during data management and collaborative analysis, and foster FAIRness and openness, e.g. by applying established standards for metadata, data, and/or scientific workflows. In the Earth System Sciences (ESS), several research data infrastructures (and components) are currently developed on different regional and disciplinary scales. However, with the upcoming amount of high-quality research products and services, they all face pressing challenges in meeting researchers' needs and developing sustainable research data infrastructure (components).
This session will explore the gap between current user needs and software solutions for research data infrastructures to answer questions, like:
How can we develop infrastructures that fit user's workflows and provide seamless integrated/linked services?
What are the most pressing challenges in re-using services that are established in the research community?
How can we face challenges on integrating existing high-quality services with different characteristics?
How far can/should RDM services and tools from data management infrastructures like EUDAT or EOSC be used ?
How should we deal with cloud repositories, such as AWS (metadata, interoperability, versioning)?
How can we implement scalable concepts for regional and (inter-)national infrastructures that foster linking across scales?
How to face and choose among different types of underlying and back-end technologies to support services, tools and software, in order to prevent technology-locking and deprecated software?
How do we envision the interoperable data space and the related role of infrastructures?
How should we facilitate one-stop concepts, in particular providing central access points for distributed research product search and publication?
What are the most pressing challenges for long-term maintenance of ESS research data infrastructures?
How can sustainability (w.r.t. funding, resilience) of RDM services be guaranteed in the long term?
What should research data infrastructures offer to support researchers for long-tail research data?