MetaStore is a metadata repository that greatly simplifies the management of large volumes of metadata documents. Metadata documents are registered and given a unique identifier, formally quality-controlled and persistently stored. Furthermore, the stored metadata documents can be versioned, retrieved and searched. Thus, the management of metadata documents complies with FAIR principles.


  • Low-threshold access due to simple web user interface.

  • Register/Edit/View (XML/JSON) schema

  • Ingest/Edit/View (XML/JSON) metadata documents

  • Versioning (history) of metadata and schema documents

  • Light-weight microservice based on Spring Boot

  • Easy installation, e.g., using available Docker images

  • (Optional) OAI-PMH support for metadata harvesting

  • (Optional) Messaging support via RabbitMQ to process repository events, e.g., resource creation or indexing.

  • (Optional) JWT-based authentication and authorization via Keycloak

Find about more about MetaStore in these HMC Use Cases

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).