type and size, amount of paper used to produce the form, number of words, number of printed words per capita). Data analysis methods are not unique to bibliographic data science; they are general methods for which general and (to a lesser extent) specialized data science teaching materials in the humanities² are available, as well as textbooks on quantitative history.³ The curriculum cannot, of course, cover every conceivable data science technique, but it should use examples to introduce the most common procedures used in the history of the discipline (the basics of statistics, time series analysis, data visualization including map representation, text analysis, network analysis).

Arnold, Taylor, and Lauren Tilton. 2024. Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text. 2nd ed. 2024. Quantitative Methods in the Humanities and Social Sciences. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-62566-4.

Bátorfy, Attila. 2024. Adatvizualizáció. Elmélet, rendszer, módszer. Bevezetés az adatok grafikus ábrázolásának elméletébe és gyakorlatába. Budapest: ELTE Eötvös Kiadó.

Feinstein, Charles H., and Mark Thomas. 2008. Making History Count: A Primer in Quantitative Methods for Historians. Transferred to digital printing (with corrections). Cambridge New York Melbourne Madrid Cape Town Singapore São Paulo: Cambridge University Press.

Hudson, Pat, and Mina Ishizu. 2017. History by Numbers: An Introduction to Quantitative Approaches. Second edition. London Oxford New York New Delhi Sydney: Bloomsbury Academic.

Karsdorp, Folgert, Mike Kestemont, and Allen Riddell. 2021. Humanities Data Analysis: Case Studies with Python. Princeton Oxford: Princeton University Press.

Klinke, Harald. 2025. Cultural Data Science: An Introduction to R. Quantitative Methods in the Humanities and Social Sciences. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-88130-5.

Lemercier, Claire, and Claire Zalc. 2019. Quantitative Methods in the Humanities: An Introduction. Translated by Arthur Goldhammer. Charlottesville: University of Virginia Press.

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1. see, among other things, the literature cited in connection with the research questions.

2. (Karsdorp, Kestemont, and Riddell 2021); (Klinke 2025); (Arnold and Tilton 2024); (Bátorfy 2024); and the Programming Historian site: https://programminghistorian.org.

3. (Lemercier and Zalc 2019); (Feinstein and Thomas 2008); (Hudson and Ishizu 2017).