(Institut für Angewandte Materialien, KIT Karlsruhe)

FAIR Data Collection in Experimental Sciences: Required Effort and Benefits

Abstract: Digitalization in experimental sciences is a process that can be seen as a strategy for harnessing rich data sets with the aim of finding more efficient solutions for investigating scientific outputs. One road to achieving full digitalization in research and development goes through the adoption of the FAIR data principles. FAIR is an acronym that stands for Findable, Accessible, Interoperable and Reusable data. Serially-produced FAIR data could be the key ingredient to enabling research results for scalable machine-learning-based analyses, and thus, it can potentially solve society’s greatest challenges. Rich, dynamic, and interoperable metadata are the foundation of any FAIR data infrastructure. However, there is hardly an established roadmap for the generation and curation of controlled vocabularies for scientific workflows. Therefore, for a few years now, our groups have been developing software solutions for the assembly of FAIR digital objects in research. In this presentation we will discuss available software, and explain why we had to develop our own. We will explore an end-to-end example of FAIR data collection, and finally discover some of the benefits of such an undertaking.

When and where: 
Friday, Nov 10, 2023, 12:30, *room J-1101* (Wiwi building on the hill, diagonally across from central library)

The talk will be in three parts with two coffee/discussion breaks. Bringing a laptop might be useful. There will be cake and coffee starting from 12:30.