Publication details
The STIRData Approach to Interoperability of European Company High-Value Datasets
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | SN Computer Science |
| MU Faculty or unit | |
| Citation | |
| Web | Full text |
| Doi | http://dx.doi.org/10.1007/s42979-024-02721-8 |
| Keywords | Interoperability;Linked data;Open data;Data spaces;High-value datasets (HVDs);Company data |
| Description | The European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data as part of the Open Data Directive. One of the HVD topics is company data. Although the HVD description contains items that must be included in these datasets, it does not prescribe any technical means of how the data should be published. This is a major obstacle to the interoperability of the datasets once they are published. In this extended paper, we elaborate on the results of STIRData, a project co-financed by the Connecting Europe Facility Programme of the European Union, focusing on various aspects of data interoperability of open data from business registries, covering the company data HVDs topic. These aspects include the semantic, technical, and legal interoperability of this data. The results include a data architecture and a data specification to make the published data technically and semantically interoperable. In addition, we present basic legal interoperability guidelines to ensure legal interoperability of the published data, which is a topic often neglected by technically focused data experts. The project results include proof-of-concept transformations of data from selected European business registries using open source tools and in accordance with the data specification. Moreover, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. Finally, we present an example of how compliant data can be processed by data experts for further analysis. |
| Related projects: |