Data Spaces for Interoperable Research DataManagement and GovernanceYuri Demchenko
Session on Data Spaces for RDM
SLICES Workshop @ IoT Week
23 June 2022

IoTWeek 2022, SLICES Workshop

Data Staces for Research Data Manag and Governance1Outline• Introduction and goals• SLICES-RI and Research Data Management• 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• EOSC Interoperability Framework and FAIR data principles• Discussion: Benefits of adopting Data Spaces concept for RDM

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Data Staces for Research Data Manag and Governance2Motivation and Goal: SLICES-RI and Research DataManagement• Can Data Spaces concept facilitate interoperability and sustainability inresearch data sharing?
• Best practices and standardisation: SLICES-RI, EOSC, RDA, and IDSA/Industry• Infrastructure services for data sharing• Interoperability in data sharing including between Research and Industry

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SLICES-RI and Research Data Management & Governance• ESFRI SLICES-RI is a future RI for digital technologies experimentation• Require infrastructure for experiments access and management• Require supporting infrastructure for experimental and research data sharing(both computer and semantic)• Experimentation data are big, distributed, domain specific, serving
specific community• Require effective models and infrastructure services for RDM and secure datasharing

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Data Staces for Research Data Manag and Governance4Many activities related to Data Spaces – Up-to-date• IDSA: Continuous activity of multiple groups in International Data Spaces Association (IDSA)• RDA: BoF on Data Spaces for Research Data Taxonomy scheduled for RDA19 Plenary on 21 June 2022
(morning due to time difference)• Reviewing existing Data Spaces definitions and suggesting harmonization and apporach• RDA: BoF on Data Commons (actually similar to Data Spaces concept in US and Australia) – 22 June2022 (night)• Data Commons Typology diagram: part od the concept by RDA activity GORC (Global Open Research Commons)• DSBA (BDVA + IDSA) organised DataWeek2022 event 24 May - 9 June 2022, online on on-site meetingon 8-9 June in Naples.
• SLICES workshop and session "Data Spaces for interoperable Research Data Management andGovernance (DS4RDM)" during IoT Week on 23 June 2022 (this event)• The BoF focus is DS for RDM and relevance to RI via SLICES.

• During IotWeek2022, few meetings about DS are organised by DSBA, IDSA, project Team Data Spaces(https://dataspaces4.eu/) and many projects focusing on Data Markets

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5RDA GORC Typology Diagram• Interoperability and standards• Governance Structures• Builds on participation• Engagement• Human capacity• Sustainability• Computer, Storage, Network• Services and tools• Research objects

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Data Spaces Taxonomy BoF - 21
June 2022
Two-three line summary of session topic / focus and discussion:
Term 'Data Spaces' is appearing recently in many contexts related to data governance andmanagement, data exchange and sharing. The session focused on the nature of the data spaces andtaxonomies. Also differences and similarities with data commons were discussed.

Key Outcomes/Actions/Takeaways:
1.
2.
3.

There are multiple use cases for data spaces concept for example in the area of the health data
Differences and similarities with “data commons” and other related concepts needs further discussions
Taxonomy of data spaces (and data commons) is needed to produce or enhance interoperability between data sets andinfrastructures

Slide courtesy Ville Tenhunen, EGI

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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

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European Data Space – Policy Aspects (since 2018)
European strategy for data COM(2020) 66 final, 19.02.2020
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020DC0066
The European data space will give businesses in the EU the possibility to build on the scale of the 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 and competition
law, are fully respected;- Rules for access to and use of data are fair, practical and clear, and there are clear and trustworthy data governancemechanisms in place; there is an open, but assertive approach to international data flows, based on Europeanvalues.
Key documents• European Data Governance Act, Nov 2020
• Digital Services Act package (Q4 2020)• Data Act, February 2022

IoTWeek 2022, SLICES Workshop

Data Staces for Research Data Manag and Governance9European Data Governance – Covered Aspects• General aspects• (a) conditions for the re-use, within the Union, of certain categories of data held by public sector bodies• (b) a notification and supervisory framework for the provision of data sharing services• (c) a framework for voluntary registration of entities which collect and process data made available foraltruistic purposes.

• Re-use of certain categories of protected data held by public sector bodies which
are protected on grounds of:• (a) commercial confidentiality• (b) statistical confidentiality• (c) protection of intellectual property rights of third parties• (d) protection of personal data

• Requirements to data sharing services and providers• Data Altruism and organisations

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Data Staces for Research Data Manag and Governance10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? What is domain
related to SLICES-RI and IoT[ref] Common European Data Spaces http://dataspaces.info/common-european-data-spaces/
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Industrial Data Spaces: Sharing Data
• Data sharing along the whole data handling flow in industrial or businessprocesses – Allow for 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 SpaceAssociation (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 HE projects• Community activities• Weekly architecture design meetings• Governance task force• Winter/Summer schools
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Data Staces for Research Data Manag and Governance13General Structure of IDS Architecture
IDS Governance Perspective• Governance aspects on different
architecture layers• Data as an economic goods• Data Ownership• Data Sovereignty• Data Quality• Data Provenance• Specification defines functionalities by layers• Details are sufficient to define processes, functional components andAPI
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Data Spaces definition: Ongoing work by the Data Spaces
Taxonomy BoF• European documents• Data Spaces are domain specific ecosystems for data sharing governed by common policies(complying EU Data Governance policies)• IDSA, OPEN DEI and GAIA-X• Data space can be defined as a federated data ecosystem within a certain application domain
and based on shared policies and rules. A data space is made up of multiple actors, which
together form a data ecosystem.
• Decentralized infrastructure for trustworthy data sharing and exchange in data ecosystem
based on commonly agreed principles

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Data Spaces Interoperability Framework – Infrastructure• Technical – Infrastructure• Semantic – Metadata + Infrastructure Services• Organisational• LegalIoTWeek 2022, SLICES Workshop

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EOSC Interoperability Framework (EOSC-IF) is about Data – To
support FAIR data sharing• FAIR Digital Object (FDO) is a key concept• Technical Interoperability:• Artefact Common Protocols and Data Formats• Semantic Interoperability:• Contextual Semantics related to Common Semantic resources• Organisational Interoperability:• Contextual Semantics related to Common process resources• Legal Interoperability:• Contextual licenses related to Common Licenses resources• FDO is actively promoted by GO FAIR Technical Center and Peter Wittenburg• EOSC Interoperability profiles to facilitate interoperability

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Example of EOSC Provider profile (EPP): Provider data model
Category

Components

Basic information

ID, Name, abbreviation, Website, legal entity(Y/N), status

Marketing information

Description, logo, multimedia

Classification

Scientific domain and sub-domain(s), tags(relevant keywords)

Location information

Street, postcode, city, region, country

Contact information

Main contact, public contact, Email, Phone number, position

Maturity information

Lifecycle status, certifications

Other information

Name of the organization, participating countries, affiliations, structure(single sited,distributed, etc.), ESFRI domain (e.g., energy, environment…), societal grand challenge
defined by EC (e.g., health, food ...), part of the National roadmap for infra, etc.

CCI 2021

EOSC Interoperability and Metadata Profiles

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Example of EOSC Resource profile (ERP): Resource data model
Category

Components

Basic information

ID, Name, resource organization, resource providers, Website

Marketing information

Description, tagline (catch-phrase), logo, multimedia, usecases supported

Classification

Scientific domain, scientific sub-domain(s), scientific category, scientific subcategory, targetusers, access type, access mode, tags (relevant keywords)

Geo and language
information

Resource Locations, languages of the UI, resource availability location

Main Contact/Resource
ownerName, position, organization Email, Phone number, public contact, Helpdesk information(email. phone), security contact

Maturity information

TRL, lifecycle, certifications, standards, open source tech supported by resource, version, lastupdate, change log

Dependency information

List of other resources required, commonly used with and platforms, etc.

Funding

Funding body, program and grant/project, etc.

Managementinformation

Helpdesk page, manual, terms of use, privacy policy, access policy, service level, training info,
status monitoring, webpage with planned maintenance info

Access and order info

Ordering procedure

CCI 2021

Financial information

EOSC Interoperability and Metadata Profiles

Payment model, pricing, etc.

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Conceptual view of SLICES
Interoperability Architecture• Provides vision and roadmap toachieving interoperability with EOSC
• Some services can be used from EOSC,some services will require API with EOSC
services of metadata mapping• Data Interoperability and sharing is an
important component of SLICES-RI• Compliance with the Open Science and FAIRdata principles• Semantic interoperability• Supported by robust data infrastructure• Data Management and Governance

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EOSC Provider and Resource Metadata Profiles• Q1. How Data Spaces concept (as domain specific data ecosystem) can facilitate data
sharing in your domain?
• Q2. Data Spaces and FAIR data principles: What place in the Data Spaces architecture?
• Q3. Is there a specifics for defining metadata profiles for experimental data sharing? Whatlevel of standardisation is required?
• Q4. What infrastructure services are needed to enable expected Data Spaces
implementation and operation?
• Q5. What aspect of inter-domain, inter-sector or international cooperation do you see?

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Data Staces for Research Data Manag and Governance21Additional/Context information• SLICES Data Lifecycle Model and Dataflow• SLICES Data Management and Governance: Infrastructure, Services, Policy• FAIR Data Principles: Metadata Management

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SLICES Data Lifecycle Model and Dataflow
Experiment/DataLifecycle Stages

Researcher

SLICES Data Storage and Management Infrastructure(multi-purpose: experimental, technological, archival)

Experiment
Plan &Implement

Data Collection& Preparation

Data Analysis
Model Building

Data Archiving &Publication/Sharing

Data Curation &Quality Assurance

Data Publishing andArchiving on EOSC
Data Sharing
Infrastructure

EOSC Data
Archiving &Sharing

Data Discovery (for experiment planning and operation)• Each Data Lifecycle stage – experiment, data collection, data analysis, and finally data archiving, works
with own data set, which are however connected. All these data sets need to be stored and possibly reused in later processes.
• Many experiments and research require already existing datasets that will be available in the SLICES datarepositories or can be obtained/discovered in EOSC data repositories

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Robust Data Infrastructure for Data Driven Research• Distributed data storage and experimental data(set) repositories• Supporting recognized data interoperability standards (data formats and metadata)• Eventually certified: RDA endorsed Maturity and certification practice• Support the whole data lifecycle• Connected to research/experiment lifecycle or workflow• Linked data and data discovery• PID (Persistent IDentifier) and FDO (FAIR Digital Object) infrastructure• Supported by EOSC and developed by RDA and number of H2020 and HE projects• (Trusted) Data exchange and transfer protocols• Supported by rules and policies• Interoperable metadata• Data management and data curation and quality assurance

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SLICES Data Management and Governance: Infrastructure,Services, Policy• Data Infrastructure• Distributed data storage and repositories (for experiments support and data publishing)• PID/FDO infrastructure for data discovery and linked data
• Data Catalogs and Metadata Catalogs• Data Management• FAIR data principles and metadata registries (schemas)• Data Management Plan and Data Stewards (to maintain DMP and data quality)• Define metadata for sharing and publication of data produced by SLICES• Define metadata for the resource and experimental facilities description• Data Governance:• Organisational and legal aspects in data handling• Policies and IPR• personal data protection

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FAIR Data Principles: Metadata Management(GO FAIR recommendations)Findable:

Accessible:• F1 (meta)data are assigned a globally unique and persistentidentifier;• F3 metadata clearly and explicitly include the identifier of thedata it describes;• A1 (meta)data are retrievable by their identifier using astandardized communications protocol;• A1.1 the protocol is open, free, and universallyimplementable;• A1.2 the protocol allows for an authentication andauthorization procedure, where necessary;• F4 (meta)data are registered or indexed in a searchable
resource;• A2 metadata are accessible, even when the data are no longer
available;

Interoperable:

Reusable:• I1. (meta)data use a formal, accessible, shared, and broadly
applicable language for knowledge representation.

• R1 meta(data) are richly described with a plurality of accurate
and relevant attributes;• F2 data are described with rich metadata;• I2. (meta)data use vocabularies that follow FAIR principles;• R1.1 (meta)data are released with a clear and accessible data
usage license;• I3. (meta)data include qualified references to other (meta)data;• R1.2 (meta)data are associated with detailed provenance;• R1.3 (meta)data meet domain-relevant community standards;

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FAIR from the technical point of view
• Findable• Metadata and PDI – infrastructure and tools• Registries and handles resolution, API• Policies and SLA• Accessible
••
•
•
•

Repositories and data storage: infrastructure and management
Policy and access control: infrastructure and API management
Data access protocols
Usage Policy and Sovereignty
Data protection, compliance, privacy and GDPR

• Interoperable• Standard data formats• Metadata and API• FAIR maturity level and certification

Require comprehensive data
infrastructure to support• Data Storage• Data publication• Data discovery• Linked data and data lineage(provenance)• Multiple datasets access foranalysis• Reusable• Data provenance and lineage• Preservation• Metadata, PID and API – linked or embedded into datasets

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