Collaborative Data Behaviors in Digital Humanities Research Teams
Wenqi Li, Zhenyi Tang, Pengyi Zhang, Jun Wang
TL;DR
Digital humanities increasingly rely on data-intensive, interdisciplinary collaboration, yet the patterns of collaborative data behavior remain unclear. The authors conducted focus group interviews across 19 DH teams from two summer courses and applied inductive coding to reveal two data activity categories (primary and supportive) and three collaborative modes (humanities-driven, technically-driven, balanced). They map how specific activities are driven by different expertise: data selection/collection and modeling tend to be humanities-driven, data processing by technical teams, and analysis/interpretation through balanced collaboration, with orientation, data quality assurance, and interdisciplinary communication supporting these activities. The study provides a preliminary framework for analyzing collaborative data behaviors in DH and offers design implications for data infrastructures and collaboration tools, while acknowledging limitations due to the early-stage, student-focused sample. These insights can help teams negotiate roles, improve data quality, and design better practice guidelines for interdisciplinary DH research.
Abstract
The development of digital humanities necessitates scholars to adopt more data-intensive methods and engage in multidisciplinary collaborations. Understanding their collaborative data behaviors becomes essential for providing more curated data, tailored tools, and a collaborative research environment. This study explores how interdisciplinary researchers collaborate on data activities by conducting focus group interviews with 19 digital humanities research groups. Through inductive coding, the study identified seven primary and supportive data activities and found that different collaborative modes are adopted in various data activities. The collaborative modes include humanities-driven, technically-driven, and balanced, depending on how team members naturally adjusted their responsibilities based on their expertise. These findings establish a preliminary framework for examining collaborative data behavior and interdisciplinary collaboration in digital humanities.