Metadata and Data validation for INSPIRE
Source |
Earlier versions of this training module have been developed within the context of the smeSpire project, 2014 (http://www.smespire.eu/). |
Ownership |
Authors: Giacomo Martirano, Fabio Vinci, Stefania Morrone (EPSILON ITALIA). The material is provided under Creative Commons Attribution Share-Alike License (http://creativecommons.org/licenses/by-sa/3.0/). |
Abstract |
This self-learning module provides examples of metadata and data validation against the requirements of the applicable Implementing Rules and Technical Guidelines of INSPIRE. Using different tools, examples are given on how to validate existing metadata and/or create compliant metadata according to INSPIRE Implementing Rules for Metadata (Commission Regulation (EC) No 1205/2008). Examples are also given on integrating the six additional metadata elements for interoperability required by INSPIRE Implementing Rules for interoperability of spatial data sets and services (Commission Regulation (EU) No 1089/2010) and relevant amendments. This module shows how to assess the degree of conformity of an INSPIRE GML dataset to the requirements specified by Commission Regulation (EU) No 1089/2010 and relevant amendments. Conformity is assessed through an Executable Test Suite (ETS), i.e. physical implementation of the Abstract Test Suite (ATS) defined in the Annex A of the INSPIRE Data Specifications. |
Structure |
The module consists of two units as follows:
|
Learning outcomes |
After completion of the module, the learner will be able to validate existing metadata, create and validate INSPIRE compliant metadata, assess the conformity of an INSPIRE GML dataset. |
Intended Audience |
The module targets GIS and ICT professionals aiming to validate their metadata and datasets against INSPIRE requirements. |
Pre-requisites |
Basic knowledge of INSPIRE Training module “Procedures for Data and Metadata Harmonization”. |
Language |
English |
Format |
PDF documents, presentation with voice, exercises, video tutorial, video. The module is a self-learning module. |
Expected workload |
2 hours |