Data Spaces Concept in European Data Policy
andData Spaces Definitions

Yuri Demchenko
BoF Data Spaces Taxonomy
RDA19, 21 June 2022
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Data Staces Taxonomy

1Outline• Why do we need taxonomy?
• European Data Spaces and related documents– Data Act - Data Governance Act – GDPR - Data Altruism• Industrial Data Spaces– IDSA Reference Architecture Model and Data Sovereignty– Open DEI Data Spaces Design principles• Data Spaces definitions by EU documents and IDSA
• Discussion: What to include into Data Spaces definition?

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Goal: Why do we need taxonomy?
• Common language/terms and data models• Interoperability in professional communication and data sharing– Interoperability between Research and Industry• Basis for interoperable and sustainable infrastructure services– Based on the specified Data Spaces properties• Standardisation: Liaison between RDA and Industry/IEEE/ISOEOSC/European Interoperability
Framework – 4 layers• Technical/infrastructure• Semantic/metadata• Organisational• Legal
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European Data Space(s)• European Data Governance Act– Support for set-up and development of common European data spaces in strategic domains,involving both private and public players• European rules and values to ensure free flow of data between EU countries andoutside– Sharing and re-using data, data monetisation– Enabling regulated environment for data processing• Industrial Data Space: Sharing data while ensuring data Sovereignty– Data Sovereignty achieved by attaching policy and usage conditions to data
– Enabling regulated environment for data processing• International Data Spaces Association and GAIA-X

RDA19 BoF Data Spaces

Data Staces Taxonomy4Regulation on European Data Governance (Data Governance Act, Nov
2020)•EU/Parliament Regulation on European data governance (Data Governance Act) SEC(2020) 405 final, Nov
2020, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020PC0767– ‘data’ means any digital representation of acts, facts or information and any compilation of such acts, facts orinformation, including in the form of sound, visual or audiovisual recording;•Facilitate Sharing and Re-use of Data
– New way of data governance will increase trust in data sharing, strengthen mechanisms to increase data availability andovercome technical obstacles to the reuse of data.

•Making public sector data available for re-use, in situations where such data is subject to rights of others.
– “Data the use of which is dependent on the rights of others” or “data subject to the rights of others” covers data thatmight be subject to data protection legislation, intellectual property, or contain trade secrets or other commerciallysensitive information.

•Allowing personal data to be used with the help of a ‘personal data-sharing intermediary’, designed to help
individuals exercise their rights under the General Data Protection Regulation (GDPR).
– Allowing data use on altruistic grounds.

Questions: What is data? How is it related to Data Spaces? What operations on data?
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Data Governance Act: Common European Data Spaces• Health• Industry andManufacturing• Agriculture• Finance• Mobility• Green Deal
• Energy• Public Administration• Skills

Questions: Are any developments in other domains, except Industry and Manufacturing?

[ref] Common European Data Spaces http://dataspaces.info/common-european-data-spaces/RDA19 BoF Data Spaces

Data Staces Taxonomy

6Industrial Data Spaces: Sharing Data
• Data sharing along the whole data handling flow in industrial or businessprocesses – To allow for the 3rd party services– Sensors - IoT – Ingest/Load – Ingest/Transform – Process – Act/Operate• Sharing and Sovereignty are key to make value of data
– Network of trusted data

• Data Sovereignty achieved by attaching policy and usage conditions to data
– In non-fungible/blockchain way or protected environment•Enabling regulated environment for data processing– Enclave computing in modern cloud technology

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International Data Spaces Association (IDSA)
https://www.internationaldataspaces.org/••••

More than 120 members
Started 2016 as Industrial Data Space
initiative (supported by German project)
Re-defined as International Data Space
Association (IDSA)
Published IDSA Reference Architecture
Model Version 3.0 (RAM3.0, 2018)– Whitepaper and use cases•••

Focused on Industrial data sharing
Key Issue is Data Sovereignty
Number od associated H2020 and HEprojects•Community activities– Weekly architecture design meetings– Governance task force– Winter/Summer schools

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Data Spaces definitions: Review• European documents• IDSA and OPEN DEI

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9Data Spaces in the European Data Governance Policy in Horizon2020
and Horizon Europe• Term “Data Space” appeared in EU documents related to data regulations and data policy in a formof “European Data Space” (European Strategy for Data,19 Feb 2020) which is defined as– “a genuine single market for data, open to data from across the world – where personal as well as nonpersonal data, including sensitive business data, are secure and businesses also have easy access toan almost infinite amount of high-quality industrial data, boosting growth and creating value, while
minimising the human carbon and environmental footprint. It should be a space where EU law can beenforced effectively, and where all data-driven products and services comply with the relevant normsof the EU’s single market.

• Common European rules and efficient enforcement mechanisms should ensure that:--Data can flow within the EU and across sectors;
European rules and values, in particular personal data protection, consumer protection legislation andcompetition law, are fully respected;
Rules for access to and use of data are fair, practical and clear, and there are clear and trustworthy data
governance mechanisms in place;
There is an open, but assertive approach to international data flows, based on European values.

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IDSA Data Spaces – No actual definition, used depending oncontext• Reference Architecture Model version 3.0 (referred to as IDSA RAM3.0) the Data Spaces
concept– The International Data Spaces (IDS) is a virtual data space leveraging existing standards and technologies, aswell as governance models well-accepted in the data economy, to facilitate secure and standardized dataexchange and data linkage in a trusted business ecosystem. It thereby provides a basis for creating smartservice scenarios and facilitating innovative cross-company business processes, while at the same time
guaranteeing data sovereignty for data owners.

• Data Sovereignty is a central aspect of the International Data Spaces. It can be defined asa natural person's or corporate entity's capability of being entirely self-determined withregard to its data.
• Strategic requirements– TRUST, SECURITY AND DATA SOVEREIGNTY, ECOSYSTEM OF DATA• IDS Connector is introduced as a mandatory element to implement IDS functionality andensure above properties.
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OPEN DEI Data Spaces Design Principles (2021)•Definition by OPEN DEI is built around infrastructure for data sharing and exchange: a data space can be defined as afederated data ecosystem within a certain application domain and based on shared policies and rules. The users of suchdata spaces are enabled to access data in a secure, transparent, trusted, easy and unified fashion.
– Abstracted from this technical definition, a data space can be defined as a federated data ecosystem within a certain application
domain and based on shared policies and rules. The users of such data spaces are enabled to access data in a secure, transparent,trusted, easy and unified fashion. These access and usage right can only be granted by those persons or organisations who areentitled to dispose of the data.

••A data space is made up of multiple actors, which together form a data ecosystem. Data will only be shared andexchanged between actors if they decide to do so. Data will only be shared and exchanged between actors if they decide
to do so. One reason for doing so may be to create business value. Such business value does not necessarily need to beof monetary nature, but can also manifest itself in a better quality of a product or service. (p.92)
A data space is defined as a decentralized infrastructure for trustworthy data sharing and exchange in data ecosystem
based on commonly agreed principles (p.23). Data spaces require the following elements:•••

Building blocks such as data platforms, providing support for effective data sharing and exchange as well as for engineering anddeployment of data exchange and processing capabilities;
Building blocks such as data marketplaces, where data providers can offer and data consumers can request data, as well as dataprocessing applications;
Building blocks ensuring data sovereignty, i.e. the ability for each stakeholder to control theirdata by making decisions as to how
digital processes, infrastructures, and flows of data arestructured, built and managed, based on an appropriate governancescheme enabling specification of terms and conditions.

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12OPEN DEI Data Spaces Design Principles (2021) - 1• Definition by OPEN DEI is built around infrastructure for data sharing and exchange: a data space can bedefined as a federated data ecosystem within a certain application domain and based on shared policies andrules. The users of such data spaces are enabled to access data in a secure, transparent, trusted, easy andunified fashion.
– Abstracted from this technical definition, a data space can be defined as a federated data ecosystem within a certain
application domain and based on shared policies and rules. The users of such data spaces are enabled to access data
in a secure, transparent, trusted, easy and unified fashion. These access and usage right can only be granted bythose persons or organisations who are entitled to dispose of the data.

• A data space is made up of multiple actors, which together form a data ecosystem. Data will only be sharedand exchanged between actors if they decide to do so. Data will only be shared and exchanged between
actors if they decide to do so. One reason for doing so may be to create business value. Such business value
does not necessarily need to be of monetary nature, but can also manifest itself in a better quality of aproduct or service. (p.92)

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OPEN DEI Data Spaces Design Principles (2021) - 2• A data space is defined as a decentralized infrastructure for trustworthy data sharing and exchange in dataecosystem based on commonly agreed principles (p.23). Data spaces require the following elements:•••

Building blocks such as data platforms, providing support for effective data sharing and exchange as well as forengineering and deployment of data exchange and processing capabilities;
Building blocks such as data marketplaces, where data providers can offer and data consumers can request data, as
well as data processing applications;
Building blocks ensuring data sovereignty, i.e. the ability for each stakeholder to control theirdata by making decisions
as to how digital processes, infrastructures, and flows of data arestructured, built and managed, based on an
appropriate governance scheme enabling specification of terms and conditions.

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State of Data Spaces, Report by Finland Ministry of Transport andCommunications (October 2021)
This document in general share the definitions by OPEN DEI both Data Space and Data Ecosystem.
• Data space: a framework and a medium that creates a secure space for data exchange.
– Decentralized infrastructure for trustworthy data sharing and exchange in data ecosystems based on commonlyagreed principles.
– Purpose-or sector-specific or cross-sectoral interoperable frameworks of common standards and practices to shareor jointly process data for, inter alia, development of new products and services, scientific research or civil society
initiatives.

• Data sharing: conditioned exchange of data aiming to create added value• Data ecosystem: integration of and interaction between stakeholders to access and share data
• Data sovereignty: self-determination in a digital world,

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Operations on data(sets) and Data/Data Spaces properties••••

The traditional POSIX file system object permission model defines three classes of users called
Data/dataset properties
owner, group, and other. Each of these classes is associated with a set of permissions: read (r),(generic) – To check RDM
write (w), and execute (x).
recommendations!
The most of traditional database define the four basic operations that can be performed• Data modelon the database records: Create, Read, Update, Delete (typically referred to as CRUD operations).
• Namespace
Data in general and datasets in particular are complex digital objects that may characterised• Language
by multiple parameters, properties and models.
• Encoding
The following are the common operations on data or datasets can be assumed:• Accuracy/Statistical• Basic• Owner• Create• Source, Origin• Store (write)• DOI/PID• Modify/update• Delete• Transfer/Copy: Send – Receive - Stream
Questions to discuss:• Extended1) What operations are subject to Data Spaces and• Metadata: Create – Read – Update – Delete
what are applications and system internal?
• DOI: Create – Delete
2) What infrastructure or system components are• To be extended
required to perform these operations?
3) From FAIR data principles to related data properties

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Discussion Questions• How to match all existing definitions that are shared by large community ofpractitioners?
• Given active development in Industrial Data Space domain, are any developments
in other domains, except Industry and Manufacturing?
• What is data? How is it related to Data Spaces? What operations on data?
• What properties to included into Data Spaces definition? – To achieve the main
goal in facilitating data sharing.

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