Infrastructure for Spatial Information in Europe

INSPIRE Infrastructure for Spatial Information in Europe

Data quality in INSPIRE: from requirements to metadata

Discussion paper

Title Data quality in INSPIRE: from requirements to metadata - Discussion paper Creator European Commission Date 2010-05-11 Subject Data quality and metadata Publisher European Commission Type Text Description The aim of this discussion paper is to outline the process of addressing the topic of data quality, to provide background information for the discussion, and to invite the INSPIRE (Data Quality) Member States Points of Contact to answer a number of questions which will help structuring the discussion. Contributor Katalin Tóth, Robert Tomas, Vanda Nunes de Lima, Paul Smits, Antti Jakobsson, Gilles Troispoux, Carol Aigus Format MS Word (doc) Source Rights INSPIRE Member States Contact Points Identifier 081ab1c0a67310b435e0ca093ec2a588.doc Language En Relation n/a Coverage Project duration Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 2 of 16 Table of contents

Foreword...... 3

1 Introduction...... 4 1.1 References and Further Readings...... 5 2 Objectives and process...... 6

3 Two faces of data quality: quality requirements and metadata...... 7

4 Experience of INSPIRE Annex I data specification process...... 10 4.1 Data Quality Requirements / Recommendations...... 12 4.2 Metadata on data quality...... 12 5 Points for discussion...... 15

List of Abbreviations

DQ Data Quality EC European Commission GCM Generic Conceptual Model INSPIRE Infrastructure for Spatial Information in Europe ISO International Standards Organisation MD Metadata NMCA National Mapping and Cadastre Agencies SDI Spatial Data Infrastructure TWG Thematic Working Group Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 3 of 16

Foreword In the process of the development and adoption of the draft INSPIRE Implementing Rules for interoperability of spatial data sets and services, it became apparent that further discussion is needed to better understand and address the aspects of data quality in the context of INSPIRE. The Commission agreed to initiate and lead this discussion.

At the meeting of the INSPIRE Member States Contact Points on 10 March 2010, the contact points were requested to inform the Commission as to who will represent the countries in the discussion on data quality. The discussion with the data quality experts will take place on 22 June 2010 in Krakow, Poland.

The aim of this discussion paper is to outline the process of addressing the topic of data quality, to provide background information for the discussion, and to invite the INSPIRE (Data Quality) Member States Points of Contact to answer a number of questions which will help structuring the discussion.

The document will be publicly available as a ‘non-paper’, as it does not represent an official position of the Commission, and as such can not be invoked in the context of legal procedures. Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 4 of 16

1 Introduction On 14 December 20009 the INSPIRE Committee approved unanimously the draft Regulation on Interoperability of Data Sets and Services for INSPIRE Annex I data themes. In the process of developing this draft Regulation, which is based on the INSPIRE data specification guidelines developed by the Thematic Working Groups (TWGs), the question of data quality was a re-occurring issue, both during the data specifications development and the consultations. During the above mentioned INSPIRE Committee meeting and following a discussion during the presentation of the proposal for a regulation on Interoperability of Data Sets and Services for INSPIRE Annex I data themes, the European Commission committed to organize a large consultation with the Member States on data quality.

This interest can be explained by the fact that quality is one of the data harmonisation components underpinning interoperability. The peculiarity of the discussion was the widely diverging of opinions, ranging from introducing strict data quality requirements for all data included in the infrastructure, to complete omission of requirements. During the discussions it became clear that the a-priori data quality requirements need to be carefully distinguished from metadata.

The draft data specifications of each Annex I theme (v 2.0) have been consulted with stakeholders’ communities. The comments related to data quality and metadata parts have been addressed by the Thematic Working Groups responsible for the specification process. The results have been incorporated in v 3.0 that has been used as technical basis of the draft Regulation on Interoperability of spatial data sets and services. The draft Regulation has been distributed to the members of the INSPIRE Committee, where two concerns have been raised:

- How is the comparability of information derived from different spatial data sets ensured in the draft Regulation? The answer of the Commission pointed out the rigorous and common data modelling principles addressed all interoperability/ data harmonisation components of the Generic Conceptual Model (GCM). It was agreed that the parts related to data quality and metadata have to be reviewed.

- The recommendation to apply a minimal absolute geometric accuracy (to be less then 2/1000 of the distance resolution). The Commission did not accept this general approach. The agreement was that the issue should be further explored in relation to the different data themes.

Following these concerns, the EC INSPIRE Team agreed to initiate and lead this discussion. This paper prepares and guides the discussions on the above two topics, clarifying the details and giving an initial position to stimulate the exchange of views. It is expected that the results of the discussions can be useful for the process of development of data specifications of Annex I, II and III data themes, and that they will be considered in future updates of other INSPIRE documents.

The remainder of this paper is organized as follows. Section 2 gives an overview of the main objectives of and the process for the discussions. Section 3 introduces the topic of data quality and metadata. Section 4 reports on previous experience on data quality in INSPIRE, followed by the discussion points (section 5). Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 5 of 16

1.1 References and Further Readings

INSPIRE Directive INSPIRE Generic Conceptual Model INSPIRE Methodology INSPIRE Data Specifications – Guidelines

In order to facilitate discussion on the data quality and metadata here are some references from different theme communities:

GEO Task DA-09-01 Data Management Subtask a.: GEOSS Quality Assurance Strategy: http://www.grouponearthobservations.org/cdb/geoss_imp.php

World Meteorological Organisation: www.wmo.int/pages/prog/www/WDM/wdm.html

Global Spatial Data Infrastructure Association: www.gsdi.org/gsdiconf/gsdi11/papers/pdf/283.pdf

Quality Assurance Framework for Earth Observation: http://lpvs.gsfc.nasa.gov/PDF/qa4eo_guide.pdf http://qa4eo.org/documentation.html

INSPIRE, Data Quality and SDIs: www.directionsmag.com/article.php?article_id=3380

Q-KEN - EuroGeographics WG on quality: www. eurogeographics .org/about/quality

ESDIN – EU eContentPlus project: www.esdin.eu Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 6 of 16

2 Objectives and process The INSPIRE data quality and metadata discussions are expected to reach the following objectives:

1. Find evidence whether specifying data quality requirements are appropriate for INSPIRE; 2. If yes, propose data quality elements, measures, and target values; 3. Fix how metadata on data quality has to be presented; 4. Provide guidance on DQ requirements and Metadata for Annex I, II and III 5. Formulate proposals amending the INSPIRE data specification template, if appropriate 6. Raise awareness about the role of data quality and metadata in spatial data infrastructures.

For reaching these objectives it is necessary to channel information exchange through a few steps, which are expected to reach agreements in a bottom up manner:

1. Drafting the discussion paper The discussion paper is expected to scope the subjects, clarify the terminology, review the initiative already in place and propose an initial position on the subject. The discussion paper is developed by the European Commission supported by a small group of experts.

2. Consultations in the Member States (Until 11 June 2010) The discussion paper will be sent for consultation in the Members States via their nominated data quality contact points, who are kindly invited to organise the review to reach an agreed and consolidated position in their countries. In order to reach a structured result, specific questions will be asked. The DQ contact points are expected to send back to the Commission the answers to these questions as well comments to this document and if necessary propose additional issues to be considered.

3. Analysis of the results of the consultation (14-18 June 2010) The answers received by the Commission will be analyzed. The outcome of this step will be a draft report, which will be prepared by the drafting group of the discussion paper. Wherever possible, it will synthesise the responses, but it will also highlight the issues where further discussions are needed. The draft report will be sent back to the national DQ contact points.

4. Face-to-face discussion (22 June 2010) The DQ contact points will be invited to the workshop in Krakow on 22 June 2010, back to back with the annual INSPIRE Conference. They will represent the official position of their countries, and may be asked, as part of preparation to the meeting, to give a presentation.

5. Final report The draft report will be updated with the results of the Krakow DQ workshop and will be disseminated to a wider public. It will provide recommendations for possible updates of INSPIRE documents and how data quality and metadata should be addressed in spatial data infrastructures in general. In may also involve creating a discussion platform that will help to keep the recommendations of the report up to date. Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 7 of 16

3 Two faces of data quality: quality requirements and metadata

INSPIRE, like any Spatial Data Infrastructure, will result in data from different providers being consumed by multiple users and applications through the INSPIRE infrastructure. The INSPIRE Directive applies to spatial data held by or on behalf of public authorities and to the use of spatial data by public authorities in the performance of their public tasks. Therefore, the aspects of quality trust and confidence in the data made available through the infrastructure is touching many different data producers, transformers, and users.

One may therefore expect that the data affected by INSPIRE is linked to existing formal or informal data quality requirements at national or sub-national (or supra-national) levels. These data quality requirements, which are normally the basis for the production of the data, the quality control, the quality evaluation and the conformance testing, are ideally reported as metadata.

Before starting discussing data quality requirements and metadata in the context of INSPIRE it is necessary to clarify their role in spatial data/information infrastructures in general. The SDI provides the technical and legal framework for accessing and reusing spatial data produced in a defined geographical zone (global, national, and sub-national), or thematic field. It is assumed that SDIs are initially built on existing data that are produced by different data providers. The flow of data from the data producers to their provision within an SDI is shown in Figure 1. Data is being produced to fulfil specific use-cases, i.e. to satisfy the requirements of users connected to well-defined tasks. These requirements are optimally formalised in data product specification, which is the basis for data production. As a rule they contain specific parts related to a priori requirements on data quality to be followed during the production process. Frequently standards or other regulations drive the data specification and production processes giving strict target values for selected quality measures. Metadata gives a posteriori statement about the data quality based on the de-facto measurements or specific aggregation rules applied to the data set. In summary, in data production process ideally both data quality requirements and metadata are present.

Conformance statements are also given as part of metadata. They are important part in the context of data semantics and structures; however do not fully replace metadata on evaluation and use. Conformance statements are mainly useful to expert users that know the content of the data product specification used for data production.

An SDI should provide access to data in interoperable way, i.e. without the need for specific ad-hoc interaction of humans or machines after the data is retrieved from the infrastructure. The interoperability target that has to be reached before the data is provided to the users is set in data specifications that have a similar structure to the data product specifications. However the role of a priori data quality requirements in SDI is different.

When establishing the data component of an SDI, two aspects need to be balanced: 1. Giving access to the widest selection of data; 2. Achieving interoperability at pan-European and cross-border levels.

The background of the first aspect is that the final decision whether a data set is useful is made by the users. This might lead to data being provided to the infrastructure without the minimal a priori requirements for the data quality. The underlying principle of this approach is that any data is better than no data. Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 8 of 16

Data flow in Spatial Data Infrastructures

Data production SDI implementation

User Definition of the requirements scope of the analysis infrastructure

Data product specification development

Interoperability target specification development Metadata creation Data production and maintenance and maintenance

Decision about including data in the infrastructure

Data transformation (if necessary )

Publication of Publication of data (updated ) (conformant to metadata the target specification )

Figure 1: Data flow in spatial data infrastructures

The second condition, promoting interoperability, implies that data from disparate sources can be combined without specific efforts. However, when the quality of data is very different, some data harmonisation measures, for example edge-matching, become meaningless and the integrated use of data is jeopardised. In addition, SDIs are expected to provide the future coherent basis for the development of new applications. From these points of view minimal a priori requirements on data quality are relevant.

Requirements on data quality are usually more stringent for reference data1 especially in terms of positional accuracy, as the geometries of reference data a frequently used for object referencing. In addition, in thematic SDIs some fundamental use-cases may also justify such requirements; otherwise the infrastructure does not reach its objective.

In summary, data quality requirements may play a discriminative role when deciding about including a specific data set in an SDI. Balancing the wide spread publication of data sets with requirements against the quality of data is a delicate decision in defining the specifications for

1 Data that can be used for linking other types of (thematic) information. Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 9 of 16 interoperability. Consequently data quality requirements may be completely absent from the target specifications if the main purpose of infrastructure is to make available every existing data set.

Naturally users have to be informed about the quality of the data that they retrieve from the infrastructure. Contrary to data quality requirements, metadata on data quality is an indispensable content of every SDI. The original metadata generated during/after the data production can be used as the starting point for the infrastructure; however it may need updating because of the eventual deterioration of data quality due to the transformations necessary for reaching the interoperability target specifications.

Updating the values of different metadata elements might be cumbersome. In topographic data production, for example, it can be based on calculations or on quality inspection models 2. For many cases, a practical alternative could be to use the original metadata with information on the process step describing the transformation methods and the possible associated errors, expressed in the MD_Lineage element.3

Interoperability also requires that data quality is measured and reported in an agreed way, otherwise it is not possible to compare the metadata associated to different datasets. Therefore the metadata part of the interoperability target data specification has to fix the data quality elements and measures to be used in a data theme. If possible, this needs to be harmonised across the data themes as well.

From the usability point of view, the significance of the same DQ element is different. As said before, positional accuracy is more important for reference data than for other thematic data, while thematic classification correctness is prime importance for some coverage data (e.g. land cover). Therefore specific attention should be paid in the data specification development process as to which are the most meaningful or expressive elements to describe the quality of data.

When a data specification contains DQ measures to express requirements or recommendations, the same measures should be used in the metadata; therefore the Data quality and the Metadata clauses of data specifications must be consistent.

The next section reports on the data quality-related experience to-date in the context of INSPIRE.

2 A model based on ISO data sampling methods is proposed by the ESDIN project. 3 Report or lineage role metadata element is mandatory when the scope of the DQ element is the dataset (ISO 19115) Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 10 of 16

4 Experience of INSPIRE Annex I data specification process Apart from logical consistency4, the INSPIRE Directive does not directly spell out requirements for data quality. However combining spatial data from different sources across the Community in a consistent way and share them between several users and applications represents a strong data quality demand. These requirements are supported by the consequent application of the data modelling elements and other provisions of the INSPIRE Generic Conceptual Model (GCM). Logical consistency can be established by different modelling methods like object referencing and constraints. In addition, the GCM lists data quality among the data harmonisation elements, but no specific details are given.

The draft Regulation on Interoperability of Spatial Data Sets and Services sets a requirement stating that all updates of data shall be made available in INSPIRE at the latest 6 months after the change was applied in the source data set5.

Based on the discussions within and across the TWGs during the process of developing Annex I data specifications it was decided not to introduce uniform mandatory minimum data quality requirements. This approach is in compliance with the recommendation of “D2.6 Methodology for development of data specifications” INSPIRE framework document. However based on the specific requirements of the data theme each TWG has included and described how data quality has to be presented (see 4.1 and Table 1). In the majority of cases, data quality information is required at dataset level6 (see 4.2 and Table 1). These recommendations that are focused on enhancing the interoperability and informing about the fitness for use of the datasets are based on the analyses of user requirements, use cases and especially on good practices.

Due to the natural diversity of the data themes, the user requirements, and no common approach, each TWG proposed slightly different data quality elements and MD elements. At the end of the process an effort harmonising the proposed MD elements and DQ recommendations took place. The of this exercise is presented in the Table1 below.

4 Art. 8(4) and Clause 20 of the GCM 5 Article 6(2) 6 In Cadastral parcels data specification positional accuracy may be also described as an attribute of spatial objects.However this is fundamentally different to evaluation type of metadata. Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 11 of 16

Table 1: Summary table of data quality information and related MD elements described/used in Annex I data specification * Data quality element Data quality sub-element Metadata element AD AU CP GN HY PS TN completeness commission DQ_CompletenessCommission X X X X 2X omission DQ_CompletenessOmission X X X X X X X Logical consistency conceptual consistency DQ_ConceptualConsistency X X X X domain consistency DQ_DomainConsistency X X X format consistency DQ_FormatConsistency X topological consistency DQ_TopologicalConsistency 2X 3X** 9X 6X Positional accuracy absolute or external accuracy DQ_AbsoluteExternalPositionalAccuracy 2X X X X X 2X X relative or internal accuracy DQ_RelativeInternalPositionalAccuracy X Temporal accuracy temporal consistency DQ_TemporalConsistency X Thematic accuracy classification correctness DQ_ThematicClassificationCorrectness X non-quantitative attribute DQ_NonQuantitativeAttributeAccuracy correctness X X X quantitative attribute accuracy DQ_QuantitativeAttributeAccuracy X Maintenance* MD_MaintenanceInformation 3X 3X 3X 3X 3X 3X 3X The reason for inclusion of Maintenance information is the fact that information about the update frequency and the scope is related to the temporal accuracy of a resource. Thus, it is related to the data quality of a dataset in general. ** It is only expressed as recommendations without given corresponding MD-DQ element/measure (Lineage template) Legend: X data quality measure used in a data quality sub-element / Nr. of measures X mandatory MD element (only one measure) AD Addresses TWG AU Administrative units TWG CP Cadastral parcels TWG GN Geographical names TWG HY Hydrography TWG PS Protected sites TWG TN Transport networks TWG 4.1 Data Quality Requirements / Recommendations

Recommendations related to data quality information that applies to all Annex I data themes has been taken from D2.6 “Methodology for Data Specification Developments”. It states that ideally the data quality information has to be collected at the level of spatial object types and has to be aggregated to the dataset (series) level metadata7

Apart from the list of data quality elements and related MD elements (Table 1) each TWG adopted several requirements and/or recommendations related to data quality and use of MD elements that are listed below.

Cadastral parcels: - Rate of missing items should be 0% for cadastral parcels and cadastral zonings (if any). - Mean value of positional uncertainties should be 1 meter or better in urban areas and 2,5 meters or better in rural/agricultural areas. Cadastral data may be less accurate in unexploited areas. - Edge-matching between cadastral parcels in adjacent data sets should be done. Ideally, there should be no topological gaps or topological overlaps between cadastral parcels in adjacent data sets. Status of edge-matching should be reported as metadata, under lineage element - There should be no topological overlaps between cadastral parcels. - There should be no topological gaps between cadastral parcels.

Transport networks: - Guarantee that a continuous transport network can be built from the elements provided in the transport network datasets, by assessing their conformance to some basic topological consistency rules aimed at ensure at least clean connections between features. This specification is compliant with EN ISO 19113 and EN ISO 19114, but it does not fix any concrete conformance criteria for the data quality information proposed, since it should be valid for a wide range of European transport network datasets, with very different levels of detail and quality requirements.

4.2 Metadata on data quality

There are two specific requirements related to metadata on data quality from the Directive: - Article 5(2): MD shall include information on data quality and validity of spatial data sets - Article 11(2): Network services (Discovery) shall include searching functionality on quality and validity of spatial data sets.

The scope of Regulation 1205/2008/EC on metadata is to set out the requirements for the creation and maintenance of metadata for spatial data sets, spatial data set series and spatial data services corresponding to the themes listed in Annexes I, II and III to Directive 2007/2/EC. This, together with the discovery services and an INSPIRE geo-portal, will help in searching for existing spatial data or establishing whether they may be used for a particular purpose.

The newly published Regulation 268/2010/EC on data and service sharing requires (Article 6 – Transparency) the Member States to make available, upon request, information for evaluation and use, on the mechanisms for collecting, processing, producing, quality control and obtaining access to the spatial data sets and services, where that additional information is available and it is reasonable to extract and deliver it. This legal requirement gives a clear evidence of the importance of reaching the common understanding among the Member States about data quality Metadata elements and their use in INSPIRE.

When additional metadata (for instance on data quality) are supplied it is possible to display them providing additional information to the users. However, it is not clear if and how these additional metadata elements can be searched (cf. Art. 11(2)). It is expected that further harmonisation between

7 For a better understanding the original text “Aggregated data quality information should ideally be collected at the level of spatial object types and included in the dataset (series) metadata” has been paraphrased Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 13 of 16

Data, Metadata, and Network services components of INSPIRE may take place upon completing their technical drafting.

During the Annex I data specification development all themes included the mandatory list of elements, required by Regulation 1205/2008/EC, in their specifications. In addition new mandatory metadata elements were introduced and accepted. These are: - Coordinate Reference System - Temporal Reference System (Mandatory, if the data set or one of its feature types contains temporal information that does not refer to the Gregorian Calendar or the Coordinated Universal Time) - Encoding - Character Encoding (Mandatory, if a non-XML-based encoding is used that does not support UTF-8) - Data Quality/Logical Consistency/Topological Consistency (Mandatory, if the data set do not assure centerline topology (connectivity of centerlines) for transport network – applies only to Hydrography and Transport networks themes)

The list of recommended data quality metadata elements that are currently in the Guidelines of Annex I themes is presented in the Table 1. The table also presents the result of the harmonisation effort that took place at the end of the data specification development process.

The Recommendations related to using metadata elements of Regulation 1205/2008/EC that are relevant to the data quality and that apply to all themes are listed bellow:

- Conformity. In order to report conceptual consistency with this INSPIRE data specification, the Conformity metadata element should be used. The value of Conformant should be used for the Degree element only if the dataset passes all the requirements described in the abstract test suite. The Specification element should be given as follows: o title: “INSPIRE Data Specification on Transport Networks – Guidelines” o date: o dateType: publication o date: - Lineage.8 part from describing the process history, if feasible within a free text, the overall quality of the dataset (series) should be included in the Lineage metadata element. This statement should contain any quality information required for interoperability and/or valuable for use and evaluation of the data set (series). - Temporal reference. If feasible, the date of the last revision of a spatial data set should be reported using the Date of last revision metadata element.

Theme specific recommendations on the use of Metadata elements:

Cadastral parcels: - Lineage. Main specificities of cadastral data should be published in the element “description of a data set”, using the relevant template. - Maintenance. Frequency with which changes are made for INSPIRE should be as close as possible to the frequency with which changes are made in a national cadastral register or equivalent. Moreover, frequency with which changes are made for INSPIRE should be one year or better. - Positional Accuracy. A cadastral data provider may give information about absolute accuracy: o at spatial object level, as attribute “estimatedAccuracy”on CadastralZoning or on CadastralBoundary o at spatial object type, as metadata element “positional accuracy”.

In case none of these solutions are feasible, the cadastral data provider should give information about positional accuracy under the “lineage” metadata element. This may occur, for instance, if the information about positional accuracy does not provide from quality measures but is just estimated

8 This use of lineage is in conflict with ISO 19115 and ISO 19114. Quality results should be reported by using appropriate quality elements that are described in ISO 19115. Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 14 of 16 from the knowledge of source data and of production processes. More generally, absolute positional accuracy should be function of the density of human activities. This recommendation may be adapted to the specific context of each Member State).

Hydrography: - For evaluation purpose the Data quality measure and Metadata element Rate of missing items (Completeness Omission) should be included for all spatial object types apart from the following list of types: HydroPointOfInterest and ManMadeObjects. - Keywords should be taken from the GEMET – General Multilingual Environmental Thesaurus where possible. - When publishing metadata for any dataset conforming to this specification; it shall have the topic category ‘Inland Waters’ for the corresponding metadata element (requirement).

Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 15 of 16

5 Points for discussion In order to propose a balanced and agreed way how DQ requirements and Metadata need to be approached in INSPIRE, we invite the (DQ) Contact Points of the Member States to organise national discussions and provide agreed and consolidated answers to the questions bellow.

1. Is there a need to include a priori data quality targets (elements, measures, and values) in INSPIRE data specifications? Yes, for each dataset addressing the same set of requirements Yes, but only for those datasets where achieving interoperability requires so No If no, please go to question 4. If yes, please answer questions 2 and 3.

2. Please indicate the theme and whether these targets should be addresses by mandatory requirements (M) or recommendations (R)? Please include justification if necessary.

Name of the data theme Condition M/R Justification / Comments

(Extend table if required.)

3. Please indicate the data quality elements, measures, and the target values to be used (add as many lines as needed). Please fill a separate table for each data theme to which a priori DQ requirements/recommendation apply.

Name of the data theme DQ element DQ measure Target value Comments

4. Do you recommend to specify mandatory metadata elements in INSPIRE when no a priori data quality requirements have been specified, or to complement those specified in the DQ section to inform users about the fitness for purpose?

Yes No Data quality in INSPIRE: from requirements to metadata Reference: 081ab1c0a67310b435e0ca093ec2a588.doc Discussion paper 2010-05-11 Page 16 of 16

5. What is the best way to generate DQ metadata about the data that has been made conformant to the INSPIRE data specifications (i.e. after the necessary data transformations?) Keep the original metadata Generate new metadata based on calculations or quality inspection by appropriate sampling Keep the original metadata and described as process step in MD_lineage (transformations performed with their possible effect on data quality)

6. Do you recommend to introduce theme-specific conformity levels (in addition to conformant, non conformant, not evaluated) in the INSPIRE Annex II-III data specifications development?

Yes No

7. Would there be value in adding theme-specific conformity levels (apart from conformant, non conformant, not evaluated) to INSPIRE Annex I data specifications?

Name of the data Yes/No Justification/comments theme