D4.6 - Use case driven validation of semantic artefact
exploitation within data repositories

Sophie Aubin, Carmen Corre, Clement Jonquet, Llorenç Cabrera-Bosquet, Ilaria Rosati, Enrica Nestola, Parham Ramezani, Vyacheslav Tykhonov, Pascal Flohr, Andrea Scharnhorst, et al.

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To cite this version:

Sophie Aubin, Carmen Corre, Clement Jonquet, Llorenç Cabrera-Bosquet, Ilaria Rosati, et al. D4.6 - Use case driven validation of semantic artefact exploitation within data repositories. D4.6, FAIR-IMPACT. 2025. hal-05024648

HAL Id: hal-05024648

https://hal.inrae.fr/hal-05024648v1

Submitted on 8 Apr 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Project Title Expanding FAIR solutions across EOSC

Project Acronym FAIR-IMPACT

Grant Agreement No. 101057344

Start Date of Project 2022-06-01

Duration of Project 36 months

Project Website https://fair-impact.eu/

## D4.6 - Use case driven validation of semantic artefact exploitation within data repositories

<table><tr><td>Work Package</td><td>WP4, Metadata and ontologies</td></tr><tr><td>Lead Author (Org)</td><td>Sophie Aubin (INRAE)</td></tr><tr><td>Contributing Author(s) (Org)</td><td>Carmen Corre (INRAE), Dimitri Szabo (INRAE), Enrica Nestola (CNR), Ilaria Rosati (CNR), Christelle Pierkot (CNRS), Guillaume Alviset (CNRS), Christian Pichot (INRAE), Pascal Flohr (KNAW-DANS), Vyacheslav Tykhonov (KNAW-DANS), Andrea Scharnhorst (KNAW-DANS), Llorenç Cabrera-Bosquet (INRAE), Julien Seinturier (CNRS), Baptiste Cecconi (OBS-PARIS), Clement Jonquet (INRAE), Renato Juacaba Neto (EMBL-EBI)</td></tr><tr><td>Due Date</td><td>2025-03-31</td></tr><tr><td>Date</td><td>2025-03-31</td></tr><tr><td>Version</td><td>V1.0 - DRAFT not yet approved by the European Commission</td></tr><tr><td>DOI</td><td>10.5281/zenodo.14917164</td></tr></table>

### Dissemination Level

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PU: Public

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PP: Restricted to other programme participants (including the Commission)

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RE: Restricted to a group specified by the consortium (including the Commission)

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CO: Confidential, only for members of the consortium (including the Commission)

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Versioning and contribution history

<table><thead><tr><td>Version</td><td>Date</td><td>Author</td><td>Notes</td></tr></thead><tbody><tr><td>0.1</td><td>2023.10.26</td><td>Sophie Aubin (INRAE)</td><td>Main structure</td></tr><tr><td>0.2</td><td>2024.12.18</td><td>Sophie Aubin, Carmen Corre (INRAE)</td><td>introduction and 7 UC overview (first part redaction) impact study writing</td></tr><tr><td>0.31</td><td>2025.01.24</td><td>Julien Seinturier (AMU)</td><td>Archeo-AMU UC overview</td></tr><tr><td>0.32</td><td>2025.01.27</td><td>Christelle Pierkot (CNRS) &amp; Guillaume Alviset (CNRS)</td><td>Earth UC overview</td></tr><tr><td>0.33</td><td>2025.01.28</td><td>Enrica Nestola (CNR) &amp; Ilaria Rosati (CNR)</td><td>LifeWatch UC overview</td></tr><tr><td>0.34</td><td>2025.01.30</td><td>Christian Pichot (INRAE)</td><td>AnaEE UC overview</td></tr><tr><td>0.4</td><td>2025.01.30</td><td>Andrea Scharnhorst (DANS)</td><td>introduction, section 2.6</td></tr><tr><td>0.5</td><td>2025.02.06</td><td>Clement Jonquet (INRAE)</td><td>General review, changes and comments</td></tr><tr><td>0.51</td><td>2025.02.10</td><td>Slava Tykhonov (DANS)</td><td>suggestions and modifications, section 2.6</td></tr><tr><td>0.6</td><td>2025.02.17</td><td>Sophie Aubin &amp; Carmen Corre (INRAE)</td><td>split with M4.5 (keep &amp; update parts 1 &amp; 2)</td></tr><tr><td>0.7</td><td>2025.02.28</td><td>Sophie Aubin &amp; Carmen Corre (INRAE)</td><td>version ready for external review</td></tr><tr><td>0.8</td><td>2025.03.24</td><td>Sophie Aubin &amp; Carmen Corre (INRAE)</td><td>Incorporating internal review from Esteban Gonzalez Guardia (UPM), Hilde Orten (sikt) &amp; Vasso Kalaitzi (DANS)</td></tr><tr><td>1.0</td><td>2025.03.31</td><td>Sophie Aubin &amp; Carmen Corre (INRAE)</td><td>published &amp; submitted to EC</td></tr></tbody></table>

Disclaimer

FAIR-IMPACT has received funding from the European Commission's Horizon Europe funding programme for research and innovation programme under the Grant Agreement no. 101057344. The content of this document does not represent the opinion of the European Commission, and the European Commission is not responsible for any use that might be made of such content.

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# Table of Contents

**Executive Summary** 6

**1. Introduction** 7

1.1. Objectives of D4.6 deliverable 7

1.2. Components of the connection 8

1.2.1. (Meta)data repositories 9

1.2.3. FAIR Semantic Artefacts (SAs) 9

1.2.4. Semantic artefacts catalogues (SACs) 10

**2. Use Cases overview** 10

2.1. PHIS-AgroPortal 11

2.1.1. Context and objectives 11

2.1.2. Implemented solutions and Challenges 13

2.2. LifeWatch DP&MDC-EcoPortal 14

2.2.1. Context and objectives 15

2.2.2. Implemented solutions and Challenges 16

2.3. DANS Data Stations 18

2.3.1. Context and objectives 18

2.3.2. Implemented solutions and Challenges 20

2.4. EaSyData-EarthPortal 21

2.4.1. Context and objectives 21

2.4.2. Implemented solutions and Challenges 22

2.5. Recherche Data Gouv-AgroPortal 23

2.5.1. Context and objectives 23

2.5.2. Implemented solutions and Challenges 24

2.6. IVOA Astronomy 25

2.6.1. Context and objectives 25

2.6.2. Implemented solutions and Challenges 26

2.7. AnaEE-Agroportal 27

2.7.1. Context and objectives 27

2.7.2. Implemented solutions and Challenges 28

2.8. ArcheoAMU 29

2.8.1. Context and objectives 30

2.8.2. Implemented solutions and Challenges 30

2.9. Identifiers.org-ROR.org 31

2.9.1. Context and objectives 31

2.9.2. Implemented solutions and Challenges 32

**3. Lessons learned from use cases** 33

3.1. Community semantics needs and capabilities 33

3.2. Connectors' impacts on data FAIRness and technical aspects 35

3.3. SACs as key element of connectors' implementation 36

**4. Conclusions and perspectives** 37

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## List of Figures

<table><tr><td>Figure 1a - (Meta)data repositories, SAs and SACs relationship.</td><td>8</td></tr><tr><td>Figure 1b - (Meta)data repositories, SAs and SACs in use in T4.5 use cases.</td><td>8</td></tr><tr><td>Figure 2 - Entity-Characteristic-Method-Unit model for representing scientific variables used in PHIS, and link to reference ontologies.</td><td>13</td></tr><tr><td>Figure 3 - Screenshot of the connector between PHIS and AgroPortal.</td><td>14</td></tr><tr><td>Figure 4 - LifeWatch Italy Data Portal Keywords wizard.</td><td>17</td></tr><tr><td>Figure 5 - LifeWatch Italy Data Portal Keywords wizard.</td><td>17</td></tr><tr><td>Figure 6 - DANS Data-stations organisation and instances.</td><td>19</td></tr><tr><td>Figure 7 - The process of term selection in DANS Data Stations</td><td>19</td></tr><tr><td>Figure 8 - User interface showing use of the EaSy Data - EarthPortal connector.</td><td>22</td></tr><tr><td>Figure 9 - Representation of Recherche Data Gouv-AgroPortal connector.</td><td>24</td></tr><tr><td>Figure 10 - AnaEE semantic workflow (from data collection to publication).</td><td>28</td></tr><tr><td>Figure 11 - SemData user interface with AgroPortal connector (model).</td><td>29</td></tr><tr><td>Figure 12 - OpenTheso GUI for adding new SA from PACTOLS within EPICHERCHELL</td><td>31</td></tr><tr><td>Figure 13 - Identifiers.org metadata record fill in for institution name space.</td><td>33</td></tr></table>

## List of tables

<table><tr><td>Table 1 - FAIR-IMPACT Task 4.5 UCs.</td><td>10</td></tr></table>

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TERMINOLOGY

<table><thead><tr><td>Terminology/Acronym</td><td>Description</td></tr></thead><tbody><tr><td>API</td><td>Application Programming Interface</td></tr><tr><td>DCAT</td><td>Data Catalogue Vocabulary</td></tr><tr><td>DP</td><td>Data Portal</td></tr><tr><td>EML</td><td>Ecological Metadata Language</td></tr><tr><td>EOSC</td><td>European Open Science Cloud</td></tr><tr><td>FAIR</td><td>Findable Accessible Interoperable Reusable</td></tr><tr><td>ID</td><td>Identifier</td></tr><tr><td>ISO</td><td>International Organization for Standardization</td></tr><tr><td>MDC</td><td>MetaData Catalogue</td></tr><tr><td>MOD</td><td>Metadata for Ontology Description and Publication</td></tr><tr><td>OWL</td><td>Web Ontology Language</td></tr><tr><td>PID</td><td>Persistent identifier</td></tr><tr><td>RDF</td><td>Resource Description Framework</td></tr><tr><td>REST API</td><td>REpresentational State Transfer API</td></tr><tr><td>RI</td><td>Research Infrastructure</td></tr><tr><td>ROR</td><td>Research Organization Registry</td></tr><tr><td>SA</td><td>Semantic Artefact</td></tr><tr><td>SAC</td><td>Semantic Artefact Catalogue</td></tr><tr><td>SKOS</td><td>Simple Knowledge Organization System</td></tr><tr><td>SSH</td><td>Social Sciences and Humanities</td></tr><tr><td>T4.5</td><td>EOSC FAIR-IMPACT project's Task 4.5</td></tr><tr><td>UCS</td><td>Use Cases</td></tr><tr><td>UI</td><td>User Interface</td></tr><tr><td>URI</td><td>Uniform Resource Identifier</td></tr><tr><td>URL</td><td>Uniform Resource Locator</td></tr><tr><td>WP</td><td>EOSC FAIR-IMPACT project's Work Package</td></tr><tr><td>XML</td><td>Extensible Markup Language</td></tr></tbody></table>

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# Executive Summary

Semantic artifacts (SAs) such as terminologies, taxonomies, thesauri, ontologies, and metadata schemas are critical for standardising data representation and documentation, by encapsulating the meaningful knowledge within interoperability frameworks. WP4 “gathers, synthesises and disseminates the materials needed to federate the approach to metadata and ontologies at various organisational and technical levels within EOSC”<fnref n="1" />. It reviews and analyses **semantic artefact (SA)** community practices and governance models within use cases, with the aim to explore SA management, sharing practices for data FAIRification, practices which are important for any data-driven sciences.

This document reports the work of Task 4.5 on FAIR semantic artefacts in use within data repositories through nine use cases, which aim is to demonstrate the impact of FAIR SAs for (meta)LD data repositories. The common objective is to facilitate and encourage the use of SAs to describe and index data. To achieve this objective, discipline specific SAs catalogues are used to give access to semantically grounded, unambiguous controlled terms. The implemented solution is an API-based connector between the (meta)LD data repositories and the SAs catalogues (SACs) which allows data depositors to select terms from selected, relevant SAs to fill in metadata with controlled values. The use of a connector understandably benefits the data repository end users by respectively facilitating metadata filling and improving indexation quality, compared to free text values. The innovation brought by the connector profits in particular managers by preventing them from manipulating SAs (import/export, format transformation) and ensuring that their content is automatically updated. Additionally, end users gain from improved metadata quality and indexing ease.

This document gathers the description of each UC and a short report of their work on connecting a SAC and a (meta)LD repository. It also aims at highlighting a set of recommendations to future implementers of a connector, based on the UC experience:

* Identify SA and SAC capabilities of the community to meet the semantic needs in (meta)LD data repositories and catalogues.

* The organisation responsible for the SAs governance needs to be sustainable and responsive to users feedback.

* Connectors' implementation induces changes in (meta)LD data repositories to exploit SAs potential through adapted search modalities and user interfaces.

* Consider users' feedback at all stages of the connectors' development.

* Take into account that the connection between SACs and (meta)LD data repositories impacts the ecosystem by bringing new interdependencies between information systems.

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<footnote n="1">¹ https://fair-impact.eu/wp4-metadata-and-ontologies</footnote>
# 1. Introduction

## 1.1. Objectives of D4.6 deliverable

This report showcases the use of semantic artefacts (SAs) in (meta)LD data repositories, to produce FAIR research data. Through nine use cases (UC), we attempt to materialise and experiment one of the FAIR principles<fnref n="2" /> related to the use of controlled vocabularies (SAs) to improve metadata FAIRness:

### I2: (Meta)LD data use vocabularies that follow the FAIR principles

When we are describing data or metadata, we often use vocabularies that provide the terms or concepts that are adequate to represent their content. However, if we use vocabularies in our data or metadata, we should make sure that they are also FAIR in their own right so that others, humans or machines, can find, access, interoperate and reuse them. The controlled vocabulary used to describe datasets needs to be documented and resolvable using globally unique and persistent identifiers. This documentation needs to be easily findable and accessible by anyone who uses the dataset.

The objectives of this report are to:

* Demonstrate the feasibility of implementing connectors between SAs and (meta)LD data repositories,

* Define the points of attention for this type of implementation in the different contexts presented.

The UCs were selected from an open call among partners at the start of the project based on their relation to the task perimeter: they needed to have plans to improve the semantic capabilities of their metadata and host and disseminate their scientific data in domain-specific public (meta)LD data repositories.

The UCs tend to be representative with a variety of data ecosystems around RI networks (AnaEE - Analysis and Experimentation on Ecosystems, Data Terra, LifeWatch, IVOA - International Virtual Observatory Alliance) or (meta)LD data repositories (DANS, PHIS - Phenotyping Hybrid Information System, Recherche Data Gouv, Identifiers.org) providing services for storing, managing and sharing data with different technologies. Needs and requirements were thus considered at different scales, from the infrastructure supported by a research lab to that managed by a country. These activities aim at validating the approach with various SAs, involving a diversity of thematics as well as types of SAs (vocabularies, thesaurus, ontology). UCs are in the fields of Life sciences, Archeology, Physics & Technical sciences, Earth & Environmental sciences, and Social Sciences & Humanities.

This document presents use cases from various fields in Section 2 following a harmonised structure of description (Context and Objectives; Challenges and Solutions). Section 3

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<footnote n="2">² https://www.go-fair.org/fair-principles/i2-metadata-use-vocabularies-follow-fair-principles/</footnote>
presents the main use cases feedback, the recommendations and points of attention to implement a connector.

## 1.2. Components of the connection

In a consistent manner, each UC involves at least one (meta)LD data repository, SAs and the SAC hosting and serving them. After an illustration of the whole landscape in Figure 1, this subsection gives a short introduction to each type of asset. Figure 1a represents the connector's basic shape: the (meta)LD data repository includes a connector that allows users to request a term from the SAC through an API call. The API provides terms from various SAs in response to the (meta)LD data repository user. Figure 1b represents the (meta)LD data repositories technologies, the SACs and SAs in use in the UCs. Notice that some elements are common to different UCs (e.g., GeoNetwork, Dataverse, AgroPortal, AGROVOC, etc.).

<figure><img src="image_14.png" /><figcaption>Figure 1a – (Meta)LD data repositories, SAs and SACs relationship.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Figure 1b – (Meta)LD data repositories, SAs and SACs in use in T4.5 use cases.</figcaption></figure>

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### 1.2.1. (Meta)Ldata repositories

(Meta)Ldata repositories are a centralised place to hold (meta)Ldata, share them publicly, and organise them in a logical manner.<fnref n="3" /> They can be multidisciplinary, thematic or specialised on a type of data and are handled at different scales (linked to a research infrastructure, institutional, national or international). Overall, they are built on shared technologies such as Dataverse,<fnref n="4" /> GeoNetwork,<fnref n="5" /> DSpace<fnref n="6" /> and OpenSILEX,<fnref n="7" /> all enabling data to be deposited, described and searched by humans. The data description, i.e. the metadata, in the repository is relevant to the work being reported here. In the present UCs, the metadata of interest include keywords, locations, methods and protocols used to obtain the data, software used to obtain or process data, institutions that have produced data, variable names and descriptions associated with observation data, etc.

The issue is the standardisation of the textual values used to fill them in. On the one hand, the same information may be given differently by several users, and on the other hand, certain values may be ambiguous and not cover the same information depending on the user. This affects the quality of the (meta)Ldata and, above all, the ability to find them in (meta)Ldata repositories. This problem can be partly resolved by using lists of controlled values embedded in the tool, but this solution contributes to information silos, duplication of work and is not optimal regarding management. The use of FAIR SA in this metadata should resolve most of these problems.

### 1.2.3. FAIR Semantic Artefacts (SAs)

SAs are defined as “a machine-actionable and -readable formalisation of a conceptualisation enabling sharing and reuse by humans and machines. These artefacts may have a broad range of formalisation, from loose sets of terms, taxonomies, thesauri to higher-order logics.”<fnref n="8" /> They are used to characterise data produced by scientific research and address different needs from data discovery to reasoning. The terms that make them up are taken from the domain of knowledge, organised and linked together to make the most accurate representation possible. Ideally, these representations are derived from a consensus of the community working on the knowledge domain. Each term can therefore be associated with a definition, synonyms, translations and hierarchical relationships, depending on the needs. In the following, “term” is used in a generic way, also covering “concept” (from thesauri) and “class” (from ontologies).

SAs play a pivotal role in implementing the FAIR principles in the (meta)Ldata repositories, acting on Findability, Interoperability and Reusability, but it became increasingly apparent that the artefacts themselves also needed to adhere to these principles. SACs were created

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<footnote n="3">³ https://datamanagement.hms.harvard.edu/share-publish/data-repositories</footnote>

<footnote n="4">⁴ https://dataverse.org/</footnote>

<footnote n="5">⁵ https://geonetwork-opensource.org/</footnote>

<footnote n="6">⁶ https://dspace.org/</footnote>

<footnote n="7">⁷ http://opensilex.org/</footnote>

<footnote n="8">⁸ Le Franc, Y., Parland-von Essen, J., Bonino, L., Lehväslaiho, H., Coen, G., & Staiger, C. (2020). D2.2 FAIR Semantics: First recommendations (1.0). FAIRsFAIR. https://doi.org/10.5281/zenodo.5361930</footnote>
to host, expose and share SAs and prove to be relevant solutions to augment their FAIRness providing means for their description, selection, evaluation, trustworthiness and interconnection.<fnref n="9" />

### 1.2.4. Semantic artefacts catalogues (SACs)

Semantic artefacts Catalogues (SACs) is a broader term that includes libraries, registries, listing or repositories of SAs and also platforms often named terminology/vocabulary service/server. Indeed, SAs are distributed in various formats, sizes, structures, and across overlapping domains, creating a need for unified platforms that can receive, host, serve, align, and enable their reuse in diverse communities and applications.<fnref n="10" /> The SACs used in the UCs presented here are domain-specific and based on either the OntoPortal or the SKOSMOS technologies. They are particularly useful for their role as aggregators of SAs by domain, making it possible to offer users a single interface and to highlight the information associated with SAs and terms in a unified way. In addition, SACs give access to their content via APIs. Yet, these APIs remain heterogeneous and often poorly documented, which should soon be leveraged by the standardisation of APIs for SACs, namely MOD-API<fnref n="11" />, developed in FAIR IMPACT WP4 and promoted through the open calls in WP2.

## 2. Use Cases overview

This section provides an overview of each UC summarised in Table 1. The (meta)data repositories and SACs are defined in each UC context and their challenges are presented. Great care is taken to identify the semantic needs of the community, as well as existing solutions to address them. Then the connectors or SA implementation/uses are detailed as solutions. Finally, the resulting challenges are exposed.

For each UC, a more detailed description is available on the FAIR-IMPACT website<fnref n="12" /> and also published on the FAIR-IMPACT Zenodo community.<fnref n="13" />

<table><caption>Table 1 – FAIR-IMPACT Task 4.5 UCs.</caption><thead><tr><td>Use case (scientific domain)</td><td>Data repository</td><td>Semantic Artefact catalog</td><td>Use case description</td><td>Leader organisation</td></tr></thead><tbody><tr><td>PHIS-AgroPortal (Life sciences)</td><td>Phenotyping Hybrid Information System</td><td>AgroPortal</td><td>Enabling interoperability between AgroPortal and PHIS information system data repository for enhanced phenomics data annotation and exchange</td><td>INRAE</td></tr></tbody></table>

<figure><img src="image_18.png" /></figure>

<footnote n="9">⁹ Jonquet, C., & GRAU, N. (2025). D4.2 - FAIR semantic artefact lifecycle from engineering, to sharing (v1.0). Zenodo. https://doi.org/10.5281/zenodo.14643279</footnote>

<footnote n="10">¹⁰ Jonquet, C., & Grau, N. (2024). M4.4 - Review of Semantic Artefact Catalogues and guidelines for serving FAIR semantic artefacts in EOSC (1.0). Zenodo. https://doi.org/10.5281/zenodo.12799796</footnote>

<footnote n="11">¹¹ Wilson, A., & Jonquet, C. (2024). D4.3 - Specification of shared metadata description of semantic artefacts and their catalogues including common reference API. Zenodo. https://doi.org/10.5281/zenodo.12579779</footnote>

<footnote n="12">¹² https://fair-impact.eu/</footnote>

<footnote n="13">¹³ https://zenodo.org/communities/fair-impact/records</footnote>
<table><thead><tr><td>Use case (scientific domain)</td><td>Data repository</td><td>Semantic Artefact catalog</td><td>Use case description</td><td>Leader organisation</td></tr></thead><tbody><tr><td>LifeWatch DP&amp;MDC-EcoPortal (Life sciences, ecology)</td><td>LifeWatch Metadata catalogue &amp; Data Portal</td><td>EcoPortal</td><td>Improving ecological (meta)data FAIRness through semantic services: integration of EcoPortal in LifeWatch Italy new platforms</td><td>LifeWatch ERIC &amp; CNR</td></tr><tr><td>DANS Data Stations (SSH, Physics &amp; Technical Sciences, Life Sciences, Archeology)</td><td>DANS data repositories</td><td>SKOSMOS Datastations Vocabularies</td><td>FAIR vocabularies in DANS Data Stations</td><td>DANS Data Stations</td></tr><tr><td>EaSyData-EarthPortal (Earth &amp; environmental sciences)</td><td>Earth System Data repository</td><td>EarthPortal</td><td>Enhance the semantic functionality of the national Earth &amp; Environmental Data Repository by integrating it with the EarthPortal</td><td>Data Terra (CNRS)</td></tr><tr><td>Recherche Data Gouv-AgroPortal (Life sciences)</td><td>Recherche Data Gouv (Data INRAE)</td><td>AgroPortal</td><td>Leveraging AgroPortal ontologies to ease metadata completion and data discovery in Data INRAE</td><td>INRAE</td></tr><tr><td>IVOA Astronomy (Physical &amp; technical sciences)</td><td>NA</td><td>Astronomy OntoPortal</td><td>Semantic Artefacts Alignment for Improving Interoperability in Astronomy</td><td>Observatoire de Paris</td></tr><tr><td>AnaEE-AgroPortal (Life Sciences)</td><td>SemData</td><td>AgroPortal</td><td>AnaEE use case: Semantic interoperability in ecosystem studies</td><td>INRAE</td></tr><tr><td>ArcheoAMU (Archeology)</td><td>EPICHERCHELL</td><td>PACTOL</td><td>Not available</td><td>Aix Marseille University</td></tr><tr><td>Identifiers.org-ROR.org (Life sciences)</td><td>identifiers.org</td><td>ROR.org</td><td>EMBL-EBI - Providing harmonized information about organisations in identifiers.org using ROR registry</td><td>EMBL-EBI</td></tr></tbody></table>

## 2.1. PHIS-AgroPortal

Enabling interoperability between AgroPortal and PHIS information system data repository for enhanced phenomics data annotation and exchange

Card: https://doi.org/10.5281/zenodo.14192644

Scientific domain: Agricultural Sciences

Leading organisation: INRAE

Contributors: Anne Tireau, Arnaud Charleroy, Llorenç Cabrera-Bosquet, Clement Jonquet (INRAE)

Code and documentation: https://github.com/OpenSILEX/opensilex

### 2.1.1. Context and objectives

PHIS<fnref n="14" /> is an information system used to integrate, organise and manage multi-scale and multi-source phenomic data. Plant phenomics is the discipline of biology related to the measurement and analysis of observable physical and biochemical characteristics of plants as they interact with their environment. This discipline produces a large amount of data of a very heterogeneous nature: environmental data, phenotypic variables, images, and metadata, obtained from multiple sources: field and greenhouse conditions and heterogeneously distributed. Assembling and organising datasets is challenging.

<figure><img src="image_19.png" /></figure>

<footnote n="14">¹⁴ Phenotyping Hybrid Information System: http://www.phis.inrae.fr/</footnote>
PHIS serves as a solution to address this challenge. It uses the generic OpenSILEX<sup>15</sup> technology, which is based on an ontology and is developed and maintained by INRAE-MISTEA. This software offers a management method for the exploitation of semantics, the production of FAIR data and proposes an architecture adapted to heterogeneity and large increasing volumes of data.

At this step, one of the main objectives in PHIS is the accurate identification and definition of measured variables. Indeed, due to the large variability of methods, study's objects, and users, the PHIS community is encouraged to use the *Entity-Characteristic-Method-Unit* model to facilitate the standardisation of measured variables (Fig. 2). For each variable in PHIS, the different components are mapped (when possible) to existing reference ontologies.

To build each “block” of these measured variables, PHIS users are encouraged to use as much as possible discipline-reference ontologies such as Plant Ontology,<sup>16</sup> Crop Ontologies and SOSA ontology.<sup>17</sup> These ontologies are hosted in AgroPortal<sup>18</sup> (SAC for Agri-food SAs) but, until recently, fetching ontology terms was a manual process, which considerably prevented and slowed down the reuse of standard ontology terms.

The goal of this UC was to build a connector between PHIS and AgroPortal to ease the reuse of ontology terms within PHIS.

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<footnote n="15"><sup>15</sup> http://opensilex.org/sandbox/app/</footnote>

<footnote n="16"><sup>16</sup> https://agroportal.lirmm.fr/ontologies/PQ</footnote>

<footnote n="17"><sup>17</sup> https://agroportal.lirmm.fr/ontologies/SOSA</footnote>

<footnote n="18"><sup>18</sup> https://agroportal.lirmm.fr/</footnote>
Figure 2 - Entity-Characteristic-Method-Unit model for representing scientific variables used in PHIS, and link to reference ontologies.

### 2.1.2. Implemented solutions and Challenges

The connector (Fig. 3) allows users to search and grab from AgroPortal either a term URI and its related information (name, synonyms, definition) or create a new term within PHIS. It's currently developed within the generic OpenSILEX technology by INRAE-MISTEA and INRAE-LEPSE. The users can describe their measured observations as variables, using the *Entity-Characteristic-Method-Unit* model through the PHIS web interface. Terms and related information are retrieved from AgroPortal thanks to its API and all the descriptions are stored in PHIS in RDF. SKOS relations allow mapping between variable descriptions and other domain ontologies. The connector provides a semi-automatic ontology term fetching tool including an ergonomic search interface, pre-selected SA and a mapping functionality (that allows users to specify the links between terms from different ontologies). The connector provides a number of benefits to PHIS users and contributes to AgroPortal's content, notably reducing time and effort and improving interoperability between respectively datasets and ontologies.

Some challenges still need to be addressed: in the medium term, the connector will open up the possibility of proposing content (e.g., terms and mappings) to SAs hosted in AgroPortal. Finally, the connector would be made completely generic to work with any instance of OntoPortal and PHIS / OpenSILEX.

<figure><img src="image_22.png" /></figure>

Funded by
the European Union
<figure><img src="image_23.png" /><figcaption>Figure 3 - Screenshot of the connector between PHIS and AgroPortal.

The connector allows: (i) searching terms and filtering for specific-domain ontologies, (ii) selecting and creating new terms and (iii) mapping to related terms.</figcaption></figure>

## 2.2. LifeWatch DP&MDC-EcoPortal

Improving ecological (meta)LData FAIRness through semantic services: integration of EcoPortal in LifeWatch Italy new platforms

Card: https://doi.org/10.5281/zenodo.14186722

Key topic: Interoperability, Metadata & Ontologies, Metrics, Certification and Guidelines

Scientific domain: Ecology and biodiversity domain

Leading organisation: LifeWatch ERIC (CNR)

Contributors: Enrica Nestola (CNR), Parham Ramezani (LifeWatch ERIC), Martina Pulieri (University of Salento) and Ilaria Rosati (CNR)

Code and documentation: Not available at the time of writing; implementation through ongoing national project<fnref n="19" />

<figure><img src="image_24.png" /></figure>

<footnote n="19">¹⁹ EU - Next Generation EU Mission 4 “Education and Research” - Component 2: “From research to business” - Investment 3.1: “Fund for the realisation of an integrated system of research and innovation infrastructures” - Project IR0000032 - ITINERIS - Italian Integrated Environmental Research Infrastructures System - CUP B53C22002150006 - https://itineris.cnr.it/about-itineris/the-project/</footnote>
### 2.2.1. Context and objectives

LifeWatch ERIC<fnref n="20" /> is an European Research Infrastructure Consortium providing e-Science research facilities to scientists investigating the biodiversity and ecosystem domain. LifeWatch Italy, the Italian Distributed Center of LifeWatch ERIC, contributes in-kind to its functioning. Coordinated by the National Research Council of Italy (CNR), LifeWatch Italy activities focuses on the management of biodiversity and ecosystem research data, as well as the development of tools and services for their sharing, integration and analysis. Among the different services offered, LifeWatch Italy provides Data Portal<fnref n="21" /> and Metadata Catalogue.<fnref n="22" />

The Data Portal is a data repository, based on DSpace,<fnref n="23" /> a free and open source software commonly used as an institutional repository. It makes it possible to share and re-use (meta)LData by scientists, including environmental researchers and the broader scientific community, in a FAIR-compliant way. The data schema is based on the Darwin Core standard and controlled vocabularies. The metadata schema associated with each dataset is the Ecological Metadata Language profile LifeWatch (EML 2.2.0²⁴; Vaira et al., 2022²⁵).

The Metadata Catalogue is an information management system based on GeoNetwork 4.2.2,<fnref n="26" /> a catalogue application to manage spatially referenced resources. The Metadata Catalogue enables access to several resources from a variety of providers through descriptive metadata, promoting the information exchange and sharing among organizations and researchers. The metadata of the resources accessible through the Metadata Catalogue are based on two main standards:

* ISO 19139²⁷ (VREs, services, workflows, scripts and research sites);

* EML 2.2.0 (datasets).

LifeWatch Italy has recently worked to enhance FAIRness by integrating the Data Portal and Metadata Catalogue with Ecoportal,<fnref n="28" /> the LifeWatch ERIC SAs catalogue for ecology and related domains. The integration aimed to ensure the annotation and description of (meta)LData using SAs. EcoPortal uses the OntoPortal technology that supports the scientific community in the creation and management of SA and their use to harmonise (meta)LData.

<figure><img src="image_25.png" /></figure>

<footnote n="20">²⁰ https://www.lifewatch.eu/</footnote>

<footnote n="21">²¹ https://data.lifewatchitaly.eu/search</footnote>

<footnote n="22">²² https://metadatacatalogue.lifewatchitaly.eu/</footnote>

<footnote n="23">²³ https://dspace.lyrasis.org/</footnote>

<footnote n="24">²⁴ https://eml.ecoinformatics.org/</footnote>

<footnote n="25">²⁵ Vaira, L., Fiore, N., & Rosati, I. (2022). LifeWatch ERIC Application Profiles (Version 1). LifeWatch ERIC. https://doi.org/10.48372/8528-9Z45</footnote>

<footnote n="26">²⁶ https://geonetwork-opensource.org/</footnote>

<footnote n="27">²⁷ https://www.iso.org/standard/67253.html</footnote>

<footnote n="28">²⁸ https://ecoportal.lifewatch.eu/</footnote>
### 2.2.2. Implemented solutions and Challenges

The main challenge addressed in this UC is enhancing metadata annotation with FAIR SA. The integration between EcoPortal and the Data Portal and Metadata catalogue of LifeWatch Italy provides improved access to SAs and facilitates data discovery, annotation, and machine-actionability of (meta)data.

In both the Data Portal and the Metadata Catalogue, users are able to search through the editing interface concepts and classes from SAs published within EcoPortal. REST API access points are used to retrieve SA term URIs, labels and definitions.

In the Data Portal, the integration permits semantic annotation, especially for the "keywords", "software" and "protocol" attributes. Users are able to access and search concepts and classes from EcoPortal SAs through the interface, directly in the metadata field. After user selection, term-related information is stored. A similar approach is followed for the attributes of the data table but it differs from the previous one because the definition of concepts or classes is also retrieved and is reported inside the element set.

In the Metadata Catalogue, users can search for concepts and classes directly in the search box. Thanks to the autocomplete function and to the connection with EcoPortal, users can retrieve data from external resources (i.e. EcoPortal). Possible values are suggested, according to the text entered in the field, then users can select relevant terms.

<figure><img src="image_26.png" /></figure>

Funded by
the European Union
<figure><img src="image_27.png" /><figcaption>Figure 4 - LifeWatch Italy Data Portal Keywords wizard.</figcaption></figure>

In this figure, the search bar is filled in by the term “biodiversity”, the results associated with this term and retrieved in various thesauri through the EcoPortal API. Many terms, according to the search, are proposed to the user, who can make a choice by consulting the information associated with the terms via the icon “i”.

<figure><img src="image_28.png" /><figcaption>Figure 5 - LifeWatch Italy Data Portal Keywords wizard.</figcaption></figure>

Keyword information associated and stored in the Data Portal: SA name (keyword thesaurus), term or concept (keyword), value label, value URI, property label and property URI. The property URI represents a semantically well-defined concept that one wants to apply to an element within EML and they are typically drawn from formal ontologies.

<figure><img src="image_29.png" /></figure>
## 2.3. DANS Data Stations

### FAIR vocabularies in DANS Data Stations

Card: https://doi.org/10.5281/zenodo.14671309

Scientific domains: Archeology, Social Sciences and Humanities, Life Sciences, Physical and Technical Sciences.

Leading organisation: DANS-KNAW

Contributors: Vyacheslav Tykhonov, Pascal Flohr, Andrea Schamhorst (DANS)

Code and documentation: https://github.com/gdcc/dataverse-external-vocab-support

James D. Myers, & Vyacheslav Tykhonov. (2023). A Plug-in Approach to Controlled Vocabulary

Support in Dataverse. https://doi.org/10.5281/zenodo.8133723

### 2.3.1. Context and objectives

Data Archiving and Networked Services (DANS)<fnref n="29" /> is the Dutch national expertise center and repository for research data. Its main objective is to facilitate and improve the long-term storing and sharing of research data and enhance their reusability. As shown in Figure 6, the DANS Data stations platform is composed of four discipline-specific data-stations all based on Dataverse software (Archaeology, Social sciences and Humanities, Life Sciences and Physical and Technical Sciences). For these domain-specific research data repository services, DANS executes the data curation and ensures long-term sustainability (e.g., with CoreTrustSeal certification<fnref n="30" />). In addition, DANS offers an institutional platform called DataverseNL,<fnref n="31" /> which currently has instances from all Dutch universities as well as numerous other institutions. To make the connection with designated communities for the DANS data services stronger and more explicit, and also to improve the FAIRness of its repository service, DANS moved from one generic repository, Electronic Archiving System (EASY), to the above mentioned four discipline-specific ‘Data Stations’:

* Archaeology<fnref n="32" />

* Social Sciences and Humanities<fnref n="33" />

* Life Sciences<fnref n="34" />

* Physical and Technical Sciences<fnref n="35" />

The implementation of discipline-specific archives on the new technical platform has facilitated the addition of discipline-specific metadata with their own specific controlled vocabularies, in addition to the already present rich generic metadata (mainly Dublin Core based).

<figure><img src="image_30.png" /></figure>

<footnote n="29">²⁹ Data Archiving and Networked Services, https://dans.knaw.nl/en/; Wals, H. (Ed.) (2020). Focus on FAIR: DANS 2021-2025. https://pure.knaw.nl/ws/portalfiles/portal/1742248224/03_DANS_Strategy_2021-2025_Focus_on_FAIR.pdf</footnote>

<footnote n="30">³⁰ https://cds.unistra.fr/about/cts/</footnote>

<footnote n="31">³¹ https://dataverse.nl/</footnote>

<footnote n="32">³² https://archaeology.datastations.nl</footnote>

<footnote n="33">³³ https://ssh.datastations.nl</footnote>

<footnote n="34">³⁴ https://lifesciences.datastations.nl</footnote>

<footnote n="35">³⁵ https://phys-techsciences.datastations.nl</footnote>
<figure><img src="image_31.png" /><figcaption>Figure 6 - DANS Data-stations organisation and instances.</figcaption></figure>

For several generic metadata fields and for many of the discipline-specific metadata fields, the depositor chooses values from a dropdown menu or automatic completion field, as shown on Figure 7.

<figure><img src="image_32.png" /><figcaption>Figure 7 - The process of term selection in DANS Data Stations</figcaption></figure>

The lists, or SAs, underlying these values are mostly hard-encoded in the standard Dataverse installation. For the discipline-specific metadata blocks, the values are derived from internationally used vocabularies, for instance in the case of SSH and Archeology. These vocabularies are available in Data Stations through a SAC connector developed by DANS earlier<fnref n="36" /> and based on a SKOSMOS instance deployed as a part of its own infrastructure. This connector provides users an easy way to fill out a dropdown list of the metadata elements

<figure><img src="image_33.png" /></figure>

<footnote n="36">³⁶ James D. Myers, & Vyacheslav Tykhonov. (2023). A Plug-in Approach to Controlled Vocabulary Support in Dataverse. https://doi.org/10.5281/zenodo.8133723</footnote>
with third-party vocabularies.<fnref n="37" /> The process of adding domain-specific metadata and vocabularies is still ongoing for the Life Sciences and Physical and Technical Sciences.

### 2.3.2. Implemented solutions and Challenges

Some challenges still need to be addressed in this UC. The first concerns the availability and interoperability of already connected SSH- and archeology-specific vocabularies and, the second concerns the connection of other vocabularies for domains of the Life Sciences and Physics and Technical Sciences. There is also no good way to track the concept drift when concepts are changing their meaning within time and therefore links to them should be verified and adjusted.

Concerning the general rich metadata from Data Stations, not all depositors use available metadata fields, especially keywords that are often free text, for various reasons. The problem is addressed by the connection between Data Stations and third-party controlled vocabularies, as already said. But, there is an issue to take into account users' needs and feedback. To give an example, in the Archeology data station, specific metadata fields are used. Some of them are free text, but many of them relate to a controlled vocabulary (Basic Archaeological Register - ABR)<fnref n="38" />,<fnref n="39" /> As a possible solution, DANS can work on a more clear guide on why and how to use the metadata fields and their controlled vocabularies, and explain to users how they can benefit from it. Another issue is multilinguality support with for instance Archeology terms taken from a controlled vocabulary which is only available in Dutch (ABR). Although, by means of the ARIADNE<fnref n="40" /> mappings to other vocabularies have been executed, those links have not been fully implemented. In the Life Sciences and Physical and Technical Sciences Data Stations, currently there are no specific connected vocabularies in use.

The implemented connector between Dataverse and Ontoportal can bring to DANS Data Stations' users many more options to better and more richly describe their data. For instance, there is a lot of potential to use the many pertinent SAs that are already present in OntoPortal instances but absent from SKOSMOS vocabularies to improve the richness and findability of metadata. One of the goals of this UC was to develop a connector between OntoPortal and Dataverse instances, which was achieved by the Recherche Data Gouv-AgroPortal UC.

In general, the implementation of connectors to make more vocabulary sources available for search and linkage, contributes to increased FAIRness but also comes with other questions<fnref n="41" />: how are updates to the original vocabularies dealt with? How to connect several controlled vocabularies to a single data repository/metadata element? And how are the semantic

<figure><img src="image_34.png" /></figure>

<footnote n="37">³⁷ https://vocabs.datastations.nl/en/</footnote>

<footnote n="38">³⁸ https://english.cultureelerfgoed.nl/</footnote>

<footnote n="39">³⁹ Lauwerier, Roel. (2017). R.C.G.M. Lauwerier 2017: Knowledge for informed choices. Tools for decision making in archaeological heritage management in the Netherlands. ISBN/EAN: 9789057992773</footnote>

<footnote n="40">⁴⁰ http://legacy.ariadne-infrastructure.eu/</footnote>

<footnote n="41">⁴¹ Knowledge Organization Systems (KOS) in the Semantic Web. A Multi-Dimensional Review (Chapter 3). In: Smiraglia, R.P., Scharnhorst, A. (eds) (2021) Linking Knowledge. Nomos doi.org/10.5771/9783956506611-34</footnote>
terms used in the repository index? How are they exported in the various export flavors or
served by APIs? DANS is already working on the possible solutions of those issues and will
share them with the Dataverse community.

## 2.4. EaSyData-EarthPortal

Enhance the semantic functionality of the national Earth & Environmental Data Repository by
integrating it with the EarthPortal

Card: https://doi.org/10.5281/zenodo.14186507

Scientific domain: Earth and environmental sciences

Leading organisation: Data Terra (CNRS)

Contributors: Guillaume Alviset (Data Terra/CNRS), Christelle Pierkot (Data Terra/CNRS), Hélène

Bressan (BRGM)

Code and documentation: https://tinyurl.com/Connector-EaSyData-EarthPortal

### 2.4.1. Context and objectives

Data Terra<fnref n="42" /> is a research infrastructure (RI) to access, process and combine multi-source
data from Earth and Environmental research. Earth System Data Repository (Easy Data)<fnref n="43" /> is
the French repository for long-tail data relating to these domains. “Long-tail” or “orphan”
data represent a significant proportion of the data generated by public research into the
Earth system and the environment. These data are not organised and/or shared and are not
systematically archived in dedicated repositories, and not sufficiently documented. EaSy
Data is a service provided by Data Terra and developed by the French Geological and Mining
Research Bureau (BRGM).<fnref n="44" /> EaSy Data will make datasets from other thematic data
repositories (e.g., Seanoe,<fnref n="45" /> Data Indores<fnref n="46" />) visible, and data stored in EaSy Data will be
referenced at the national level in the Recherche Data Gouv<fnref n="47" /> repository. Easy Data uses
Geonetwork<fnref n="48" /> to store metadata, and the ISO 19115 standard to describe them. An ad-hoc
interface has been specially developed for the deposit functionality of the data repository.
The first goal of this UC is to improve EaSyData's datasets FAIRness, which can be improved
by better referencing of these datasets correlated with the advanced search improvement,
thanks to metadata completion.

To complete metadata, such as title, topics and keywords, SAs are already used in Easy Data:
EaSy Data Thesauri are community controlled vocabularies defined by scientists and
maintained in a registry based on UKGovLD.<fnref n="49" /> This technology has limited services to
administer and suggest new keywords and contains incomplete SAs (original vocabularies
from Data Terra).

<figure><img src="image_35.png" /></figure>

<footnote n="42">⁴² https://www.data-terra.org/</footnote>

<footnote n="43">⁴³ https://www.easydata.earth/#/public/home</footnote>

<footnote n="44">⁴⁴ https://www.brgm.fr/en</footnote>

<footnote n="45">⁴⁵ https://www.seanoe.org/</footnote>

<footnote n="46">⁴⁶ https://data.indores.fr/</footnote>

<footnote n="47">⁴⁷ https://recherche.data.gouv.fr/en</footnote>

<footnote n="48">⁴⁸ https://geonetwork-opensource.org/</footnote>

<footnote n="49">⁴⁹ https://github.com/ukgovld</footnote>
### 2.4.2. Implemented solutions and Challenges

<figure><img src="image_36.png" /><figcaption>Figure 8 - User interface showing use of the EaSy Data - EarthPortal connector.
An additional button has been added to the Keywords field, providing the user the ability to query the EarthPortal for term suggestions and pick the relevant ones.</figcaption></figure>

In this UC, EarthPortal,<fnref n="50" /> a SAC dedicated to earth sciences created within T4.2, was used to add vocabularies, allowing users to access other resources and improve the semantic capabilities of EaSy Data. EarthPortal uses the OntoPortal technology which provides the ability to store, share and manage SAs. The use of this technology provides access to more relevant SAs (Theia/Ozcar,<fnref n="51" /> AerisThesaurus,<fnref n="52" /> GCMD,<fnref n="53" /> etc.). External applications can use SAs stored in EarthPortal through its Web service REST API. Moreover, EarthPortal provides tools such as the Annotator (makes term suggestions based on text input), the Recommender (suggests relevant SAs based on text input) and Mappings (generates, stores and displays mappings between SAs), that can be used in EaSyData. The goal is that users can add terms, from SAs, directly into EaSyData metadata form, as keywords and topics. Users should be able to check various SAs from the EarthPortal catalog, and to use the Annotator service to suggest new terms from the user-written abstract to enrich the metadata with new terms. Using EarthPortal's allows the SAs to be arranged in a domain-specific way and its API improves the relevance of suggested terms.

<figure><img src="image_37.png" /></figure>

<footnote n="50">⁵⁰ https://earthportal.eu/</footnote>

<footnote n="51">⁵¹ https://earthportal.eu/ontologies/TOZ</footnote>

<footnote n="52">⁵² https://earthportal.eu/ontologies/AERIS_THES</footnote>

<footnote n="53">⁵³ https://earthportal.eu/ontologies/GCMD</footnote>
## 2.5. Recherche Data Gouv-AgroPortal

Leveraging AgroPortal ontologies to ease metadata completion and data discovery in Data INRAE

Card: https://doi.org/10.5281/zenodo.14191077

Scientific domain: Life Sciences

Leading organisation: INRAE

Contributors: Sophie Aubin (INRAE), Carmen Corre (INRAE), Dimitri Szabo (INRAE), Clement

Jonquet (INRAE)

Code and documentation :

https://github.com/gdcc/dataverse-external-vocab-support/blob/main/examples/keywordsUsingOntoPortal.md

### 2.5.1. Context and objectives

Recherche Data Gouv is the french data repository, created in 2022, described as "An ecosystem for sharing and opening research data" for French scientific research and is organised with 4 Institutional reference centers (institutional Research Data Management - RDM - support), 5 thematic reference centers (specific scientific domains support) and a common, generic repository offered by default to scientists whose institutions do not yet have an institutional space.

This repository aims to host data from all scientific domains and institutional data. It uses the Dataverse<fnref n="54" /> technology (Open source research data repository software). This UC focuses on Data INRAE,<fnref n="55" /> the institutional collection (within the Recherche Data Gouv repository) for agri-food and environmental research data maintained by INRAE.

To describe and contextualise data, the repository's softwares such as Dataverse allow associated metadata to the dataset. The metadata field "keyword" is originally textual information for human readers. It can also be used to search for datasets (or collections) on data repositories and add an extra level of description to the dataset.

The depositors can enter a term, from a controlled vocabulary or not. Without connector, to fill in a term from a controlled vocabulary, they needed to search on their own a vocabulary (INRAE Thesaurus<fnref n="56" /> is recommended), find and fill in the name of vocabulary, the vocabulary URL and the term URI. This procedure is complex for the uninitiated and time-consuming because of the opening of numerous web pages. This resulted in Data INRAE containing few keywords, which rarely belonged to controlled vocabularies.

The first goal was to facilitate access to controlled vocabularies: AgroPortal<fnref n="57" /> is a SAC that permits sharing ontologies and other types of SAs dedicated to agri-food. AgroPortal is based on the generic OntoPortal<fnref n="58" /> technology. The SAC permits to "get feedback and

<figure><img src="image_38.png" /></figure>

<footnote n="54">⁵⁴ https://dataverse.org/</footnote>

<footnote n="55">⁵⁵ https://entrepot.recherche.data.gouv.fr/dataverse/inrae</footnote>

<footnote n="56">⁵⁶ https://thesaurus.inrae.fr/thesaurus-inrae/fr/</footnote>

<footnote n="57">⁵⁷ https://agroportal.lirmm.fr/</footnote>

<footnote n="58">⁵⁸ https://ontoportal.org/</footnote>
suggestion", and "precisely describe ontology with relevant metadata". The last point provides a homogeneous and qualitative description of SAs and the terms they contain.

A connector between AgroPortal and Data INRAE has therefore been developed and set up in this UC to seamlessly fill in keywords.

### 2.5.2. Implemented solutions and Challenges

This UC aims to develop a connector in Data INRAE to be able to use SAs from AgroPortal in a user-friendly way. This connector allows the dataverse manager and data depositors to select relevant SAs from AgroPortal (cf: Fig. 9).

<figure><img src="image_39.png" /><figcaption>Figure 9 - Representation of Recherche Data Gouv-AgroPortal connector.
In the metadata record, the search bar allows users to type a keyword, here "association". With autocompletion, thanks to the connector, AgroPortal API retrieves term-related information, such as term, URI, vocabulary, URL, synonyms and, traduction.</figcaption></figure>

When the data depositor picks keywords from ontologies and thesauri that have been connected in the metadata form, Dataverse records the URIs, original vocabulary and term's related information (synonyms and translations) for this keyword.

This information is then used to enrich metadata exports and data searches with translations and synonyms. This connector must ease the work of describing and searching datasets while remaining intuitive and fast-responding.

<figure><img src="image_40.png" /></figure>
In the future, the main challenge is to keep improving the connector according to users' feedback. Some developments are planned, concerning the information associated with terms (synonymous, translations and hierarchical). Which information do depositors require to select a term as a keyword? Which information is useful for data users? Various user tests have been set up during the connector development, and the communication process around the connector availability insists on the feedback's consideration.

Like all developments carried out within a project timeframe, the maintenance after the project has to be planned in order to avoid the code becoming obsolete. Other domain specific OntoPortal instances (MatPortal, BioPortal, EcoPortal, etc.) could be connected with other institutional or thematic parts of Recherche Data Gouv, at the Dataverse' collection level. The sustainability challenge also concerns the genericity and the reusability of the connector between Dataverse and OntoPortal technologies. It also involves adapting to changing technologies.

## 2.6. IVOA Astronomy

Semantic Artefacts Alignment for Improving Interoperability in Astronomy

Card: https://doi.org/10.5281/zenodo.14192464

Scientific domain: Astronomy, Heliophysics, Solar System Sciences

Leading organisations: Observatoire de Paris

Contributors: Baptiste Cecconi, Laura Debisschop (Observatoire de Paris)

Code and documentation: This UC reused the OntoPortal existing framework

https://ontoportal.github.io/documentation/

### 2.6.1. Context and objectives

International Virtual Observatory Alliance (IVOA)<fnref n="59" /> is dedicated to "facilitating the international coordination and collaboration necessary for the development and deployment of the tools, systems and organisational structures necessary to enable the international utilisation of astronomical archives as an integrated and interoperating virtual observatory." The astronomy community is composed of three main sub-communities, with different levels of semantic organisations:

* Celestial astronomy (objects are referenced to with their sky coordinates, e.g., stars, galaxies, etc). This sub-community is organised around the IVOA, which is maintaining an operational interoperability framework used by data repositories and science application platforms. In this community, SAs are composed of terms (vocabularies) and schemas (data models). The Semantics Working Group of the IVOA is managing the vocabularies used in the IVOA standards. The vocabularies are available from a dedicated web page<fnref n="60" /> and are accessible using IVOA or RDF tooling.

* Planetary sciences (the study of the Solar System objects, e.g., planets, comets, asteroids, etc). In this sub-community, teams studying the planetary surfaces are

<figure><img src="image_41.png" /></figure>

<footnote n="59">⁵⁹ https://ivoa.net</footnote>

<footnote n="60">⁶⁰ https://ivoa.net/rdf</footnote>
organised around Open Geospatial Consortium (OGC)<fnref n="61" /> and International Planetary Data Alliance (IPDA).<fnref n="62" /> They propose an advanced data archiving information model for planetary exploration datasets, respectively.

* Heliophysics (the study of the Sun, the plasma environments throughout the Solar System). This sub-community is organised around the International Heliophysics Data Environment Alliance (IHDEA),<fnref n="63" /> which is proposing a set of tools and standards for finding and accessing datasets. It's semantically organised around:

* SPASE ontology: [Space Physics Archive Search and Extract](https://space-physics.org/search-and-extract/),

* SOLARNET: Metadata Recommendations for Solar Observations (keywords).

The initiative/project to bring these communities closer started only recently, with the ongoing development of two common SAs: a vocabulary for "observation facilities" and another one for "reference frames".

The goal of this UC is to improve the semantic interoperability between these three sub-communities and improve SAs FAIRness. This may be allowed by bringing all SAs in the same catalogue.

### 2.6.2. Implemented solutions and Challenges

To address this goal, OntoPortal technology is used. This technology allows to gather SAs from the different sub-communities in a unique SAC that would be called Astronomy OntoPortal and for which a prototype has been deployed during FAIR-IMPACT.<fnref n="64" /> At this time, this SAC includes 42 SAs from the celestial astronomy and the heliophysics communities.

The main challenge of this UC is to produce FAIR SAs that can be ingested into the OntoPortal-Astro, which means adopting RDF, OWL or SKOS representation languages. The challenge is due to the different organisation' levels in the 3 sub-communities. In heliophysics and planetary sciences, SAs are in diverse forms : lists of terms in XML schemas (SPASE schema) or unformatted, requiring frequent release of new versions and updates. In celestial astronomy, in the IVOA context, the community is semantically organised better. All their SAs (lists of terms) were available on a web page and a RDF version of each SA was available on the respective landing pages. But the IVOA vocabularies are managed according to an IVOA recommendation,<fnref n="65" /> which specifies how to design the RDF version of the IVOA SA, with an adhoc subset of SKOS and OWL properties. It means that these SAs need to be transformed to better align with the Semantic Web standards. These SAs are managed and updated thanks to users' proposals and community discussion. The change of

<figure><img src="image_42.png" /></figure>

<footnote n="61">⁶¹ https://www.ogc.org/</footnote>

<footnote n="62">⁶² https://planetarydata.org</footnote>

<footnote n="63">⁶³ https://ihdea.net</footnote>

<footnote n="64">⁶⁴ http://voparis-ontoportal-dev.obspm.fr/</footnote>

<footnote n="65">⁶⁵ https://ivoa.net/documents/Vocabularies</footnote>
accommodation will also require an update of the SA management practices (e.g., new version of the Vocabulary in the VO recommendation<fnref n="66" />).

In a second step, the OntoPortal technology could be used to improve astronomy research data. The SAs can be plugged into smart Data Management Plan tooling (URIs for terms, rather than free text) or search interfaces, and in turn allow more refined queries and selections on data discovery interfaces. These aspects are expected to be developed later, in other projects as OSTrails.<fnref n="67" />

Finally, based on the same principle and issues as this UC has, the astronomy community should explore interoperability with neighboring fields such as the Earth and environmental sciences, or particle physics.

## 2.7. AnaEE-Agroportal

### AnaEE use case: Semantic interoperability in ecosystem studies

**Card:** https://doi.org/10.5281/zenodo.14673208

**Scientific domain:** Earth and environmental sciences

**Leading organisation:** INRAE

**Contributors:** Christian Pichot, Philippe Clastre, Brett Choquet, Clement Jonquet (INRAE)

**Code and documentation:** Soon available

### 2.7.1. Context and objectives

The Analysis and Experimentation on Ecosystems (AnaEE)<fnref n="68" /> Research Infrastructure offers experimental facilities for studying ecosystems and biodiversity. One of its goal is to help AnaEE RI users to generate interoperable datasets and associated metadata thanks to a 3 step workflow:

* Observed/measured variables and their acquisition contexts are modeled as a generic graph based on the ontology.

* These data are annotated and a graph-hosted semantised data is produced.

* The semantised data are used to produce standardised metadata records (presently GeoDCAT and ISO 19139) and data files (presently in NetCDF and csv format) from selected perimeters (acquisition sites, measured variables, experimental factors, years, etc. Fig. 10).<fnref n="69" />

<figure><img src="image_43.png" /></figure>

<footnote n="66">⁶⁶ https://ivoa.net/documents/Vocabularies/</footnote>

<footnote n="67">⁶⁷ https://ostrails.eu</footnote>

<footnote n="68">⁶⁸ https://www.anaee-france.fr/en/</footnote>

<footnote n="69">⁶⁹ Christian Pichot, Cécile Callou, Andre Chanzy, Philippe Clastre, Chloé Martin, et al.. Developing semantic interoperability in ecology and ecosystem studies. RDA 17th Plenary Meeting, Apr 2021, Edinburgh, United Kingdom. <hal-03234155></footnote>

<footnote>Christian Pichot, Damien Maurice, Ghislaine Monet, Rachid Yahiaoui, Philippe Clastre, et al.. Semantic Management of Data from Biodiversity and Ecosystem Studies: Toward an Integrated Workflow from Collection to Publication. Application to Plankton Data from Lake Geneva. Joint Ontology Workshops 2021 Episode VII: The Bolzano Summer of Knowledge, JOWO, Sep 2021, Bolzano, Italy. <hal-03579553></footnote>
<figure><img src="image_44.png" /><figcaption>Figure 10 - AnaEE semantic workflow (from data collection to publication).</figcaption></figure>

The datasets and metadata records generated from this workflow are pushed to the Recherche Data Gouv repository.<fnref n="70" /> That’s why great care is taken to the interoperability of AnaEE metadata with that of Recherche Data Gouv. Use of SA, especially in the keywords field, is able to improve i) the dataset’s description, ii) the interoperability of metadata and iii) the user experience through the interface allowing for dataset generation (SemData<fnref n="71" />).

Based on existing resources and adapted to the needs of AnaEE community, relevant SAs are developed: AnaeeThes thesaurus<fnref n="72" /> and OBOE-based AnaeeOnto ontology (to be published). Availability of these resources and others SA in the field of ecology, on AgroPortal makes it possible to use them through APIs.

### 2.7.2. Implemented solutions and Challenges

This UC is focused on the connection between AgroPortal and AnaEE semantic tools to permit keyword completion and enrichment at the third step of the workflow. At this step, some keywords are simple strings, without semantic reference. The AgroPortal API allows searching in AnaeeThes<fnref n="73" /> for the matching concept(s). As shown in Figure 11, the user is able to access keyword full description (and synonyms and URI; eye icon), and for keywords that do not match (unchecked box), decide to get rid of them (trash icon).

<figure><img src="image_45.png" /></figure>

<footnote n="70">⁷⁰ https://entrepot.recherche.data.gouv.fr/dataverse/anaee-france</footnote>

<footnote n="71">⁷¹ https://semantic-data-flow.anaee-france.fr/hugo/help/</footnote>

<footnote n="72">⁷² https://opendata.inra.fr/anaeeThes/page/</footnote>

<footnote n="73">⁷³ https://agroportal.lirmm.fr/ontologies/ANAEETHES</footnote>
<figure><img src="image_46.png" /><figcaption>Figure 11 - SemData user interface with AgroPortal connector (model).</figcaption></figure>

To enrich the metadata record with additional keywords, users can manually search for new concepts through the SemData interface (Fig. 11). The user starts writing keywords and the AgroPortal API call proposes several candidate terms, especially from AnaeeThes (as the default SA). The user can select one or more of these concepts and add them to the metadata record.

Then, new keywords from SA can be proposed by matching on a narrative description of dataset content. This description is automatically generated by SemData from information collected by the workflow from the database and the semantic graph and can be completed by the depositor. The user can press the “suggest new concept” button and launch an annotator call to the AgroPortal API, which returns candidate concepts.

For each implemented solution, keywords are stored with their preferred label, URI, SA name and URL, as required in dataverse metadata. This point induced a change in the SemData application data model and an adaptation of the API call to the Dataverse repository.

Finally, for datasets already published, there will be no impact of this development, unless they are regenerated through the ad hoc functionality offered by the AnaEE pipeline.

## 2.8. ArcheoAMU

**Towards interoperability and cross-disciplinary of epigraphic corpora: The case study of Epicherchell**

Scientific domain: Social Sciences and Humanities

Leading organisation: Aix-Marseille University (AMU)

Contributors: Patrice Bellot, Julien Seinturier (AMU)

Code and documentation: https://gitlab.lis-lab.fr/julien.seinturier/epi2dap (available on demand using a GitLab access)

<figure><img src="image_47.png" /></figure>
### 2.8.1. Context and objectives

The ArcheoAMU initiative is a demonstrator that aims at illustrating the impact of data FAIRness for accessing archeological data. As a first step, the project focuses on epigraphic corpora to provide a set of methods and implementations for semantising existing corpora and to integrate SAs in order to make underlying data more findable and accessible. The proposed approach relies on 3 axes:

* Proposal and implementation of a uniform representation for epigraphic data that relies on ontologies.

* Provide automated modules for exporting existing epigraphic corpora within a semantic database (triple store, JENA<sup>74</sup>).

* Connect the semantic database with a SAC (PACTOLS) dedicated to humanities and archaeology to provide metadata that improve FAIRness.

The produced semantic database is accessible directly from the triple store or from the semantic web platform OpenARCHEO hosted and maintained by the Humanum consortium. With this integration, epigraphic corpora can be found and accessed through a general interface using keywords and metadata that are common to all domains of History and Archaeology.

The first implementation is built on the Epicherchell database, a corpus that contains about 1 800 ancient writings.

### 2.8.2. Implemented solutions and Challenges

The first challenge was to instantiate the proposed ontology within a semantic database that relies on a triple store based on JENA. This triple store enables to set up a data server and to add specific modules for connecting to SACs. To validate the approach, the EPICHERCHELL<sup>75</sup> database, was used as the initial data source. A software that connects to the existing EPICHERCHELL database and that automatically populates the semantic database has been set-up.

The second challenge was to integrate FAIR vocabularies into the corpus. For that, some concepts and values of the database are aligned with PACTOLS thesaurus,<sup>76</sup> which provides a controlled, standardised, multilingual vocabulary for archaeology, from prehistory to the contemporary period, and for all aspects of the sciences of antiquity. The alignment has been done by using the REST API provided by OpenTheso (The thesaurus engine on which PACTOLS is made) and by integrating this API through a specific module added to the JENA FUSEKI implementation. For example, associating the SA *Epitaph* to an inscription is made by setting a relation *hasType* that points to PACTOLS' *Epitaph* entry using the REST query:

<figure><img src="image_48.png" /></figure>

<footnote n="74"><sup>74</sup> https://jena.apache.org/</footnote>

<footnote n="75"><sup>75</sup> https://ccj-epicherchel.huma-num.fr/interface/</footnote>

<footnote n="76"><sup>76</sup> https://pactols.frantiq.fr/opentheso/</footnote>
```xml
<owl:NamedIndividual rdf:about="#EPI_012568">
  ...
  <epi:hasType
    rdf:resource="https://ark.frantig.fr/ark:/26678/pcrtmG1RAoyFJ2"/>
  ...
</owl:NamedIndividual>
```

A graphical user interface (GUI) has been set up on top of the SA connector. This GUI enables users to search for SA within PACTOLS using keywords and to add them to the data (Fig. 12).

<figure><img src="image_49.png" /><figcaption>Figure 12 - OpenThesaurus GUI for adding new SA from PACTOLS within EPICHERCHELL</figcaption></figure>

## 2.9. Identifiers.org-ROR.org

**EMBL-EBI - Providing harmonized information about organisations in identifiers.org using ROR registry**

Card: https://doi.org/10.5281/zenodo.14773873

Scientific domain: Life sciences

Leading organisation: EMBL-EBI

Contributors: Henning Hermjakob, Renato Juacaba Neto (EMBL-EBI)

Code and documentation:

https://github.com/identifiers-org/monorepo/tree/master/react-frontend/registry

https://docs.identifiers.org/pages/faq.html

### 2.9.1. Context and objectives

The Bioinformatic laboratory of the European Molecular Biology Laboratory (EMBL-EBI)<fnref n="77" /> is an intergovernmental research organisation. Its goal is to collect, store and add value to Life

<figure><img src="image_50.png" /></figure>

<footnote n="77">77 https://www.ebi.ac.uk/</footnote>
sciences data so that researchers in many fields can retrieve and analyse them efficiently. EMBL-EBI data resources span genomics, protein function and structure, expression, ontologies, imaging and scientific literature.

Identifiers.org<sup>78</sup> has an established resolving system that enables the referencing of data for the scientific community, with a current focus on the Life Sciences domain but open to all areas of research. It handles persistent identifiers in the form of URIs and CURIES. This allows the referencing of data in both a location-independent and resource-dependent manner. To each resource, a descriptive metadata record is associated.<sup>79</sup> These records are filled in by depositors (often resources managers) and dedicated curators and community feedback ensure that information in the Registry remains up-to-date and accurate.

### 2.9.2. Implemented solutions and Challenges

This UC focuses on the addition of institution/organisation relevant information in the record. Indeed, keeping institutional information up-to-date requires a lot of time and effort from the curators. The goal is here to allow depositors and curators to obtain information from the ROR registry.

ROR.org<sup>80</sup> is a community-led registry of open persistent identifiers for research organisations. It acts here as a catalogue that provides organisations information through their ROR ID. It means that identifiers.org users (depositor or curator) can write the ROR ID of their organisation in the record and the ROR API will autofill institution information (location, description, name, etc. ; cf: Fig. 13). The user can then overwrite this information if necessary.

The main challenge of the EMBL-EBI identifier registry is the huge amount of data/entries due to the proliferation of research and resources in life sciences. That induces frequent data updates and barriers to the adoption of global PID systems.

Then, this UC can make the Identifiers.org registry more accessible through the use of semantic artifact catalogues (here, the ROR catalogue).

Finally, a more efficient use of metadata in repositories will greatly improve interoperability and findability of resources.

<figure><img src="image_51.png" /></figure>

<footnote n="78"><sup>78</sup> https://registry.identifiers.org/</footnote>

<footnote n="79"><sup>79</sup> https://docs.identifiers.org/pages/api.html</footnote>

<footnote n="80"><sup>80</sup> http://ror.org</footnote>
<figure><img src="image_52.png" /><figcaption>Figure 13 - Identifiers.org metadata record fill in for institution name space.</figcaption></figure>

Data depositors need to retrieve institute ROR ID from the ROR.org register and fill in the dedicated field, related information is collected from the ROR API and added to the record. Information is updated as long as the ROR ID is identical.

## 3. Lessons learned from use cases

Through the different UCs presented in the previous section, the specificities of each community have been identified and described. The purpose of this section is to highlight the joint reflection process, driven by follow-up meetings and animations on the Klaxoon board provided by INRAE (see report M4.5⁸¹, which provides details of the methodology and preliminary results). This work has enabled the implementation of connectors between the (meta-)data repositories and the relevant SAs and SACs, and share these thoughts with the community:

* Identify its SA needs and how to meet them with relevant SAs and SACs already available or to be constructed.

* Consider the impacts of the introduction of these connectors in the (meta-)data repositories, particularly regarding the FAIRness of the data, but also on the various technical adaptations induced in the data repository.

* Consider the role of SACs in connectors' implementation and the possible impacts on the SAs life cycle and SACs.

### 3.1. Community semantics needs and capabilities

<figure><img src="image_53.png" /></figure>

<footnote n="81">⁸¹ Aubin, S., Corre, C., & Jonquet, C. (2025). M4.5 - Internal and external use case evaluation and demonstrators. Zenodo. https://doi.org/10.5281/zenodo.14967303</footnote>
The main common objective of the various UCs presented in this deliverable is to improve the FAIRness of data stored in (meta-data repositories to, as a final goal, address end user's needs.

First, the connector allows **data managers** to address the I2⁸² FAIR principle: (Meta-)data use vocabularies that follow the FAIR principles:

* The connector provides persistent and global identifiers (generally URIs) for values of a variety of metadata and for mappings; these PIDs and term associated information are managed within SACs by the community;

* SACs provide standardised representations of SAs (SKOS vocabularies in particular), making them easier to reuse;

* SACs can offer FAIRness scores for SAs using tools like O'FAIRe<fnref n="83" /> or FOOPS<fnref n="84" />, facilitating the selection of FAIR SAs.

Improvements in the harmonisation of APIs to access SAs are much-awaited by data managers. The MOD-API, currently deployed and tested, tends to address this limitation.

To be useful to **data depositors**, **data curators** and **data users**, the connector must allow richer and more relevant metadata. The SAs used in the connector should be 1) in the relevant thematic perimeter 2) in the user's familiar language(s) 3) sufficiently lexicalised and not ambiguous. Involving end users in the specification and test phases is highly recommended. User interviews can give precious elements to evaluate their terms and (meta-)data needs and practices (how they fill in records, how they select terms and how they could be able to use SAs?)

Addressing these needs using a connector requires that a SAC is available with the SAs relevant for **the community of users**. The institutions and RIs involved in the UCs questioned their own semantic capabilities (according also to the way the communities they serve are organised):

* What are the existent SAs? (e.g., thesaurus, ontologies, lexicals);

* Who produced these SAs? (e.g., is there a persistent organisation, are there more or less specific vocabularies);

* How are they organised technically? (e.g., storage, accessibility);

* Are the SA accessed through a SAC already or are they coupled/integrated within the data repositories?

<figure><img src="image_54.png" /></figure>

<footnote n="82">⁸² https://www.go-fair.org/fair-principles/i2-metadata-use-vocabularies-follow-fair-principles/</footnote>

<footnote n="83">⁸³ Emna Amdouni, Syphax Bouazzouni, Clement Jonquet. O'FAIRe: Ontology FAIRness Evaluator in the AgroPortal Semantic Resource Repository. ESWC 2022 - 19th Extended Semantic Web Conference, Poster and demonstration., May 2022, Hersonissos, Greece. https://doi.org/10.1007/978-3-031-11609-4_17</footnote>

<footnote n="84">⁸⁴ https://foops.linkeddata.es/about.html</footnote>
Several UCs presented in this deliverable had to get organised and launch a new SAC and engage in its long term maintenance and governance. Others could depend on an already-existing SAC and use the SAs that were available or seek assistance from the SAC teams to add the ones that were lacking in order to meet their needs. The SAs governance recommendations on lifecycle are available in previous project work, D4.1<sup>85</sup>, to which a couple of UCs have contributed.

## 3.2. Connectors' impacts on data FAIRness and technical aspects

To measure this improvement in the FAIRness of the data, several approaches were considered and can be combined:

* **FAIRness assessment tools.** Tools like F-UJI<sup>86</sup> and FAIR-checker<sup>87</sup> can take into account the use of SAs in (meta-)data repositories depending on the space of the associated URI and if its root namespace is present in a predefined set of SAC. The main limitation of these tools is precisely that not all SACs used by the UCs are actually taken into account in the evaluation. The lists of SACs considered by FAIRness assessment tools should be extended using the FAIR-IMPACT Review of Semantic Artefact Catalogues and guidelines for serving FAIR semantic artefacts in EOSC.<sup>88</sup>

* **(Meta-)data quantitative metrics.** Thanks to the APIs or UIs of the various (meta-)data repositories, other factors can be taken into account to evaluate the effect of the use of SAs in the repositories, such as the number of times the datasets are consulted (Findability), the number of terms associated with the datasets (Reusability), etc. These metrics can be impacted by other factors but when they are associated, they can give hints of the effects of the connectors.

Methods to measure the impact, their limits and first results are presented in the FAIR-IMPACT report "M4.5 - Internal and external use case evaluation and demonstrators".<sup>89</sup>

To ensure the success of the connector, it is important that it is seamlessly implemented in the existing UI, not changing the users' habits too much, and not degrading the repository performance. Here are a few aspects to consider:

* **Ergonomy.** The UCs tested three different features:

* **Autocompletion** gives the depositors a great deal of autonomy but may limit the number of terms added as its capacity to show terms-associated information is limited and the process remains fully manual;

<figure><img src="image_55.png" /></figure>

<footnote n="85"><sup>85</sup> Ramezani, P., Grau, N., Jonquet, C., & Fiore, N. (2024). D4.1 - Semantic artefact governance models and disciplinary approaches for inclusion within EOSC. Zenodo. https://doi.org/10.5281/zenodo.13142842</footnote>

<footnote n="86"><sup>86</sup> https://www.f-uji.net/</footnote>

<footnote n="87"><sup>87</sup> https://fair-checker.france-bioinformatique.fr/</footnote>

<footnote n="88"><sup>88</sup> Jonquet, C., & Grau, N. (2024). M4.4 - Review of Semantic Artefact Catalogues and guidelines for serving FAIR semantic artefacts in EOSC (1.0). Zenodo. https://doi.org/10.5281/zenodo.12799796</footnote>

<footnote n="89"><sup>89</sup> Aubin, S., Corre, C., & Jonquet, C. (2025). M4.5 - Internal and external use case evaluation and demonstrators. Zenodo. https://doi.org/10.5281/zenodo.14967303</footnote>
* **Tree navigation**, which after searching a term, allows the user to navigate in the hierarchy and see complementary information on terms;

* **An annotator**, which suggests terms according to the title and description of a dataset. If this solution saves users' efforts, it is highly dependent on the annotator's quality and can restrict the depositors' autonomy and expertise.

* **Usability.** Whatever feature is implemented, the terms-associated information (e.g. name of the vocabulary, close hierarchical relationships, synonyms, translations, definition...) presented to the end user need to be questioned. All this information can be useful, but should not overload the interface. Finding the right balance is aided by user interviews.

Some adaptations of the (meta)LD data repositories and their existing features must also be considered to maximise the benefits of the connector:

* **Search index adaptation.** To optimise the use of SAs for dataset findability, search index adaptations have been made in the various UCs. In particular, the weight in search results of metadata using SAs was increased, or searches on synonyms and translations of terms were allowed.

* **Metadata exports.** The function of these is to extract all the metadata from a dataset for later use or by another IS. The quantity and format of information exported or exposed by the (meta)LD repository API must meet the needs of their users. For each export format, it is important to document the adaptations made necessary to include terms-associated information (URI, synonyms, etc.).

### 3.3. SACs as key element of connectors' implementation

As explained above, SACs have a crucial role in the connector's implementation: they give access to relevant SAs, improve SAs FAIRness, and offer a unique interface to users.

Conversely, the connector should increase the visibility and use of the SAs and SACs by the community to which they are dedicated. Generally, the connector may have an impact on the SAs life cycles<fnref n="90" />, which was not possible to demonstrate in the project lifetime. Yet, the UCs identified some aspects that need to be anticipated:

* Users need ways to suggest SA modifications including error fixing, addition of terms, mappings, etc. SACs can offer features to connect SA users with their authors;

* SAs used in the connector need to be maintained if susceptible to evolve; SACs should improve and facilitate SAs governance and maintenance<fnref n="91" /> by disseminating best practices and tools, and serving as fora for SA developers;

<figure><img src="image_56.png" /></figure>

<footnote n="90">⁹⁰ Jonquet, C., & GRAU, N. (2025). D4.2 - FAIR semantic artefact lifecycle from engineering, to sharing (v1.0). Zenodo. https://doi.org/10.5281/zenodo.14643279</footnote>

<footnote n="91">⁹¹ Ramezani, P., Grau, N., Jonquet, C., & Fiore, N. (2024). D4.1 - Semantic artefact governance models and disciplinary approaches for inclusion within EOSC. Zenodo. https://doi.org/10.5281/zenodo.13142842</footnote>
* SACs should offer guarantees in terms of availability, speed and reliability. Sustainability and governance models need to be defined within communities.

Also, the promotion of the catalogue as the single interface for SAs should limit the anarchic proliferation of redundant SAs and encourage the reuse of existing ones. At the same time, new SACs should emerge in various disciplines and communities that are not covered today. As well, the increased use of connectors should lead to the FAIRification of more existing SAs that have been embedded in information systems.

## 4. Conclusions and perspectives

The main outcomes from this document are:

* The nine T4.5's UCs have implemented or will implement a connector between a SAC and a (meta)data repository on one or more metadata elements. These UCs represent various scientific domains (SSH, Archeology, Astronomy, Life Sciences, Ecology, Earth and Environmental Sciences, Physics and technical Sciences) and implicate various technologies (cf: Table 1), demonstrating the feasibility of this type of connector, and the genericity of the approach.

* Lessons learnt by the UCs when implementing a connector: (1) the need to consider both semantic needs and capabilities of the served scientific community; (2) technical changes in the (meta)data repository are necessary to address the users needs and integrate the connector in an existing environment; (3) the seeds of a reflection on SA and SAC long-term management to serve multi-users/multi-environment' requirements.

To the UCs scale, main challenges are to ensure the connectors' sustainability, the adequacy with their users' needs, the technological evolution of the (meta)data repositories and SACs, of the standards at stake, taking into account the requirements of EOSC. In a general way, the common challenges that have been identified to date (a few months after the implementation of the connector in the (meta)data repositories) relate to:

* The management of SAs and SACs: these must remain up to date with technological and standards evolutions, but also be able to develop their content in line with user feedback and changes in the knowledge domain they represent.

* In the long term, each UC is committed to maintain the connector and ensure its sustainability taking into account the evolutions of both connected parties, i.e. the (meta)data repository and the SAC. The standardisation of APIs for SACs through MOD-API will make the connector's maintenance easier and will facilitate the implementation of more connectors.

Due to the very recent implementation of connectors in the various UCs, very few have had time to see and measure the effects of these connectors on the FAIRness of (meta)data over the duration of the FAIR-IMPACT project. However, in order to meet the objectives under the

<figure><img src="image_57.png" /></figure>
activities in relation to FAIR semantic artefacts in use within data repositories, the
measurement methods, first results and expected impacts are detailed and discussed in
M4.5.<fnref n="92" />

According to the EOSC Interoperability Framework, the work carried out by UCs tends to address some identified needs about semantic interoperability, starting with the lack of a common explicit definition of terms, of reference repositories of SAs and of SAs across communities. The work on SAC creation, consolidation and appropriation by end-users allows the SAs managers better management capacities and creates a common framework in and across communities. Then the availability of SAs in the (meta)LD data repositories improved the expertise and skills related to semantics of the scientific communities. Finally, the work carried out by UCs provides proof of concept of the EOSC nodes' capacities to "provide support for maintenance of a repository of SA and a governance framework for such a repository".

<figure><img src="image_58.png" /></figure>

<footnote n="92">⁹² Aubin, S., Corre, C., & Jonquet, C. (2025). M4.5 - Internal and external use case evaluation and demonstrators. Zenodo. https://doi.org/10.5281/zenodo.14967303</footnote>