Digital scholarship/academic practice
Contents
Digital scholarship/academic practice¶
Introduction¶
Digital scholarship here refers to research practices centered around the usage of digital systems.
Due to the evolution of computers and increasingly faster communication technologies science has evolved beyond simply collecting data for a specific project, running some (simple) analysis and publishing results in a scientific journal. Buzzwords like “Big Data”, “cloud computing” and Machine learning/A.I have taken the world, Academia included, by storm. In the following lesson we’ll provide a general introduction regarding relevant concepts and technlogogies, how and when to apply them and guide you towards additional ressources that may help you evolve these skillsets yourself.
Roadmap¶
We’ll mostly concentrate on the trio of Big Data Analytics, Machine Learning/AI and Cloud Computing in the following content blocks. You’ll also be more generally introduced about relevant concepts like Social Media Analytics, Natural Language Processing, Human-Computer Interaction, Internet of Things (IoT) approach and Social Media Analytics.
The following lessons will mainly consist of introdcutory chapters to the most important aspects where digital litearcy meets modern fields of science.
Follow these links or use the table of contents on the left side to navigate this module
Big Data Analytics: Collecting and analyzing large amounts of data FAIR principles
Machine Learning/AI: In-depth, practical lesson on the usage of Machine learning/Artifical Intelligence in research.
Cloud Computing: Old school: BASH & SSH new approaches: Google colab
Digital research practices: Social Media Analytics, Natural Language Processing, Human-Computer Interaction, Internet of Things (IoT) approach and Social Media Analytics