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, https://dhlab.hypotheses.org/1952.
  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.“ []

Exploring connections: Digital workshop on Network Analysis with Python

by Sarah Büttner

In times of covid-19, virtual workshops can be quite hard for organizers as well as for participants. On the other hand, they offer a unique opportunity to try unconventional methods to improve the situation for both sides. Dr. Demival Vasques Filho, in cooperation with Anna Aschauer, grasped such an opportunity at the DARIAH-DE-workshop Network Analysis with Python for Beginners, when he decided to simultaneously code and explain the basis on network analysis. Furthermore, he managed to address academics from different research areas with the help of some well-known fictional characters.
Exploring connections: Digital workshop on Network Analysis with Python weiterlesen

60 Minutes of DH: eScriptorium for Handwritten Text Recognition in Humanities Research

by Sofie Sonnenstatter

In January, the DH Lab launched its series of (online) events “60 Minutes of DH” with a webinar on the automatic transcription tool eScriptorium.

The monthly events, planned as one-hour long afternoon sessions, are mainly intended for academic staff at the IEG and focus on joint discussions of tools, methods and literature from the field of Digital Humanities, as well as insights into the international project landscape. The goal is to encourage and support researchers when it comes to digital solutions supporting their history- and religion-related research. For this kick-off, however, the invitation was extended to a wider audience and was met with overwhelming interest by researchers from all over Europe.

60 Minutes of DH: eScriptorium for Handwritten Text Recognition in Humanities Research weiterlesen

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

Geohumanities II: Gestaltung und Druck einfacher Karten in QGIS

by Monika Barget

Als Ergänzung zur Einführung in die Erstellung von GeoJSON-Dateien beschäftigt sich der heutige Blogbeitrag mit der Visualisierung dieser Geodaten im Open Source Geoinformationssystem QGIS. Das Tutorial führt Schritt-für-Schritt durch die Erstellung einer einfachen Karte und gibt Hinweise auf weiterführende Lernressourcen. Geohumanities II: Gestaltung und Druck einfacher Karten in QGIS weiterlesen

Über den Umgang mit digitalen Forschungsdaten: Virtueller Forschungsdatentag der RMU 2020

by Sarah Büttner

Große Ziele alleine anzugehen ist ein oftmals unmögliches Unterfangen. Daher war es nicht nur eine, sondern gleich drei Universitäten, die sich der Aufgabe widmeten, den Zweck und die Möglichkeiten von Forschungsdatenmanagement an ein breites Publikum heranzutragen. Die Goethe-Universität Frankfurt am Main, die Johannes Gutenberg-Universität Mainz und die Technische Universität Darmstadt bilden den Verbund der Rhein-Main-Universitäten (RMU) und richteten als solche den ersten virtuellen Forschungsdatentag der RMU am 13. Oktober 2020 in Mainz aus. Hierbei ließen sich die Einladenden auf ein spannendes Experiment ein: Wie kann eine Veranstaltung dieses Formats digital umgesetzt werden?
Über den Umgang mit digitalen Forschungsdaten: Virtueller Forschungsdatentag der RMU 2020 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

Calling for data publication workshops in historical research

by Fabian Cremer

This September, an online workshop on the publication of research data in the fields of History, which we offered for the first time, exceeded our expectations. The overwhelming interest, the engaging participants and the smooth flow of the event led us to a better understanding and (three) notable thoughts we would like to share. Calling for data publication workshops in historical research 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

DH fun on the road: 3D models from your photos using photogrammetry

A How-to have fun with DH on the road by generating 3D models from photos using Structure From Motion Photogrammetry

by Sarah Lang

The Digital Humanities have such a huge inventory of digital methods that it is pretty hard to keep up with the learning of new methods while working with others just recently learned. Shifting academic work into free time can be annoying, but learning new things in a fun way – may keep the leisure factor. This is why I wanted to use this blog post to share a fun way making first steps in 3D while beeing on the road – be that a holiday or conference travel. DH fun on the road: 3D models from your photos using photogrammetry weiterlesen

The Use and Study of Special Documents (ReIReS-School)

by Anna Aschauer

The IEG and our DH Lab is involved in ReIReS, a joint project building infrastructure on religious studies, about which my colleague Jaap Geraerts already wrote here. In February 2020 I had a chance to visit a ReIReS-school on the “Use and Study of Special Documents”, organized by Mathijs Lamberigts (Professor at the Faculty of Theology and Religious Studies) and Isaak Deman at KU Leuven (Katholieke Universiteit Leuven). The aim of the school was to introduce the scholars to the  collections of the libraries and archives of the University related to theology and religious studies.

I want to share some topics that I found interesting from a perspective of a digital humanist. I also want to share my experience, since in the time of the pandemic the organisation of “train-the-trainer” (like this or this) sessions are currently problematic. The Use and Study of Special Documents (ReIReS-School) weiterlesen

Geohumanities I: Ortsdaten mit Geojson.io erfassen und bearbeiten

by Monika Barget

Für viele Geisteswissenschaftler:innen und Mitarbeiter:innen in der Forschungskommunikation sind Karten methodische Werkzeuge, um räumliche Beziehungen und Entwicklungen zu analysieren und zu vermitteln. Allerdings fällt es oft schwer, geeignete Werkzeuge zur Kartenerstellung auszuwählen, da die Möglichkeiten klassischer Einsteigertools wie Palladio und DARIAH-DE Geobrowser besonders im Bereich des Exports hochwertiger Kartenbilder beschränkt sind, während viele andere Werkzeuge aufwendigere Softwareinstallationen, Kenntnisse in der Arbeit mit Terminal / Kommandozeile oder sogar Erfahrung mit Programmiersprachen erfordern. In diesem Tutorial möchte ich deshalb eine einfache Methode der Aufbereitung geographischer Informationen mit Geojson.io vorstellen. Geohumanities I: Ortsdaten mit Geojson.io erfassen und bearbeiten weiterlesen