Category: Research Techniques

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.

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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.
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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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LoGaRT and RISE: Two multilingual tools from the Max Planck Institute for the History of Science

LoGaRT and RISE: Two multilingual tools from the Max Planck Institute for the History of Science

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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Annotation Guidelines For narrative levels, time features, and subjective narration styles in fiction (SANTA 2).

Annotation Guidelines For narrative levels, time features, and subjective narration styles in fiction (SANTA 2).

Introduction: If you are looking for solutions to translate narratological concepts to annotation guidelines to tag or mark-up your texts for both qualitative and quantitative analysis, then Edward Kearns’s paper “Annotation Guidelines for narrative levels, time features, and subjective narration styles in fiction” is for you! The tag set is designed to be used in XML, but they can be flexibly adopted to other working environments too, including for instance CATMA. The use of the tags is illustrated on a corpus of modernist fiction.
The guidelines have been published in a special issue of The Journal of Cultural Analytics (vol. 6, issue 4) entirely devoted to the illustration of the Systematic Analysis of Narrative levels Through Annotation (SANTA) project, serving as the broader intellectual context to the guidelines. All articles in the special issue are open peer reviewed , open access, and are available in both PDF and XML formats.
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BERT for Humanists: a deep learning language model meets DH

BERT for Humanists: a deep learning language model meets DH

Introduction: Awarded as Best Long Paper at the 2019 NACCL (North American Chapter of the Association for Computational Linguistics) Conference, the contribution by Jacob Devlin et al. provides an illustration of “BERT: Pre-training of Deep Biredictional Transformers for Language Understanding” (https://aclanthology.org/N19-1423/).

As highlighted by the authors in the abstract, BERT is a “new language representation model” and, in the past few years, it has become widespread in various NLP applications; for example, a project exploiting it is CamemBERT (https://camembert-model.fr/), regarding French. 

In June 2021, a workshop organized by David Mimno, Melanie Walsh and Maria Antoniak (https://melaniewalsh.github.io/BERT-for-Humanists/workshop/) pointed out how to use BERT in projects related to digital humanities, in order to deal with word similarity and classification classification while relying on Phyton-based HuggingFace transformers library. (https://melaniewalsh.github.io/BERT-for-Humanists/tutorials/ ). A further advantage of this training resource is that it has been written with sensitivity towards the target audience in mind:  in a way that it provides a gentle introduction to complexities of language models to scholars with education and background other than Computer Science.

Along with the Tutorials, the same blog includes Introductions about BERT in general and in its specific usage in a Google Colab notebook, as well as a constantly-updated bibliography and a glossary of the main terms (‘attention’, ‘Fine-Tune’, ‘GPU’, ‘Label’, ‘Task’, ‘Transformers’, ‘Token’, ‘Type’, ‘Vector’).

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The First of May in German Literature

The First of May in German Literature

Introduction by OpenMethods Editor (Erzsébet Tóth-Czifra): Research on date extractions from literature brings us closer to answering big questions of “when literature takes place”.  As Frank Fischer’s blog post, First of May in German literature shows, beyond mere quantification, this line of research also yields insights on the cultural significance of certain dates. In this case, the significance of 1st of May in German literature (as reflected in the “Corpus of German-Language Fiction” dataset) was determined with the help of a freely accessible data set and the open access tool HeidelTime. The brief description of the workflow is a smart demonstration of the potential of open DH methods and data sharing in sustainable ways.

Bonus one: the post starts out from briefly touching upon some of Frank’s public humanities activities.

Bonus two: mention of the Tiwoli (“Today in World Literature”) app, a fun side product built on to pof the date extraction research.

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What Counts as Culture? Part I: Sentiment Analysis of The Times Music Reviews, 1950-2009 – train in the distance

What Counts as Culture? Part I: Sentiment Analysis of The Times Music Reviews, 1950-2009 – train in the distance

Introduction: This blog post by Lucy Havens presents a sentiment analysis of over 2000 Times Music Reviews using freely available tools: defoe for building the corpus of reviews, VADER for sentiment analysis and Jupiter Notebooks to provide a rich documentation and to connect the different components of the analysis. The description of the workflow comes with tool and method criticism reflections, including an outlook how to improve and continue to get better and more results.

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OpenMethods Spotlights #3 Keeping a smart diary of research processes with NeMO and the Scholarly Ontology

OpenMethods Spotlights #3 Keeping a smart diary of research processes with NeMO and the Scholarly Ontology

In the next episode, we are looking behind the scenes of two ontologies: NeMO and the Scholarly Ontology (SO) with Panos Constantopoulos and Vayianos Pertsas who tell us the story behind these ontologies and explain how they can be used to ease or upcycle your daily works as a researcher. We discuss the value of knowledge graphs, how NeMO and SO connect with the emerging DH ontology landscape and beyond, why Open Access is a precondition of populating them, the Greek DH landscape …and many more!

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