SLICES infrastructure and services integration with
EOSC and Open Science

Defining SLICES Interoperability Framework
Yuri Demchenko, Kishor Joshi
CCI, University of Amsterdam

SLICES-RI Workshop
23 September 2021

Outline• SLICES-DS WP4 Integration and compatibility with EOSC, FAIR and external RIs• Deliverable D4.2 SLICES infrastructure and services integration with EOSC and Open Science• Deliverable D4.3 Definition of the SLICES metadata profiles to support FAIR principles• European Open Science Status Overview• Minimum Viable EOSC – core elements to enable EOSC and Federated Scientific Data Infrastructure• EOSC Interoperability Framework• Components and technical view

• FAIR data principles: Technical aspects• Discussion

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2Deliverable D4.2 SLICES infrastructure and services integration with EOSC
and Open Science – SLICES Interoperability Framework
1. INTRODUCTION
2. SLICES USE CASES OVERVIEW
3. RECOMMENDATIONS/ANALYSIS OF EOSC INTEROPERABILITY FRAMEWORK
3.1. EOSC INTEROPERABILITY CHALLENGES AND REQUIREMENTS
3.2. EOSC INTEROPERABILITY RECOMMENDATIONS
3.3. EOSC FAIR DIGITAL OBJECT AND PID FRAMEWORK
3.4. EOSC PID ARCHITECTURE AND PID POLICY
3.5. EXISTING PID FRAMEWORKS
3.6. EOSC FDO METADATA MODEL3.7. EOSC METADATA PROFILES
3.8. CONCLUSION OF EOSC IF

4. SLICES INTEROPERABILITY FRAMEWORK AND INTEGRATION WITH EOSC
4.1. REQUIREMENTS OF SLICES-IF
4.2. SLICES-INTEROPERABILITY FRAMEWORK
5. SLICES INTEROPERABILITY COMPONENTS/DESIGN RECOMMENDATIONS
5.1. SLICES AAI
5.2. SLICES FDO AND PID INTEROPERABILITY
5.3. SLICES METADATA PROFILES
5.4. PROPOSAL FOR SLICES APIS

6. ADHERENCE TO THE OPEN SCIENCE PRINCIPLES
7. CONCLUSIONSLICES Workshop 2021

SLICES and EOSC Interoperability

3Deliverable D4.2 SLICES infrastructure and services integration with EOSC
and Open Science – SLICES Interoperability Framework
1. INTRODUCTIONSLICES Interoperability Framework –Goals

2. SLICES USE CASES OVERVIEW
3. RECOMMENDATIONS/ANALYSIS OF EOSC INTEROPERABILITY FRAMEWORK• Requirements to SLICES interoperability
and connection with EOSC
• What infrastructure services from EOSC
can be re-used?
• How to access EOSC data resource andhow to publish SLICES data to EOSC?
• What and how SLICES can contribute to
EOSC?

3.1. EOSC INTEROPERABILITY CHALLENGES AND REQUIREMENTS
3.2. EOSC INTEROPERABILITY RECOMMENDATIONS
3.3. EOSC FAIR DIGITAL OBJECT AND PID FRAMEWORK
3.4. EOSC PID ARCHITECTURE AND PID POLICY
3.5. EXISTING PID FRAMEWORKS
3.6. EOSC FDO METADATA MODEL3.7. EOSC METADATA PROFILES
3.8. CONCLUSION OF EOSC IF

4. SLICES INTEROPERABILITY FRAMEWORK AND INTEGRATION WITH EOSC
4.1. REQUIREMENTS OF SLICES-IF
4.2. SLICES-INTEROPERABILITY FRAMEWORK
5. SLICES INTEROPERABILITY COMPONENTS/DESIGN RECOMMENDATIONS
5.1. SLICES AAI
5.2. SLICES FDO AND PID INTEROPERABILITY
5.3. SLICES METADATA PROFILES
5.4. PROPOSAL FOR SLICES APIS

6. ADHERENCE TO THE OPEN SCIENCE PRINCIPLES
7. CONCLUSIONSLICES Workshop 2021

Why interoperate and connect with EOSC

• EOSC initiatives are accepted by ESFRI and EC• Mandatory in Horizon Europe proposals: DMP, FAIR,
Open Science, PID/Zenodo, ORE (new) as well as
liaison with EOSC
• EOSC is a model Federated Scientific Data
Infrastructure and community

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Conceptual view of SLICES Interoperability Architecture• Provides vision and roadmap to achieving
interoperability with EOSC
• Some services can be used from EOSC, some
services will require API with EOSC services of
metadata mapping

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SLICES and EOSC Interoperability

5European Open Science Cloud (EOSC) – Overview• EOSC is an overarching concept and framework to integrate existing RIs and facilitate information and dataexchange between RI, organisations and researchers• First phase 2016-2020 with funded projects 2016-2022•

53 projects in total• EOSC main projects and co-creation activities• EOSCpilot – Initial EOSC architecture and requirements (10 Mln, ended 2018)• EOSChub – Technical integration platform, RI marketplace and API/services directory (33 Mln, ended 31 March
2021)• EOSCsecretariat https://www.eoscsecretariat.eu/ (10 Mln, ends 31 October 2021)• Establishment of the Governance structure and EU EOSC association to be co-funded by EC and Member States (MS)Co-creation model and European Open Science Commons• SRIA (Strategic Research and Innovation Agenda) document (195 pages, Final version dated 15 Feb 2021) https://www.eosc.eu/sites/default/files/EOSC-SRIA-V1.0_15Feb2021.pdf• Built on experience of the past successful initiatives and project• EGEE and WLCG, EGI, RDA (co-founded by EC and NSF), GEANT/TERENA• Can provides a model experience for future EU initiatives, such as GAIA-X European Federated Data Cloud

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EOSC Conceptual Architecture

EOSC Docs• EOSC-Core• Minimum architecture elements to enable theFederation• EOSC-Exchange• Evolving Federation to serve the needs of researchcommunities• Widening to the general public and the private sector• EOSC Federation (RIs and e-Infra)• Research and Innovation Community• Minimal Viable EOSC (aka MVE)
• Minimum Federation to bring value to users
3

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EOSC Status: Minimum Viable EOSC and EOSC Core
• Architecture defining (infrastructure) components
•
•
•
•
•
•

Metadata Framework (FAIR data enabling services)
PID framework/Infrastructure and service
Federated Authentication, Authorisation Interoperability (AAI) Framework
Data Access framework
Service Management and Access framework
Open Metrics Framework• Policy and Governance• Shared Open Science policy framework• A minimum legal metadata framework as
part of the FAIR compliance framework• An open metrics framework

Demand for modern RI platform using recent
development by industry (for future technology
exchange)
EOSC challenges: to incorporate recent technology
development into ERA• Portal providing web access to the EOSC services• EOSC is an important stage in the European RI integration

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Future EOSC Development and SLICES:
Timeline e-RI evolution and SLICES positioning
2025+

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EOSC Interoperability Framework (Feb 5, 2021)• Defines 4 layers• Technical• Semantic• Organisational• Legalhttps://op.europa.eu/en/publication-detail/-/publication/d787ea54-6a87-11eb-aeb5-01aa75ed71a1/language-en/format-PDF/source-194348068

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EOSC Interoperability Framework Feb 5, 2021
Layer

Recommendation

Technical(services but
not yet infra)• Open Specifications for EOSC Services.
●

●
●
●
●

Semantic(Metadata)

•
●
●
●

●

●
●

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A common security and privacy framework (including Authorisation andAuthentication
Infrastructure).
Easy-to-understand Service-Level Agreements for all EOSC resourceproviders.
Easy access to data sources available in different formats.
Coarse-grained and fine-grained dataset (and other research object) searchtools.
A clear EOSC PID policy.
Clear and precise, publicly-available definitions for all concepts, metadata anddata schemas.
Semantic artefacts preferably with open licenses.
Associated documentation for semantic artefacts.
Repositories of semantic artefacts, rules with a clear governance framework.
A minimum metadata model (and crosswalks) to ease discovery over existingfederated
research data and metadata.
Extensibility options to allow for disciplinary metadata.
Clear protocols and building blocks for the federation/harvesting of semanticartefacts
catalogues.
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EOSC Interoperability Framework Feb 5, 2021
Layer

Recommendation

Organisational •LegalInteroperability-focused rules of participation recommendations.
●
Usage recommendations of standardised data formats and/or vocabularies,and with theircorresponding metadata.
●
A clear management of permanent organisation names and functions.
• Standardised human and machine-readable licenses, with a centralised source of knowledge andsupport on copyright and licenses.
●
Permissive licenses for metadata (and preferably for data, wheneverpossible). And CC0 preferredover CC BY 4.0.
●
Identification of different parts of a dataset with different licenses.
●
Clearly marked instances of expired or non-existent copyright, as well as fororphan data.
●
EOSC-recommended licenses and their compatibility with Member States’ recommended licenses.
●
Tracking of license evolution over time for datasets.
●
Harmonised policy and guidance to dealing with cases where patent filing or trade secrets may becompromised by disclosure.
●
GDPR-compliance for personal data.
●
Additional restrictions on access and use of data only applied in cases ofapplicable legislation orlegitimate reasons.
●Harmonised terms of use across repositories●
Alignment between Member States national legislations and EOSC.

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EOSC-IF Components• Authentication and Authorization Interoperability (AAI)
framework• PID Framework• Metadata framework•

Resource and Provider Metadata profiles• Data access framework• Service management and access framework• Open metrics framework•• Security framework•• Support framework••

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Needs translation from “EOSC”
language to technical language
Still no clear design approach

SLICES and EOSC Interoperability

Danger of silos and re-inventing

No infrastructure vision

13

EOSC-IF: Structured View (EOSC Iron Lady)• Technicalterms to beclarified

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EOSC-IF is about Data – To support FAIR
• 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• Recent presentation at e-IRG meeting (e-Infrastructure Reflection Group - EC policy consulting body)

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FAIR is an Overloaded Concept (and a term)• Primarily, FAIR is (set of) principles for sustainable Research Data Management (RDM) and OpenScience• Findable – Accessible – Interoperable – Reusable• FAIR is an initiative• FAIR is a key policy area of EOSC
• FAIR data management is part of Data Management Plan (DMP) and required by Horizon Europe
and many national funding bodies• FAIR impose a number of requirements to Research Infrastructure• Existing RIs run dedicated projects on FAIR adoption: ENVRI-FAIR, ELIXIR• Universities should play important role in FAIR and RDM adoption• Still slow adoption at all levels: Bachelor, Master, Doctoral, teachers

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FAIR from the technical/infrastructure point of view
• Findable•••

Metadata and PID – 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

New profession of the Data Steward is promoted by
EOSC an currently demanded by industry. Maincompetences include:• Data management• Domain research or business knowledge• Reusable•••

Data provenance and lineage
Preservation, archiving
Metadata, PID and API – linked or embedded into datasets

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FAIR Digital Object – A core for EOSC-IF• In EOSC, a digital object can be research data,
software, scientific workflows, hardware designs,
protocols, provenance logs, publications,presentations, etc• FAIR Digital Object Extends Digital Object concept
for better FAIRness• FAIR Digital Object (FDO) is a core building block of
EOSC-IF• Four interoperability layers applied• Requires infrastructure support

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FDO (FAIR Digital Object) and PID Infrastructure Requirements•

General requirements include machine actionability, technology independence, persistent binding, abstraction andstructured hierarchical encapsulation, compliance with standards and community policies (as specified in the FDO
general requirements G3-G9);•

FDO is identified by PID; there are possible multiple PID frameworks defined by PDI scheme, namespaces, ontologies orcontrolled vocabularies (FDOF1);•A PID resolves to a structured record (PID record) with attributes that are semantically defined within a (data) typeontology (which may be defined for different application or science domains) (FDOF2);•

PID record may include other attributes that are important to characterize specific types of FDO or that are required byapplications. Additional attributes must be registered in a data types registry (FDOF4);•

Metadata used to describe FDO properties should use standard semantics and registered schemes to allow machinereadability and actionability (FDOF8-FDOF10).

• FAIR Digital Object Framework, Technical implementation guideline, version 1.02, [online]
https://datashare.rzg.mpg.de/s/RTeYZGe3QMgEciH/download?path=%2F&files=FAIR%20Digital%20Object%20Framework-v1-02.pdf

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Discussion• FAIR and Open Science implementation in organisations• What do you think is the most important challenge in achieving FAIRness• Challenges with technical interoperability SLICES and EOSC
• Challenges with semantic interoperability SLICES and EOSC

• Go to www.menti.com and use the code 4309 9563

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Mentimeter: FAIR and Open Science implementation in organisations –“Maturity Level Indicators”• FAIR awareness: Researchers and RI operators• Top management aware and organization has a plan to implement FAIR
• Organisation has approved DMP including FAIR requirements• Organisation has DMP, Data Governance Policy and Data Steward or Data Curator• Open Data Policy accepted and research data are mandatory published in open access repositories• Metadata catalog and data publishing tools used
• Metadata profile defined for discipline/domain data
• Metadata are added at the initial stage of data collection (from experiment)• To ensure Reusability, data are published with API and application• Use of existing data evaluated: Data search and review at the initial stage of the project

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Mentimeter: What do you think is the most important
challenge in achieving FAIRness• Cast your short few words reflection

Technical Interoperability with EOSC Challenges-> Mentimeter(i) fAir: Separate authentication and authorization are often required when accessing services across different
infrastructures and communities, which generally requires transfer of personal information among identity
and service providers. To address this issue, there is a need to develop an AAI framework that is community
independent and minimally obstructive;(ii) faIr: Research data may be stored in different formats which are either general purpose (CSV, XML, JSON,etc.) or community specific (Darwin core, FITS, VOTable, VOResource, etc.) which are difficult to reuseacross communities. To solve this, a common-minimum metadata model is needed to allow seamlessdiscovery and reuse of data across multiple formats;(iii) FaiR: Research data is often not available in multiple granularities. This makes it difficult to be found andreused by different scientific domains requiring different granularity of data. Thus, there is a requirement
for research data to be stored at multiple levels of abstractions (fine grain and coarse grain) so that a wide
variety of scientific and application domains can benefit from it;(iv) PID: Generally, scientific communities employ community-specific persistent identifiers (PURL, IUPAC
international chemical identifier, DOI, etc.) with a different set of policies. This sometimes results in
identifiers which can be difficult to resolve. Therefore, a community-agnostic PID policy is required for acommon understanding.

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Semantic Interoperability Challenges -> Mentimeter• Semantic interoperability refers to the ability that the exchanged data is understood welland have a common meaning across different entities of the EOSC ecosystem.
(i) Semantic artefacts are poorly documented and definitions of terms used are not precisely
defined. This makes it difficult to be used across communities;(ii)Furthermore, common reference repositories/registries for semantic artifacts are noteasily available or maintained for long enough;(iii) Lack of common metadata schemas across communities. Different communities use
different metadata schemas such as DarwinCore, RDA metadata, DCAT, DDI4, etc.

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EOSC Reference Information• EOSC Minimal Viable EOSC and EOSC Core

• EOSC Technical Architecture by EOSC-hub

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25https://www.eosc.eu/task-force-faq• Who are the members of the Task Forces?
This is still to be defined but will be agreed by September 2021. Much depends on the rules defined by each Task Force.
Some will restrict the size to 25-30 members whilst others will not set a limit in membership. The Board will select themembers in collaboration with the charter drafting coordinators.
• The open call for drafters of the Task Forces in March 2021 resulted in 255 applications.
• The Board nominated four coordinators per Task Force to steer the work and assigned applicants to a single Task Force ensuringbalance in expertise, preference of applicants for specific Task Forces, gender, and organisations.

• Do I need o formally apply if the membership of a Task Force is open?
Yes. You need to apply so that all members of the Task Force match the skillset and selection criteria for that Task Forceand so that we have a record of the names and contact details of all members of the Task Forces.
• Will there be funding for activities in the Task Forces?
There is no budget foreseen in the first months for conducting studies or supporting activities in the Task Forces.

EOSC Technical Architecture
https://documents.egi.eu/public/ShowDocument?docid=3495
Users
GUI/API

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

Workflow
Management

Big Data
Analityics

Data
Annotation

Making science
products FAIR

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

Cloud IaaS

Cloud
Orchestrator

Data Discovery

Metadata
management

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

APIs and std interfaces

Data/Compute/Storage Resources

Other Portals and Marketplace

Common services

Federated Discovery and Brokerin,/supporting (Federated AAI)

APIs and std interfaces

EOSC Portal

Federations tools
AAI, Accounting, monitoring, Helpdesk, etc.

Thematic services

27
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EOSChub Reference architecture, interfaces andinteroperability guidelines
How to make yourservice scale out to 3rdparty clouds forcompute-intensive
workloads

How to makeusers able to
SSO into yourservice

Makingservices
discoverable
and orderable

SLICES Workshop 2021

Cloud
computeguideline

Data
guideline

How to save data in
EOSC-compliant
storage in a FAIR
mannerContainer
computeguideline

AAI
Guideline

EOSC Portal
website andMarketplace

How toautomate thedeployment ofa cluster ofcontainers

Metadata
guideline

SLICES and EOSC Interoperability

How to definea metadata
schema28

From EOSC-1 to EOSC-2: Four Technology Aspects
2020-2025

2016-2018

RI Type

EOSC-1

Definition

Interoperable Federated (European)
RI, FAIR RI

Virtualised Pan-European RI platform as a Service andEcosystem (PRIaaS)

Network &Compute

•
•
•
•
•
•
•

Distributed scalable computing
Cloud based Big Data technologies
High performance networks
5G technologies, wireless access
IoT sensor networks
Portal and Services Catalog
Industry standards and IDSA adoption• Composable virtualized RI provisioning on demand (including for services
integration)• Common federated computing and networking platform/environment,
enabling virtual RIs• Cloud based and cloud enabled• DevOps and AI enabled services• Digital Twins• Interoperability and Integration with Industry infrastructure (e.g. IDSA+,
Industrial Internet)

Data Infra

•
•
•
•

FAIR data
Data Factories and PID
Metadata registries
Interoperable/common Data Management
model

•
•
•
•

Fully adopted FAIR principles, extended to ontologies
Semantically enabled scientific data lakes, common vocabularies
Secure/trusted data exchange (data markets)
Full data value chain supported (cross-domain)

Security• Federated Identity Management, Federated
Access Control• Automated identity provisioning

•
•
•
•
•

Zero trust security, Trust Bootstrapping
Homomorphic encryption and data processing
Quantum ready encryption, Quantum enabled key management
Federated Identity Management, Federated Access Control
Automated identity provisioning

Infra Managnt
Technolog• Integrated Operation and Automation• Fully automated RI and services provisioning, management and operation• Optimisation of infrastructure and operation• DevOps and AI enabled (re-usable design patterns)

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EOSC-2 (future)

SLICES and EOSC Interoperability

Beyond2025

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