Category: Relational Analysis

Relational Analysis refers to computational techniques serving to discover specific relations between several objects of study. In textual studies, this could mean discovering overlap between several different texts (study of text reuse / plagiarism), or textual variations between several versions of one text (collation), or assessing the similarity of texts in terms of stylistic features (stylometry). By analogy, such methods can also be applied to other cultural artefacts, such as music, film or painting. Relevant techniques include Sequence Alignment, Collation, and techniques associated with Stylistic Analysis.

Tools for Critical Discourse Analysis – and introduction to tool critizism

Tools for Critical Discourse Analysis – and introduction to tool critizism

In this video, Drs. Stephanie Vie and Jennifer deWinter explain some of the tools digital humanists can use for critical discourse analysis and visualization of data collected from social media platforms. Although not all the tools they mention are open source, the majority of them have free to use or freemium versions, including AntConc, a free-to-use concordancing tool, or several Twitter data visualisation tools such as Tweeps map or Tweetstats.

Even though the video does not provide just-as-good open source alternatives to Atlas.ti or MAXQDA (an obviously a recurrent question or shortcoming that is recurrently discussed on OpenMethods), it sets an excellent example for how to introduce tool criticism in the classroom alongside introduction to certain Digital Humanities Tools. After briefly touching upon both advantages and disadvantages of each tool, they encourage their audience (students in Digital Humanities study programs) to pilot each of them by using the same data-set and not only compare their results but also reflect on the epistemic processes in-between.

Sharing the video on Humanities Commons with stable archiving, DOI and rich metadata is among the best things that could happen to teaching resources of all kinds.

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Humanities Data Analysis: Case Studies with Python — Humanities Data Analysis: Case Studies with Python

Humanities Data Analysis: Case Studies with Python — Humanities Data Analysis: Case Studies with Python

Introduction: Folgert Karsdorp, Mike Kestemont and Allen Riddell ‘s  interactive book, Humanities Data Analysis: Case Studies with Python had been written with the aim in mind to equip humanities students and scholars working with textual and tabular resources with practical, hands-on knowledge to better understand the potentials of data-rich, computer-assisted approaches that the Python framework offers to them and eventually to apply and integrate them to their own research projects.

The first part introduces a “Data carpentry”, a collection of essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. This sets the stage for the second part that consists of 5 case studies (Statistics Essentials: WhoReads Novels? ; Introduction to Probability ; Narrating with Maps ; Stylometry and the Voice of Hildegard ; A Topic Model of United States Supreme Court Opinions, 1900–2000 ) showcasing how to draw meaningful insights from data using quantitative methods. Each chapter contains executable Python codes and ends with exercises ranging from easier drills to more creative and complex possibilities to adapt the apply and adopt the newly acquired knowledge to their own research problems.

The book exhibits best practices in how to make digital scholarship available in an open, sustainable ad digital-native manner, coming in different layers that are firmly interlinked with each other. Published with Princeton University Press in 2021, hardcopies are also available, but more importantly, the digital version is an  Open Access Jupyter notebook that can be read in multiple environments and formats (.md and .pdf). The documentation, coda and data materials are available on Zenodo (https://zenodo.org/record/3560761#.Y3tCcn3MJD9). The authors also made sure to select and use packages which are mature and actively maintained.

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What is PixPlot? (DH Tools) – YouTube

What is PixPlot? (DH Tools) – YouTube

Introduction: This short video teaser summarizes the main characteristics of PixPlot, a Python-based tool for clustering images and analyzing them from a numerical perspective as well as its pedagogical relevance as far as
machine learning is concerned.

The paper “Visual Patterns Discovery in Large Databases of Paintings”, presented at the Digital Humanities 2016 Conference held in Poland,
can be considered the foundational text for the development of the PixPlot Project at Yale University.
[Click ‘Read more’ for the full post!]

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Navigating the centuries with the ‘Mapping of the Republic of Letters’ project.

Navigating the centuries with the ‘Mapping of the Republic of Letters’ project.

In this post, we reach back in time to showcase an older project and highlight its impact on data visualization in Digital Humanities as well as its good practices to make different layers of scholarship available for increased transparency and reusability.  

Developed at Stanford with other research partners (‘Cultures of Knowledge’ at Oxford, the Groupe d’Alembert at CNRS, the KKCC-Circulation of Knowledge and Learned Practices in the 17th-century Dutch Republic, the DensityDesign ResearchLab), the ‘Mapping of the Republic of Letters Project’ aimed at digitizing and visualizing the intellectual community throughout the XVI and XVIII centuries known as ‘Republic of Letters’ (an overview of the concept can be found in Bots and Waquet, 1997), to get a better sense of the shape, size and associated intellectual network, its inherent complexities and  boundaries.

Below we highlight the different, interrelated
layers of making project outputs available and reusable on the long term (way before FAIR data became a widespread policy imperative!): methodological reflections, interactive visualizations, the associated data and its data model schema. All of these layers are published in a trusted repository and are interlinked with each other via their Persistent Identifiers.

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La poética dramática desde una perspectiva cuantitativa: la obra de Calderón de la Barca

La poética dramática desde una perspectiva cuantitativa: la obra de Calderón de la Barca

Introduction: In this paper, Ehrlicher et al. follow a quantitative approach to unveil possible structural parallelisms between 13 comedies and 10 autos sacramentales written by Calderón de la Barca. Comedies are analyzed within a comparative framework, setting them against Spanish comedia nueva and French comedie precepts. Authors employ tool DramaAnalysis and statistics for their examination, focusing on: word frequency per subgenre, average number of characters, their variation and discourse distribution, etc. Autos sacramentales are also evaluated through these indicators. Regarding comedies, Ehrlicher et al.’s results show that Calderón: a) plays with units of space and time depending on creative and dramatic needs, b) does not follow French comedie conventions of character intervention or linkage, but c) does abide by its concept of structural symmetry. As for autos sacramentales, their findings brought forth that these have a similar length and character variation to comedies. However, they also identified the next difference: Calderón uses character co-presence in them to reinforce the message conveyed. Considering all this, authors confirm that Calderón’s comedies disassociate from classical notions of theatre – both Aristotelian and French –ideals. With respect to autos sacramentales, they believe further evaluation would be needed to verify ideas put forward and identify other structural patterns.

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Cultural Ontologies: the ArCo Knowledge Graph.

Cultural Ontologies: the ArCo Knowledge Graph.

Introduction: Standing for ‘Architecture of Knowledge’, ArCo is an open set of resources developed and managed by some Italian institutions, like the MiBAC (Minister for the Italian Cultural Heritage) and, within it, the ICCD – Institute of the General Catalogue and Documentation), and the CNR – Italian National Research Council. Through the application of eXtreme Design (XD), ArCO basically consists in an ontology network comprising seven modules (the arco, the core, the catalogue, the location, the denotative description, the context description, and the cultural event) and a set of LOD data comprising a huge amount of linked entities referring to the national Italian cultural resources, properties and events. Under constant refinement, ArCo represents an example of a “robust Semantic Web resource” (Carriero et al., 11) in the field of cultural heritage, along with other projects like, just to mention a couple of them, the Google Arts&Culture (https://artsandculture.google.com/) or the Smithsonian American Art Museum (https://americanart.si.edu/about/lod).

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When history meets technology. impresso: an innovative corpus-oriented perspective.

When history meets technology. impresso: an innovative corpus-oriented perspective.

Historical newspapers, already available in many digitized collections, may represent a significant source of information for the reconstruction of events and backgrounds, enabling historians to cast new light on facts and phenomena, as well as to advance new interpretations. Lausanne, University of Zurich and C2DH Luxembourg, the ‘impresso – Media Monitoring of the Past’ project wishes to offer an advanced corpus-oriented answer to the increasing need of accessing and consulting collections of historical digitized newspapers.
[…] Thanks to a suite of computational tools for data extraction, linking and exploration, impresso aims at overcoming the traditional keyword-based approach by means of the application of advanced techniques, from lexical processing to semantically deepened n-grams, from data modelling to interoperability.
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Research COVID-19 with AVOBMAT

Research COVID-19 with AVOBMAT

Introduction: In our guidelines for nominating content, databases are explicitly excluded. However, this database is an exception, which is not due to the burning issue of COVID-19, but to its exemplary variety of digital humanities methods with which the data can be processed.AVOBMAT makes it possible to process 51,000 articles with almost every conceivable approach (Topic Modeling, Network Analysis, N-gram viewer, KWIC analyses, gender analyses, lexical diversity metrics, and so on) and is thus much more than just a simple database – rather, it is a welcome stage for the Who is Who (or What is What?) of OpenMethods.

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Analyzing Documents with TF-IDF | Programming Historian

Analyzing Documents with TF-IDF | Programming Historian

Introduction: The indispensable Programming Historian comes with an introduction to Term Frequency – Inverse Document Frequency (tf-idf) provided by Matthew J. Lavin. The procedure, concerned with specificity of terms in a document, has its origins in information retrieval, but can be applied as an exploratory tool, finding textual similarity, or as a pre-processing tool for machine learning. It is therefore not only useful for textual scholars, but also for historians working with large collections of text.

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