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.
|Human Rights: The Rights of Refugees||
This short course will enable you to find the answers and empower yourself to defend and promote the rights of refugees and discover how you can be part of the solution.
This is Amnesty International's second human rights MOOC. Be prepared for active learning, connecting with course participants from across the world and to become part of a global community campaigning for the rights of refugees to be upheld everywhere.
|Open Science Training Handbook||
A group of fourteen authors came together in February 2018 at the TIB (German National Library of Science and Technology) in Hannover to create an open, living handbook on Open Science training. High-quality trainings are fundamental when aiming at a cultural change towards the implementation of Open Science principles. Teaching resources provide great support for Open Science instructors and trainers.
The Open Science training handbook will be a key resource and a first step towards developing Open Access and Open Science curricula and andragogies. Supporting and connecting an emerging Open Science community that wishes to pass on their knowledge as multipliers, the handbook will enrich training activities and unlock the community’s full potential.
|Temporal Network Analysis with R||
Learn how to use R to analyze networks that change over time.
Temporal Network Analysis is still a pretty new approach in fields outside epidemiology and social network analysis. This tutorial introduces methods for visualizing and analyzing temporal networks using several libraries written for the statistical programming language R. With the rate at which network analysis is developing, there will soon be more user friendly ways to produce similar visualizations and analyses, as well as entirely new metrics of interest. For these reasons, this tutorial focuses as much on the principles behind creating, visualizing, and analyzing temporal networks (the “why”) as it does on the particular technical means by which we achieve these goals (the “how”). It also highlights some of the unhappy oversimplifications that historians may have to make when preparing their data for temporal network analysis, an area where our discipline may actually suggest new directions for temporal network analysis research.
|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.