Abstract

This paper examines standards, datasets, and transparency norms as foundational pillars of reproducibility in 
Digital Humanities (DH). It situates reproducibility within the broader computational turn in humanities 
scholarship, where large-scale datasets, algorithmic methods, and digital infrastructures increasingly shape 
research practices. While reproducibility has become a central ideal in DH, the paper argues that its 
implementation remains complex due to the interpretive, contextual, and diachronic nature of humanities inquiry. 
It critically explores the distinctions between reproducibility, replication, and reusability, highlighting how these 
concepts intersect yet serve different epistemic purposes. The study further investigates how standards, metadata 
frameworks, and interoperability protocols, such as FAIR principles, Dublin Core, RDF, and DOI systems attempt 
to structure data sharing and enhance transparency. Attention is also given to workflows, provenance tracking, 
ethical constraints, licensing regimes, and infrastructural limitations that influence dataset accessibility and long
term sustainability. Ultimately, the paper contends that while transparency norms and standardized datasets 
enhance accountability and research visibility, they also introduce tensions related to interpretive flexibility, 
intellectual property, and disciplinary diversity. It concludes that reproducibility in Digital Humanities should be 
understood not as a fixed technical benchmark but as a negotiated, context-sensitive practice embedded in 
scholarly ecosystems.