Programming software

The aim of the course is to introduce digital humanities and to describe various aspects of digital content processing. The practical aims consist of introducing current data sources, annotation, pre-processing methods, software tools for data analysis and visualisation, and evaluation methods.

Currently, we identified that students are somewhat aware of digital humanities but it is difficult for them to dive in and, mainly, to anticipate what they should learn for their future research. A more detailed goal of this course is to present some current projects, show the datasets and technologies behind, and encourage students to explore the datasets and use the technologies on data they already know. A high level goal is to set the knowledge of the technologies and available datasets into the research iteration loop (create hypotheses -> design instruments -> collect data -> analyze and evaluate).

Taken from: Teaching with CLARIN: https://www.clarin.eu/content/introduction-digital-humanities

These learning materials consist of a two-course module that seeks to provide humanities majors with a basic understanding of language technology and the practical skills needed to apply language technology using Python. The module is intended to empower the students by showing that language technology is both accessible and applicable to research in the humanities.

Taken from: Teaching with CLARIN 

https://www.clarin.eu/content/applied-language-technology 

The original code plus some variants that contain extensions, especially a new function extracting relations between editors. Note that extended versions might include additional computational steps that can lead to higher runtimes than the original. 

Service based on Jupyter Notebook enables the creation and sharing of documents containing “live” code, equations, visualisations and texts. It allows to write simple programs, numerical simulations, statistical modeling, data visualization and much more.

This workshop was a chance for research support staff to learn how they could design their own games using the Python programming language. The workshop used the case study of a game to help raise awareness of research data management, but the method would have applications in many differenttraining scenarios.

Games for research support professionals, Research Data Management awareness raising (starting with Python).

A IIIF powered, Chrome extension that displays a random map from the David Rumsey Map Collection everytime you open a new tab in your browser. Built using Leaflet-IIIF and React.js.

Learn about the use of programming for data analysis, data management, and statistical analysis techniques.

EOSC-Synergy training platform is set of tools for the creation and conduction EOSC related training courses. It facilitates cloud related courses providing tools for interactive computing. The platform is based on the container’s technologies, that allows for combining together in a suitable learning setup for students/training participants.

This repository contains resources (links to training, policies, best practices, and other information) that enable Belmont Forum researchers to meet the Data Management expectations described in the Data and Digital Objects Management Plan, as well as a step-by-step guide to completing the DDOMP at the stages of pre-proposal, full proposal, and awarded projects. "Toolkit Training" video tutorials are available on YouTube for information about the material featured here and the structure of the site.

The International Image Interoperability Framework (IIIF) is a group of standard APIs around sharing and reuse of media. It is also a growing community of galleries, libraries, archives, museums, companies, and others who develop the standards and interoperable software implementations. Content includes helpful links around each of the standards, demonstrations of their use, and tutorials and presentations. The list is especially helpful for orienting new community members and developers.