This work-in-progress paper offers for critical review the current challenges of an ambitious project to create a digital framework for interpreting the dissolution of monasteries in Europe. The most dramatic episode of the European Reformation (
c.1517-
c.1648), the state suppression of monasteries, the dispersal of their populations, the re-distribution of their property and the re-deployment of their infrastructure, represented the largest and furthest-reaching re-ordering of society, economy and culture before the Industrial Revolution (Chadwick, 2001; Youings, 1971). The scale, scope, pace and reach of the process make it perhaps the most formidable of all pre-modern territories for the data-driven researcher, and have ensured that narrative histories founded on conventional methods of data analysis have consistently failed to provide perspectives of adequate breadth, depth, and accuracy.

In respect of research data, the medieval monastery presents both the best and worst of all prospects. A world in microcosm, possessed of its own demographic, economic, social, cultural and environmental imprint, in principle there are multiple layers to its source-base. It also runs deep through time, passing any polity, dynasty, and even place of settlement to reach back to the remote beginnings of Christian-occupied Europe. Yet for these same reasons, the sources of the medieval monastery are also uniquely unstable. The self-containment of the monastery was such that while the form and function of its documentary record might be comparable one to another, it is never quite the same. A durable – but not always enduring – presence in a world that was chronically disturbed, the record underwent repeated and extended interruptions. The monastery invited the manipulation of those in power, and its records are susceptible to conscious distortion. Even well-preserved monastic records can confound the researcher.

The closure and re-constitution of these centuries-old institutions brings these data complexities into collision with the records of the state, city, commune and of private individuals at a moment when these constituencies were in transition to a post-medieval world. The bare historical record may give the impression of the dissolution as an event bounded by the dates of specific acts of state, but in fact its course and consequences were a collective experience which unfolded over several generations. This means that for effective interpretation, datasets should be defined not by the intrinsic criteria of a particular monastery but rather by those that can be related to the contexts in which it was situated, relating to a range of organisational and social networks and to physical place and space. This requires drawing from the monastery’s records data that they were not originally created to document. For example, a contextualised approach to data on the monastery’s population profile demands not only a raw numeral but also a measure of its geographical origin, social status and generational mix, each in relation to other neighbourhood constituencies. Because the dissolution was experienced over
la longue durée, a wide chronological frame is needed: only by capturing data from 1450 to 1650 can the process of dissolution be traced in real time. Given the inherent characteristics of the records, this can be no conventional time-frame bringing a strict linear order to each dataset. With unequal interruptions in every category of record, instead the timeline must be drawn between irregular census points derived from individual documents.

Presently, we are applying these principles to a single case-study, the English Benedictine abbey of Battle, in the county of Sussex, dissolved in 1538. A substantial foundation, holding territory across seven counties of England and Wales, overseeing diverse agricultural, commercial and industrial interests and governing a network of satellite churches and communities, Battle presents sufficient scale and complexity to guide, and test, our emerging methodology (Evans, 1941-2; Searle, 1974). We are creating datasets which aim to measure (1) every aspect of the monastery’s presence in and imprint upon its neighbourhood in the period before its dissolution and (2) the pattern and pace of change in that presence and imprint as the monastery was suppressed. We have defined data categories to evaluate its dynamic role in its neighbourhood, providing a series of key performance indicators at those census points which can be established. Although these do not always directly reflect the categories of the monastic records, generally it has been possible for data to be anchored by a specific documentary reference. However, it is sometimes necessary to make use of proxies. For example, because the family origins of monks are rarely documented, surnames are taken as an index of origin and social position, and because the precise site and proportions of monastic buildings are not consistently documented we have adopted a ‘best-guess’ principle, utilising historic mapping, field-, excavation and environment surveys, realising the benefits of the Archaeology Data Service (

www.archaeologydataservice.ac.uk
) and Heritage Gateway (

www.heritagegateway.org.uk
).

Our paper explores how we are addressing these complexities and challenges in our sources by combining a webapp built on an open source XML database (xQuery-based eXist-db,

http://exist-db.org/
) with highly customisable mapping using jQuery and GoogleMaps API to create a digital framework for analysing the process of dissolution across Europe. The framework allows researchers to interpret its events and sources at levels from regional to site-specific, utilising a comparative approach to reveal and visualise patterns that have been largely obscured within this often chaotic set of sources. We are building the webapp to be redeployed by others: its source code and documentation will be released freely on GitHub, enabling others to reuse it for their own research aims. The complexity of the process of dissolution might suggest that certainty in interpretation is an impossible goal, but our work to date suggests that far from this being a deterrent to digital approaches, it instead raises important questions about how we describe our datasets and how we can represent with honesty and clarity the uncertainty, inconsistency and gaps in our sources. These are questions with which every digital humanities scholar must grapple, and having set out the solutions we have identified so far, we are keen to invite discussion on how we might resolve or improve our approach for the benefit of current and future projects encountering similar issues.