Mining multiple sources of historical data: The example of a standardized dataset of medieval monasteries and convents in France

Adam Mertel,David Zbíral

doi:10.5194/ica-proc-2-85-2019

Abstract

Abstract. In this paper, we present a dataset of medieval monasteries and convents on the territory of today’s France and discuss the workflow of its integration. Spatial historical data are usually dispersed and stored in various forms – encyclopedias and catalogues, websites, online databases, and printed maps. In order to cope with this heterogeneity and proceed to computational analysis, we have devised a method that includes the creation of a data model, data mining from sources, data transformation, geocoding, editing, and conflicts solving. The resulting dataset is probably the most comprehensive collection of records on medieval monasteries within the borders of today’s France. It can be used for understanding the spatial patterns of medieval Christian monasticism and the implantation of the official Church infrastructure, as well as the relation between this official infrastructure and phenomena covered in other datasets. We open this dataset, as well as scripts for mining, to the public (https://github.com/adammertel/dissinet.monasteries) and provide a map tool to visualize, filter, and download the records (http://hde.geogr.muni.cz/monasteries).

Highlights

Christian monasteries and convents were among the most important institutions of medieval Europe
For centuries rather than decades, Christian monasticism has been a prominent subject of historiography as well as encyclopedic efforts
1970; Moorman, 1983; Pelliccia & Rocca, 1974; Poras & Cottineau, 1935) or digital form (e.g., Wikipedia; the Digital Atlas of Roman and Medieval Civilizations). These sources present a comprehensive picture of Christian monasticism in medieval Europe

Summary

Introduction

Christian monasteries and convents were among the most important institutions of medieval Europe. 1970; Moorman, 1983; Pelliccia & Rocca, 1974; Poras & Cottineau, 1935) or digital form (e.g., Wikipedia; the Digital Atlas of Roman and Medieval Civilizations) These sources present a comprehensive picture of Christian monasticism in medieval Europe. Data integration has been a focus of research in information science for a long time, and is gaining more and more attention in the disciplines included under the umbrella term ‘the digital humanities’ (Oldman, de Doerr, de Jong, Norton, & Wikman, 2014). Bilke, Bleiholder, & Weis (2006) recognize three steps – schema matching, duplicate detection, and data fusion, where each step resolves inconsistencies at a different level (schematic heterogeneity, duplicates, and data conflicts)

Methods

Results

Conclusion