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Data Interoperability (RP 2024)

(A.) Policy and legislation 

(A.1)   Policy objectives

In its communication A European strategy for data  ( COM (2020) 66 final), the Commission describes the vision of a common European data space, a Single Market for data in which data could be used irrespective of its physical location of storage in the Union in compliance with applicable law. Various policy initiatives that have been undertaken since then, see section 3.0.1 Data Economy (RP2024) for an overview.

The EU data strategy communication specifically mentions the importance of data interoperability and quality:  Data interoperability and quality, as well as their structure, authenticity and integrity are key for the exploitation of the data value, especially in the context of AI deployment. Data producers and users have identified significant interoperability issues which impede the combination of data from different sources within sectors, and even more so between sectors. The application of standard and shared compatible formats and protocols for gathering and processing data from different sources in a coherent and interoperable manner across sectors and vertical markets should be encouraged through the rolling plan for ICT standardisation and (as regards public services) a strengthened European Interoperability Framework.

Open data

The DIRECTIVE on open data and the re-use of public sector information ( (EU) 2019/1024) mentions the need for the use of formal open standards:

Article 5 Available formats:

1. Without prejudice to Chapter V, public sector bodies and public undertakings shall make their documents available in any pre-existing format or language and, where possible and appropriate, by electronic means, in formats that are open, machine-readable, accessible, findable and re-usable, together with their metadata. Both the format and the metadata shall, where possible, comply with formal open standards. 

The COMMISSION IMPLEMENTING REGULATION laying down a list of specific high-value datasets and the arrangements for their publication and re-use ( (EU) 2023/138 ) mentions interoperability in its opening statements:

(3) Harmonising the implementation of the re-use conditions of high-value datasets entails the technical specification for making the datasets available in a machine-readable format and via application programming interfaces (APIs). Making high-value datasets available under optimal conditions strengthens the open data policies in the Member States, building on the principles of findability, accessibility, interoperability and reusability (FAIR principles).

(9) In addition to Directive (EU) 2019/1024, other Union legal acts, including Directive 2007/2/EC of the European Parliament and of the Council and Directive 2005/44/EC of the European Parliament and of the Council may be of relevance for the re-use of public sector information falling within the scope of this Implementing Regulation, notably where those Union acts lay down common requirements for data quality and interoperability.

Data spaces

The need for interoperability is mentioned in several places in the Data Governance Act, in particular in the article on the European Data Innovation Board  - a new expert group introduced in the DGA: 

Article 30, Tasks of on the European Data Innovation Board

(g) to assist the Commission, in particular taking into account the input from standardisation organisations, in addressing fragmentation of the internal market and the data economy in the internal market by enhancing cross-border, cross-sector interoperability of data as well as data sharing services between different sectors and domains, building on existing European, international or national standards, inter alia with the aim of encouraging the creation of common European data spaces;

(h) to propose guidelines for common European data spaces, namely purpose- or sector-specific or cross-sectoral interoperable frameworks of common standards and practices to share or jointly process data for, inter alia, the development of new products and services, scientific research or civil society initiatives, such common standards and practices taking into account existing standards, complying with the competition rules and ensuring non-discriminatory access to all participants, for the purpose of facilitating data sharing in the Union and reaping the potential of existing and future data spaces, addressing, inter alia:

(i) cross-sectoral standards to be used and developed for data use and cross-sector data sharing, cross-sectoral comparison and exchange of best practices with regard to sectoral requirements for security and access  
procedures, taking into account sector-specific standardisation activities, in particular clarifying and distinguishing which standards and practices are cross-sectoral and which are sectoral; 
(ii) requirements to counter barriers to market entry and to avoid lock-in effects, for the purpose of ensuring fair competition and interoperability; 
(iii) adequate protection for lawful data transfers to third countries, including safeguards against any transfers prohibited by Union law; 
(iv) adequate and non-discriminatory representation of relevant stakeholders in the governance of common European data spaces; 
(v) adherence to cybersecurity requirements in accordance with Union law;

Article 33 of the Data Act specifies the interoperability requirements for participants in data spaces:

1. Participants in data spaces that offer data or data services to other participants shall comply with the following essential requirements to facilitate the interoperability of data, of data sharing mechanisms and services, as well as of common European data spaces which are purpose- or sector-specific or cross-sectoral interoperable frameworks for common standards and practices to share or jointly process data for, inter alia, the development of new products and services, scientific research or civil society initiatives:

(a) the dataset content, use restrictions, licences, data collection methodology, data quality and uncertainty shall be sufficiently described, where applicable, in a machine-readable format, to allow the recipient to find, access and use the data;

(b)the data structures, data formats, vocabularies, classification schemes, taxonomies and code lists, where available, shall be described in a publicly available and consistent manner;

(c)the technical means to access the data, such as application programming interfaces, and their terms of use and quality of service shall be sufficiently described to enable automatic access and transmission of data between parties, including continuously, in bulk download or in real-time in a machine-readable format where that is technically feasible and does not hamper the good functioning of the connected product;

(d)where applicable, the means to enable the interoperability of tools for automating the execution of data sharing agreements, such as smart contracts shall be provided. (a)

(A.2) EC perspective and progress report

Overall, the application of standard and shared formats and protocols for gathering and processing data from different sources in a coherent and interoperable manner across sectors and vertical markets should be encouraged. This applies to the sharing of open data via data portals, as well as to the sharing of restricted data via data spaces. 

Standardisation at different levels (such as metadata schemata, data representation formats and licensing conditions of open data) is essential to enable broad data integration, data sharing and interoperability with the overall goal of fostering innovation and generating value based on data. This refers to all types of (multilingual) data, including both structured and unstructured data, and data from different domains as diverse as geospatial data, statistical data, weather data, public sector information (PSI) and research data, to name just a few.

Three main processes can be distinguished in the sharing of data, be it via data spaces or via open data portals:

  • Data Discovery
  • Data Sharing
  • ·Data Usage

A fourth process covers the data management aspects that enable organisations to share high-quality data:

  • Data Governance  

In each of the processes, data holders, data users and data intermediaries need to collaborate, requiring interoperability and standards. Various kinds of standards need to come into play to support the 3 processes. Below some aspects are listed, clustered by layer of the European Interoperability Framework (EIF).

Legal aspects:

  • Data protection and cybersecurity
  • Intellectual property / trade secrets
  • Identification of parties

Organisational aspects:

  • Data quality (criteria and classification)
  • Data provenance, lineage
  • eArchiving
  • Data sharing agreements (contracts, licences, service level agreements, terms of use)
  • Types of data sharing, e.g. event streams, file transfer, large data sets, ....

Semantic aspects:

  • Data catalogues (description & discovery)
  • Ontologies, linked data
    • including specific cross-domain topics such as Data Privacy Vocabulary
  • Documentation: metadata
  • Semantic integration (i.e., a shared and formalized understanding on the meaning of the used terminology / concepts) 
  • Definition of upper and/or commonly agreed ontologies as well as controlled vocabularies
  • Mapping rules and commonly agreed principles 
  • persistent URIs of data models / ontologies

Technical aspects:

  • Data formats and syntaxes
  • Data sharing protocols (APIs, file transfer)
  • Aggregation techniques 
  • Anonymisation and pseudonymisation techniques
  • Obfuscation techniques

The FAIR data principles (https://www.go-fair.org/fair-principles/) originating from the research community, should be used as a guide to identify the standardisation needs. FAIR data principles are not standards themselves, but rather provide a set of criteria against which standards can be evaluated to make data Findable, Accessible, Interoperable and Reusable (FAIR). A set of specifications for an object oriented implementation of the FAIR principles, the so-called FAIR Digital Objects, has been released by the FDO Forum (fairdo.org) in 2022.

Open source developments also provide a lot of foundational technologies for data interoperability and data exchange. Open source may complement standardisation efforts, but may also provide additional standards for addressing the respective challenges. It will increasingly be important to establish collaboration mechanisms between standardisation and open source and to look at transposing open source technologies into standards and specifications in support of the EU data strategy.

PROGRESS

Looking at the data standards landscape, the challenge lies not so much in a lack of standards, but rather in the multitude of standards and involved standards development organisations. There is a need for convergence and clarity.

I. Data Discovery

The DCAT standard (W3C) is a vocabulary designed to facilitate interoperability between data catalogues. The Application Profile for data portals in Europe (DCAT-AP) has been implemented for the referencing of open data in the European open data portals. The DCAT Application Profile has been developed as a common project under the former ISA2 programme, currently Interoperable Europe, the Publications Office (PO) and CNECT to describe public-sector data catalogues and datasets and to promote the specification to be used by data portals across Europe. The common application profile and promoting this among the Member States is substantially improving the interoperability among data catalogues and the data exchange between Member States.

The DCAT-AP related work, including its extensions to geospatial data (GeoDCAT-AP) and statistical data (StatDCAT-AP) also highlights the need for further work on the core standard. These are topics for the W3C smart descriptions & smarter vocabularies (SDSVoc) under the VRE4EIC Project.

The DCAT standard does not include API specifications for publication and querying. FIWARE has developed open stack-based specification and open standards APIs. The FIWARE solution has now been integrated into the Connecting Europe Facility “Context Broker” building block. The CEF has agreed meanwhile to upgrade the “Context Broker” to use the ETSI NGSI-LD specification (ETSI GS 009 V1.7.1 of the NGSI-LD API), and also the FIWARE Foundation is evolving its API to the same ETSI standard for exchange of open data. Now further effort is needed to demonstrate good examples of proper usage of NGSI-LD. This has been promoted within the EC Large Scale Pilot project SynchroniCity, however more dissemination and training is required (as recognized by CEF efforts to promote training webinars).

The DCAT standard can potentially also be used to support Data Discovery in data spaces, but it's applicability needs to be investigated (see actions), both in peer-2-peer scenarios as well in scenarios supported by data intermediaries. Currently its use (with possible extensions) is considered under the scope of the mobility data space (Metadata  | NAPCORE) and the health data space (by the TEHDAS project Joint Action Towards the European Health Data Space – TEHDAS - Tehdas). 

II. Data Sharing

For open data, the topics of data provenance and licensing (for example the potential of machine-readable licenses) need to be addressed, as encouraged in the current and proposed revision of the PSI Directive (see section B.1). The Open Data Directive encourages the use of standard licenses which must be available in digital format and be processed electronically (Article 8(2)). Furthermore, the Directive encourages the use of open licenses available online, which should eventually become common practice across the EU (Recital 44). In addition, to help Member States transpose the revised provisions, the Commission adopted guidelines which recommend the use of such standard open licenses for the reuse of PSI. Currently, Interoperable Europe vocabularies and solutions are used for the implementation of Single Digital Gateway Regulation (SDGR) in the design of common data models for evidences that are going to be exchanged between Member States. SDGR highlights the need to insure functional, technical and semantic interoperability in the exchange of evidence which can only be assured by using standard and shared data representation formats. The High Value Datasets Implementing Act put additional emphasis on the licensing part by stating that " High-value datasets shall be made available for re-use under the conditions of the Creative Commons Public Domain Dedication (CC0) or, alternatively, the Creative Commons BY 4.0 licence, or any equivalent or less restrictive open licence, allowing for unrestricted re-use."

Restricted data, standards are needed to facilitate the secure and trusted sharing of data. Apart from commercial data, restricted data also includes personal data (based on consent) and sensitive data from public institutions. This means that a complex combination of requirements will need to be addressed, including:

  • legal aspects, such as data protection, consent management
  • commercial aspects, such as licences, contracts, prices, terms of use, and the handling of trade secrets
  • technical aspects, such as establishing secure connections, transaction logging and data lineage.  

Common cross-domain data sharing mechanisms are needed to manage this complexity.

III. Data Usage

Ontologies are a key enabler for Data Usage, since they enable to create a common semantic layer across disparate data sets. The RDF (resource Description Framework) is a well established standard methodology to describe domain and cross-domain concepts. Data integration tools enable to combine data sets for data analytics purposes, either by ingesting the data or by virtual integration techniques.

In the eGovernment domain, Core Vocabularies have been established (i.e., Core Person, Core Organization, Core Location, Core Public Event, Core Criterion and Core  Evidence), Core Public Service Application Profile and Asset Description Metadata Schema (for describing reusable solutions), under the former ISA2 programme, currently Interoperable Europe. They were used in the TOOP-OOP (Once-Only Principle) project and now are currently used in the Once Only Principle (OOP) Technical System under the scope of the Single Digital Gateway Regulation EU 2018/1724.

The mapping of existing relevant ontology standards for other big data areas would be beneficial. Moreover, it will be beneficial to select or develop domain ontologies for the common European data spaces, supported by common best practices and tools. The use of AI tools such as Large Language Models can prove to be useful. 

IV. Data Governance

Data governance is relevant to individual participants, as well as to collaborative structures such as portals and data spaces. Standards that address data availability, data quality, data security and data retention fall under this process.   

(A.3) References 

(B.) Requested actions

Action 1: SDOs to optimize the management of DCAT and DCAT-AP (data discovery)

  • Related legal requirements: Open Data Directive, implementing act on High Value Data Sets, Data Act
  • Help to establish a governance framework for DCAT profiles
  • Develop rules for the management of new requirements, including criteria to decide whether to implement these in the base DCAT standard or as extensions
  • Consider the development of interface standards for DCAT publication and querying
  • Evaluate the suitability of DCAT for sharing of closed data, for example in a data spaces context

Action 2: SDOs to define a framework for the sharing of data from smart devices (data sharing, data usage)

  • Related legal requirements: Data Governance Act, Data Act, GDPR

Action 3: SDOs to define a framework for the sharing of consent-based data based (data altruism by organisations or persons), including metadata standards to define the consent attributes (e.g. purpose) and mechanisms to manage withdrawal of consent. (data sharing, data governance).

  • Related legal requirements: Data Governance Act, GDPR

Action 4: SDOs to optimize the management of domain ontologies:

  • Related legal requirements: Open Data Directive, implementing act on High Value Data Sets, Data Act
  • Help to establish a common governance framework for ontologies
  • Address long-term sustainability aspects such as maintenance funding
  • Develop standard criteria to evaluate quality of ontologies, e.g. avoiding bias, ensuring completeness, use of persistent URLs
  • Develop standard methods for the automated testing of ontologies

Action 5: SDOs to identify standards for data integration, semantic mapping / tagging, data fabric. Also addressing the way this can help to leverage common domain ontologies (data usage)

Action 6: SDOs to define standards for data governance, addressing two levels:

  • Data governance standards to support individual parties, for example certification of internal data governance processes and tools.
  • Data governance standards to support collaborative data sharing, in particular open data portals and data spaces.

Action 7: Support standardisation needs of the European open data infrastructure, especially the European Data Portal and the SEMIC ….

(C.) Activities and additional information 

(C.1) Related standardisation activities 

This chapter on Data Economy covers the high-level, horizontal aspects as outlined in part A above. The ongoing activities listed below are focused on that level, as well. There are many more ongoing activities and available standards relevant for data which are included in the other, sector-specific chapters.

CEN & CENELEC

CEN/TC 468 ‘Preservation of digital information’ works on the functional and technical aspects of the preservation of digital information. In this field, the committee will develop a structured set of standards, specifications and reports, addressing business requirements, including compliance with the European legislative and regulatory framework (e.g. GDPR, eIDAS).

ETSI

ETSI TC SmartM2M is developing a set of reference ontologies, mapped onto the oneM2M Base Ontology. This work has commenced with the SAREF ontology, for Smart Appliances, but is being extended to add semantic models for data associated with smart cities, industry and manufacturing, smart agriculture and the food chain, water, automotive, eHealth/aging well and wearables (https://saref.etsi.org/).

ETSI ISG CIM (cross-cutting Context Information Management) has developed the NGSI-LD API (GS CIM 009 v1.7.1) which builds upon the work done by OMA Specworks and FIWARE. NGSI-LD is an open framework for exchange of contextual information for smart services, aligned with best practice in linked open data. The NGSI-LD API is based on the NGSI-LD Information Model (GS CIM 006 v1.2.1). It is now capable of attesting provenance of information as well as supporting fine-grained encryption (GS CIM 019). Ongoing activities involve increased interoperability with oneM2M data sources. Applications and use cases are extended to Digital Twins, eHealth, analytics for government services, federated data spaces, GDPR-compatible data sharing.

ETSI’s ISG MEC is developing a set of standardized Application Programming Interfaces (APIs) for Multi-Access Edge Computing (MEC). MEC technology offers IT service and Cloud computing capabilities at the edge of the network. Shifting processing power away from remote data centres and closer to the end user, it enables an environment that is characterized by proximity and ultra-low latency, and provides exposure to real-time network and context information.

ETSI’s TC ATTM committee has specified a set of KPIs for energy management for data centres (ETSI ES 205 200-2-1). These have been combined into a single global KPI for data centres, called DCEM, by ETSI’s ISG on Operational energy Efficiency for Users (OEU), in ETSI GS OEU 001.

SC USER: has produced a set of documents related to “User-Centric approach in the digital ecosystem”. 
Note: this body of work also applies to several other sections of the ICT rolling plan, such as, IoT, eHealth, Cyber security, e-privacy, accessibility, but are documented only once. 

ETSI TR 103 438            User Group; User centric approach in Digital Ecosystem
ETSI EG 203 602            User Group; User Centric Approach: Guidance for users; Best practices to interact in the Digital Ecosystem
ETSI TR 103 603            User Group; User Centric Approach; Guidance for providers and standardisation makers
ETSI TR 103 604            User Group; User centric approach; Qualification of the interaction with the digital ecosystem
ETSI TR 103 437            Quality of ICT services; New QoS approach in a digital ecosystem

SC USER has initiated an action to finalise the project by defining and implementing a proof of Concept of a “Smart interface for digital ecosystem”, which is a user interface that meets the needs and expectations of the user at his request, and is an "Intelligent", "highly contextualised" personalisation, agile and proactive interface with an integrated QoS. This project will be based on the Smart Identity concept.

GS1

Digital Link -  GS1 Digital Link | GS1

Electronic Data Interchange - https://www.gs1.org/standards/edi

EPCIS 2.0 Standard https://ref.gs1.org/standards/epcis/"

ISO

ISO/TC 46/SC 4 Technical interoperability

  • ISO 15836 Information and documentation — The Dublin Core metadata element set
ISO/IEC JTC1

In 2018 JTC 1/SC 42 Artificial Intelligence was formed, and contains a WG 2 which is responsible for the Big Data work program.

SC 42  has published the following published big data standards:

ISO/IEC 20546:2019 Information technology -- Big Data -- Overview and Vocabulary (https://www.iso.org/standard/68305.html?browse=tc)

ISO/IEC TR 20547-2:2018 Information technology -- Big data reference architecture -- Part 2: Use cases and derived requirements (https://www.iso.org/standard/71276.html?browse=tc)

ISO/IEC TR 20547-5:2018 Information technology -- Big data reference architecture -- Part 5: Standards roadmap (https://www.iso.org/standard/72826.html?browse=tc)

ISO/IEC 20547-1: Information technology -- Big Data reference architecture -- Part 1: Framework and application process

ISO/IEC 20547-3: Information technology -- Big Data reference architecture -- Part 3: Reference architecture

SC 42 is progressing the following current big data projects, which are expected to complete in the next year:

ISO/IEC 24688: Information technology -- Artificial Intelligence -- Process management framework for Big data analytics

See for further information: https://www.iso.org/committee/6794475.html 

Built on its foundation standard that is ISO/IEC 38500 (Information technology - Governance of IT for the Organization), JTC 1/SC 40 IT service management and IT governance has developed or is developing the following standards on Governance of Data:

38505-1: Information technology - Governance of IT - Part 1: Application of ISO/IEC 38500 to the governance of data

38505-2: Information technology - Governance of IT - Part2: Implications of ISO/IEC38505-1 for Data Management

38505-3: Information technology - Governance of Data - Part3: Guidelines for Data Classification

See for further information https://www.iso.org/committee/5013818.html

ISO/IEC JTC1 SC32 on "Data management and interchange" work on standards for data management within and among local and distributed information systems environments. SC 32 provides enabling technologies to promote harmonization of data management facilities across sector-specific areas. https://www.iso.org/committee/45342.html

ISO/IEC JTC 1/SC 7 Software and systems engineering

ISO/IEC 25024:2015 Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Measurement of data quality

ISO/IEC CD 25040.2 Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – Quality evaluation framework

ITU-T

ITU-T SG11 is developing interfaces for data exchange between Equipment Identity Registers which can be used to combat counterfeit and the use of stolen ICT devices as well as for other purposes. Also, based on the increasing demand of data-based smart agriculture services, ITU-T SG11 is developing new standards defining data management interfaces for intelligent edge computing-based smart agriculture service (Q.IEC-SAINF) and flowing-water smart aquaculture system (Q.IEC-FWINF).

ITU-T SG13 Recommendation ITU-T Y.3600 “Big data - Cloud computing based requirements and capabilities" covers use-cases of cloud computing based big data to collect, store, analyse, visualize and manage varieties of large volume datasets:
https://www.itu.int/rec/T-REC-Y.3600/en

Also, SG13 published Y.3600-series Supplement 40 "Big Data Standardisation Roadmap” which will be revised in 2022:
https://www.itu.int/rec/T-REC-Y.Sup40/en

SG13 has 10 ongoing work items on big data, in particular, it is working on big data functional requirements for data integration (Y.bdi-reqts). It approved

Recently approved ITU-T Recommendations on big data, includes Y.3605 (09/2020) with big data reference architecture and functional architecture of big data-driven networking Y.3653 (04/2021).

See a flipbook “Big Data - Concept and application for telecommunications":
https://www.itu.int/en/publications/Documents/tsb/2019-Big-data/mobile/index.html.

The work programme of SG13 is available at:  http://itu.int/itu-t/workprog/wp_search.aspx?sg=13

More info:  https://www.itu.int/en/ITU-T/studygroups/2017-2020/13

ITU-T SG20 “Internet of things (IoT) and smart cities & communities (SC&C)” is studying big data aspects of IoT and SC&C. ITU-T Study Group 20 developed Recommendation ITU-T Y.4114 “Specific requirements and capabilities of the IoT for big data” which complements the developments on common requirements of the IoT described in Recommendation ITU-T Y.4100/Y.2066 and the functional framework and capabilities of the IoT described in Recommendation ITU-T Y.4401/ Y.2068 in terms of the specific requirements and capabilities that the IoT is expected to support in order to address the challenges related to big data. This Recommendation also constitutes a basis for further standardization work such as functional entities, application programming interfaces (APIs) and protocols concerning big data in the IoT.

ITU-T SG20 also published Recommendation ITU-T Y.4461 “Framework of open data in smart cities” that clarifies the concept, analyses the benefits, identifies the key phases, roles and activities and describes the framework and general requirements of open data in smart cities, Recommendation ITU-T Y.4473 “SensorThings API - Sensing” that specifies the SensorThings application programming interface (API) which provides an open standard-based and geospatial-enabled framework to interconnect Internet of things (IoT) devices, data, and applications over the Web, Recommendation ITU-T Y.4472 “Open data application programming interface (APIs) for IoT data in smart cities and communities” which presents a complete set of Open APIs dedicated to smart cities offering different features covering the needs of interoperable smart city framework development, and Supplement ITU-T Y.Suppl.61 “Features of application programming interface (APIs) for IoT data in smart cities and communities” which studies the concept and potential of developing a secured open and interoperable APIs in the context of IoT deployment and open data management in smart cities.

The work programme of SG20 is available at:  https://www.itu.int/ITU-T/workprog/wp_search.aspx?sg=20

More info:  https://itu.int/go/tsg20

The ITU-T Focus Group on Data Processing and Management (FG-DPM) to support IoT and Smart Cities & Communities was set up in 2017. The Focus Group played a role in providing a platform to share views, to develop a series of deliverables, and showcasing initiatives, projects, and standards activities linked to data processing and management and establishment of IoT ecosystem solutions for data focused cities. This Focus Group concluded its work in July 2019 with the development of 10 Technical Specifications and 5 Technical reports. The complete list of deliverables is available here  https://itu.int/en/ITU-T/focusgroups/dpm

ITU-T SG17 has approved six standards on big data and open data security:

ITU-T X.1147 “Security requirements and framework for big data analytics in mobile internet services”

ITU-T X.1376 “Security-related misbehaviour detection mechanism based on big data analysis for connected vehicles”

ITU-T X.1603 “Data security requirements for the monitoring service of cloud computing”

ITU-T X.1750 “  Guidelines on security of big data as a service for Big Data Service Providers”

ITU-T X.1751 “Security guidelines on big data lifecycle management for telecom operators”

ITU-T X.1752 “Security guidelines for big data infrastructure and platform” (under approval as of Sept 2021).

More info:  https://www.itu.int/en/ITU-T/studygroups/2017-2020/17

ITU-T Focus Group on Artificial Intelligence (FG-AI4H), established in partnership with ITU and WHO, is working towards establishing a standardized assessment framework for the evaluation of AI-based methods for health, diagnosis, triage or treatment decisions.

https://www.itu.int/en/ITU-T/focusgroups/ai4h/

IEEE

The IEEE “Big Data Governance and Metadata Management: Standards Roadmap” has been developed to guide IEEE standards and pre-standardization projects related to Big Data (mobile health, energy efficient processing, personal agency and privacy) and open data. 

Relevant standards activities include:

  • IEEE 1752 series of standards on mobile health data
  • IEEE 3652.1, Guide for Architectural Framework and Application of Federated Machine Learning
  • IEEE 7002, Standard for Data Privacy Process
  • IEEE 7005, Standard for Transparent Employer Data Governance
  • IEEE P3800, Data Trading System: Overview, Terminology, and Reference Model
  • IEEE P7004, Standard for Child and Student Data Governance
  • IEEE P7015, Standard for Data and Artificial Intelligence (AI) Literacy, Skills, and Readiness.

There also are pre-standards programs, including:

  • IC16-001 Open Data
  • IC19-004 Technology and Data Harmonization for Enabling Clinical Decentralized Clinical Trials
  • IC21-007 Ethical Assurance of Data-Driven Technologies for Mental Healthcare
  • IC21-013 Synthetic Data

 For more information, see: https://ieee-sa.imeetcentral.com/eurollingplan/ .

IETF

The  A Semantic Definition Format for Data and Interactions of Things (asdf) Working Group  is tasked with developing  Semantic Definition Format (SDF)  into a standards-track specification for thing interaction and data modelling. In the process of developing this specification, further functional requirements that emerge in the usage of SDF for model harmonization will be addressed.

https://wiki.ietf.org/en/group/iab/Multi-Stake-Holder-Platform#h-313-data-interoperability

OASIS

The OASIS Open Data Protocol (Odata) TC works to simplify the querying and sharing of data across disparate applications and multiple stakeholders for re-use in the enterprise, Cloud, and mobile devices. A REST-based protocol, OData builds on HTTP and JSON using URIs to address and access data feed resources. OASIS OData standards have been approved as ISO/IEC 20802-1:2016 and ISO/IEC 20802-2:2016.

The NIEMOpen OASIS project (formerly known as the "National Information Exchange Model") is a framework for exchanging information between public and private sector organizations, with particular focus on issued and vocabularies for e-government and public administration issues. The framework includes a reference data model for objects, properties, and relationships, allowing data elements to be shared, extended and harmonized across vertical topics and governmental functions, as well as a set of technical specifications for using and extending the data model in information exchanges. 

The Code List Representation (genericode) v1.0 final standard is a semantic model of code lists and accompanying XML serialization that can encode a broad range of lists of information elements such as country codes, abbreviations and lookup tables.  This serialization is designed to enable automatic interchange or distribution of machine-readable code list information between systems, and so more widely reuse existing categorizations.

The OASIS ebCore TC maintains the ebXML RegRep ('registry and repository') standard, also approved as ISO 15000-3:2023, that defines the service interfaces, protocols and information model for an integrated registry and repository. The repository stores digital content while the registry stores metadata that describes the content in the repository. RegRep was used in the EU TOOP project, which was concluded in 2021.

RegRep can be used in conjunction with ebXML Messaging including AS4 using a recently developed binding for the Registry Services of the OASIS ebXML RegRep Version 4.0 OASIS Standard. This binding is compatible with the AS4 profile of ebXML Messaging as used, for example, in the European Commission’s eDelivery Building Block, and complements the existing protocol bindings specified in OASIS RegRep Version 4.0. This AS4 binding is also of relevance to the Once-Only Technical System for the Single Digital Gateway (see section 3.2.4, eGovernment).

OGC

The Open Geospatial Consortium (OGC) defines and maintains standards for location-based, spatio-temporal data and services. The work includes, for instance, schema allowing description of spatio-temporal sensor, image, simulation, and statistics data (such as "datacubes"), a modular suite of standards for Web services allowing ingestion, extraction, fusion, and (with the web coverage processing service (WCPS) component standard) analytics of massive spatio-temporal data like satellite and climate archives. OGC also contributes to the INSPIRE project.

http://www.opengeospatial.org

oneM2M

The oneM2M Partnership Project has specified the oneM2M Base Ontology (oneM2M TS-0012, ETSI TS 118 112) to enable syntactic and semantic interoperability for IoT data. The oneM2M standard defined a middleware layer, residing between a lower layer, comprising IoT devices and communications technologies, and an upper layer of IoT applications. Thus, it enables a wide range of interactions between applications and the underlying technologies needed to source data from connected devices and sensors as well as sharing of data from many sensors that are managed by different device owners and service providers. All oneM2M specifications are publicly accessible at at Specifications (onem2m.org).

W3C

DCAT vocabulary (done in the linked government data W3C working group) 

http://www.w3.org/TR/vocab-dcat/

After a successful Workshop on Smart Descriptions & Smarter Vocabularies (SDSVoc) ( www.w3.org/2016/11/sdsvoc/) W3C created the Dataset Exchange Working Group ( https://www.w3.org/2017/dxwg) to revise DCAT, provide a test suite for content negotiation by application profile and to develop additional relevant vocabularies in response to community demand. 

Work on licence in  ODRL continues and has reached a very mature state:  https://www.w3.org/TR/odrl-model/ and  https://www.w3.org/TR/vocab-odrl/

The Data on the web best practices WG has finished its work successfully ( https://www.w3.org/TR/dwbp) also issuing data quality, data usage vocabularies ( https://www.w3.org/TR/vocab-dqv; https://www.w3.org/TR/vocab-duv)

(C.2) Other activities related to standardisation
ISA and ISA2 programme of the European Commission

The DCAT application profile (DCAT-AP) has been defined. DCAT-AP is a specification based on DCAT (a RDF vocabulary designed to facilitate interoperability between data catalogues published on the web) to enable interoperability between data portals, for example to allow metasearches in the European Data Portal that harvests data from national open data portals.

Extensions of the DCAT-AP to spatial (GeoDCAT-AP: https://semiceu.github.io/GeoDCAT-AP/drafts/latest/) and statistical information (StatDCAT-AP: https://joinup.ec.europa.eu/collection/semantic-interoperability-community-semic/solution/statdcat-application-profile-data-portals-europe ) have also been developed.

https://joinup.ec.europa.eu/asset/dcat_application_profile/description

https://joinup.ec.europa.eu/collection/semantic-interoperability-community-semic/solution/dcat-application-profile-data-portals-europe/release/211

Core Vocabularies can be used and extended in the following contexts:

  • Development of new systems: the Core Vocabularies can be used as a default starting point for designing the conceptual and logical data models in newly developed information systems.
  • Information exchange between systems: the Core Vocabularies can become the basis of a context-specific data model used to exchange data among existing information systems.
  • Data integration: the Core Vocabularies can be used to integrate data that comes from disparate data sources and create a data mesh-up.
  • Open data publishing: the Core Vocabularies can be used as the foundation of a common export format for data in base registries like cadastres, business registers and public service portals.

The Core Public Service Vocabulary Application Profile allows harmonised ways and common data models to represent life events, business events and public services across borders and across-sectors to facilitate access.

ADMS is a standardised vocabulary which aims at helping publishers of semantic assets to document what their assets are about (their name, their status, theme, version, etc) and where they can be found on the Web. ADMS descriptions can then be published on different websites while the asset itself remains on the website of its publisher.

More info can be found in the following links:

      https://joinup.ec.europa.eu/collection/semantic-interoperability-community-semic/core-vocabularies

      https://ec.europa.eu/isa2/solutions/core-public-service-vocabulary-application-profile-cpsv-ap_en

      https://joinup.ec.europa.eu/collection/semantic-interoperability-community-semic/adms

CEF

Under the framework of the Connecting Europe Facility programme support to the interoperability of metadata and data at national and EU level is being developed through dedicated calls for proposals. The CEF group is also promoting training and webinars for using the “context broker”, in collaboration as appropriate with the NGSI-LD standards group ETSI ISG CIM.

AquaSmart

AquaSmart enables aquaculture companies to perform data mining at the local level and get actionable results.

The project contributes to standardisation of open data in aquaculture. Results are exploited through the Aquaknowhow business portal.

www.aquaknowhow.com

Automat

The main objective of the AutoMat project is to establish a novel and open ecosystem in the form of a cross-border Vehicle Big Data Marketplace that leverages currently unused information gathered from a large amount of vehicles from various brands.

This project has contributed to standardisation of brand-independent vehicle data.

www.automat-project.eu

BodyPass

BodyPass aims to break barriers between health sector and consumer goods sector and eliminate the current data silos.

The main objective of BodyPass is to foster exchange, linking and re-use, as well as to integrate 3D data assets from the two sectors. For this, BodyPass adapts and creates tools that allow a secure exchange of information between data owners, companies and subjects (patients and customers).

The project aims at standardizing 3D data

www.bodypass.eu

EU Commission

A smart open data project by DG ENV led directly to the establishment of the Spatial Data on the Web Working group, a collaboration between W3C and the OGC.

G8 Open Data Charter

In 2013, the EU endorsed the G8 Open Data Charter and, with other G8 members, committed to implementing a number of open data activities in the G8 members’ collective action plan (publication of core and high-quality datasets held at EU level, publication of data on the EU open data portal and the sharing of experiences of open data work).

Future Internet Public Private Partnership programme

Specifications developed under the Future Internet public-private-partnership programme (FP7):

FIWARE NGSI extends the OMA Specworks NGSI API for context information management that provides a lightweight and simple means to gather, publish, query and subscribe to context information. FIWARE NGSI can be used for real-time open data management. ETSI’s ISG for cross-cutting Context Information Management (CIM) has developed the NGSI-LD API (GS CIM 004 and GS CIM 009) which builds upon the work done by OMA Specworks and FIWARE. The latest FIWARE software implements the newest ETSI NGSI-LD specification.

FIWARE CKAN: Open Data publication Generic Enabler. FIWARE CKAN is an open source solution for the WG10 publication, management and consumption of open data, usually, but not only, through static datasets. FIWARE CKAN allows its users to catalogue, upload and manage open datasets and data sources. It supports searching, browsing, visualising and accessing open data

Big Data Value cPPP TF6 SG6 on big data standardisation

In the big data value contractual public-private-partnership, a dedicated subgroup (SG6) of Task Force 6: Technical deals with big data standardisation.

Fair Principles and the GO Fair Initiatives 

FAIR Principles stands for F indability,  A ccessibility,  I nteroperability, and  R euse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) data as a result of the increase in volume, complexity, and creation speed of data. GO FAIR is a community that has been working towards implementations of the  FAIR Guiding Principles

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