Dealing with uncertainty and capturing the underrepresented

by Jaap Geraerts

Since I started my project on the schism in the Catholic Church in the eighteenth-century Dutch Republic in the summer of 2019, I have been creating a dataset that comprises the information contained in lists of baptisms, burials, and marriages. This information enables me to trace the movement of Catholics to another, competing Catholic Church in the context of the schism. Consider, for example, Henricus Verbruggen and Maria Blomevelt. They baptised their first two children in a mission station that was part of the Church of Utrecht but had their third and last child baptised in the Roman Catholic Church (see Fig. 1).

Fig. 1.

As can be gleaned from the image above, I express this data in a graph database, which enables me to capture the various relationships between people and their roles at the events in which they participated. Moreover, a graph database allows for a great deal of flexibility. Recently I encountered a fascinating list of Catholics who had “converted” from the Church of Utrecht to the Roman Catholic Church. The list contains extremely valuable information and required me to include a new edge (=relationship), namely ‘converted_at’, and a new node (=event), conversion (see Fig. 2).

Fig. 2.

Often, these lists are relatively easy to work with, safe from abysmal handwriting or a paucity of information due to the lack of interest (or time) of the serving priest. However, a more frequent and persistent problem is the uncertainty about whether person A in event B was the same person in event C. Countless spelling variants and the common occurrence of particular names render it sometimes near impossible to tell whether we are dealing with the same person or not. In case of great uncertainty, I generally decided to refrain from making a decision. Sometimes, however, in the case of less uncertainty, I did treat these people as if they were actually one and the same. Luckily, it is possible to capture such uncertainty – I have done so in the Excel spreadsheet, which functions as a temporary staging database (from which I import the data into my graph database). It is not a problem to include this data in the graph database, but when visualizing the data, as done above, it becomes much trickier to account for this uncertainty. For example, one could capture the uncertainty as an attribute of an edge. However, only one attribute can be visualised, so either someone’s role at an event (e.g. ‘godparent_at’) can be shown or the attribute that denotes the uncertainty. Hence when dealing with images, the uncertainty in the data easily slips to the background, creating the misleading idea that all the data is of equal certainty.

When presenting this and related problems pertaining to data visualizations with several colleagues from the IEG DH Lab at the recent conference ‘Digital History: Konzepte, Methoden und Kritiken’ (see the video presentation by Monika Barget), a conference attendant, Moritz Feichtinger, if I remember correctly, asked the following intriguing question:

‘How do you consider it operationalisable to identify the unknown/unrecorded, the “unrepresented,” so to speak, in order to prevent statistical distortions or a problematic claim of “total” recording of the past in data? Does this correspond to the “humanistic approach”? I think that the globally and socially unequal (digital) representation of the past should also be included in analyses in the form of a clearly identified "blind spot” or “fuzziness”1

Virtually all the data from the early modern period (or, perhaps better phrased, data based on early modern sources) is incomplete and biased. This incompleteness can be circumstantial, through the accidental loss of sources, or deliberate, through targeted destruction. Biases can result from the mindset, preferences, and outlook of the people and institutions who created the sources. Phrased differently, many sources reflect the hierarchies and power relations of the early modern period, causing things to be underrepresented or portrayed in a negative light. For example, many Catholic priests only mentioned the mother’s name when they baptized an illegitimate child, possibly because the name of the father was unknown to them or because they sought to protect the father’s honour (and hence only recorded his first name in some instances).

Arguably, a one-size-fits-all approach to this vexing issue does not exist. Rather, one must assess whether the information derived from one body of source material can be enriched by information stemming from other (related) sources. This will not always be possible. Moreover, we need to reflect on the implications of our editorial interventions. For instance, I could try to find the names of the fathers of illegitimate children or, to give another example, infer that a couple had married (even if I cannot find any record thereof) because their children were not called illegitimate in the baptismal registers. Doing this, however, would greatly increase the uncertainty regarding some data in my dataset and would only augment the problems described earlier. Hence, my approach to this issue when working with this specific data would be to provide a lengthy introduction about the primary source material as well as my editorial policy and decisions. In addition, I aim to capture all the relevant information where possible (e.g., signifying when a child was deemed illegitimate) but refrain from inferring information and including it in the dataset. In the end, as the questioner already indicated, instead of glossing over gaps, inconsistencies, and biases, pointing at and signalling them is the best way to account for the fact that both the sources and the dataset created by me are the creation of fallible human beings and are a flawed and incomplete representation at best.

Cite this article as: Jaap Geraerts, "Dealing with uncertainty and capturing the underrepresented," in Digital Humanities Lab, 16/04/2021,
  1. The original question in German: „Wie hielten Sie es für operationalisierbar, Unbekanntes/Unerfassstes, gewissermaßen das ‚Nicht-repräsentierte‘ auszuweisen, etwa um statistische Verzerrungen zu verhindern oder auch ein problematischen Anspruch ‚totaler‘ Erfassung der Vergangenheit in Daten? Entspricht das dem ‚Humanistic approach‘? Ich denke, auch die global und sozial ungleiche (digitale) Repräsentiertheit von Vergangenheit müsste in Form einer deutlich ausgewiesenen ‚Blindstelle‘ oder ‚Unschärfe‘ in Analysen mit aufgenommen werden.“ []

LinkedArt: exploring network analysis in art history

by Sophia Renz and Vanessa Tissen

The beginning

It all started with the seminar on network analysis in the summer semester of 2020. After learning about the basics of network theory and building networks in Python ourselves, the teachers Aline Deicke and Demival Vasques Filho asked us students to work in groups to develop a project combining our individual humanities backgrounds with network analysis. We are specialists in art history, which we wanted to include in the project. On top of that, the IEG DH Lab provided us with funds and support to further explore the application of network analysis in the field, e.g. whether art history datasets are available and to what extent they are usable or which art historical analyses or topics have already been done. The research project was kept relatively open, so we were able to look at the subject matter first. Tasks and questions developed during the following research. LinkedArt: exploring network analysis in art history weiterlesen

„Hello, World!“: a Python course for beginners with the Codingschule Düsseldorf

By Alessandro Grazi

My adventure in the world of the Digital Humanities, which started about a year ago in Innsbruck, continued last October and November with a Python course for beginners offered by the Codingschule Düsseldorf.

I did not know what to expect that Autumn Wednesday evening, when at 6 pm I connected to the Zoom link of the Python course I was going to attend. „Hello, World!“: a Python course for beginners with the Codingschule Düsseldorf weiterlesen

Text zu XML mit Python auf Basis des „Bomber’s Baedeker“

von Felix Bach und Cristian Secco

Die Transformation von digitalisierten Druckwerken von einer Bilddatei zur maschinenlesbaren XML-Datei ist für zahlreiche Methoden der Digital Humanities ein wichtiger Schritt in der Datenaufbereitung. In diesem Beitrag präsentieren wir einen Ansatz auf Basis eines Python-Skripts am Beispiel eines Werkes mit einer besonderen Binnenstruktur: Der Bomber’s Baedeker war ein „Reiseführer“, welcher von der Royal Air Force genutzt wurde, um während des 2. Weltkrieges deutsche Industriestandorte anzugreifen. Text zu XML mit Python auf Basis des „Bomber’s Baedeker“ weiterlesen

Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn

by Monika Barget

In April 2020, we started a series of case studies to introduce researchers working with historical sources to data analysis and data visualisation with Python. Today’s blog post covers topic modelling with the Python packages Gensim, spaCy, NLTK and SciKit learn.

Topic modelling is one of the central methods of Natural Language Processing (NLP), the „automatic manipulation of natural language, like speech and text, by software.“ (Jason Brownlee: What Is Natural Language Processing?, in: Deep Learning for Natural Language Processing, 22nd September 2017) In its most basic form, a „topic“ modelled by software displays word co-occurrences in texts, assuming that the frequency of co-occurrences defines certain areas of meaning. Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn weiterlesen

RESILIENCE – A Research Infrastructure for Religious Studies

by Sofie Sonnenstatter

The IEG is involved in RESILIENCE, a research infrastructure project for Religious Studies and related disciplines, involving twelve partner institutions from ten European countries.

The acronym stands for “REligious Studies Infrastructure: tooLs, Experts, conNections and CEnters in Europe” and the goal is a pan-European research infrastructure (RI) which provides access to sources, research results, expertise and tools for researchers and individuals interested in religion-related topics.

Digital Humanities have been transforming research in Europe and RESILIENCE aims for driving forward the digital turn in Religious Studies by stimulating the applicating of innovative methodological approaches in this field. Digital data and services designed for the needs of transdisciplinary research related to religions will be made available within a single ecosystem accessible for researchers as well as non-academics worldwide. RESILIENCE – A Research Infrastructure for Religious Studies weiterlesen

Research in times of COVID

by Jaap Geraerts

COVID-19 has posed a challenge, to put it mildly, to how most of us go about living our lives. Either in our personal life or work life, most if not all of us had to make significant adaptions in order to deal with a world that still is in the grasp of pandemic. The experience I’d like to focus on in this blog post, is that of doing research at these unprecedented times. Fairly soon after COVID-19 broke out, it became clear that it would have huge implications for researchers and teachers. Due to travel bans and other restrictions, virtually all conferences were either canceled or were held online in some form or another. In a short time span, teachers across the world had to go to extraordinary lengths to move their courses online. As libraries and archives closed their doors and traveling to or from particular countries became severely restricted or even impossible, the access to primary and secondary sources, the very fuel of our research, was hampered to a large degree. Research in times of COVID weiterlesen

Doing Digital History with Python II: creating custom Word Clouds

by Monika Barget

In the second edition of Doing digital history with Python, I would like to address word clouds as a visual method of finding patterns in texts (see critical reflection in Basic Text Mining: Word Clouds, their Limitations, and Moving Beyond Them). Word clouds display the frequency or importance of individual keywords in individual texts or entire corpora. There are many ready-made tools in multiple languages that help you create word clouds in different designs, such as the in-built word cloud generator in Voyant Tools or browser-based tools such as However, not all of them may be suitable for your specific use case. Doing Digital History with Python II: creating custom Word Clouds weiterlesen


von Thorsten Wübbena


Vor ungefähr drei Monaten fand die DHd 2020 „Spielräume: Digital Humanities zwischen Modellierung und Interpretation“ in Paderborn statt und wie jedes Jahr brachte auch diese DHd-Tagung nicht nur Einblicke in aktuelle Forschungsfragen, sondern versammelte eine Community und schaffte Gelegenheiten für Diskussionen. In der mittlerweile siebten Auflage der Konferenz kam im Austausch mit meiner Peergroup – also jenen, die schon ein paar Jahre länger im Job sind – mehrfach das Gespräch auf beobachtete und/oder selbst vollzogene berufliche Veränderungen der jüngeren Zeit. Die Überlegung, wer von uns denn in den vergangenen Jahren schon wo und mit wem zusammengearbeitet hatte, nahm ich mit und fragte mich: Wie könnte ein Personen-Affiliationen-Netzwerk der DHd-Tagung 2020 aussehen bzw. wie erstelle ich es auf möglichst geschmeidige Art und Weise?

ORCIDgraph weiterlesen

Research visit in New Zealand: Modelling the spread of COVID-19 on higher-order networks

At the beginning of March, I went to New Zealand for a research visit with a planned duration of four weeks. This visit was part of a collaboration involving the IEG DH Lab and Te Pūnaha Matatini, a New Zealand centre of research excellence for complex systems and data science hosted by the University of Auckland’s Department of Physics.

The main goal of the visit was to further develop our research on the structure of higher-order networks, that is, networks which account for interactions between individuals stemming from their membership to groups. Higher-order networks are usually represented by either bipartite (two-mode) networks, or hypergraphs, or simplicial complexes. Our last publications on this topic can be seen in the Journal of Complex Networks and the Physical Review E (also available on arXiv, here and here).

I arrived about one week before New Zealand closed its borders to the world, allowing only citizens and long-term residents to enter into the country. Research visit in New Zealand: Modelling the spread of COVID-19 on higher-order networks weiterlesen

Doing Digital History with Python I: reading (messy) XML & JSON data

by Monika Barget

During our DH brownbag lunches at the IEG, colleagues have repeatedly asked us if we could recommend Python packages for digital history. We have therefore set up a list of packages we at the IEG DH Lab are using for the analysis of text (stored, for instance, in XML/TEI or JSON formats), the modelling of historical networks, or the creation of interactive maps.

The list Python for digital history is based on our personal experiences and, though by no means exhaustive, may serve as an appetizer for “Doing Digital History with Python”. In a series of blog posts, we will try and introduce you to some of the packages mentioned through case studies from current IEG research.

Today’s post covers the extraction of data from XML and JSON files with xml.etree.ElementTree, lxml, json(5) and beautifulsoup(4) as reading structured text is often a starting point of digital history projects. Doing Digital History with Python I: reading (messy) XML & JSON data weiterlesen

The Archaeology of Reading, COVID-19, and online teaching

by Jaap Geraerts

Prior to taking up my current position at the IEG, I was based at the Centre for Editing Lives and Letters (CELL) at University College London where I served as the postdoctoral fellow on the digital humanities project “The Archaeology of Reading in Early Modern Europe” (AOR). An international collaboration between CELL, Johns Hopkins University, and Princeton University, the project team, consisting of historians, computer engineers, and librarians, aimed to enable a more structured analysis of historical reading practices through the creation of an online resource. The end result is a modified IIIF-compliant viewer comprising a corpus of 36 early modern books annotated by Gabriel Harvey and John Dee and the fully searchable transcriptions of all the “interventions” they made in these books. This viewer is accompanied by a WordPress site which contains contextual information of the scholarly and technological aspects of the project.
The Archaeology of Reading, COVID-19, and online teaching weiterlesen

Redesigning the “engage!” board game – my public humanities internship

by Isabelle Tanja Reiß

Last winter I had the pleasure of working at IEG Mainz for ten weeks. This internship was part of my master programme in Digital Humanities at JGU Mainz. My task was to rework the board game “engage!” created by Monika Barget, Jack Kavanagh, Susan Schreibman, Kathleen Fitzpatrick and Sharon M. Leon in 2018. The redesign and reworking of the game was supposed to happen on the basis of  user feedback collected beforehand. “Engage!” is an educational game that introduces cultural heritage experts and humanities scholars to public engagement / engaged research, though one of the main points of critique was, that the game wasn’t engaging enough. Many praised the great discussions that arose throughout the gameplay, but criticised the missing drive to win the game in the same breath.
Redesigning the “engage!” board game – my public humanities internship weiterlesen