This comment examines the rapidly evolving ecosystem of historical research data in the United Kingdom, where cultural heritage collections are increasingly digitised, commercialised and fragmented. Historians face growing challenges in discovering, accessing and reusing data as resources move behind paywalls, and repositories remain scattered, without a national infrastructure to ensure long-term preservation or discoverability. Drawing on examples from major digital initiatives, we analyse the life cycle of historical research data and highlight the complex interplay of commercial, institutional and scholarly interests that shape access. We distinguish three types of data that emerge from historians’ typical engagements with digitised collections: derived, enhanced and aggregated data. We argue that historians must actively participate in the practices relating to the creation, maintenance and reuse of such data. This will involve new forms of citation, favouring open datasets, improving digital skills and building communities around shared resources. The comment concludes with proposals to improve discoverability, sustainability and reuse, urging the discipline to establish common standards and infrastructures to secure an equitable data commons for future research.

This article implements a critical method for assessing bias in large historical datasets that we term the “Environmental Scan.” The Environmental Scan sheds new light on newspaper collections by linking newly available “reference metadata” gathered from historical sources to existing full-text and catalogue metadata. The rise of computational methods in history and the social sciences, in tandem with newly “datafied” source materials, creates a challenge for researchers to adapt their existing critical practices to the increasing scale and complexity of computational research. To help address this challenge, the Environmental Scan situates big historical datasets in much greater context, including estimating what materials are missing, thereby revealing the ways digital collections can be “oligoptic” in nature. Using the British Newspaper Archive (BNA) as a case study, we diagnose the biases and imbalances in the digitised Victorian press. We determine which voices are under- or over-represented in relation to the political composition of the collection as well as its content and we trace the origins of these biases in the digitisation process. This article informs future interdisciplinary discussions about data bias and offers a conceptual model adaptable to diverse historical datasets. The Environmental Scan provides a more nuanced and accurate understanding of how newspaper data reflects past societies, making it a valuable tool for researchers.

Within disciplines that aim to understand past cultures by studying the objects they made, research methodologies can move between example-based object-specific investigations and whole-of-corpus data-driven analyses. And when the count of extant objects is relatively small, every single individual object has the potential to uniquely contribute to new knowledge or transform existing paradigms. But how does a researcher know how many objects there are, where they are, how they have been studied and written about in the past, and which may be awaiting closer examination? This article introduces an object-literature framework that connects objects to the literature that mention them and creates an error-corrected resource that enables the tracing of objects through published literature and through time. The specific example described here applies the framework to khipus (knotted-cord recording devices from the Andes), to create the “khipu-biblio cross-reference.” Key findings include comprehending the pattern of khipu publications, identifying understudied khipus, and updating the count of known khipus and their locations. By applying the framework to any collection of objects, researchers and collections teams can draw substantial benefits and accelerate the generation of new knowledge.