Introduction
Today’s research landscape is supported by a broad and evolving array of digital research infrastructure (DRI) tools and techniques. The training materials provided here are intended to address gaps and advance the skills of researchers and research support staff in this DRI space.
We aim to improve the utilization of technologies by describing the technologies, identifying important uses, describing best practices, and providing step by step guides to reduce learning curves. This content targets issues in the spaces of research software, research data management, research impact, and supporting open research.
Learning Outcome
By the end of this learning module, readers will be able to:
- Understand and operate computational notebooks, and share interactive, reproducible research outputs.
- Use computational notebooks to access linked open data (LOD), such as to evaluate individual and organizational research impact through LOD bibliometric sources.
- Measure and acknowledge the contributions of software developers that support research.
- Improve data documentation quality by using generative artificial intelligence to evaluate data dictionaries.
- Understand the importance of data de-identification and use DRI tools to apply practical techniques to anonymize sensitive datasets.