MetaStore

FAIR Data Commons infrastructure component

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.

Features

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

Webpage
https://kit-data-manager.github.io/webpage/metastore/index.html

Demo
https://demo.datamanager.kit.edu/dashboard.html

API Docs
https://kit-data-manager.github.io/webpage/metastore/documentation/api-docs.html

SourceCode
https://github.com/kit-data-manager/metastore2

Documentation
https://kit-data-manager.github.io/webpage/metastore/documentation/REST/index.html

Issues
https://github.com/kit-data-manager/metastore2/issues

Interactive Tutorial
https://killercoda.com/0d38ffbe-8e9d-494b-b04a-fddaae6537fa/scenario/metastore

FAIR Data Commons

hmc-fair-data-commons@lists.kit.edu

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.

details

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).

details

overview of all HMC tools & services