FactGrid – a database for historians

FactGrid – a database for historians

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.

Linked Data from TEI (LIFT): A Teaching Tool for TEI to Linked Data Transformation

Linked Data from TEI (LIFT): A Teaching Tool for TEI to Linked Data Transformation

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.

Spanish Paleography Digital Teaching and Learning Tool

Spanish Paleography Digital Teaching and Learning Tool

The Spanish Paleography (http://spanishpaleographytool.org) tool helps to bridge this gap for those interested in learning paleography of the early modern Spanish period, covering the late 15th to the 18th centuries. The tool is intended to allow users to learn how to decipher and read handwriting from documents of this era. Full transcriptions of the documents can be viewed in a facing-page format, or users can highlight individual words. This tool could be used as a teaching tool to introduce students to paleography.

Mediate: A Collaborative Time-Based Media Annotation Tool for the Web

Mediate: A Collaborative Time-Based Media Annotation Tool for the Web

Mediate is a collaborative time-based media annotation tool for the web that can be used both individually and collaboratively for synchronous and asynchronous digital annotation. One of its highlighting features is accessibility and customization, i.e. the ability to customize the schema that forms the basis of the analysis or the purpose of the project.

“Multilingual Research Projects: Non-Latin Script Challenges for Making Use of Standards, Authority Files, and Character Recognition”.

Everyone of us is accustomed to reading academic contributions using the Latin alphabet, for which we have already standard characters and formats. But what about texts written in languages featuring different, ideographic-based alphabets (for example, Chinese and Japanese)? What kind of recognition techniques and metadata are necessary to adopt in order to represent them in a digital context?

SPARQL for music: when melodies meet ontology

SPARQL for music: when melodies meet ontology

Introduction: Developed in the context of the EU H2020 Polifonia project, the investigation deals with the potentialities of SPARQL Anything to
to extract musical features, both at metadata and symbolic levels, from MusicXML files. The paper captures the procedure that has applied by starting from an overview about the application of ontologies to music, as well as of the so- called ‘façade-based’ approach to knowledge graphs, which is at the core of the SPARQL Anything software. Then, it moves to an illustration of the passages involved (i.e., melody extraction, N-grams extraction, N-grams analysis and exploitation
of the Music Notation Ontology). Finally, it provides some considerations regarding the result of the experiment in terms of effectiveness of the queries’ performance. In conclusion, the authors highlight how further studies in the field may cast an increasingly brighter light on the application of semantic-oriented methods and techniques to computational musicology.
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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.

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.
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