The SSH Training Discovery Toolkit provides an inventory of training materials relevant for the Social Sciences and Humanities.

Use the search bar to discover materials or browse through the collections. The filters will help you identify your area of interest.

 

Python/Jupyter

Item
Title Body
Wikiwho source code

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. 

Interactive computing

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.

Virtual Games Workshop

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).

Utrecht University : workshop data handling in R

Educational materials used in the workshop 'Data handling in R' run at Utrecht University

It focuses on raw data handling using R, though a Jupyter notebook, run on CoCalc or SURF.

Contents covered: 

- Naming conventions for files

- Ordering scripts

- Layout of scripts

- Commenting code: best practices

Code Reuse and Modularity in Python

Computer programs can become long, unwieldy and confusing without special mechanisms for managing complexity. This lesson will show you how to reuse parts of your code by writing functions and break your programs into modules, in order to keep everything concise and easier to debug.

 

Core Curriculum

The lessons introduce terms, phrases, and concepts in software development and data science, how to best work with data structures, and use regular expressions in finding and matching data. We introduce the Unix-style command line interface, and teach basic shell navigation, as well as the use of loops and pipes for linking shell commands. We also introduce grep for searching and subsetting data across files. Exercises cover the counting and mining of data. In addition, we cover working with OpenRefine to transform and clean data, and the benefits of working collaboratively via Git/GitHub and using version control to track your work.

Source
Title Body
EOSC-Synergy Training Platform

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.

GESIS Notebooks

Have a Binder-Ready repository? With GESIS Notebooks, turn this repository into a persistent Jupyter environment, allowing you to continue your analysis from anywhere at any time.

This service is intended for use by social scientists. You can build and launch all binder-ready projects without logging in. If you want to have more persistent projects, you need to log in.

GESIS Training

At GESIS we offer a wide range of events, especially training courses on empirical social research methods. Our theory founded and hands-on courses develop participants’ methods skills and are aimed at both early career and senior researchers from Germany, Europe, and the whole world.

Library Carpentry

Library Carpentry workshops teach people working in library- and information-related roles how to:

  • Cut through the jargon terms and phrases of software development and data science and apply concepts from these fields in library tasks;
  • Identify and use best practices in data structures;
  • Learn how to programmatically transform and map data from one form to another;
  • Work effectively with researchers, IT, and systems colleagues;
  • Automate repetitive, error prone tasks.