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.

Novels in distant reading: the European Literary Text Collection (ELTeC).

Novels in distant reading: the European Literary Text Collection (ELTeC).

Introduction: Among the most recent, currently ongoing, projects exploiting distant techniques reading there is the European Literary Text Collection (ELTeC), which is one of the main elements of the Distant Reading for European Literary History (COST Action CA16204, https://www.distant-reading.net/). Thanks to the contribution provided by four Working Groups (respectively dealing with Scholarly Resources, Methods and Tools, Literary Theory and History, and Dissemination: https://www.distant-reading.net/working-groups/ ), the project aims at providing at least 2,500 novels written in ten European languages with a range of Distant Reading computational tools and methodological strategies to approach them from various perspectives (textual, stylistic, topical, et similia). A full description of the objectives of the Action and of ELTeC can be found and read in the Memorandum of Understanding for the implementation of the COST Action “Distant Reading for European Literary History” (DISTANT-READING) CA 16204”, available at the link  https://e-services.cost.eu/files/domain_files/CA/Action_CA16204/mou/CA16204-e.pdf

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Programmable Corpora: Introducing DraCor, an Infrastructure for the Research on European Drama

Programmable Corpora: Introducing DraCor, an Infrastructure for the Research on European Drama

Introduction: The DraCor ecosystem encourages various approaches to the browsing and consultation of the data collected in the corpora, like those detailed in the Tools section: the Shiny DraCor app (https://shiny.dracor.org/), along with the SPARQL queries and the Easy Linavis interfaces (https://dracor.org/sparql and https://ezlinavis.dracor.org/ respectively). The project, thus, aims at creating a suitable digital environment for the development of an innovative way to approach literary corpora, potentially open to collaborations and interactions with other initiatives thanks to its ontology and Linked Open data-based nature.
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GROBID: when data extraction becomes a suite

GROBID: when data extraction becomes a suite

Introduction: GROBID is an already well-known open source tool in the field of Digital Humanities, originally built to extract and parse bibliographical metadata from scholarly works. The acronym stands for GeneRation Of BIbliographic Data.
Shaped by use cases and adoptions to a range of different DH and non-DH settings, the tool has been progressively evolved into a suite of technical features currently applied to various fields, like that of journals, dictionaries and archives.
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Pipelines for languages: not only Latin! The Italian NLP Tool (Tint)

Pipelines for languages: not only Latin! The Italian NLP Tool (Tint)

The StandforCore NLP wishes to represent a complete Java-based set of tools for various aspects of language analysis, from annotation to dependency parsing, from lemmatization
to coreference resolution. It thus provides a range of tools which
can be potentially applied to other languages apart from English.

Among the languages to which the StandfordCore NLP is mainly applied there is Italian, for which the Tint pipeline has been developed as described in the paper “Italy goes to Stanford: a collection of CoreNLP modules for Italian” by Alessio Palmero Apostolo and Giovanni Moretti.

On the Tint webpage the whole pipeline can be found and downloaded: it comprises tokenization and sentence splitting, morphological analysis and lemmatization, part-of-speech tagging, named-entity recognition and dependency parsing, including wrappers under construction. [Click ‘Read more’ for the whole post.]

Zur Epistemologie digitaler Methoden in den Geisteswissenschaften

Introduction: What is the precise impact of digital humanities on the humanities in general? That this influence exists seems a given, but how the digital humanities impact humanities methodology en epistemology is still an open question. This article delves deeper into this problem of epistemology and presents a model of five ‘polarities’ along which these influences can be positioned.

Teaching Quantitative Methods: What Makes It Hard (in Literary Studies)

Introduction: This article reflects on the lessons learnt by the author as he first taught a graduate course in digital analysis of literary texts. He stresses the importance of methodologies over technologies, the need for well-curated, community-created teaching datasets and the implications of the practical, discipline-based organisation of the curricula.