Big Meetingroom

There is an ever-increasing amount of data, projects, and publications hosted across several platforms coming from different disciplines. For one part, funding agencies require that as many results as possible are made available to be accessible even after a project is finished. Furthermore, there should be an intrinsic motivation by scientists to make their data as well described and easily findable as possible. Thoroughly described metadata is key to increase findability and subsequently increase reusability, which can increase the impact of one’s own research. Such metadata can be rated using the FAIR principles. However, only less than half of all scientists have ever heard, and even less used them to efficiently describe their research data. Consequently, metadata found on scientific repositories shows often strongly varying levels of completeness, or is simply faulty. There are various guidelines and initiatives led by e.g. research institutes to improve metadata quality, as it really is as famously noted “A love letter to the future”. We, the National Centre for Environmental and Nature Conservation Information based at the German Environment Agency, aim to integrate Germany’s scattered data and information landscape on environmental and nature knowledge into one central access point. We have encountered a wide …

📢 You're Invited: Advancing FAIR Data with NetCDF – Join the Conversation!

Ensuring that scientific data is Findable, Accessible, Interoperable, and Reusable (FAIR) is more important than ever. In Earth System Science, NetCDF has become the quasi-standard for storing multidimensional data. But to truly unlock its potential, we need rich, standardized metadata .

Join us for an insightful discussion where we’ll explore what are the key challenges in metadata compatibility and completeness. What tools do we need for improving the metadata of our scientific output? And how can we  guarantee seamless metadata integration, AI-readiness, and improved data discoverability.

🔍 What to Expect:

• An overview of the HMG NetCDF Initiative and its goals
• A first look at the NetCDF metadata attribute guidelines
• Discussion on aligning metadata fields across disciplines
• A Discussion on tools for machine-readable templates and user-friendly metadata entry

🌍 This is a collaborative effort across German research centers and contributes to broader Helmholtz initiatives like HMC .

Let’s shape the future of geoscientific data together. We look forward to your participation and insights!

Data Stewards play an important role in institutional, project and national data infrastructures to support a sustainable, FAIR, and efficient management of research data. The goal of this coffee meeting is to foster exchange among domain-specific (embedded) data stewards with data experts engaged in support infrastructures like the NFDI4Earth helpdesk, the DataHUB support group or federal state networks, to collaboratively develop strategies for enhancing data visibility and reusability by supporting researchers and ensuring a sustainable management of research data. The focus is on practical solutions and best practices that advance Data Stewardship in Earth System Sciences and beyond.

Lab and field notebooks are essential tools for documenting structured information during measurement campaigns or field and laboratory work. Modern Electronic Lab Notebooks (ELNs) offer advanced features to support this documentation process and can enrich records with additional metadata—such as instrumentation details, personnel involved, sample registration, and more. To fully harness this potential, it is desirable to integrate ELNs seamlessly into the center’s data workflows—supporting information flow from sample and data acquisition, through measurement activities, all the way to data publication in repositories.

However, many laboratories face significant barriers: ELNs are not readily available, may require costly licenses, and often lack institutional support or training opportunities. As a result, their use is not yet widespread.

In this coffee round, we invite participants to explore the potential of ELNs in scientific workflows. Together, we’ll discuss desirable features, briefly review a few existing solutions, and consider whether centrally provided ELN services across Helmholtz could be a sustainable way forward.

Modern AI tools for academic literature research can be divided into finders and connectors .

Finders operate in a quite similar way like catalogues: you enter a keyword, a phrase or – even better – a complete question and receive responding matches. Finders work particularly well in the natural sciences, where semantic analyses are less dependent on monographs or older literature sources then in humanities or social sciences. This is due to the fact that finders primarily index and analyse English journal articles with given DOIs and open access status. Connectors , on the other hand, start with a pre-existing literature source, designated as a seed . You enter part of the seed’s metadata (ideally a DOI) into the AI tool to identify further publications. This process automatically identifies literature, which is cited, thematically related or methodologically relevant.

In this Collaboration Coffee session, I will demonstrate how to use both options effectively: The start into a specific research question can be aided by a finder such as Semantic Scholar or Elicit. Based on a selected publication, connector such as ResearchRabbit or Inciteful then help to tap into further literature sources. Alongside my specific examples, I would like to use an …