Mapping PhysicalInstance and DCAT

INSEE, the French national statistical office, has launched an overhaul of its dissemination processes in order to improve efficiency and services to producers. Part of this project involves developing a data dissemination platform where data can be discovered and consistency between data and metadata verified. In terms of metadata, this boils down to describing the structure of the data and creating a catalogue to document the data. In practice, and in the context of INSEE's RMéS metadata repository, this means using different standards and different parts of the repository. On the one hand, we need to describe the micro-data with DDI L, in particular with PhysicalInstance objects, and on the other hand, we need to use the DCAT RDF model which is convenient for publishing data semantically on the web. The questions of this project are therefore the correspondence between the PhysicalInstance DDI-L objects and the DCAT standard. We will present the extent to which these two standards can be used together, and we will highlight the issues and difficulties of this mapping which are linked to the limited possibility of defining the data structure at the level of the RepresentedVariables in DDI-L. Practical considerations will be presented regarding the management and requirements of this type of multi-repository metadata description, and recommandations could be made to streamline the relation between these standards.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-04
DOI
https://doi.org/10.5281/zenodo.14823393
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mapping PhysicalInstance and DCAT

Zenodo (CERN European Organization for Nuclear Research)
Research Data Management Practices
article

Mapping PhysicalInstance and DCAT

article en

Abstract

INSEE, the French national statistical office, has launched an overhaul of its dissemination processes in order to improve efficiency and services to producers. Part of this project involves developing a data dissemination platform where data can be discovered and consistency between data and metadata verified. In terms of metadata, this boils down to describing the structure of the data and creating a catalogue to document the data. In practice, and in the context of INSEE's RMéS metadata repository, this means using different standards and different parts of the repository. On the one hand, we need to describe the micro-data with DDI L, in particular with PhysicalInstance objects, and on the other hand, we need to use the DCAT RDF model which is convenient for publishing data semantically on the web. The questions of this project are therefore the correspondence between the PhysicalInstance DDI-L objects and the DCAT standard. We will present the extent to which these two standards can be used together, and we will highlight the issues and difficulties of this mapping which are linked to the limited possibility of defining the data structure at the level of the RepresentedVariables in DDI-L. Practical considerations will be presented regarding the management and requirements of this type of multi-repository metadata description, and recommandations could be made to streamline the relation between these standards.

Zenodo (CERN European Organization for Nuclear Research)
Institut National de la Statistique et des Etudes Economiques (FR)
Industry, innovation and infrastructure
Openalex Percentile: Top 41%
Research Data Management Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.