New release of Metadata4Ing Ontology now online!

The latest release of the metadata4ing ontology (m4i) comes with a practical hands-on guide: Get to know basic classes and properties of m4i and see how a JSON-LD file is built up step by step applying the ontology to a real-world example.

What is Metadata4Ing – a short recap

In 2022, Metadata4Ing (m4i) was released as an ontology for a process-based description of research activities and their results, focusing on the provenance of both data and material objects. m4i is intended as a general process model applicable to many disciplines and focuses on general concepts like in- and output, employed methods and tools as well as the investigated entity, reusing terms of existing ontologies where possible.

Where to find Metadata4Ing

The source code of Metadata4Ing is hosted at the GitLab repository of RWTH Aachen, in the NFDI4Ing group. Here, you can get involved with its developers, make suggestions or report any issues you might have. The ontology itself can be accessed via its w3id. Call this URL with a web browser and you will be guided to m4i’s detailed documentation. Or use it in an ontology editor like Protégé to retrieve its machine-readable RDF/XML serialization.
m4i is also indexed in the NFDI4Ing Terminology Service (TS). Via the TS API you can retrieve information about m4i and its terms in the structured JSON format. Here is a Swagger documentation for the API.

What is new?

The new release 1.1.0 adds a First Steps Tutorial to m4i demonstrating m4i’s application in easily reproducible steps to a real-world example from the domain of experimental XRCT scanning for the microstructural characterization of materials (cf. [1], [2]). The exemplary application can be transferred to other research settings, with minimal knowledge about ontology engineering or Semantic-Web applications and with custom tools.

The guide focuses on the JSON-LD format [3], which adds semantic information to normal JSON data in form of a so called context file. Through the semantic context, metadata in JSON-LD files are machine-readable and -actionable thanks to the exact semantic identification of classes and properties. At the same time, the files remain readable and manageable for humans that have experience with JSON-based data – which is the case for a great number of engineers, especially in scientific computing.

What is the benefit?

Semantically enriched metadata in JSON-LD can be published together with the research data or code they describe and will be understandable for both humans and machines. The structure of m4i allows to describe and connect datasets, persons, projects, methods and tools and therefore supports complex search queries, improving the retrieval of information, especially as data pools continue to grow and connect. In addition, a machine-actionable documentation of the data also facilitates publishing or archiving data in data repositories in a citable way.

References

[1] M. Ruf, & H. Steeb. “An open, modular, and flexible micro X-ray computed tomography system for research”. Review of Scientific Instruments, vol. 91 no.11, 2020, p. 113102. https://doi.org/10.1063/5.0019541 ↩

[2] M. Ruf, & H. Steeb. “micro-XRCT data set of open-pored asphalt concrete.” DaRUS. 2020. https://doi.org/10.18419/darus-639 ↩

[3] M. Sporny , D. Longley, G. Kellogg, M. Lanthaler, P.-A. Champin, N. Lindström (2020). “JSON-LD 1.1. A JSON-based Serialization for Linked Data”. https://www.w3.org/TR/json-ld11/ (accessed 2023-04-19) ↩