What do we mean by "data"? A proposed classification of data types in the arts and humanities

TL;DR

This study explores how researchers in the arts and humanities define and manage 'data', identifying 13 data types and highlighting their openness to FAIR data practices despite some confusion about the term.

Contribution

It provides a novel classification of 13 research data types in humanities and insights into researchers' perceptions of 'data' and FAIR principles.

Findings

Identified 13 distinct research data types in humanities.

Researchers are generally open to making their data FAIR.

Methodologies and collaboration are central to research practices.

Abstract

Purpose: This article describes the interviews we conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of Bologna. The main purpose was to shed light on the definition of the word "data" in the humanities domain, as far as FAIR data management practices are concerned, and on what researchers think of the term. Methodology: We invited one researcher for each of the official disciplinary areas represented within the department and all 19 accepted to participate in the study. Participants were then divided into 5 main research areas: philology and literary criticism, language and linguistics, history of art, computer science, archival studies. The interviews were transcribed and analysed using a grounded theory approach. Findings: A list of 13 research data types has been compiled thanks to the information collected from participants. The term "data" does not emerge as especially problematic, although a good deal of confusion remains. Looking at current research management practices, methodologies and teamwork appear more central than previously reported. Originality: Our findings confirm that "data" within the FAIR framework should include all types of input and outputs humanities research work with, including publications. Also, the participants to this study appear ready for a discussion around making their research data FAIR: they do not find the terminology particularly problematic, while they rely on precise and recognised methodologies, as well as on sharing and collaboration with colleagues.