Research Data Management (RDM) addresses crucial cornerstones of modern research.
On one hand, ever increasing complexity in our experiments mean that we need to improve the way we track our experimental samples, the experiments we conduct and the machines and tools we use at each step. The resulting data needs to be organised such that we can adhere to FAIR data principles and best practices.
On the other hand, many modern analysis techniques, from statistical modelling to machine learning, require data as input and machine learning in particular typically requires large amount of data. RDM helps us to lay the foundation, so that we easily find relevant data, and use them in the next step of the analysis.
The workshops aims at all levels, and we welcome, in particular, all members who are just about to embark on research data management.
We look forward to welcome you to Aachen in September.
Volker Hofmann & Ulrich Kerzel
Aims
In this first topical workshop, we want to lay the foundation to use reseach data management tools and best practices to enable the next generation of experiments and analysis. We will exchange ideas, discuss use-cases, hear from groups and experts about their experiences, as well as external speakers on RDM related topics.
Two invited speakers will give us an introduction to research data management:
MecaNano stands for "European Network for the Mechanics of Matter at the Nano-Scale" is a European Cooperation in Science & Technology (COST) Action running 2022-2026. The Action is intended as a broad international cooperation aiming to advance the multiscale understanding of the mechanical behavior of nanostructured materials. By combining the expertise of its participants – from experimentalists to simulation, data management and machine learning experts – it aims to overcome the different bottlenecks limiting the exploration of mechanical size effects. MecaNano provides its members with numerous opportunities to interact and collaborate, e.g. through dedicated workshops, symposia and summer schools, or by funding the mobility between participants.