Alex Spike Gibbs, Juniorprofessur für Wirtschaftsgeschichte des Mittelalters: Historische Datensätze (September 2024)
What is your current research topic?
Our project examines the remuneration of agricultural workers in late medieval England during the age of the Black Death. Workers in this period were paid most of their wages in kind, rather than in money, in our case receiving large amounts of different types of grain to sustain them throughout the year. We combine datasets on the quantities of grain paid to workers, the types of grains these workers received and the prices of these different grains, to calculate a cash equivalent value for each worker’s in-kind payment. This allows us to turn this mass of qualitative information into a set of values which can be compared across space and time revealing the ways in which economic, social and legal changes in the wake of the Black Death improved the living standards of ordinary people.
For those who have not yet delved deeply into the topic of Data Science: How would you explain to a child what you are working on?
We try to understand how much workers earned in the past by looking at the food they were given and seeing how much this would have cost them to buy in a marketplace. This provides insights into how the Black Death, one of the worst epidemics in human history, changed society by providing new bargaining power to workers in their relationship with their employers.
Everyone talks about Data Science – how would you describe the importance of the topic for yourself in three words?
Empirically testing theories
What points of contact with Data Science does your work have? Which methods do you already use, and which would be interesting for you in the future?
While medieval economic history datasets are typically relatively small due to the difficult task of extracting information from archival sources, data science techniques allow us to create structured data out of material such as tax lists and estate accounts which were originally written for very different purposes than the questions we ask of them today. Data science allows us to stitch small and fragmentary samples together to create representative series of data to track large-scale economic trends. In future, I am excited to start a new collaborative project which will apply social network analysis techniques to medieval manorial court records for the first time to better understand the nature of village life in a more structured way than has hitherto been possible.
How high is the value of Data Science for your work? Would your research even be possible without Data Science?
Data Science has considerable value for my work. Not only do these techniques allow us to create new datasets, but also cloud storage enables us to share data instantly while working in completely different parts of Europe. This is invaluable in creating cutting-edge medieval economic history, as it allows someone like me who has expertise in the sources to collaborate with colleagues who are experts in econometric techniques which can be used to analyse this data. While it seems pretty inconceivable to me to do this kind of research without the databases and analytical tools which are now available to anyone with a computer, I always try to remember that much of the pioneering work on which we draw was done with very limited access or even no access to computing power. This shows how an imaginative and determined researcher can produce amazing results even with limited tools!
What development opportunities do you see for the topic of Data Science in relation to your field?
The most significant area where Data Science is providing new opportunities in my field is the ability to quickly transcribe pre-modern texts using AI tools such as Transkribus. These can be trained to read specific types of historical handwriting by someone without specialist technical knowledge. This promises to revolutionise the field by allowing economic historians to create databases from far larger corpuses of sources that those available to date, allowing us to explore processes of change at a greater level of resolution.