This is a growing list of Use Cases of HMC's solutions applied. More will be added soon. Stay tuned!
HMC has direct access to scientific use cases within the Helmholtz research fields where example implementation generates transferable results. We strive to feed back our experience with these metadata concepts to national and international RDM working groups. This disseminates developed solutions as well as lessons learned and transfers this knowledge to stakeholders outside of Helmholtz.
You can search the examples using keywords or filter them using the tags listed below.
A4 instrument to data publication
Hub matter curated data from the decommissioned A4 experiment, making it FAIR (Findable, Accessible, Interoperable, Reusable) and providing a blueprint for similar experiments. The project has now become a peer-advisory forum with PUNCH4NFDI for data management in ongoing experiments, welcoming participation from all.
Development of a Photovoltaic System
We developed a novel approach to integrate FAIR digital objects (FDO) and ontologies as metadata models to support data access for energy researchers, energy research applications, operational applications, and energy information systems. We employed a photovoltaic system as a conceptual example. The goal of the PV system project is to provide a complete description of an existing PV system in digital, machine-readable, and FAIR form to support system operators.
Enabling FAIR Metadata Management on the European Level by using HMC Base Services
NEP made available for its users MetaRepo, an instance of the MetaStore, which offers a generic metadata repository and metadata schema registry. It enables data curators to register metadata schemas in one of the supported formats (XML Schema Definition or JSON Schema), and it allows users to store metadata documents, linked to the datasets they describe.
FAIR Digital Object Application Case: Composing Machine Learning Training Data
Collecting data from different storages and using it to compose a training data set for Machine Learning (ML) is a time-consuming task. Even if FAIR principles are fulfilled, scientists still need to perform several steps to obtain a ready-to-use training dataset. This application case presents a solution for supporting scientists while relabeling training datasets for ML by representing their single elements as FAIR Digital Objects (FDOs).
Making FAIR vocabularies Findable – VocPopuli and PIDA
Within the MetaCook project funded by HMC, the team at KIT and Hereon is developing VocPopuli - a tool for the collaborative development and harmonization of domain terminologies. HMC's PID service "PIDA" supports content negotiation for findable and harmonized vocabularies.
RO-Crate Time Series Exporter
A unified database is being developed to merge smart meter data from two databases for easier access by researchers. A web service exports the data as a RO-Crate, enriching it with metadata to make it FAIR, saving researchers time in manually describing the data.