How to make data valuable in digital humanities projects? Stages, archetypes, and guidelines for data practice

Abstract

A cornerstone of digital humanities (DH) is its engagement with diverse data practices across disciplines, which create academic, educational, cultural, historical, social, technological, and economic value. However, data value theories derived from business and management domains are not entirely applicable to the DH domain. Humanities data practices remain insufficiently theorized, with limited insights into the implementation of these value-centric practices. To address these gaps, this study employs a multiple-case study and content analysis to examine data practices in 17 DH projects. The analysis identifies four stages of the humanities data value chain (DVC): data collection, data processing, data exposition, and data sharing. Humanities data practices are clustered into three archetypes: data transformation by expanding data scale, data enrichment by enriching data context, and data revitalization by facilitating data reuse. Synthesizing these findings, the study proposes a conceptual model illustrating how humanities data practices create multi-dimensional value across the DVC stages. Furthermore, four guidelines are proposed to support the implementation of these value-centric data practices. As a pivotal effort to theorize value-centric data practices in DH, this study extends data value theory in the DH domain, thereby fostering interdisciplinary collaboration and advancing the development of robust humanities data infrastructure.