TRAINING


 

Metadata and Data validation for INSPIRE

 

Source
Earlier versions of this training module have been developed within the context of the smeSpire project, 2013 (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).

This module shows how to assess the degree of conformity to the requirements specified by Commission Regulation (EU) No 1089/2010 relevant to a GML dataset belonging to INSPIRE Annex II/III data themes.

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 Data Specifications.

Structure
  1. “Discovery Metadata” validation
  2. “Metadata for interoperability” validation
  3. Data validation: from ATS to ETS
Learning outcomes

After the module, the participant will be able to validate existing metadata, create and validate INSPIRE compliant metadata, assess the conformity of an INSPIRE GML dataset.

Intended Audience
GIS and ICT professionals aiming to validate their metadata and datasets against INSPIRE requirements. 
Pre-requisites

Basic knowledge of INSPIRE.

LINKVIT module:Procedures for Data and Metadata Harmonization”.

Language
English
Format
PDF documents, presentations, Weblecture. The module is a self-learning module.
Expected workload
2 hours