There is a shortage of software trainers at research organizations, which hinders the (re)use of open, FAIR and sustainable research software. Addressing this skill gap is important for the quality and impact of academic research in the Netherlands. We propose to bring together the software trainers from local Digital Competence Centers into the existing network Research Software Training NL (RSTNL). The Netherlands eScience Center will appoint a Network Coordinator who will organize train-the-trainer workshops, collaborative lesson development workshops, skill-up sessions and community networking activities. Together, this project will ensure capacity building and a solid foundation of a national training network.

HPC-DAT trains NES researchers to make effective and easier use of high performance computing systems necessary to address fundamental research questions. We will organize multi-day hackathons, dedicated training events and build upon the established Python-PyData-Jupyter-Dask ecosystem. Close collaboration with researchers is achieved through selected use cases, a broad user’s committee and an open user forum. The hackathon approach will furthermore enable the project to directly engage with existing and future users from a broad set of disciplines and their evolving needs for large-scale computing.

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Geospatial machine learning (ML) models are widely used in natural and engineering science (NES). These models and the methods to develop them rapidly evolve, making it challenging to keep up with them and reap their benefits. Besides this, many NES researchers do not have the required geospatial knowledge to develop, apply and (re)use these models because “spatial is special”, and they do not know how to document their creative process, making model (re)use unnecessarily hard. To address these issues, we propose developing training modules that increase geospatial ML literacy and geospatial ML models (re)usability.

CLOUD-NES aims to stimulate the use of cloud-native methods to publish, access, and process research data efficiently by building a prototype open cloud-native data repository, transforming selected datasets in traditional formats into cloud-native ones, demonstrating the benefits of cloud-native approaches with reliable and reproducible benchmarks in comparison to traditional methods, developing open training material and providing tailored training to the researchers to improve their skills in using cloud-native methods, and sharing the experience and lessons learned with relevant (inter)national stakeholders.