Interpretation is the activity of ascribing meaning to phenomena observed in Analysis. Therefore, interpretation usually follows analysis, although it could also be considered that interpretation defines the hermeneutic perspective of any method of analysis.
FactGrid is both a database as well as a wiki. This project operated by the Gotha Research Centre and the data lab of the University of Erfurt. It utilizes MediaWiki and a Wikidata’s “wikibase” extension to collect data from historic research. With FactGrid you can create a knowledge graph, giving information in triple statements. This knowledge graph can be asked with SPARQL. All data provided by FactGrid holds a CC0-license.
TEI editions are among the most used tool by scholarly editors to produce digital editions in various literary fields. LIFT is a Python-based tool that allows to programmatically extract information from digital texts annotated in TEI by modelling persons, places, events and relations annotated in the form of a Knowledge Graph which reuses ontologies and controlled vocabularies from the Digital Humanities domain.
Every scholar in digital humanities and/or social sciences has probably already faced the challenge posed by consulting large digital newspaper archives in order to extract detailed information about a topic. It is beyond any doubt that computational-oriented methods and tools currently available may provide a great contribution; however, applying such methods and tools could pose several difficulties, especially in dealing with large ensembles of items.
Based on the ancient name for the gate of the Athenian Acropolis, the PROPYLÄEN open up a variety of approaches to Johann Wolfgang von Goethe’s life, work, communication and actions.
The Chinese Text Project is a well-established resource in Sinology, providing open access to a large number of ancient Chinese texts. As a digital medium, it utilizes crowdsourcing, linked data, knowledge graph and other computational technologies to provide an interactive interface for users who are interested in ancient Chinese texts. Beyond its main aim of providing open access to Chinese literature and philosophy texts, the project features an integrated Chinese character dictionary tool, images of scanned source texts, a search function for parallel passages, and much more. In terms of structured data, the project’s data wiki contains a wealth of records on entities such as persons, locations, and works.
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.
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.
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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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The conversation below is a special, summer episode of our Spotlight series. It is a collaboration between OpenMethods and the Humanista podcast and this it comes as a podcast, in which Alíz Horváth, owner of the Humanista podcast series and proud Editorial Team member of OpenMethods, is asking Shih-Pei Chen, scholar and Digital Content Curator at the Max Plank Institute for the History of Science about the text analysis tools LoGaRT, RISE and SHINE; non-Latin scripted Digital Humanities, why local gazetteers are goldmines to Asian Studies, how digitization changes, broadens the kinds research questions one can study, where are the challenges in the access to cultural heritage and liaising with proprietary infrastructure providers… and many more! Enjoy!
Introduction: This post introduces two tools developed by the Max Planck Institute for the History of Science, LoGaRT and RISE with a focus on Asia and Eurasia. […]The concept of LoGaRT – treating local gazetteers as “databases” by themselves – is an innovative and pertinent way to articulate the essence of the platform: providing opportunities for multi-level analysis from the close reading of the sources (using, for example, the carousel mode) to the large-scale, “bird’s eye view” of the materials across geographical and temporal boundaries. Local gazetteers are predominantly textual sources – this characteristic of the collection is reflected in the capabilities of LoGaRT as well, since some of its key capabilities include data search (using Chinese characters), collection and analysis, as well as tagging and dataset comparison. That said, LoGaRT also offers integrated visualization tools and supports the expansion of the collection and tagging features to the images used in a number of gazetteers. The opportunity to smoothly intertwine these visual and textual collections with Chinese historical maps (see CHMap) is an added, and much welcome, advantage of the tool, which helps to develop sophisticated and multifaceted analyses.
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