Web Scraping with Python for Beginners | The Digital Orientalist

Web Scraping with Python for Beginners | The Digital Orientalist

Introduction: In this blog post, James Harry Morris introduces the method of web scraping. Step by step from the installation of the packages, readers are explained how they can extract relevant data from websites using only the Python programming language and convert it into a plain text file. Each step is presented transparently and comprehensibly, so that this article is a prime example of OpenMethods and gives readers the equipment they need to work with huge amounts of data that would no longer be possible manually.

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.]

Document ALL the things!| The Center for Digital Humanities at Princeton

Document ALL the things!| The Center for Digital Humanities at Princeton

Introduction: Sustainability questions such as how to maintain digital project outputs after the funding period, or how to keep aging code and infrastructure that are important for our research up-to-date are among the major challenges DH projects are facing today. This post gives us a sneak peek into the solutions and working practices from the Center for Digital Humanities at Princeton. In their approach to build capacity for sustaining DH projects and preserve access to data and software, they view projects as collaborative and process-based scholarship. Therefore, their focus is on implementing project management workflows and documentation tools that can be flexibly applied to projects of different scopes and sizes and also allow for further refinement in due case. By sharing these resources together with their real-life use cases in DH projects, their aim is to benefit other scholarly communities and sustain a broader conversation about these tricky issues.

Little package, big dependency

Little package, big dependency

Introduction: The world of R consists of innumerous packages. Most of them have very little download rates because they are limited to certain functions as part of a larger argument. Based on a surprising experience with the small package clipr Matthew Lincoln shares his thoughts about this reception phenomenon especially in the digital humanities.

Creating Web APIs with Python and Flask | Programming Historian

Creating Web APIs with Python and Flask | Programming Historian

Introduction: This very complete tutorial by Patrick Smyth will help digital humanists or any interested person on digital technologies applied to projects how to make data more accessible to users through APIs (Application Programming Interfaces). After explaining the basics about APIs and databases, an API is built and put into practice. Python 3 and the Flask are the web frameworks used for developing this API.

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.