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
by Sophia Renz and Vanessa Tissen
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
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
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
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
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 Wortwolken.com. However, not all of them may be suitable for your specific use case. Doing Digital History with Python II: creating custom Word Clouds weiterlesen
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