A Green Paper on AI, Data Governance, and Metadata Policies for Europe's Music Ecosystem

A Green Paper on AI, Data Governance, and Metadata Policies for Europe’s Music Ecosystem Practical Steps Towards a Decentralised and Open European Music Observatory Daniel Antal, CFA 2024-12-16 Table of contents Introduction 4 Glossary 11 Musicterms...................................... 11 Dataterms ...................................... 12 AI&SystemsTerms................................. 14 Dataprotectionterms ................................ 16 Data curation and collection terms . . . . . . . . . . . . . . . . . . . . . . . . . 16 Rightsmanagementterms.............................. 17 Statisticalterms ................................... 17 Registers, authorities, standards and identifiers . . . . . . . . . . . . . . . . . . 18 Organisations..................................... 21 Otherabbreviations ................................. 22 1 Policy context and problem map 23 1.1 Three structural pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.2 National and European pilots as anchors . . . . . . . . . . . . . . . . . . . 28 1.2.1 The Slovak Comprehensive Music Database (SKCMDb) . . . . . . 29 1.2.2 Unlabel ................................. 31 1.3 Questforefficiency............................... 32 1.4 Potentialsolutions ............................... 35 2 Fixing Music Data at the Source 38 2.1 Discussion.................................... 38 2.1.1 Structural fragmentation of data and value flows . . . . . . . . . . 38 2.1.2 Cost barriers in documentation and claims . . . . . . . . . . . . . . 41 2.1.3 Why one grand collection model will not work . . . . . . . . . . . . 41 2.1.4 Legacymetadata............................ 42 2.1.5 Named-entity resolution, attribution, and privacy . . . . . . . . . . 45 2.2 Policyproposals................................. 47 2.2.1 Reducing redundancy . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.2.2 Reconciling attribution and privacy . . . . . . . . . . . . . . . . . . 48 2.2.3 Pragmatic metadata alignment . . . . . . . . . . . . . . . . . . . . 50 3 Open Music Observatory: Building a Shared Music Data Space 53 3.1 Discussion.................................... 54 3.1.1 Why centralisation is a futile model . . . . . . . . . . . . . . . . . . 54 3.1.2 Open Data Directive: right without means . . . . . . . . . . . . . . 57 2 3.1.3 Why voluntary workarounds do not scale . . . . . . . . . . . . . . . 57 3.1.4 Public infrastructures bypass music’s real data flows . . . . . . . . 58 3.1.5 Subsidiarity and infrastructures for scaling music data . . . . . . . 60 3.1.6 Economies of scale in metadata . . . . . . . . . . . . . . . . . . . . 62 3.2 PolicyProposals ................................ 63 3.2.1 Workflow playbooks and provenance trails . . . . . . . . . . . . . . 65 3.2.2 Federated infrastructure as a cost and governance solution . . . . . 66 3.2.3 Legal, standards, and funding levers . . . . . . . . . . . . . . . . . 69 3.2.4 Alignment with the European Open Science Cloud . . . . . . . . . 70 4 AI that Works for Music, Not Against It 71 4.1 Discussion.................................... 75 4.1.1 Structural problems for music businesses to apply AI . . . . . . . . 75 4.1.2 European regulation that misses the point . . . . . . . . . . . . . . 76 4.1.3 Policy issues at the intersection of AI, copyright, and GDPR . . . . 78 4.1.4 AI design without awareness of limits . . . . . . . . . . . . . . . . . 79 4.1.5 Unfreezing frozen assets . . . . . . . . . . . . . . . . . . . . . . . . 80 4.1.6 AI support for investment into new repertoire assets . . . . . . . . 81 4.2 Policy Proposals: Aligning AI with Governance and Value Creation . . . . 81 4.2.1 EU-Level Policy: Compass and Guardrails . . . . . . . . . . . . . . 82 4.2.2 Industry-Level Policy: Standards and Collaboration . . . . . . . . . 82 4.2.3 Organisational-Level Policy: Playbooks for CMOs, Publishers, Archives................................. 83 4.2.4 Curative AI and Reparative AI as a Remediation Solution . . . . . 83 4.2.5 Lowering Documentation Barriers . . . . . . . . . . . . . . . . . . . 84 4.2.6 Observatory: European = Open . . . . . . . . . . . . . . . . . . . . 84 4.2.7 The Open Music Observatory as a Collective Guardrail . . . . . . . 85 5 What Europe Should Do Next for Music Data & AI 87 Sources & Further Reading 90 3 Introduction There are musical works that are reinterpreted thousands of times across centuries. A symphony by Beethoven or a folk song from the Baltic coast can be heard again and again, each performance offering a new reading of something that never becomes “final.” The same is true of sound recordings: some are rediscovered decades later, remastered, and brought back into circulation for new audiences. Music assets, in other words, have an unusually long lifecycle. This is equally true of their documentation — the metadata that accompanies them from creation to archiving. Metadata does not freeze a work or recording in time. Instead, it evolves alongside it: from the moment of rights registration, through commercial distribution and playlisting, to preservation in a library or archive. Each new interpretation, remix, or reissue generates new metadata, and each new information system demands new connections and contexts. This Green Paper builds on a substantial body of prior European policy analysis concerning copyright data, metadata interoperability, and the implications of new technologies for the cultural and creative sectors. In particular, it draws on the Study on copyright and new technologies: copyright data management and artificial intelligence, commissioned by the European Commission (DG CNECT) and published in 20221. That study identified persistent structural weaknesses in rights metadata management across creative industries, including the music sector, despite long-standing identifier systems and standardisation efforts. These weaknesses include fragmentation between identifiers and registries (ISRC, ISWC, ISNI, VIAF, IPI), limited interoperability between 1Study on copyright and new technologies: copyright data management and artificial intelligence (SMART 2019/0038) (European Commission et al. 2022) 4 sectoral systems, high administrative costs in rights management, and difficulties in ensuring authoritative, machine-readable information across the full lifecycle of creative works and recordings. The study further noted that emerging technologies, including artificial intelligence, are increasingly deployed in environments where metadata quality and legal clarity are insufficient, amplifying existing inefficiencies and legal uncertainties. It therefore emphasised the need for lifecycle-aware metadata governance, improved integration of existing standards and identifiers, and institutional arrangements that enable trust, transparency, and interoperability rather than further centralisation. These findings form an important analytical baseline for the policy considerations and implementation examples discussed in this Green Paper. ĹWhy this Green Paper matters for music professionals? • Streaming has centralised power in platforms, while leaving rightsholders with micro-royalties and growing administrative burdens. • Metadata mistakes translate directly into lost revenue — each unlinked ISRC or ISWC is money left unclaimed. • AI is already reshaping music ecosystems: it can either support documentation and remuneration, or flood systems with untracked works. • Europe needs federated, cooperative solutions so independents, collective management organisations, and heritage institutions can compete on fairer terms. There is rarely a single moment when music metadata can be considered complete. Metadata, like music itself, is open to reinterpretation. A name may later be reconciled with an identifier; a work may be linked to a new performance; a recording may be embedded in new formats or platforms. Each act of documentation adds layers of meaning and makes music intelligible in new environments. This is not an invitation to reinvent the wheel. We can still read Beethoven’s early prints as well as Iris Szeghy’s twenty-first-century scores because music notation — a standardised way of expressing the metadata of musical works — has remained remarkably stable for centuries. Notation demonstrates that standardisation can endure, and that shared conventions make music legible across time, geography, and institutions. 5 Figure 1: In Slovakia, we implemented, using the European Interoperability Framework for shared digital services, public-private service integration to make music more visible and its handling more cost efficient. Our implementation aligns well with the requirements set by the Copright Infrastructure Task Force. The invention of the computer, and later the internet, introduced new ways to document and transmit music. These innovations brought powerful efficiencies: identifiers such as the ISRC and ISWC, digital distribution pipelines, and networked catalogues enabled the global circulation of music at unprecedented scale. At the same time, they produced new fragmentation. Standards proliferated, identifiers failed to interconnect, and workflows designed for one purpose often broke down in another. What was intended as progress frequently resulted in overlapping, incompatible, or incomplete metadata — a legacy that now requires systematic repair. ĹNote This Green Paper is a maturing policy document developed within the Open Music Europe (OpenMusE) Horizon Europe Research and Innovation Action (Grant Agreement No. 101095295). It deliberately combines policy research with implementation piloting, reflecting the project’s emphasis on innovation, experimentation, and real-world validation rather than abstract policy design alone. Prepared in line with the Guidelines for Open Policy Analysis (available at https://www.bitss.org/opa/community-standards/) and the Horizon Europe Data Management Guidelines, the document has been released early to support consultation, incorporate stakeholder input, and ensure transparency throughout its development. It extends the analysis developed in the first OpenMusE policy brief on 6 music metadata mainstreaming and EU law (Deliverable D5.6), and its core findings are further condensed in the second policy brief (Deliverable D5.7), which integrates wider stakeholder consultations2. Transparency note: This Green Paper does not present a purely conceptual or speculative policy proposal. Substantial parts of the analysis synthesise lessons from policy-embedded implementation activities conducted using Open Policy Analysis methods, including openly documented data, code, workflows, and methodological decisions. These activities were carried out in cooperation with public authorities and sectoral institutions, allowing the paper to reflect both policy ambitions and real-world constraints. In accordance with Open Policy Analysis principles3, all related deliverables and technical documentation are publicly accessible to foster engagement and ensure a clear audit trail. The current version (and future White Paper drafts) is available at https: //zenodo.org/records/17075796. Standardised folders, figures, and bibliographies are available at https://github.com/dataobservatory-eu/open-music-data-white-paper. This document situates the Open Music Observatory as a central reference point. The Observatory is a prototype of a modern European Music Observatory developed by the OpenMusE consortium, currently populated with data on economy, diversity, society, and innovation, and operating multiple federated modules. Technical documentation and versioned DOIs are available via Zenodo, with an overview at https://openmusicobservatory.eu/. Funding acknowledgement: This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No. 101095295. The views expressed are those of the authors only and do not necessarily reflect those of the European Commission or its agencies4. Citation note: When citing this Green Paper, please use the latest versioned DOI available on Zenodo, and include the date of access if referring to material hosted on our GitHub repository.5This is an early version (0.9.3.) 7 Our document has been presented and discussed with industry specialists on the following forums: • Big Data Value Association, Gaia-X: Dataweek²�: Introducing a new European music dataspace6 • Echoes/ECCH: The ECHOES (European Cloud for Heritage OpEn Science) policy even and workshop, 12-13 December 2024 at the Royal Institute for Cultural Heritage (KIK-IRPA), Brussels, • Hungarian stakeholders interested in replication of the Slovak pilot versions in various meetings throughout 2024 and 20257. • CISAC: Protecting Creators’ Rights in the AI Era: OpenMusE at the European Committee Meeting, Vilnius, 29-30 April 8. • The Fair MusE - Prelude to a fairermusic industry Fair MusE project9 • IAMIC 10: The International Association of Music Information Centres and several 5The Policy Brief 1: Music Metadata Mainstreaming and EU Law (Senftleben et al. 2024) provides the legal and institutional framing for metadata mainstreaming in European copyright and data law. The present Green Paper builds on that foundation with a lifecycleand sovereignty-oriented conceptual framework, tested in pilots such as the Slovak Comprehensive Music Database. Its key recommendations are further condensed in OpenMusE Policy Brief 2: An Open, Scalable Data-to-Policy Pipeline for European Music Ecosystems (Deliverable D5.7, 2025) (Open Music Europe Consortium 2025), which integrates broader stakeholder consultations (CISAC, IAMIC, IAML, FairMusE, Music360, ECCCH forums, among others) and translates them into policy actions for EU institutions. 5The Guidelines for Open Policy Analysis form an actionable and practical set of directives that the OpenMusE consoritum was mandated to use under the Grant Agreement. It can be seen as good implementation framework for Evidence-based policy making in the European Commission and The practice of reproducible research [BITSS (2019); Open Music Europe (2023); (J 2015; Kitzes, Turek, and Deniz 2018). 5This document has been prepared by Open Music Europe (OpenMusE) project partners as an account of work carried out within the framework of this contract. Any dissemination of results must indicate that it reflects only the author’s view and that the Commission Agency is not responsible for any use that may be made of the information it contains. Neither Project Coordinator, nor any signatory party of Open Music Europe (OpenMusE) Project Consortium Agreement, nor any person acting on behalf of any of them: (a) makes any warranty or representation whatsoever, express or implied, (i) with respect to the use of any information, apparatus, method, process, or similar item disclosed in this document, including merchantability and fitness for a particular purpose, or (ii) that such use does not infringe on or interfere with privately owned rights, including any party’s intellectual property, or (iii) that this document is suitable to any particular user’s circumstance; or (b) assumes responsibility for any damages or other liability whatsoever (including any consequential damages, even if advised of the possibility) resulting from your selection or use of this document or any information, apparatus, method, process, or similar item disclosed herein. 5Always use the latest versioned DOI when citing this Green Paper, available via Zenodo. If you rely on supporting material hosted in the GitHub repository, please add the date of access in your reference. The figures and charts can be found on FigShare and may be reused separately, citing their DOI and, for context, the Green Paper that contains them. 6Jun 5, 2024, Dataweek²�, Leuven, Belgium. 7Federation possibilities of the Slovak music data sharing space in Hungary (Antal 2024a) 8Protecting Creators’ Rights in the AI Era: OpenMusE at the European Committee Meeting, our presentation (Mikš 2025) 9We received useuful feedback for this Green Ppaer from the project and see further synergies in presenting our policy findings together. https://fairmuse.eu/about/ 10We presented and discussed these ideas at the International Association of Music Information Centres 8 key members of the organisation. • IAML: The International Association of Music Libraries, Archives and Documentation Centers and several national chapters and key members 11. • Polifonia: In October 2023 Polifonia invited a few stakeholders - Podiumkunst.net, the Open Music Observatory, Uni Firenze, IC Fonseca School, Joséphine Simonnot/PRISM, Maria Luisa Onida/D’Istruzione Superiore Leonardo Da Vinci, Carnegie Hall Archive, Municipality of Bologna - for a work session, which gave us a great opportunity to strengthen the metadata framework of our policy recommendations and infrastructure planning. At this stage, we started to record user stories for the design of system competences of the Open Music Europe software ecosystem and the observatory’s semantic architecture12. • Music Futures: the AHRC Creative Industries Cluster project MusicFutures in the United Kingdom. • Slovak national stakeholders interested in cultural data.13 • Wikimedia community and developers14. • European music industry stakeholders on LineCheck 2025 15 This Green Paper is aligned with the European Commission’s Culture Compass for Europe and its ambition to strengthen shared European cultural data infrastructures. It proceeds from the premise that cultural data challenges are largely shared across domains, but that effective implementation requires domain-specific, federated units where sectoral complexity exceeds what generic frameworks can capture. Music is one such domain. The CITF’s First Project Report (Ministry of Education and Culture, Finland, 2025) validates and extends the policy logic of this Green Paper. CITF formulates cross-sectoral requirements for trustworthy, machine-readable copyright infrastructures in the AI era — focusing on identifiers, rights management information, provenance, and federated governance. OpenMusE provides a concrete domain implementation of these ideas within the on the General Assembly and Annual Conference 2024 on November 21, 2024, at Music Austria, Vienna. See the presentation and its poster format (Antal 2024d). 11We presented and discussed these ideas at the International Association of Music Libraries, Archives and Documentation Centers on the General Assembly and Annual Conference 7th and 9th of July 2025 in Salzburg, Austria. See the presentation and its poster format (Antal 2025b, 2025c). 12The user stories were placed in an OPA compliant folder on GitHub. This work was not carried on after the initiated grant agreement change, when the WP4 did no longer participate in building open source software for this purpose, but we did not abandon the competency requirements (Open Music Europe Consortium 2023). 13Based on a memorandum of understanding with a broad range of public and private stakeholders, (Ministerstvo kultúry SR and Open Music Europe 2023) we developed a model for renewing statistical production for better cultural and music statistics (Antal 2023). 14Our work was presented in the Technology session of the Wikimedia CEE Meeting 2024 in Istanbul, and the Wikimedia CEE Meeting 2025 in Thessaloniki, and the Wikidata Conf 2025 online; we have built relationships with various national chapters and the Wikidata and Abstract Wikipedia teams, and joined the Wikidata Ontology Cleanup Task Force and the Wikidata Mereology Task Force to help the coordiantion of our open source technology, data curation and dissemination efforts. (Antal 2024b, 2025d; Antal, Pigozne, and Federico 2025). 15Open Access Music Dataspaces – Open Music Observatory presented on LineCheck 2025 (Mikš and Antal 2025) 9 Data protection terms audit trail: chronological record documenting actions taken on a digital artefact or metadata object, required for traceability and accountability. CITF stresses its role in rights and AI governance. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 101) DPIA: Data Protection Impact Assessment (DPIA) is a process used to identify and minimize the risks associated with processing personal data. DPO: the Data Protection Officer (DPO) is an individual designated by an organization to oversee its compliance with data protection laws, such as the GDPR. They act as a point of contact for data subjects and supervisory authorities, and they advise on and monitor data protection practices within the organization. GDPR: The General Data Protection Regulation (GDPR) is a legal framework made by the European Union that sets guidelines for the collection and processing of personal information from individuals who live in and outside of the European Union. Data curation and collection terms aggregation: acquisition of sensitive information by collecting and correlating information of lesser sensitivity (ISO 2023b) collection: gathering of items assembled on the basis of some common characteristic, for some purpose, or as the result of some process (ISO 2017b) holdings: totality of documents in the custody of an information and documentation organization (ISO 2017b) digital collection: collection formed by a collection process on existing data and data sets where the collected data is in digital form (ISO 2017b) library collection: all documents provided by a library for its users(ISO 2017b) anthology: document consisting of a collection of full documents or of extracts, usually of literary works (ISO 2017b) exhibition: curated display of objects on a clear concept and communicating a message [SOURCE:ISO 18461:2016, definition 2.4.6 modified] (ISO 2017b) curator: person responsible for overseeing a collection or exhibition (ISO 2017b) data curation: managed process, throughout the data lifecycle, by which data/data collections are cleansed, documented, standardized, formatted and interrelated (ISO 2017b) register: an official list or record of names or items; it aims to be a complete list of the objects in a specific group of objects or population, for example, all copyright-protected musical works in a country, or all legal person enterprises in another country; 16 a document, usually a volume, in which data are entered in a formal manner by a statutory authority Note 1 to entry: In modern usage, usually a database. (ISO 2017b) registration: act of giving an entity a unique identifier on its entry into a system (ISO 2017b) a set of rules, operations, and procedures for inclusion of an item in a registry (ISO 2023a) registrant: organization or person that has either registered an authentication protocol or registered the adoption of an authentication protocol [SOURCE: ISO/IEC 24727-6:2010, definition 3.4] (ISO 2017b); an entity wishing to assign an ISRC to an applicable recording (ISO 2019a); a party that requests an ISNI from the Registration Authority (ISNI 3.2 (ISO 2012, p15)) party: natural person or legal person, whether or not incorporated, or a group of either (ISO 2012) usage logging: recording of access, reproduction, or other interactions with an asset to support proportional remuneration, compliance monitoring, and transparency obligations. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, MiklūnaŽukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 102) Rights management terms rights-management information: machine-readable metadata describing the ownership, licensing terms, permitted uses, and exceptions related to an asset. CITF requires RMI to be trustworthy, interoperable, and protected against alteration. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 84) rights expression: structured, machine-readable statement describing copyright status, rights holders, exceptions, limitations, and licensing conditions of an asset, supporting automated interpretation across jurisdictions. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 84) usage conditions: machine-readable description of permitted or restricted uses of an asset under copyright, contract, or statutory provisions. Required for automated rightsaware systems. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 84) Statistical terms administrative records: data generated by a non‐statistical source, usually a public body, the main aim of which is not the provision of statistics. 17 code list: predefined list from which some statistical coded concepts take their values (ISO 2013) data pipeline: a method in which raw data is ingested from various data sources and then ported to data store. FAIR or FAIR Guiding Principles for scientific data management and stewardship: guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets, emphasising machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention.) indicator: the representation of statistical data for a specified time, place or any other relevant characteristic, corrected for at least one dimension (usually size) so as to allow for meaningful comparison. microdata: non‐aggregated observations or measurements of characteristics of individual units, without direct identifier. observation unit: an identifiable entity about which data can be obtained, it is also often called a statistical unit or data subject in case of a natural person. Open Policy Analysis Guidelines: a set of information management rules to make policy analysis more transparent. personal data: any information relating to an identified or identifiable natural person. pseudonymisation: processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information. survey: a systematic examination and record of a physical or social area and its features so as to construct a map, plan, or description. In social sciences it usually refers to a well-structured questionnaire and answers given to its items by a target population. statistics: quantitative and qualitative, aggregated and representative information characterising a collective phenomenon in a considered population. visualisations: schematic charts, drawings, photographs, and their collages will as still image files that help to explain the relationship between information carriers, data points, or processes. Registers, authorities, standards and identifiers agent identifier: persistent identifier assigned to an author, performer, contributor, or other agent. CITF requires agent identifiers to be standardised, trustworthy, and interoperable. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 85–86) asset identifier: persistent identifier used for musical works, sound recordings, editions, audiovisual items, or other cultural objects. CITF requires asset identifiers to be resolvable 18 and interoperable across systems. (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, 86) IČO: The organisation identification number (IČO) is an identifier assigned to all types of legal entities, entrepreneurs and public authorities by the Statistical Office of the Slovak Republic. The Czech Republic’s organisation identifier is also called IČO. (→ agent identifier) OpenCorporates: a public corporation database which sources data from national business registries. (→ agent identifier) ISNI: an ISO certified global standard number for identifying the millions of contributors to creative works and those active in their distribution. (→ agent identifier) VIAF: The Virtual International Authority File (VIAF) is an international service that consolidates multiple name authority files into a single database. Their primary goal is to enhance the efficiency and usability of library authority files by linking and merging widely used authority records and making them accessible online. VIAF ID: The VIAF (Virtual International Authority File) combines multiple name authority files into a single OCLC-hosted name authority service. (→ agent identifier) ISRC: The International Standard Recording Code (ISRC) is a standard identifying code that can be used to identify sound recordings and music video recordings so that each such recording can be referred to uniquely and unambiguously. (→ asset identifier) ISWC: The purpose in creating an ISWC for musical works is to enable more efficient administration of rights to those works on a worldwide basis. The ISWC provides an efficient means of identifying musical works in computer databases and related documentation and for the exchange of information between rights societies, publishers, record companies and other interested parties on an international level. ISBN: the International Standard Book Number is an identification system for the publishing industry and its supply chains. (→ asset identifier) ISMN: The International standard music number (ISMN) was developed by, and for, the music publishing sector as a separate system to complement the International standard book number (ISBN). The existence of the ISMN as a separate identifier system makes it possible to identify printed and notated music as a distinct category of publication within the global supply chain and to develop trade directories and similar services for the specialized market for music publications. (→ asset identifier) ISCC: The International Standard Content Code (ISCC) is an identifier for numerous types of digital assets. (→ asset identifier) DOI: The Digital Object Identifier is a standardised unique number given to many (but not all) articles, papers and books, by some publishers, to identify a particular publication. ORCID: the Open Researcher and Contributor ID is a unique, persistent identifier free of charge to researchers. (→ agent identifier) 19 URI: A Uniform Resource Identifier (URI) is a string of characters used to identify a resource on the internet. This resource can be either abstract or physical, such as a website, an email address, or a file. URIs are essential for enabling interactions with resources over a network using specific protocols. DDI: The Data Documentation Initiative is originating for the world of social sciences data archives and more and more in use in statistical organisations for the documentation of microdata. Wikibase: Wikibase is a software system that help the collaborative management of knowledge in a central repository. It was originally developed for the management of Wikidata, but it is available now for the creation of private, or public-private partnership knowledge graphs. It is developed by Wikimedia Deutschland. SDMX: Statistical Data and Metadata eXchange (SDMX), is an international initiative that aims at standardising and modernising (“industrialising”) the mechanisms and processes for the exchange of statistical data and metadata among international organisations and their member countries. CIDOC-CRM: The conceptual model of CIDOC, the standard conceptualisation of collection management systems in heritage organisations. RiC:Records in Context is a new conceptual model that replaces the four most important international archiving standards. DCTERMS or DCMI: the Dublin Core Metadata Terms is a vocabulary of metadata terms developed and maintained by the Dublin Core Metadata Initiative (DCMI). These terms are used to describe various aspects of digital resources, such as web pages, documents, and other online content. They provide a standardized way to assign metadata to resources, making them easier to discover, manage, and exchange. RDFS: the Resource Description Framework Schema is an extension of the Resource Description Framework (RDF) that provides a vocabulary for describing classes and properties of resources within an RDF graph. EDM: the Europeana Data Model is a framework for collecting, connecting, and enriching cultural heritage metadata. It’s designed to facilitate the sharing and reuse of cultural heritage information by providing a standardized way to represent and link data. PROV-O: the Provenance ontology is a formal ontology developed by W3C to represent and interchange provenance information. MARC: MAchine-Readable Cataloging, is a standard digital format used by libraries to represent and exchange bibliographic information. DCAT: an RDF vocabulary designed to facilitate interoperability between data catalogues published on the Web. 20 Organisations AEPO-ARTIS: Organisation representing European artists-performers. Regroups most of the European CMO representing performers. ALOADED: is a company which distributes and exploits recordings. CISAC: The International Confederation of Societies of Authors and Composers is an international non-governmental, not-for-profit organisation that aims to protect the rights and promote the interests of creators worldwide. CNM (former CNV): the Centre National de la Musique is a public organisation managing a tax on concert tickets EMO: The European Music Observatory (EMO) is envisioned as a hub for collecting and analysing data on the music sector across Europe. Its primary aim is to address the current gaps and inconsistencies in music data collection, which have been a significant challenge for the sector. Europeana: a digital platform provided by the European Union that aggregates digitized cultural heritage from institutions across Europe. GESAC: The European Grouping of Societies of Authors and Composers (GESAC) comprises of 32 European authors’ societies in music, audiovisual, visual arts, literature and drama. IAML: International Association of Music Libraries, Archives and Documentation Centres IAMIC: International Association of Music Centres, an international network of organisations that collectively and collaboratively provides information and promotes the music of their countries or regions. ICMP: the global trade body representing the music publishing industry worldwide. SCAPR: International association for the development of the practical cooperation between performers’ collective management organisations (CMOs) SOZA: SOZA (Slovenský ochranný zväz autorský pre práva k hudobným dielam, Slovak Performing and Mechanical Rights Society) is a legal entity, non-profit civic association of authors and publishers of musical works, association of natural persons and legal entities. Hudobné Centrum: Music Centre Slovakia is a music organisation with a mission to promote Slovak contemporaly music. 21 Other abbreviations CEEMID: the Central European Music Industry Databases is a multi-country project that was a predecessor of Reprex’s Digital Music Observatory DSP: Digital service providers (DSPs), or Digital Streaming Platforms are companies or organisations that provide access to services online. EIF: The European Interoperability Framework (EIF) is a set of recommendations and guidelines that aims to facilitate communication and collaboration between public administrations, businesses, and citizens within the European Union and across national borders. ECCCH: The European Collaborative Cloud for Cultural Heritage is a European Union initiative for a digital infrastructure that will connect cultural heritage institutions and professionals across the EU. EOSC: The European Open Science Cloud (EOSC) aims to create a trusted, open, and multidisciplinary environment for researchers and innovators in Europe. PPP: A Public-Private Partnership (PPP) is a collaborative arrangement between government entities and private sector companies aimed at financing, designing, implementing, and operating projects or services traditionally provided by the public sector. RDM: Research Data Management refers to the suite of practices, policies, and processes used to handle data throughout the lifecycle of a research project. W3C: The World Wide Web Consortium (W3C) is an international community that develops standards for the World Wide Web. Their mission is to lead the Web to its full potential by creating technical specifications and guidelines that are designed to be open and royaltyfree. These standards include HTML, CSS, and other web technologies, which ensure that web content is accessible across different browsers and devices. Our glossary is harmonised with relevant music-sector specific standards and with the • ISO Information technology Vocabulary (ISO 2023b); Cloud computing — Taxonomy based data handling for cloud services (ISO 2020); Cloud computing — Interoperability and portability (ISO 2017a); Metadata registries (MDR) — 1. Framework (ISO 2023a) standards and the Information and documentation — Foundation and vocabulary (ISO 2017b) standard. • ISO Information technology Artificial intelligence — Concepts and terminology (ISO/IEC 2022) and Artificial intelligence — Management system and (ISO/IEC 2023) standard’s vocabulary. 22 1 Policy context and problem map The European music ecosystem has undergone disruptive transformations in recent decades. In the 2010s, the arrival of agentic AI in streaming platforms radically reconfigured distribution and consumption. These systems centralised global sales, expanding the commercially available repertoire in a typical EU country from roughly 100,000 titles to over 100 million titles competing for attention. At the same time, the average transaction value collapsed from around €18 (in current prices) to less than €0.005. This shock hollowed out much of the traditional infrastructure — record stores, radios, and music television — and shifted value capture toward data-driven platforms able to control access through recommender algorithms. In the 2020s, the rise of generative AI further exacerbates this situation. Large-scale models can mass-produce new compositions and recordings, often imitating or plagiarising patterns of human creators. This inflates supply, undermines the position of professional authors and performers, and aggravates existing problems of remuneration and discoverability.1 EU-level studies and policy frameworks have recognised these dynamics and increasingly frame them as systemic challenges. The Feasibility Study for the Establishment of a European Music Observatory diagnosed the fragmented, scarce, and poorly harmonised nature of music data collection across Member States, calling it the fundamental reason for an EU-level observatory. The Music Ecosystem 2025 study reframes the sector as an interconnected ecosystem, where platformisation, market consolidation, and emerging technologies like AI interact with broader societal challenges such as precarity, gender inequality, and sustainability. The European Parliament, in its Resolution on cultural diversity and the conditions for authors in the European music streaming market, echoed these concerns with explicit calls for reform.2 1Music Ecosystem 2025: Study on the Music Ecosystem (Music Moves Europe 2024); it frames the sector as an adaptive, networked ecosystem, highlights AI’s ability to disrupt on pp. 6–7, and mentions it as an opportunity particularly on p. 23. Feasibility Study for the Establishment of a European Music Observatory (Commission et al. 2020); stresses the fragmented, scarce, and poorly harmonised nature of music data (pp. 9–10), the need for cooperation with rights organisations, statistical agencies, and industry stakeholders (p. 61), and introduces CEEMID as a best practice (pp. 147–148). CEEMID emerged from Budapest, Bratislava, and Zagreb as an early effort to address data poverty in Eastern EU Member States. 2European Parliament Resolution on cultural diversity and the conditions for authors in the European music streaming market (European Parliament 2024); it recognises streaming as the dominant global revenue source while leaving many authors with very low income (recitals F–H), stresses accurate metadata allocation at the time of creation using identifiers ISWC, ISRC, ISNI, IPI, and IPN (recital R, and 9.), highlights the lack of quality data to properly identify authors, performers, and rights holders (recital L), and warns that AI-generated tracks are flooding streaming platforms, aggravating discoverability and remuneration imbalances (recital O). 23 Ĺddddd This Green Paper was developed as part of the Open Music Europe Horizon Europe Research and Innovation Action and reflects a deliberate methodological choice: to integrate policy analysis with implementation piloting rather than treating them as separate phases. The project combined research, stakeholder consultation, and handson experimentation to test how European music data infrastructures can be built incrementally, under real institutional constraints, and across multiple governance levels. The Open Music Observatory (OMO) is both a research output and an operational prototype. During the project, the consortium implemented: • a national music data sharing space (the Slovak Comprehensive Music Database), available at https://hudobnadatabaza.sk/en/ • a partial national replication in another Member State (Hungary), • a sub-national and cultural-identity–based module (the Finno-Ugric Data Sharing Space), available at https://finnougric.net/en/, which also shows that like the EU Culture Data Hub, it is not practical to work with music as an isolated domain, and • a pan-European core module aggregating datasets, indicators, and studies, available at https://openmusicobservatory.eu/ Together, these pilots demonstrate how a federated, decentralised model can function across supranational, national, regional, and community levels without forcing centralisation. They show how research concepts such as interoperability, provenance, FAIR data, and subsidiarity translate into governance agreements, workflows, and technical interfaces. This implementation-driven approach responds directly to long-standing European policy calls for a European Music Observatory, including the European Parliament Resolution on Cultural Diversity and the Conditions for Authors in the European Music Streaming Market (P9_TA(2024)0020), and aligns with emerging frameworks such as the European Interoperability Framework, the EU Data Strategy, the European Open Science Cloud, and the European Collaborative Cloud for Cultural Heritage. The Green Paper therefore serves both as a policy synthesis and as a documented reference implementation that can inform future European action. The European Commission’s feasibility study for the establishment of a European Music Observatory explicitly highlighted CEEMID as a best-practice example of decentralised, open, and reproducible music data integration, recommending that its approach be further explored during the start-up phase of a future Observatory3. The Open Music Observatory 3Measuring and Reporting Regional Economic Value Added, National Income and Employment by the Music Industry in a Creative Industries Perspective. Memorandum of Understanding to Create a Regional Music Database to Support Professional National Reporting, Economic Valuation and a Regional Music Study (Artisjus et al. 2014) and Central And Eastern European Music Industry Report (2020) 24 can therefore be understood as a direct response to this recommendation, updated to reflect subsequent European data space, copyright infrastructure, and AI policy developments. Beyond metadata coordination, a key ambition of the Slovak pilot was to address a deeper policy problem: the absence of music-specific economic evidence produced in line with best statistical practice. The pilot was explicitly designed around established methods for business satellite accounts, with the aim of harmonising authoritative collective rights management data with administrative registers, industry datasets, and statistical surveys. This approach sought to generate music-specific statistical indicators that Slovakia’s existing cultural and creative satellite accounting system cannot provide, and which are unavailable in most EU Member States due to the absence of dedicated cultural or creative satellite accounts altogether. In practical terms, this meant treating rights management data—often the most complete and economically meaningful source of information on music activity—not merely as sectoral metadata, but as a statistical input capable of feeding official economic indicators when properly harmonised and governed. By linking these data sources while preserving methodological transparency, reproducibility, and comparability, the pilot anticipated a model in which music could become visible in official economic statistics without requiring centralised data extraction. Although this experiment was not implemented within the OpenMusE project due to resource constraints and changing institutional conditions, its design remains methodologically sound and operationally feasible, and is directly aligned with current efforts to develop the EU Cultural Data Hub as a foundation for evidencebased cultural policy4. In line with the European Commission’s Culture Compass for Europe, this Green Paper argues that Europe’s cultural data future must be built on shared, interoperable infrastructures. At the same time, it demonstrates that certain cultural domains — notably music — require specialised, federated implementation layers due to the density of rights, frequency of reuse, and exposure to algorithmic systems. Music therefore provides a strategically valuable reference case for implementing the Culture Compass’s data and AI ambitions in practice. From the outset of this work, we have taken the view that Europe should avoid building siloed cultural data infrastructures by domain. Wherever possible, cultural data should be shared across domains through common principles, identifiers, and governance frameworks. Domain-specific solutions should emerge only where generic infrastructures reach their limits. This brings us to a third major contribution to this landscape in the form of the Copyright Infrastructure Task Force, a voluntary cooperation to solve one aspect of the puzzle with willing member states and stakeholders. We see the Copyright Infrastructure Task Force as a possible vechicle to carry on some of our findings, and also a role model to find (Antal 2020). 4Pilot Program for Novel Music Industry Statistical Indicators in the Slovak Republic (Antal 2023). The pilot was not implemented within the OpenMusE project due to resource constraints and institutional changes, but its methodological design remains valid and suitable for continuation in the context of EU Cultural Data Hub preparatory work. 25 we translated and enriched her records, reconciled them with international authorities, and extended them with DDEX catalogue transfer metadata, enabling release via Spotify, YouTube, and Apple Music. Our multi-layer model (DDEX, DCTERMS, RiC patterns, and rights metadata) aligns with CITF’s three-layer structure: DCTERMS in the foundational layer, RiC and DDEX conceptual mappings in the semantic layer, and DDEX catalogue-transfer formats in the technical layer. ĹNote Infobox: Unlabel and Hilda Griva • Metadata repair began with archival records in the Latvian Archives of Folklore. • Records were translated, enriched, and reconciled with Wikidata, MusicBrainz, and VIAF. • DDEX-compliant catalogue transfer metadata enabled digital distribution. • The enriched catalogue allowed Hilda Griva’s recordings to be released and discovered globally. Unlabel demonstrates how public heritage institutions and private distributors can cooperate through shared standards. It anchors both the curative AI approaches in Chapter 4 and the observatory perspective in Chapter 3. 1.3 Quest for efficiency Technological progress, digitisation, automation, and now AI have transformed the music industry more dramatically than most sectors. After the collapse of the CD era under peerto-peer piracy, a newly configured recording industry emerged around global platforms. Traditional retail and wholesale jobs largely disappeared, replaced by streaming platforms such as YouTube, Apple Music, and Spotify. This shift coincided with a structural devaluation of music. The licensed streaming model never recovered the real revenues of the pre-collapse recording market, and from this diminished base, platforms take a significant share. Where a CD sale once brought around €10–18 in today’s terms, the unit of account in streaming is a fraction of a cent — typically $0.003–0.005 per play. To replace the economic weight of a single album sale, a rightsholder must now process and account for roughly 4,000 successful streams. This is not merely an economic shift, but an administrative revolution. The documentation efficiency needed to handle millions of micro-transactions profitably is far higher than in the pre-streaming era. Streaming platforms are genuine big-data companies. Alphabet’s YouTube, Apple, and Spotify operate at a scale where billions of transactions and hundreds of millions of assets 32 can be managed by autonomous agents and recommender engines. But the typical rightsholder — a self-releasing artist, an independent label, or even a national collective rights agency — works at a scale where each metadata mistake means lost royalties, and where IT or documentation specialists are often absent altogether. This asymmetry is so stark that even major CMOs rely on shared infrastructures like the digital services of “Mint” to manage repertoire at scale. Music, then, is now sold in extremely low-value transactions mediated by autonomous agents. This reality enforces a very strong pressure on the entire ecosystem to improve data interoperability and metadata quality. As the CITF report emphasises, AI introduces legal lifecycle pressures: both the training and deployment of AI systems may trigger reproduction, distribution, adaptation, and communication-to-the-public rights. This amplifies the economic consequences of metadata fragmentation: missing or inconsistent identifiers now propagate across algorithmic pipelines as well as financial ones13. By contrast, in most industries administrative overhead is modest: • Retail/distribution: ~2–5% of net sales • Manufacturing: ~3–7% • Professional services: 10–15% (because administration blurs into the product) • OECD/EU cross-industry averages: 3–8% of turnover In “normal” industries, then, €50 of administrative cost is justified on €1000 of revenue. By comparison, in the recorded music industry, achieving that same 5% efficiency requires delivering faultlessly some 200,000 streaming transactions. This is a very tall order for a sector dominated by micro-enterprises and small independents without dedicated IT or metadata teams. The pressure for efficiency is not only present on the production side of the music business. In the non-profit sector, digitisation has profoundly transformed the workflows of archives, libraries, and heritage institutions as well. Streaming has reduced demand for physical collections, forcing libraries to reframe their role around digitisation, knowledge organisation, and community functions rather than lending CDs or scores. New spaces like creative studios and digital repositories are expected, but funding is limited, so efficiency is critical. At the same time, the vast amount of born-digital assets — and now the endless output of generative AI systems — creates a puzzle for archives that remains unsolved today.14 Metadata as provenance 13See (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, p31). 14See for example the Katona József Library’s adaptive strategies (Virág 2024). Archives, on the other hand, face a problem that instead of receiving records on paper, they are becoming gigantic data silos in the age of born-digital documents. They are being transformed into data through digitisation and born-digital records, face volumes too large for manual processing. This pressures traditional archival concepts such as provenance, original order, fixity, and authenticity (Colavizza et al. 2022). 33 In today’s music ecosystem, almost every asset is born digital. A modern composer’s score is produced in notation software; a performer’s recording originates as a digital file; even printing, distribution, and promotion leave their own digital traces. From the very start, each musical work and each recording comes with a dense digital fingerprint. As these works move through their lifecycle — composition, registration, performance, recording, distribution, preservation — they accumulate provenance statements:“X composed this,” “Y registered that,” “Z archived this file.” Taken together, these traces form a chain of knowledge about the history of the work. Unlike in earlier centuries, this history is now almost continuously captured, though often fragmented or messy — the “shadows” that Karabinos has described. Figure 1.2: The PROV model helps us describe the lifecycle of music: who did what, when, and with what. A composer, performer, or software tool (agent) engages in an activity such as composing or recording, which results in a musical work or a sound recording (entity). Capturing these links over time makes provenance transparent, ensures correct attribution, and supports trustworthy data exchange across the music sector. Reuse: DOI: 10.6084/m9.figshare.30073210 Metadata is “data about data.” But in practice, what counts as data or metadata is relative: a duration may be descriptive for one actor, identifying for another, and algorithmic input for a third. This distributed record of provenance resembles a chain of statements, some verifiable, some contradictory, some lost in the shadows. The challenge is not to build a single immutable blockchain, but to make the distributed record reliable, reusable, and interoperable. As shown in Section 1.2, pilots like SKCMDb and Unlabel provide two complementary responses: preventive governance of metadata at creation (Chapter 2), and curative repair 34 of legacy repertoires (Chapter 4; Chapter 3). The earlier introduced Open Future and Europeana policy brief on the publication of cultural heritage data in the age of artificial intelligence highlights why provenance failures can no longer be treated as a tolerable archival imperfection. When incomplete, ambiguous, or degraded provenance metadata is incorporated into automated and AI-driven systems, those systems tend to treat it as authoritative, propagating errors, omissions, and misattributions at scale rather than correcting them. In this context, broken provenance is not merely a documentation problem but a structural risk: once embedded in algorithmic pipelines, it shapes discovery, attribution, reuse, and economic outcomes in ways that are difficult to reverse. This reinforces the need for lifecycle-aware metadata governance that treats provenance as a core infrastructural concern in the AI era, rather than as a secondary administrative afterthought. 1.4 Potential solutions The challenges described above call for coordinated responses that combine technical, organisational, regulatory, and governance measures. This policy brief develops them in detail across three thematic chapters — curation (Chapter 2), the observatory (Chapter 3), and AI (Chapter 4). Here we present an integrated overview of the solution families. 1. Reducing redundancy and improving efficiency. Shared registries and federated pipelines ensure that data is captured once and reused many times. The Slovak Comprehensive Music Database (SKCMDb, Chapter 2) demonstrates how libraries, rights societies, and archives can align their catalogues while retaining institutional autonomy. 2. Reconciling attribution and privacy. Metadata must balance GDPR requirements with author attribution duties under copyright law. Identifier pilots such as PRS Nexus and Teosto ISNI show preventive strategies at the point of creation, while SKCMDb offers curative repair of legacy repertoires. 3. Pragmatic metadata alignment. Instead of one universal ontology, modular and pattern-based approaches allow interoperability across domains. Initiatives such as Polifonia,MusicBase, and the Unlabel pipeline provide practical bridges between archival, library, and distribution metadata (Chapter 2, Chapter 3). 4. Cross-sector observatories and data spaces. The Open Music Observatory (Chapter 3) applies the European Interoperability Framework and 8-Star FAIR model to connect rights societies, libraries, archives, and statistical offices. Data sharing spaces provide governance, semantic, and technical layers that make public and private infrastructures interoperable. 35 5. Curative strategies with AI. Many repertoires remain invisible due to incomplete or inconsistent documentation. Curative AI (Chapter 4) can support enrichment, translation, duplicate detection, and plagiarism monitoring, extending the principles of Unlabel to broader repertoires. 6. Bridges to public infrastructures. Europe already invests in the European Open Science Cloud (EOSC), the European Collaborative Cloud for Cultural Heritage (ECCCH), and Europeana. These infrastructures should be aligned with the music sector to support both cultural preservation and competitive participation in digital markets. 7. Shared AI services. Micro-enterprises, NGOs, and CMOs cannot build in-house AI capacity. Cooperative AI utilities — reconciliation-as-a-service, metadata repair pipelines, watchlists for duplicates — can be pooled under shared governance (Chapter 4). This integrated roadmap frames the more detailed analysis and recommendations in the chapters that follow. The analysis and examples presented in this Green Paper are informed by a set of closely related analytical, legal, and implementation documents, including: •Study on copyright and new technologies: copyright data management and artificial intelligence •Interoperable, trustworthy, and machine-readable copyright data in the AI era (CITF First Project Report) • Memorandum of Understanding on the reuse of Open Policy Analysis outputs in Slovak cultural policy • OpenMusE technical deliverables documenting implemented data infrastructures and workflows These materials provide additional detail on the legal, technical, and institutional assumptions underlying the policy synthesis offered here. 36 ĹCITF Figure 1.3: You can download this report at <https://julkaisut.valtioneuvosto.fi/server/api/core/bitstreams/3fb3f9ede93a-48fb-92e8-93c61a999e48/content> The CITF report, based on the Study on copyright and new technologies: copyright data management and artificial intelligence, defines a structured approach to future copyright infrastructures through three layers (foundational, semantic, technical). The solution pathways proposed in this Green Paper can be mapped onto these layers: identifier governance corresponds to the foundational layer; pragmatic ontology patterns map to the semantic layer; and federated pipelines align with the technical layer. 37 2 Fixing Music Data at the Source Figure 2.1: Curating data from multiple sources ensures that music information stays accurate, visible, and reusable over time. DOI: 10.6084/m9.figshare.30073888.v1 (click on image to reuse) 2.1 Discussion 2.1.1 Structural fragmentation of data and value flows In the music ecosystem, data is not simply decentralised by design but structurally scattered. Rights metadata is maintained by hundreds of collective management organisations and publishers, while recordings and distribution data are spread across labels, distributors, and global platforms. Libraries and archives manage their own authority files, often linked only imperfectly to international standards such as ISNI, VIAF, or ISBN. Independent projects and community-driven infrastructures, such as Wikidata and Wikibase, add yet another layer of documentation. CITF identifies similar fragmentation across the wider copyright infrastructure. It notes that rights metadata, identifiers, and RMI are 38 distributed across many actors with differing mandates and data models, and that inconsistent identifier governance contributes to systemic opacity and recurring reconciliation costs [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, MiklūnaŽukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp10–18; p23]. This fragmentation is not an anomaly but the normal condition of the sector: tens of thousands of micro-enterprises and NGOs in Europe each manage slivers of data about works, recordings, or performances. As the Feasibility Study for a European Music Observatory underlined, “the fragmented, scarce and poorly harmonised nature of the data collection landscape in the field of music has led to calls … for a European Music Observatory” (Commission et al. 2020, p9). Likewise, the Music Ecosystem 2025 study frames the sector as an ecosystem, where knowledge and value are distributed across many small actors, each with partial perspectives (Music Moves Europe 2024, pp6–7). The institutional anchoring of a future European Music Observatory is indeed a critical question. In our own feasibility planning we reviewed approximately 80 functional and discontinued data observatories, understood here as permanent institutions for ongoing data collection and dissemination. The majority in Europe were initiated by the European Commission and maintained under various public–private partnership (PPP) formats, rather than as heavy agencies or autonomous bodies. In this sense, Europeana offers a useful analogy: it coordinates metadata and access across hundreds of institutions without requiring the scale or mandate of entities such as the EUIPO or the European Audiovisual Observatory. In our interim report deliverable we suggested a similar creation path like that of the Europeana Foundation and its various layers of stakeholders (Antal 2024c). From this perspective, we believe the Observatory should follow the lighter, federated PPP model: anchored by the Commission to ensure continuity and legitimacy, but implemented through a distributed network of partners across the public, private, and research domains. This strikes a balance between stability and flexibility, while staying true to the cooperative, federated spirit that underpins our proposal. The CITF report reaches a similar conclusion: copyright data cannot be centralised at European scale and must instead be organised through layered, federated arrangements where national libraries, rights organisations, and cultural institutions each retain their roles while interoperating through open standards1. Recognising this scattered landscape is essential. It explains why reconciliation overheads are high, why identifier coverage is incomplete, and why “capture once, reuse many” pipelines are necessary. It also provides the foundation for the next chapter: explaining why attempts at centralisation are futile in such an ecosystem, and why sustainable solutions must build on federation and interoperability. Yet fragmentation is not only institutional — it is also economic. Classic value-chain analyses describe three main income streams — live performance, publishing, and recordings 1See (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp13–18). 39 — that still structure industry practice.2Digital distribution has blurred these categories without unifying the underlying infrastructures. Each handover in the lifecycle — authoring, performing, recording, distributing, streaming — generates both a financial flow and a data event. Business flows are continuous, but metadata flows are siloed. ISWCs do not connect seamlessly with ISRCs; ISRCs are rarely linked to ISNIs or VIAF authority files. The result is redundancy, inconsistency, and costly reconciliation work. Figure 2.2: Adoption of the value chain model of the European music ecosystem in the CEEMID report To address these challenges, we have adopted the value chain model of the European music ecosystem3. This approach is especially useful for designing data collection that measures 2This value-chain framing originates in Hull’s The Music Business and Recording Industry (Hull et al. 2011) and Leurdijk et al.’s Statistical, Ecosystem and Competitiveness Analysis of the Media and Content Industries (Adnra Leurdijk and Ottilie 2012), and was adapted in subsequent CEEMID reports Antal (2020). The CEEMID work was recognised as a best practice in the Feasibility Study for a European Music Observatory (Commission et al. 2020), which highlighted its role in linking fragmented data sources into a coherent economic analysis. 3For the standard American/global analytical breakup of the music industry is described in (Hull et al. 2011), its European adaptation in (Andra Leurdijk and Ottilie 2012); our more detailed Central European breakup and the figure can is described in (Antal 2020, 2021), for the reuse of the figure please refer to (Antal 2022). 40 cash flows, gross value added, and zero-price uses of music. It highlights both typical price points (e.g. averages or medians) and the interlocking metadata flows that accompany transactions. For policymakers, the model provides a way to trace how consumption — such as a consumer buying a recording through a shop, distributor, and label — translates into revenues for performers and composers. For data governance, it illustrates why capturing the metadata trail of cash flows is essential not only for valuation and cultural statistics but also for building an audit trail for fair remuneration. In the context of this policy brief, the value chain perspective therefore complements the current ecosystem analysis by clarifying which agents must be accounted for in conceptual models of data interoperability. 2.1.2 Cost barriers in documentation and claims For small publishers, labels, and self-publishing artists, the economics of documentation create a vicious circle. Most European repertoire is released by micro-enterprises that cannot afford dedicated staff for accounting or metadata. They save costs by using spreadsheets or freelance accountants, but this is efficient only in total terms — on a per-unit basis, the costs of documentation and claims are very high. Poor metadata then leads to poor discoverability on platforms, which in turn depresses revenues and leaves even less money for proper documentation. Capital investments (CAPEX) present the same dilemma. Enterprise IT systems or royalty accounting platforms may be cost-effective for catalogues with millions of assets, but are unsustainable for catalogues of a few thousand. As a result, many small actors are locked into obsolete systems that are costly to maintain but too expensive to replace. This structural imbalance means that metadata costs are proportionally higher for small entities than for large ones. Without a way to share infrastructure or reduce per-unit costs, small rightsholders remain stuck: they cannot spend more on documentation and claims than their total royalty income allows, yet under-documentation ensures that much of their income is never collected. These cost barriers are not isolated bookkeeping problems — they are structural features of music data curation. How a data sharing space can provide scale effects and relieve these constraints is discussed in Section 3.2.2. 2.1.3 Why one grand collection model will not work Every actor in music — a library, an archive, a label, or a rights society — has its own way of defining what counts as music, what is a sound recording, how to collect such things, and what belongs in a “collection.” These logics are shaped by their missions, legal obligations, and incentives. A library may collect under a national deposit law, a collective management organisation must register what its members submit, and a distributor includes whatever its clients release. None of these logics are wrong, but they are different. This is why attempts to force everything into one universal collection model have failed. 41 to business and statistical identifiers (e.g. OpenCorporates, NACE, ISCO). This allows music creators and organisations to benefit from smoother workflows, while downstream users gain more reliable data for royalty distribution, cultural visibility, and AI-driven discovery. The Open Music Registers deliberately avoid centralisation. Each registrar — collective management organisations, libraries, archives, or statistical offices — retains ownership of its data but contributes to a shared semantic framework.10 By connecting rather than merging registers, redundancy is reduced while subsidiarity, accountability, and trust are safeguarded across public and private actors. This distributed model directly answers European Parliament’s call for metadata systems that are reliable, inclusive, and supportive of creators.11 2.2.2 Reconciling attribution and privacy The problem of reconciling copyright attribution with GDPR obligations cannot be solved by ignoring either side: both are binding legal requirements. Our approach, tested in the Slovak Comprehensive Music Database (SkCMDb), shows that progress is possible through layered governance and careful balancing. Academic institutions and libraries, with their cultural and research mandates, can lawfully handle personal data under derogations for public-interest processing. Collective management organisations (CMOs) and private actors, by contrast, must rely on legitimate interest tests, supported by transparent documentation, notification to rightsholders, and opt-out mechanisms where possible. ĹInteroperability is a means, not a goal Our Slovak pilot, the Slovak Comprehensive Music Database (SKCMDb), links libraries, rights management, streaming services, and the statistical office. This is not “interoperability for its own sake.” Ontologies and crosswalks are valuable only insofar as they enable better services: •For audiences: making music findable and accessible across cultural and commercial platforms. •For rightsholders: ensuring that attribution, identifiers, and royalty flows are correct. 10Technically, this corresponds to a provenance-oriented modelling approach such as the W3C PROV-O standard (W3C 2013b, 2013a), which connects actors, activities, and entities in chains of attribution (“a composer authors a work, a performer interprets it, a producer records it…”). These chains can be expressed in the layered terms of the European Interoperability Framework (EIF), ensuring legal, organisational, semantic, and technical interoperability (Commission and Digital Services 2017). 11The Data Spaces Support Centre (DSSC) Blueprint v2.0 underlines that identifiers and rulebooks are the foundation of any common European data space (Data Spaces Support Centre 2025b). In the music sector, however, attribution identifiers themselves are caught in the GDPR contradiction (see Section 2.1.5), which underscores the importance of redundancy-free but legally robust registration practices. 48 •For policymakers: providing reliable data to support cultural policy and to measure the music economy. In short, interoperability at the data level is the condition for usable services at the societal level. The Slovak Memorandum of Understanding shows how attribution and data protection can be balanced in practice. -Names of authors, performers, and producers are treated as public-interest information necessary for copyright and royalty flows, justified under legitimate interest. -Sensitive fields (e.g., addresses, nationality, pseudonyms) are excluded from public layers and restricted to controlled-access tiers. -Governance is distributed across CMOs, libraries, and archives, ensuring subsidiarity and trust. This layered compliance model demonstrates that copyright attribution and GDPR obligations can coexist — and offers a template for other Member States and for the European-level Open Music Observatory. These conclusions are consistent with the CITF report, which recommends separating public-interest attribution data from sensitive fields and managing both through tiered access and provenance-tracked RMI. Balancing tests play a central role: each dataset is audited, divided into public and nonpublic categories, and then assessed again for personal vs. non-personal data. Public information such as names of authors, performers, and work titles—already widely available in catalogues and concert programmes—can justifiably be shared under legitimate interest, especially when linked to rights management purposes. Sensitive data (e.g. addresses, nationality, pseudonyms) require stricter access tiers and are only made available to selected stakeholders under contractual safeguards. This layered compliance model does not eliminate GDPR challenges, but it creates a robust defence: it demonstrates that the legitimate interest in accurate attribution and royalty distribution outweighs the minimal risks of publishing already public information. In practice, this means rights metadata can circulate across the ecosystem while privacysensitive data are contained. Building such workflows into federated observatories and data spaces allows the music sector to comply with data protection rules without undermining attribution, and provides a model for European-scale solutions. More broadly, these governance practices are supported by existing provisions in EU copyright and data legislation that already give metadata a central role. Rights Management Information (RMI) is explicitly protected under Article 7 of the InfoSoc Directive (2001/29/EC), making the removal or alteration of attribution data unlawful. The CRM Directive (2014/26/EU) obliges collective management organisations to maintain accurate and transparent repertoire and membership data. Under the CDSM Directive (2019/790/EU), Article 17(4)(b) requires platforms to act expeditiously on notices where metadata enable rightholders to identify and claim their works, while Article 4(3) uses metadata as the operational basis for text and data mining opt-outs. Beyond copyright, the Data Governance Act (2022/868), the Data Act (2023/2854), and the Open Data Di49 rective (2019/1024) provide the horizontal framework for treating music metadata as part of Europe’s emerging common data spaces.12 2.2.3 Pragmatic metadata alignment Attempts to build one comprehensive, harmonised schema for music metadata have repeatedly failed. The sector is too diverse: collective management organisations, libraries, archives, distributors, and platforms all operate with different standards and governance models. Trying to impose a single “grand schema” has proven brittle, costly, and unrealistic. A more workable solution is modular alignment. Instead of a single heavy ontology, small reusable building blocks can be combined to describe recurring patterns — for example, how people, works, recordings, and performances are related. This approach allows interoperability to grow step by step, without forcing any actor to abandon its systems.13 It also helps to separate two complementary tasks. On the one hand, we need conceptual scaffolding that lets different databases describe similar structures in comparable ways. On the other, we need identifier reconciliation to make sure that the same person, work, or recording can be linked across different registers. Neither of these tasks is sufficient on its own: they must work together if metadata is to remain reliable at scale.14 Other domains show how this can be done. Research infrastructures have reconciled ORCID with VIAF authority files, and libraries have mapped DataCite metadata to Dublin Core. Both examples show how two different standards can be aligned systematically while keeping their distinct scopes.15 12See Policy Brief 1: Music Metadata Mainstreaming and EU Law (Senftleben et al. 2024) (Deliverable D5.6, OpenMusE project). That brief analyses how these instruments can be mobilised to improve the reliability and circulation of music metadata. The present Green Paper complements this by showing how federated observatories and interoperability strategies can operationalise these obligations in practice. 13On ontology design patterns and modular approaches, see (Gangemi 2005; Blomqvist, Hammar, and Presutti 2016; Carriero et al. 2021). The Polifonia project applied these methods at European scale (Berardinis et al. 2023), aligning with MusicBrainz and the ChoCo knowledge graph (Albanese et al. 2023). While Polifonia did not focus on rights metadata, it provides a strong foundation for connecting musicological knowledge with industry identifiers. 14This distinction between ontology modelling and identifier reconciliation clarifies why both layers are necessary. Ontology patterns provide conceptual scaffolding (e.g. work–recording–performance), while identifier reconciliation ensures that an author in ISNI is the same as a VIAF authority record or a performer in MusicBrainz. 15For ORCID–VIAF reconciliation via OpenRefine, see (OpenRefine Community 2021; Jegan et al. 2023). For systematic mappings between DataCite and Dublin Core, see (DataCite 2021). 50 Figure 2.4: Pragmatic metadata alignment relies on modular patterns, not “giga-schemas.” The example shown here from our Wikibase pilot encodes roles, events, and provenance using reusable ontology design patterns. This allowed identifiers from rights management (ISWC, ISRC) to be reconciled with library authorities (ISNI, VIAF), proving that interoperability can be achieved incrementally without forcing any actor to abandon its systems. DOI: [10.6084/m9.figshare.30075379.v1](https://doi.org/10.6084/m9.figshare.30075379.v1) Music metadata needs the same periodic reconciliation. Rights identifiers such as ISRC, ISWC, and ISMN were designed separately and drift apart if not actively maintained. The same applies to personal and organisational identifiers such as ISNI, VIAF, and IPI. Without active cross-checking, records fragment, causing duplication and inconsistency.16 In our pilots, this modular alignment has already been tested. The Slovak Comprehensive Music Database reconciled rights identifiers with library authorities without schema unification. MusicBase used Wikibase to encode roles, events, and provenance in a way that let corrections propagate across systems. The Unlabel workflow streamlined metadata capture for self-releasing artists and libraries, allowing once-only documentation to be reused across distribution and preservation. These cases extend our proposal for Open Music Registers, which argued for federated, redundancy-free metadata workflows, into the broader governance framework of this Green Paper, similarly to the CITF three-layer interoperability model17. 16On the divergence of identifiers if not maintained, see (Paskin 2006). 17Our modular ontology patterns correspond to CITF’s semantic layer, while identifier reconciliation aligns with the foundational layer, and federated registries reflect the technical layer. This mapping demonstrates that music-sector practices can evolve within the broader European framework envisioned by CITF (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, 51 Finally, this approach is consistent with work in the heritage sector. The Heritage Digital Twin Ontology (HDTO), developed within the European Cultural Heritage Cloud, uses the same principles of modularity and federation to describe tangible and intangible assets. Where HDTO provides a semantic framework for heritage “digital twins,” the Open Music Observatory extends the same logic to music. Both models show how cultural and rights metadata can integrate with wider European data spaces while preserving subsidiarity and institutional diversity.18 Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp23–33). 18The ECHOES Heritage Digital Twin Ontology (HDTO) builds on CIDOC CRM extensions to model tangible and intangible heritage with space–time–cultural identity (ECHOES Ontology Task Force 2025). 52 3 Open Music Observatory: Building a Shared Music Data Space ĹOpen Music Observatory Our ambition with the development of the Open Music Observatory is to provide the technological basis and a practical roadmap for creating a European Music Observatory in a bottom-up, decentralised way. Instead of waiting for a grand, central agreement, any data owners or collectors who satisfy quality and cooperation rules can add their data. Once the Observatory reaches sufficient maturity, its long-term institutional form can be decided. The Open Music Observatory https://openmusicobservatory.eu/ is a cornerstone task of the OpenMusE project (running until 31 December 2025), delivering data collection, processing, dissemination, and innovative services. It is a digital service provider for the music industry, aligned with the European Interoperability Framework, and introduces a unique governance model that adapts best practices from the EU and other sectors. Transparency note: Following the principles of Open Policy Analysis, we have made all key deliverables (including versions 0.99, 1.01, and 1.1 of the Open Music Observatory document) publicly accessible to foster broad stakeholder engagement and to provide a clear audit trail. These versions are available at https://zenodo. org/records/11564114, while version 1.0 remains internal and was shared only with OpenMusE evaluators. Minor edits, as well as access to the standardised folders, figures, and bibliographies, can be found at https://github.com/dataobservatory-eu/ open-music-observatory. You can access the documentation in PDF, EPUB, and docx [in case you would like to give us comments] here. Citation note: If you refer to the specification of the Open Music Observatory in correspondence, publications, or blog posts, please cite the latest versioned DOI available on Zenodo and, if applicable, include the date of access when referring to material on our GitHub repository.1 The Music Ecosystem 2025 report already emphasised that the music sector should be understood as a distributed ecosystem where value and knowledge are held by many small actors (Music Moves Europe 2024, pp6–7). This perspective reinforces why centralised 1Always use the latest versioned DOI when citing this Open Music Observatory technical report, available via Zenodo. If you rely on supporting material hosted in the GitHub repository, please add the date of access in your reference. 53 repositories fail and why federated observatories, built on cooperation and interoperability, are more realistic. Figure 3.1: Over the past decade, feasibility studies, national reports, and EU pilot projects have laid the foundation for the Open Music Observatory. The roadmap (2014– 2026) shows a gradual build-up: from local experiments, through cross-border collaborations, to a European-wide federation aligned with cultural data spaces and interoperability frameworks. This trajectory underlines the Observatory’s pragmatic, step-by-step approach to scaling music data infrastructure. DOI: [10.6084/m9.figshare.30073291.v1](https://doi.org/10.6084/m9.figshare.30073291.v1) 3.1 Discussion ¾Caution This will be removed consultation - EMO feasibility on scarcity/fragmentation and the need for regular, comparable data; EU dataspace thinking (EIF, FAIR); Music Ecosystem 2025 on systemic view. - Industry positions on centralisation vs. federation; CMOs’ reliance on shared infra (e.g., Mint); heritage sector’s openness requirements. 3.1.1 Why centralisation is a futile model Calls for a centralised European database of music often reappear in policy debates, but in practice such proposals are neither realistic nor aligned with current EU strategies. 54 Centralisation assumes that highly diverse data sources can be harmonised within a single repository. In an ecosystem where knowledge is held by tens of thousands of microenterprises, NGOs, collective management organisations, and heritage institutions — each operating under distinct legal frameworks — this assumption is untenable. CITF reaches the same conclusion. It notes that copyright data is inherently distributed across many custodians with incompatible mandates and governance models, and that no single centralised registry can meet the legal, operational, and semantic requirements of modern copyright workflows. Instead, it argues that future-proof infrastructures must rely on federated, lifecycle-aware registries capable of exchanging trustworthy provenance and rights metadata while preserving institutional autonomy. ĹLessons from the Global Repertoire Database Between 2008 and 2014, European and global stakeholders pursued the Global Repertoire Database (GRD) as a solution to the chronic fragmentation of musical works data. Backed by collective management organisations (CMOs), major publishers, and digital service providers, the GRD aimed to establish a single, authoritative global database of musical works and rightsholders. Its promise was that licensees—especially online platforms—could obtain reliable rights information from one source, reducing duplication and disputes. However, the GRD ultimately collapsed before launch, despite several years of investment and the establishment of a London-based operating company. A similar project, the International Music Registry project, which was backed by the World Intellectual Property Organization, ended with similar results2. Post-mortems identified several reasons: - Governance conflicts: disagreements between major publishers, CMOs, and other stakeholders over who would control and fund the database. •High costs and unclear incentives: the project’s projected maintenance costs exceeded what many participants—particularly smaller CMOs—were willing or able to sustain. •Asymmetries of power: large publishers and CMOs were reluctant to share sensitive commercial data on equal terms with competitors. •Lack of trust: concerns over who would “own” the data and how revenues would be redistributed undermined cooperation. The failure of the GRD is now widely cited in policy and industry discussions as evidence of the limits of centralised, “single-database” solutions in the music sector. Similar initiatives even failed on national levels. We can also add that centralisation, even if it was possible, would pose a new risk of creating monopolistic gatekeepers to the music ecosystem. The predecessor of the Open Music Europe project, CEEMID, was based on the lessons of the following problems and on the insights of a decentralised, dataspace like approach (Antal 2020). In such federated, interoperable approaches—where data 55 remains with its custodians but can be linked through shared identifiers, standards, and protocols—have proven more viable. CISAC’s CIS-Net, Europeana in the heritage field, and emerging European data space initiatives exemplify this more distributed model of governance. CITF’s analysis reinforces these lessons. It identifies governance opacity, unclear mandates, and incompatible identifier regimes as recurring causes of failure in large-scale copyright registries. It stresses that unless registries adopt transparent governance, open identifiers, and shared semantic profiles, centralised projects inevitably collapse under conflicting incentives3. EU infrastructure initiatives have already moved beyond this logic. Since the 2000s, projects such as Europeana, the European Open Science Cloud (EOSC), the European Collaborative Cloud for Cultural Heritage (ECCCH), and DARIAH have all adopted federated architectures, linking distributed collections through shared standards and interoperability frameworks rather than consolidating them into one database. The Audiovisual Observatory, established in 1993 as a centralised reporting body, represents an earlier institutional logic that is now being phased out in favour of federation. The heritage sector, including music heritage, has consistently stressed the need for open, federated models. Libraries, archives, and museums use authority files and collaborative platforms (e.g. VIAF,Wikidata,Wikibase) to enable interoperability while preserving institutional autonomy. Commercial infrastructures do the same: the ISRC system, managed by IFPI, is inherently decentralised, while CISAC’s CIS-Net gives access to rights data without centralising ownership. Even the Mint initiative, launched by CISAC and Armonia Online, shows how shared infrastructure can deliver economies of scale for identifier allocation and metadata management while avoiding dependence on a single repository.4 Even official governmental statistics, often seen as centralised, are in reality decentralised. The ESSnet-Culture project, coordinated under Eurostat, produced the first comprehensive framework for cultural statistics in 2012, adapted from the UNESCO model, and remains a “basic reference” for the field. More broadly, national statistical offices, labour force surveys, and administrative registers each collect partial data, which are harmonised at EU level for comparability. Increasingly, surveys and administrative datasets are complemented by flows from platforms, rights management organisations, and other industry actors. Indicators therefore emerge from hybrid constellations of public and private data sources, confirming that decentralisation is a structural feature of European 3See for example Goldenfein and Hunter (n.d.); Milosic (2015). 3See (Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula 2025, pp15–18). 4On heritage practices, see (Bianchini, Bargioni, and Pellizzari di San Girolamo 2021, p210) and (Sardo and Bianchini 2022, p297), which describe how VIAF, Wikidata, and Wikibase function as authority tools in libraries and archives. On identifiers, the ISRC Handbook (International ISRC Registration Authority 2021, p5) explains the decentralised structure of the ISRC system, while CISAC’s Mint Digital Services (CISAC/SUISA/SESAC 2017) illustrates how federated allocation works in practice. Together, these examples show how distributed stewardship and shared standards underpin global metadata infrastructures. 56 evidence creation.5 3.1.2 Open Data Directive: right without means The Open Data Directive grants a right of reuse for public-sector information and requires that certain “high-value datasets” be made freely available across Europe (Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on Open Data and the Re-Use of Public Sector Information 2019). This includes cultural heritage institutions such as libraries, museums, and archives. However, the Directive stops short of providing the means to ensure that such data is actually usable. Studies consistently show that open data often remains more of a promise than a reality. In practice, much open data is poorly documented, lacks common identifiers, and is released in unstandardised formats. While it may be free of charge or available at marginal cost, making it interoperable and trustworthy for cross-border use requires significant additional effort. The burden of curation, harmonisation, and enrichment falls on downstream users, which can be prohibitively expensive for smaller organisations. As the CEDAR project put it, “Public authorities are only required to make existing data available, not to create new data or improve existing systems. This leads to significant disparities in usability and accessibility” (Project 2023). A recent EU-wide usability study adds that “many open data portals remain difficult to navigate, poorly documented, and inconsistent in their metadata quality, limiting actual reuse” (Jachimczyk and Nowak 2024). These structural weaknesses of open data provision set the stage for the Observatory’s role in providing workflow playbooks and redundancy-free registration, discussed in Section 3.2.6 3.1.3 Why voluntary workarounds do not scale The Slovak pilot shows that voluntary workarounds for attribution under GDPR are possible (see Section 2.1.5), but they do not scale. Even with strong communication and 5The ESSnet-Culture framework (Commission et al. 2020, p9) demonstrates how cultural statistics are built on national contributions harmonised at EU level, not on central databases. A Slovak pilot (Antal 2023) further illustrates how decentralisation works in practice, integrating public and private sources into coherent cultural indicators. 6Early modelling stressed the economic potential of open data but also identified major obstacles in practice: lack of availability, uneven quality, and poor usability (Carrara et al. 2015, p7; Huyer and van Knippenberg 2020, p14). Comparative studies show that simply granting a right to reuse rarely produces machine-actionable datasets. In complex domains like music, where attribution depends on precise identifiers, these shortcomings become particularly costly. Cross-sector reviews underline persistent fragmentation: heterogeneous formats and divergent practices across Member States (Buttow and Meijer 2024, p12); variability even in high-value geospatial datasets (Kević, Kuveždić Divjak, and Welle Donker 2023, p3); and sectoral case studies (e.g. mineral intelligence) repeatedly call for shared profiles beyond legal openness (Simoni, Aasly, and Schjøth 2021, p5). Additional evidence shows that preparing legacy administrative data for reuse requires cleansing and enrichment that impose real costs, even when the data are nominally “open” (EuroSDR 2021, p9; Schnurr 2021, p14; Nakos and Tsoulos 2022, p6). 57 study and the CITF process can be implemented in practice through federated, noncentralised infrastructures. The national and regional pilots referenced throughout this section — including the Slovak Comprehensive Music Database, its replication in Hungary, the Baltic and Latvian pilots, and the Finno-Ugric Data Sharing Space — function as policy-embedded test cases that validate, refine, and contextualise these requirements within existing legal competences, institutional mandates, and public–private cooperation frameworks. In this sense, the Open Music Observatory is positioned neither as a standalone technical solution nor as a new central authority, but as a convening, conformance, and observability layer that operationalises the findings of the Study on copyright and new technologies and the first CITF report. It provides a practical mechanism for translating earlier Commission analysis and task-force recommendations into reusable governance patterns, interoperable workflows, and auditable data practices suitable for the digital and AI era. ĹPublic–private reconciliation in practice Reconciling public and private infrastructures: the ALOADED pilot in Latvia The Unlabel workflow was tested with Latvian archives and the distributor ALOADED, demonstrating how public heritage metadata can be reconciled with private music supply chains. • Archival recordings (including Hilda Griva’s songs and Latvian/Latgalian midsummer songs) were identified in the Latvian Archives of Folklore. • Metadata was translated, enriched, and aligned with international authority files. • ALOADED extended this material with DDEX-compliant catalogue transfers and ingested it into Spotify and other platforms. This pilot demonstrates that reconciliation between public infrastructures (archives) and private infrastructures (distributors and platforms) is both technically and institutionally feasible. It reconnects suppressed or marginalised repertoires with contemporary audiences without requiring centralisation or loss of institutional control. A more technical description of the workflow is available here: https://downloads.reprex.nl/2025/open-music-observatory/coordination.html#secfuture-proofing Conformance and observability rules in the Open Music Observatory should be designed in line with the European Interoperability Framework (EIF) and the FAIR data principles. This alignment ensures compatibility with wider European data-space initiatives and reduces integration costs for institutions already adapting to these standards (Commission et al. 2020, p9). 64 Figure 3.3: The [Open Music Observatory](https://openmusicobservatory.eu/) sits where open science, public sector information reuse, and music industry workflows overlap. By aligning with the European Interoperability Framework, it creates a shared space where libraries, rights managers, publishers, and researchers can collaborate. This positioning highlights OMO’s role as a bridge between cultural heritage, commercial distribution, and open knowledge. DOI: [10.6084/m9.figshare.30073267.v1](https://figshare.com/articles/dataset/The_Open_Music_Observatory_at_the_Intersection_of_Open_Science_Open_Data_and_Music_Industry_Workflows/30073267/1?file=57754399) 3.2.1 Workflow playbooks and provenance trails The Observatory should not only harmonise data formats, but also document workflow playbooks that describe how metadata moves across the music lifecycle: • from rights registration, • to distribution and royalty attribution, • to charting and visibility, • to long-term preservation. Each step should define change-propagation rules: when a correction is made in one register, it should propagate to dependent systems. Provenance trails must survive system boundaries, using standards such as PROV-O to document who did what, when, and under what authority. This enables auditability, cross-border comparability, and long-term trust, and prevents “data death” when assets move between systems. 65 3.2.2 Federated infrastructure as a cost and governance solution The Cultural Compass for Europe establishes a clear strategic direction for European cultural policy: shared, interoperable, and federated data infrastructures that support evidence-based policymaking, cultural diversity, and trustworthy use of artificial intelligence. The planned EU Cultural Data Hub reflects this ambition at cross-domain level. At the same time, the Compass does not prescribe how sector-specific complexities should be handled in practice. This section argues that music represents a domain where such complexity exceeds what generic cultural data infrastructures can reasonably encode, and therefore requires a specialised, federated implementation layer that remains fully compatible with the broader European framework. The Open Music Observatory should be understood precisely in this role: not as an alternative to the EU Culture Data Hub, but as a musicand music-rights– specific implementation layer that operationalises the Hub’s objectives for one of Europe’s most complex cultural sectors The imbalance described in Section 3.1.6 makes one point clear: small and medium-sized actors cannot compete on metadata quality without shared infrastructure. In a sector characterised by extreme fragmentation, federation — rather than centralisation — is the only viable path forward. A data sharing space provides the appropriate governance framework for such federation. Instead of forcing participants into a single schema, database, or legal agreement, it enables organisations to share and reuse data on an as-needed or as-permitted basis while retaining stewardship over their own assets. For music — where rights, identifiers, and content are distributed across hundreds of micro-actors and institutions — this approach reduces duplication without creating dependency, while remaining compatible with cross-domain cultural data infrastructures such as the EU Cultural Data Hub. Crucially, federation avoids the risks inherent in centralisation: technical fragility, governance lock-in, and the emergence of a single gatekeeper capable of restricting access or imposing unilateral conditions on others18. These risks are particularly acute in copyrightrelevant environments, where errors in attribution or provenance have direct legal and economic consequences. Music represents one of the most demanding test cases for European data governance. Attribution interacts directly with privacy law, identifiers are applied unevenly across the sector, and most music enterprises lack the resources to maintain their own compliance, documentation, and audit infrastructures. If a federated model can function in this environment, it can function elsewhere. At the same time, decentralisation also creates incentives 18This definition paraphrases (Curry 2020) and reflects the view that a data sharing space is an ecosystem of exchange, processing, sharing and provision of data between trusted partners (EBU and Gaia-X 2022, p16). The CITF further emphasises that trustworthy provenance, machine-readable rights metadata, and auditable rights management information are essential components of such federated copyright infrastructures, particularly in the AI era [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), p31; pp101–102]. 66 for data hoarding. Effective governance must therefore make participation more attractive than isolation — through lower administrative costs, legal clarity, shared compliance benefits, and increased visibility. In practice, this requires combining hard alignment (minimal metadata profiles and baseline identifiers) with soft alignment (mappings, crosswalks, and shared workflow playbooks). This combination allows interoperability to be achieved without forcing all actors into a single organisational or technical model, and ensures that music-specific solutions remain interoperable with the EU Cultural Data Hub and other cross-domain infrastructures. For these reasons, a future European Music Observatory, as part of the emerging EU Culture Data Hub ecosystem, cannot be designed as a single central database. Instead, it must function as a federated, domain-level observability and coordination layer. In this role, the Observatory complements the EU Culture Data Hub by providing validated, rights-aware, provenance-rich music data that can be aggregated at European level without centralising ownership or governance. The Open Music Observatory is conceived as a musicand music-rights–specialist federated unit that connects shared cultural data infrastructures—including the European Collaborative Cloud for Cultural Heritage,Europeana and its emerging data sharing space, and the EU Culture Data Hub—with sector-specific workflows in music creation, distribution, rights management, and preservation. Rather than duplicating cross-domain functions, it supplies the missing domain logic required for music to be meaningfully represented within European cultural data infrastructures. Architecturally, the Open Music Observatory inherits CEEMID’s core principles: federation rather than centralisation, reproducible indicators rather than static datasets, and collaboration across public, private, and academic actors. What changes is not the logic, but the scale and governance context. OMO aligns these principles with the European Interoperability Framework, FAIR data principles, the common European data space for cultural heritage, and the work of the Copyright Infrastructure Task Force, ensuring coherence across cultural, data, and copyright policy domains. Earlier experimentation, including the CEEMID initiative and the Open Music Observatory’s Slovak cultural statistics pilot within the Open Music Europe project, already anticipated this architecture by demonstrating how music-specific data coordination could feed policy-relevant indicators without centralising data—an approach that aligns closely with the design principles now articulated for the EU Culture Data Hub. A central objective of the Slovak pilot was to demonstrate how music-specific statistical indicators could be produced in line with best statistical practice, where existing cultural and creative satellite accounts fall short, which are themselves rarely implemented in EU member states19.Drawing on established methods for business satellite accounts, the pilot 19Pilot Program for Novel Music Industry Statistical Indicators in the Slovak Republic (Antal 2023). The pilot was not implemented within the OpenMusE project due to resource constraints and institutional changes, but its methodological design remains valid and suitable for continuation in the context of EU Cultural Data Hub preparatory work. 67 sought to harmonise authoritative collective rights management data with administrative registers, industry datasets, and statistical surveys in order to generate indicators that are not available through Slovakia’s current cultural and creative satellite accounting system. In practice, this involved linking rights management information—often the most complete and economically meaningful source for music activity—with official statistical frameworks, while preserving methodological transparency, reproducibility, and comparability. This approach responds to a broader structural gap at European level: most EU Member States do not operate dedicated cultural or creative satellite accounts at all, and where such systems exist, they rarely capture the distinctive revenue structures, cross-border flows, and attribution dynamics of music. The pilot therefore addressed not only a national shortcoming, but a systemic limitation of European cultural statistics, anticipating the need for domain-specific data pipelines capable of producing robust, music-aware indicators. While the OpenMusE project was ultimately unable to carry out this experiment due to resource constraints and changing institutional conditions, the design remains methodologically sound and operationally feasible, and could be taken forward as part of the preparatory piloting of the EU Cultural Data Hub. Recent analysis of cultural heritage data governance in the context of artificial intelligence clarifies that the limitations of existing heritage infrastructures should not be interpreted as a reason to exclude libraries and memory institutions from future data architectures. On the contrary, these institutions occupy a distinctive position in the AI era precisely because they combine long-term custodianship with public accountability, authority control, and responsibility for provenance and attribution20. From the perspective of the Cultural Compass, integrating these institutions more directly into copyright-relevant data flows is essential for ensuring that attribution, provenance, and accountability are preserved across the full lifecycle of music data. Europe already has the necessary policy and technical foundations for this approach. The European Strategy for Data, the Data Governance Act, and the Data Act define data spaces as federated by design21. The Data Spaces Support Centre (DSSC) has translated these principles into operational blueprints that can be applied directly to the music sector22. Comparable logics already exist in music and adjacent domains: ISRC governance through national agencies, CISAC’s CIS-Net, and European statistical systems based on subsidiarity rather than central repositories. Concerns about digital sovereignty make this approach urgent. In the absence of a European solution, metadata infrastructures risk drifting toward US-style centralisation, such as the Mechanical Licensing Collective (MLC), where legislative mandates and market power converge in a single hub. OpenMusE Deliverable D5.6 explicitly warns against this outcome23. This Green Paper complements that legal-institutional analysis by demonstrating 20Publishing Cultural Heritage Data in the Age of AI (Keller 2025) 21The European Strategy for Data (2020), the Data Governance Act (2022), and the Data Act (2023) establish federated data spaces supported by trust frameworks and shared services (European Commission 2020; European Parliament and Council 2022). 22The Data Spaces Support Centre (DSSC) provides blueprints and building blocks for implementing federated data spaces across sectors (Data Spaces Support Centre 2025b, 2025a). 23See Policy Brief 1: Music Metadata Mainstreaming and EU Law (Senftleben et al. 2024). 68 how a federated, culture-led infrastructure can translate EU law into practice. In practical terms, this requires capture-once, reuse-many pipelines across the music lifecycle: from registration of works and recordings, through distribution and royalty attribution, to preservation and cultural statistics. The Observatory should therefore function as a redundancy-minimising registration and coordination space, aligned with the European Interoperability Framework and provenance-oriented models such as PROV-O24. When implemented effectively, this approach lowers entry barriers for smaller actors, improves interoperability across institutions, and grounds Europe’s cultural and economic policies in reliable evidence rather than fragmented silos. Broader debates on AI governance caution that technological systems can reproduce existing hierarchies when governance structures remain unequal. Guest, Suarez, and van Rooij warn that AI may extend “projects of domination, of hierarchies, of extractivism of cognitive labour”25. Federated, transparent, and participatory infrastructures therefore play a corrective role by preventing the concentration of informational and cultural power. This consideration is particularly salient in the Finno-Ugric Data Sharing Space music module (https://finnougric.net/en/), which addresses decolonisation and subsidiarity by consolidating dispersed knowledge about the musical heritage of Livonians, Sámis, Maris, and Setos. The posthumous publication of Hilda Griva’s recordings illustrates how federated infrastructures can enable cultural self-representation rather than algorithmic erasure. 3.2.3 Legal, standards, and funding levers For these proposals to succeed, they must be supported by legal clarity, lightweight standards, and sustained public investment: • Legal: explicit GDPR legal bases per data flow (for example, legitimate interest for attribution; research and cultural-heritage exemptions for archives). • Standards: codes of conduct and minimal conformance profiles that remain achievable for micro-enterprises. • Funding: targeted support through ECCCH pilots, national ministries, and EU programmes for metadata fitness and data-quality improvements as public-interest infrastructure. 24The European Interoperability Framework defines interoperability across legal, organisational, semantic, and technical layers (Commission and Digital Services 2017). The W3C PROV model and PROV-O ontology enable standardised provenance chains linking actors, activities, and entities (W3C 2013b, 2013a). 25Towards Critical Artificial Intelligence Literacies (Guest, Suarez, and Rooij 2025, p3) 69 3.2.4 Alignment with the European Open Science Cloud Position the Open Music Observatory as the convening, conformance, and observability layer connecting ECCCH, Europeana, and GLAM authority files with industry workflows. Concretely: 1. Capture once, reuse many across creation, registration, distribution, and preservation. 2. Require minimal profiles that smaller actors can realistically implement. 3. Prioritise identifier crosswalks (ISRC–ISWC–ISNI–VIAF/Wikidata) and change propagation. 4. Use Wikibase and Wikidata as a low-friction backbone where appropriate. 5. Govern through EIFand FAIR-aligned rules with auditability and public–private participation. This reframes Europe’s investments from siloed repositories into a shared music data space that respects subsidiarity, lowers reconciliation costs, and enables interoperability across public and commercial contexts — the practical foundation for any future European Music Observatory. 70 4 AI that Works for Music, Not Against It Most AI projects fail because they chase hype. MIT’s Project NANDA found that 95% of enterprise initiatives with generative AI delivered no measurable value. Budgets were spent on flashy pilots in sales or marketing, while the real potential — reducing back-office costs, prolonging the life of legacy systems, and avoiding constant IT churn — was overlooked. Our approach is different. We do not see AI as “for its own sake.” Instead, we treat it as a way to reduce IT churn, keep legacy systems alive longer, and cut both capital and operating expenses. Where once every new regulation, distributor change, or catalogue migration required costly upgrades, curative AI can patch outputs from existing software, extend the lifespan of old systems, and make them interoperable with new ones. Shared infrastructures make this practical for micro-enterprises, NGOs, and collective management organisations (CMOs), who could never maintain such capacity in-house. The European Parliament’s resolution on the music streaming market warns of the risks that AI-generated content poses for discoverability, attribution, and fair remuneration if metadata remains incomplete or unreliable. At the same time, the Music Ecosystem 2025 study highlights that AI will be both a disruption and an opportunity: while it can overwhelm systems with synthetic material, it also offers tools to automate documentation, reduce costs, and strengthen evidence-based policymaking (Music Moves Europe 2024, 23– 24). 71 Artificial intelligence is therefore central to the future of Europe’s music ecosystem. On one hand, it threatens to exacerbate existing inequalities by concentrating technological advantages in platforms and major rights holders. On the other, it can repair, enrich, and automate processes that are otherwise prohibitively costly for small actors. The challenge is not whether AI will be used, but whether its benefits will be distributed fairly across the ecosystem. Figure 4.1: The Culture Compass for Europe recognises both the opportunities and risks of AI for cultural and creative sectors. The Culture Compass for Europe recognises both the opportunities and risks of AI for cultural and creative sectors. Music illustrates these risks earlier and more sharply than most domains, because AI systems rely heavily on large-scale music datasets for training, recommendation, and generation. In this context, trustworthy provenance and rightsaware metadata are not optional safeguards, but prerequisites for lawful and ethical AI deployment. The use, development and governance of artificial intelligence (AI) systems should foster human creativity through a fair human-centric and rights-based 72 approach. It should: respect cultural rights, accessibility, inclusivity and cultural diversity; develop and promote discoverability of European, national and local content; foster competitiveness; counter digital divides; and foster digital inclusion. We commit to: • Promoting human creation and European cultural and linguistic digital sovereignty, and addressing ethical risks of biases and cultural homogenisation. • Protecting intellectual property rights, by addressing the impacts of AI on creators’ remuneration, while embracing innovation. • Monitoring and mitigating the impact of AI on jobs, as well as supporting the cultural and creative sectors and industries adapt to technological change and acquire digital skills. • Fostering the use of AI as a tool to support cultural and creative professionals, and enabling the cultural and creative sectors and industries to harness the opportunities offered by these technologies. Draft Joint Declaration “Europe for Culture — Culture for Europe” (European Parliament, Council of the European Union, and European Commission 2025) European policy provides guidance and evolving regardingto this balancing act. The Ethics Guidelines for Trustworthy AI underline that AI must be lawful, ethical, and robust throughout its lifecycle (Commission, Directorate-General for Communications Networks, and Technology 2019). The Getting the Future Right report by the Fundamental Rights Agency stresses the need to align AI with fundamental rights, especially where vulnerable groups and cultural participation are concerned (European Union Agency for Fundamental Rights 2020). Most recently, the AI Act enshrines a risk-based regulatory framework, defining obligations for providers and deployers of AI systems while reaffirming the principles of subsidiarity and proportionality in EU digital policy (European Parliament and Council 2024). Our own engagement with these issues began with the Listen Local feasibility study in 2020. By experimenting with the Spotify API, we discovered that Slovak users were rarely recommended Slovak music — not because Spotify was at fault, but because the data about local repertoire was sparse. Spotify’s open API was, in fact, uniquely transparent compared to competitors, and it enabled us to see a larger policy problem: without structured, machine-readable knowledge of diverse repertoires, algorithms cannot deliver fair outcomes. This lesson has guided our work ever since: improving metadata and interoperability is the first step to better AI governance.CITF reaches the same conclusion1. 1The CITF report stresses that AI-related obligations cannot be met unless rights metadata, identifiers, and provenance chains are trustworthy and repairable, and that fragmented RMI makes it impossible to ensure fair attribution or compliant AI outputs [Partanen, Rixhon, Bandere, Ziediņš, Dutt, Bolšteins, Frosterus, Lehtinen, Miklūna-Žukeviča, Ozerskis, Pihlaja, Sauka, Sornova, and Uzula (2025), pp20; p31]. 73 4.1.5 Unfreezing frozen assets Many music assets remain “frozen” because their documentation costs exceed their current commercial value. This applies to non-commercial repertoires, small-label releases, and culturally valuable but low-market recordings. Without affordable workflows, these works cannot enter modern distribution systems, regardless of their cultural or artistic significance. The Unlabel pilot illustrates this problem: by treating catalogue transfers and documentation as high-cost, high-friction processes, valuable repertoires remain locked away. AIassisted metadata repair and DDEX-compliant catalogue transfer workflows provide a pathway to lower costs and bring neglected repertoires back into circulation. ĹNote Example: Old SQL Database in a Cultural Institution • A label or archive has a recording stored in a 20–30 year-old SQL database, built on a schema that was never fully documented. The system’s author is retired (or no longer alive). • The institution wants to re-release the recording, but to distribute it today, the metadata must be expressed in DDEX Catalogue Transfer messages — a completely different schema, designed decades later. Curative AI • Acts at the system level: it can “read” the old database structure, infer undocumented field meanings, and patch outputs so the legacy database can still talk to modern pipelines. • Instead of rebuilding or migrating the old database (expensive, risky), curative AI extends its lifespan by making its outputs usable. Reparative AI • Acts at the metadata/epistemic level: it can detect inconsistencies or missing fields (e.g., composer names stored in free-text notes, titles in mixed languages) and reformat or enrich them into structured DDEX-compliant fields. • This not only enables distribution but also restores visibility for works that might otherwise remain trapped in inaccessible formats. The policy point • Without curative/reparative AI, such recordings risk becoming “frozen assets”: legally owned but practically undistributable because the metadata cannot be transformed. • By investing in these AI uses, Europe can preserve access to cultural heritage, reduce IT churn, and ensure that both heritage archives and independent labels can connect to modern digital value chains. 80 Unlike U.S.-style copyright, Europe’s author’s rights regime contains a moral component. Authors (and, for a period, their heirs) retain certain rights over how their works are used, even after economic rights expire. This recognises that works are part of a creator’s moral and cultural heritage, not only economic assets. Various legal norms, for example, local content guidelines, also gave tool earlier to national or ethnic communities to provide some guardrails to the use of their shared heritage, even this means community stewardship and not inheritance in legal terms. Metadata repair and publication strengthen visibility, but also create risks that generative AI will use these works in ways that undermine moral rights, where heirs object to uses they see as distorting or trivialising an author’s legacy and community stewardship norms, where groups perceive their folk or minority heritage as being misappropriated, even when no legal infringement occurs. While we do not identify these challenges at this point as similarly actionable public policy challenges as the problems of GDRP and the creation of trustworthy music AI, regulators do face political risk if ethical expectations of communities around cultural stewardship are not addressed. Even if no author’s rights or other legal norms are breached, the ability to create “fake” Livonian, Latvian or Basque folk songs may strongly conflict with the expectation of communities on the ethical use of AI. 4.1.6 AI support for investment into new repertoire assets While generative AI that disregards human repertoires can undermine cultural value, AI also has constructive roles. Just as photographers benefit from embedded AI in tools like Photoshop or GIMP, musicians and producers can use AI to reduce the costs of composition, recording, and documentation. In practice, this means that creating new works and registering them with identifiers can become less burdensome and more accessible. This perspective aligns with the European Parliament’s call for “metadata from birth” (European Parliament 2024), but it goes further. AI can not only generate metadata automatically at the moment of creation, but also support sound recording, scoring, and archiving processes directly, ensuring that new assets enter circulation with complete, interoperable metadata. 4.2 Policy Proposals: Aligning AI with Governance and Value Creation Generative, agentic, and inference AI are now woven into the global creative economy. But value is not created by algorithms alone — it comes from governance, curated data, and institutions that ensure trust. Policy interventions are needed on three levels: EU,industry, and organisational. 81 Our focus is the metadata and data needs of the music ecosystem — labels, distributors, publishers, managers, CMOs, archives — not the creative act of composing music itself. 4.2.1 EU-Level Policy: Compass and Guardrails •Embed cultural sectors in the EU AI Act & Data Spaces so music and cultural industries are not treated as “low risk.” •Subsidise shared AI utilities for identifier reconciliation, metadata repair, and fraud/plagiarism detection. •Adopt “metadata from birth” principles: embed ISNI/ISWC/ISRC identifiers at the point of creation. •Tax incentives for onboarding frozen assets, supporting digitisation and enrichment of under-documented catalogues. •Resolve attribution vs GDPR conflicts through legal clarification or jurisprudence, enabling fairness testing and copyright compliance. 4.2.2 Industry-Level Policy: Standards and Collaboration •Codes of conduct for AI in music, modelled on GDPR codes. •Identifier crosswalks across ISRC, ISWC, ISNI, VIAF, etc. •Federated AI services for claims, reconciliation, multilingual enrichment. •Training and reskilling to close the AI/data talent gap. •Working capital optimisation through AI-assisted claims and faster distributions. These principles do not stand in isolation: they echo and extend ongoing work such as the Responsible AI Music framework, ensuring that sector-specific practices in Europe are consistent with emerging international standards.8 8The Responsible AI Music framework (RAIM) sets out principles for transparency, fairness, sustainability, and accountability in the use of AI in music (Herremans, Sturm, et al. 2025). Several of the codes of conduct proposed here — such as clarity around data provenance, safeguards for attribution, and limits on exploitative recommendation practices — align closely with RAIM’s recommendations. Where RAIM defines broad principles, this Green Paper provides concrete mechanisms for their operationalisation within European music data spaces and observatories. 82 4.2.3 Organisational-Level Policy: Playbooks for CMOs, Publishers, Archives •Embed AI in workflows so metadata is generated and validated during creation/distribution. •Capture once, reuse many times, reducing redundant re-entry. •Invest in knowledge capital, not IT churn (ontologies, vocabularies, multilingual enrichment). •Subscribe to shared AI utilities instead of bespoke in-house builds. •Develop internal AI governance — even small actors can appoint an “AI steward.” 4.2.4 Curative AI and Reparative AI as a Remediation Solution While data spaces establish rules for new data flows, they do not address the legacy backlog of poorly formatted or incomplete open data. Here, curative AI provides a complementary solution. AI-assisted services can detect duplicates, infer missing identifiers, reconcile heterogeneous formats, and enrich metadata with multilingual descriptions. In effect, they transform datasets that are legally open but practically unusable into resources that can circulate across the ecosystem. ĹNote Curative AI as regeneration, not replacement Figure 4.2: ���� (Ise Grand Shrine): a wooden sanctuary in continuous use for 1,600 years thanks to regeneration practices handed down through generations. 83 The Ise Grand Shrine in Japan has been in continuous use for 1,600 years — not because its wooden beams never rotted, but because the knowledge of renewal was embedded and transmitted across generations. The true asset was the embedded know-how of regeneration, not any single plank of wood. Curative AI can play the same role in the digital domain: - Extend the life of legacy systems by fixing patchy outputs from old ERPs, catalogues, or distributor software. - Preserve the methods of repair: how to reconcile corrupted records, reshape data for new systems, and upgrade databases while remaining compatible with older formats. - Transform investment logic: instead of constant capex for new IT systems, shared data infrastructures with curative AI reduce costs, smooth opex, and deliver futureproof and past-proof services. Our pilots — such as Unlabel and SKCMDb — show that new value can be created without additional IT investment or system upgrades by the participating companies, libraries, and rights management agencies. Thus, governance and remediation are two sides of the same coin: -Data sharing spaces ensure that new data is created in interoperable ways. -Curative AI repairs the inherited stock of legacy and low-quality datasets. Together, they close the gap between the right of reuse (granted by the Open Data Directive) and the means of reuse required for music, culture, and AI-driven innovation. 4.2.5 Lowering Documentation Barriers We propose to adapt Unlabel’s approach as a model for unfreezing frozen assets. By leveraging AI-assisted metadata repair and DDEX-compliant catalogue transfer workflows, documentation costs can be reduced enough to enable non-profits, small labels, and community archives to register and redistribute neglected repertoires. Public support should subsidise onboarding costs, create standardised pipelines, and incentivise low-friction reuse of metadata across systems. 4.2.6 Observatory: European = Open When we call for a European Music Observatory, the adjective “European” should not be read as a cultural filter that limits scope to European repertoires. Music is, and always has been, global. The task of the Observatory is not to create an insular archive of “European music,” but to build a governance and data architecture rooted in European values: •Data sovereignty — ensuring that creators, communities, and institutions have meaningful control over how their metadata and works are represented. 84 •Subsidiarity — solutions should be built at the lowest effective level, allowing national archives, collective management organisations, and industry actors to contribute without being absorbed into a single monolith. •Inclusiveness — minority repertoires, independent artists, and small markets must be equally visible alongside the global catalogues of multinational platforms. Our Finno-Ugric case studies show how fragile metadata can be repaired without erasing community perspectives — a model that must be embedded at Observatory scale9. This is why we chose the name Open Music Observatory (OMO). Even if the policy framework ultimately labels it the “European Music Observatory,” the essential principle must remain openness — of infrastructure, of governance, and of participation. The Observatory should be a federated, open knowledge space, not a centralised database. Europe has an opportunity to take a step that resonates beyond its borders. The U.S. Music Industry Licensing Collective (MILC) demonstrated how a single initiative could set standards and ripple globally. An Open Music Observatory, grounded in European governance but open to the world, could play a similar role — aligning sovereignty with interoperability, and showing how collective data architectures can provide guardrails for AI in a truly global music ecosystem. 4.2.7 The Open Music Observatory as a Collective Guardrail AI will only create sustainable value for music when governance, interoperability, and human capital are aligned. But building effective guardrails for agentic and generative AI cannot be done by individual firms or even national markets. - At the business level, companies lack the scale and incentives to police AI use of metadata. - At the industry level, cooperation is necessary but often fragmented by competing interests. This is where the European Union can play a decisive role: - Coordinating and aligning existing investments in Europeana, the European Collaborative Cloud for Cultural Heritage (ECCCH), and the new data sharing spaces. - Anchoring these initiatives in an Open Music Observatory (OMO) built around federated, Wikibase-compatible knowledge graphs. - Ensuring that metadata repair and publication feed into collective data architectures that double as guardrails — improving attribution and interoperability while reducing the risk of generative AI misuse. 9We have created the second federated module of the Open Music Observatory with contemporary popular and authentic folk music of European Finno-Ugric minorities who do not have a nation state. (Antal et al. 2025) 85 ĹWikidata Embedding Project: An Open Model for AI Guardrails In 2024–25, Wikimedia Deutschland, in collaboration with Jina.AI and DataStax, launched the Wikidata Embedding Project. - Its goal is to add vector-based semantic search to Wikidata, combining its multilingual knowledge graph with modern embedding models. - This enables context-aware retrieval for AI systems while anchoring results in a public, verifiable knowledge base. Why it matters for music policy - Shows that guardrails for generative AI can be built on open, communitymanaged graphs rather than proprietary black boxes. - Demonstrates how semantic search and retrieval-augmented generation can: - Reduce hallucinations by grounding outputs in human-verified data. - Combat misinformation with verifiable references. - Amplify underrepresented knowledge by balancing global visibility. Implication for the Open Music Observatory (OMO) - By adopting Wikibase-compatible knowledge graphs and existing ontological patterns, OMO can build similar guardrails for music. - This positions metadata repair and publication not just as technical fixes, but as part of a collective data architecture that keeps AI accountable. The OMO model would provide: -Compass and coordination at the EU level. -Standards and shared utilities through industry cooperation. -Flexible governance and playbooks for organisations. With this architecture, AI becomes an infrastructure for continuous renewal: prolonging legacy systems, unfreezing frozen assets, and supporting both heritage and new repertoires — while embedding guardrails against substitution and misappropriation into the very data fabric of Europe’s music ecosystem. 86 5 What Europe Should Do Next for Music Data & AI Europe’s music ecosystem is under pressure. Streaming pays in micro-royalties, metadata mistakes cost real money, and AI threatens to overwhelm platforms with untracked content. But solutions are within reach. This Green Paper sets out a path forward, built on three pillars: better metadata, shared data spaces, and AI that works for everyone. (See Chapter 1for the background and policy context.) These priorities align closely with the emerging framework proposed by CITF: trusted identifiers, lifecycle-aware provenance, and interoperable, federated rights metadata. The evidence presented in this Green Paper suggests that the primary challenge for European music data policy is no longer the absence of conceptual frameworks or technical standards, but the limited scaling and coordination of solutions that already exist and have been partially implemented. Future European action should therefore prioritise the consolidation, federation, and long-term governance of proven approaches, rather than the repeated creation of new pilot structures detached from policy and institutional contexts. In this respect, the Open Music Observatory and related national implementations should be understood as components of an evolving policy infrastructure, capable of informing regulatory discussion, institutional coordination, and future standardisation efforts through documented practice rather than abstract design alone. The first step is to fix metadata at the source. Rights societies, platforms, labels, libraries, and archives all capture fragments of information about works and recordings. Today this is done in parallel, wasting effort and creating errors. Smarter pipelines, shared identifiers, and pragmatic exchange patterns can make documentation “capture once, reuse many.” This is not just a technical upgrade — it is the foundation for fair royalties, legal certainty, and cultural visibility. It also directly supports the CITF view that robust identifiers and transparent RMI are prerequisites for trustworthy AI and lawful reuse. See Chapter 3for how shared infrastructures can make this possible. The second step is to build federated data sharing spaces. Instead of a single giant database, Europe should connect what already exists: collective management systems, heritage archives, and platform catalogues. Each actor stays in control of its own data but agrees to shared profiles, identifiers, and rules. This approach lowers costs, improves trust, and makes cross-border reuse realistic. The Open Music Observatory is our proposal for such a space: not a central repository, but a convening layer that makes decentralisation work. This federated model reflects CITF’s conclusion that copyright infrastructures must remain distributed, but linked through shared semantics, APIs, and provenance trails. 87 This Green Paper shows that the strategic ambitions of the Culture Compass for Europe — particularly regarding cultural data, AI, and evidence-based policy — cannot be realised through generic infrastructures alone1. They require federated, domain-specialised implementation units where cultural complexity demands it. Music, as one of the most demanding cultural domains, provides a robust reference case. The Open Music Observatory demonstrates how such a unit can operate in practice while remaining fully interoperable with Europe’s shared cultural data spaces. By tracing its origins to CEEMID, the Open Music Observatory demonstrates how longstanding, practitioner-led solutions can be scaled and embedded within European policy frameworks. This continuity underscores that Europe’s cultural data challenges do not require abstract reinvention, but the systematic extension of proven, decentralised approaches to new domains such as copyright infrastructure and AI governance. An important lesson from the national pilots discussed in this paper is that music data infrastructures should be designed not only for interoperability and rights management, but also to support the production of policy-relevant evidence. Experiments such as the Slovak pilot showed how authoritative rights management data can be harmonised with administrative and statistical sources to generate music-specific indicators aligned with best statistical practice — an area where most Member States currently lack dedicated cultural or creative satellite accounts. Embedding this capability into future European data spaces is essential if music is to be visible in evidence-based cultural and economic policymaking. See Chapter 4for how artificial intelligence can be used to strengthen, not weaken, this foundation. The third step is to treat AI as a shared utility. Big platforms already use AI to document millions of tracks and to steer attention. Smaller players cannot compete unless Europe provides common tools: AI to reconcile identifiers, repair legacy datasets, enrich metadata in multiple languages, and help creators embed information “from birth.” If deployed in a federated way, AI reduces costs and unfreezes neglected repertoires — while respecting rights, attribution, and diversity. The use, development and governance of artificial intelligence (AI) systems should foster human creativity through a fair human-centric and rights-based approach. Taken together, these steps close the gap between the right of reuse granted by the Open Data Directive and the means of reuse that the music industry actually needs. They also bring Europe’s approach into alignment with the emerging international consensus: that cultural data infrastructures must be trustworthy, distributed, and provenance-rich in order to support both human creativity and responsible AI. 1 “Pursuing the ambitions of this declaration and fully leveraging culture’s transversal impacts means promoting culture in the EU’s internal and external policies, investing in the cultural and creative sectors and industries, and monitoring progress. We commit to: …. Advancing initiatives aimed at improving the availability of sound and comparable data on culture to support the development of evidence-based cultural policies.”(European Parliament, Council of the European Union, and European Commission 2025, p4) 88 Europe should therefore: • Support metadata capture and cross-domain identifiers, ensuring that attribution is reliable, repairable, and legally secure. • Invest in federated data sharing spaces like the Open Music Observatory, enabling decentralised actors to work together. • Provide pooled AI services for reconciliation, repair, enrichment, and documentation, accessible to SMEs, CMOs, and heritage institutions alike. This is how Europe can make its music ecosystem fair, efficient, and future-proof — not only safeguarding its cultural heritage, but ensuring that AI strengthens rather than erodes the foundations of musical creativity, diversity, and economic sustainability. 89 standard/44292.html. ———. 2013. ISO 17369:2013(en) Statistical Data and Metadata Exchange (SDMX). London:United Kingdom: International Organization for Standardization. https:// www.iso.org/obp/ui/en/#iso:std:iso:17369:ed-1:v1:en. ———. 2017a. ISO/IEC 19941:2017(en), Information Technology — Cloud Computing — Interoperability and Portability. London:United Kingdom: International Organization for Standardization. https://www.iso.org/obp/ui/#iso:std:iso-iec:19941:ed-1:v1:en. ———. 2017b. ISO/IEC 5127:2017(en), Information and Documentation — Foundation and Vocabulary. London:United Kingdom: International Organization for Standardization. https://www.iso.org/obp/ui/en/#iso:std:iso:5127:ed-2:v1:en. ———. 2019a. International Standard Recording Code (ISRC). ISO 3901:2019. 3901. Version 2019. International Organization for Standardization. https://www.iso.org/ standard/64817.html. ———. 2019b. ISO/IEC 20546:2019 Information Technology — Big Data — Overview and Vocabulary. London:United Kingdom: International Standards Organisation. https://www.iso.org/obp/ui/en/#iso:std:iso-iec:20546:ed-1:v1:en. ———. 2020. ISO/IEC 22624:2020(en), Information Technology — Cloud Computing — Taxonomy Based Data Handling for Cloud Services. London:United Kingdom: International Organization for Standardization. https://www.iso.org/obp/ui/en/#iso:std:isoiec:22624:ed-1:v1:en. ———. 2021. ISO 10957:2021 - Information and Documentation — International Standard Music Number (ISMN). 10957. Version 2021. International Organization for Standardization. https://www.iso.org/standard/83122.html. ———. 2022. International Standard Musical Work Code (ISWC). ISO 15707:2022. 15707. Version 2022. International Organization for Standardization. https://www.iso.org/ standard/83125.html. ———. 2023a. ISO/IEC 11179-1:2023(en), Information Technology — Metadata Registries (MDR) — Part 1: Framework. London:United Kingdom: International Organization for Standardization. https://www.iso.org/obp/ui/en/#iso:std:iso-iec:11179:- 1:ed-4:v1:en. ———. 2023b. ISO/IEC 2382:2015(en), Information Technology — Vocabulary. London:United Kingdom: International Standards Organisation. https://www.iso.org/ obp/ui/en/#iso:std:iso-iec:2382:ed-1:v2:en. ISO/IEC. 2022. Information Technology — Artificial Intelligence — Concepts and Terminology. ISO/IEC 22989:2022. 22989. Version 2022. International Organization for 96 Standardization. https://www.iso.org/standard/74296.html. ———. 2023. Information Technology — Artificial Intelligence — Management System. ISO/IEC 42001:2023. 42001. Version 2023. International Organization for Standardization. https://www.iso.org/standard/81230.html. J, Wilson. 2015. Evidence-Based Policy Making in the European Commission. Edited by Elisabeth Lannoo. Oslo (Norway): CICERO Centre for International Climate; Environmental Research. http://www.cicero.uio.no/en/posts/news/report-from-scienceto-policy-how-to-improve-the-dialogue/. Jachimczyk, Bartosz, and Katarzyna Nowak. 2024. “Exploring Open Government Data (OGD) Portals: A Usability Study Across the EU.” arXiv Preprint, 2024. https://arxiv. org/abs/2406.08774. Jegan, Robin, Leon Fruth, Tobias Gradl, and Andreas Henrich. 2023. “Integrating Access to Authority Data for Improved Interoperability of Research Data in the Digital Humanities.” Datenbanksysteme Für Business, Technologie Und Web (BTW 2023), 2023, 829–36. https://doi.org/10.18420/BTW2023-54. Keller, Paul. 2025. Publishing Cultural Heritage Data in the Age of AI. Policy report. Open Future; Europeana. https://openfuture.eu/wp-content/uploads/2025/12/ 251202PublishingCulturalHeritageDataInTheAgeOfAI.pdf. Kević, Kristijan, Ana Kuveždić Divjak, and Frederika Welle Donker. 2023. “Benchmarking Geospatial High-Value Data Openness Using GODI Plus Methodology: A Regional Level Case Study.” Geographies 12 (6): 222. https: //doi.org/10.3390/geographies12060222. Kitzes, Justin, Daniel Turek, and Fatma Deniz, eds. 2018. The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. 1st ed. University of California Press. http://www.practicereproducibleresearch.org/. Leurdijk, Adnra, and Nieuwenhuis Ottilie. 2012. Statistical, Ecosystems and Competitiveness Analysis of the Media and Content Industries. The Music Industry. 25277 EN. Edited by Jean Paul Simon. Luxembourg: Publications Office of the European Union, 2012: Joint Research Centre Institute for Prospective Technological Studies (IPTS). https://doi.org/10.2791/796. Leurdijk, Andra, and Nieuwenhuis Ottilie. 2012. Statistical, Ecosystems and Competitiveness Analysis of the Media and Content Industries. The Music Industry. 25277 EN. Edited by Jean Paul Simon. Luxembourg: Publications Office of the European Union, 2012: Joint Research Centre Institute for Prospective Technological Studies (IPTS). http://ftp.jrc.es/EURdoc/JRC69816.pdf. Magnus, Bart, and Olivier Van D’huynslager. 2021. “Podiumkunstendata Op Wikidata: 97 De Stap Naar Echte Linked Open Data.” Kunstenpunt / Flanders Arts Institute, February 11. https://www.kunsten.be/nu-in-de-kunsten/podiumkunstendata-op-wikidatade-stap-naar-echte-linked-open-data/. Mechanical Licensing Collective. 2021. 2021 Annual Report.https://www.themlc.com/ annual-report-2021. Mikš, Tomáš. 2025. OpenMusE: Towards a Sustainable Licensing Market for AI Use of Protected Works. April 29–30, 2025. Vilnius, Lithuania: Slovak Performing; Mechanical Rights Society (SOZA); OpenMusE Consortium; Presentation at the CISAC European Committee Meeting. https://doi.org/10.5281/zenodo.17944096. Mikš, Tomáš, and Dániel Antal. 2025. Open Access Music Dataspaces – Open Music Observatory. Open Music Observatory. https://doi.org/10.5281/zenodo.17669739. Milosic, Klementina. 2015. “The Failure of the Global Repertoire Database (GRD).” Hypebot, August 2015. https://www.hypebot.com/hypebot/2015/08/the-failure-of-theglobal-repertoire-database-effort-draft.html. Ministerstvo kultúry SR, and Open Music Europe. 2023. Memorandum o porozumení o využití výsledkov analýz otvorených politík v kontexte slovenského kultúrneho a kreatívneho priemyslu a sektorových verejných politík v spolupráci s konzorciom pre výskum a inovácie s názvom OpenMuse. [Memorandum of Understanding on utilizing the Open Policy Analysis results of the OpenMuse Research and Innovation Consortium in the context of Slovak cultural and creative industries and sectors’ public policies]. https://www.crz.gov.sk/zmluva/7645338/. MIT Sloan School of Management. 2025. Project NANDA: Enterprise Generative AI Value Creation. Massachusetts Institute of Technology. https://mitsloan.mit.edu. Music Moves Europe. 2024. Music Ecosystem 2025: Study on the Music Ecosystem. Publications Office of the European Union. Luxembourg: European Commission, Directorate-General for Education, Youth, Sport; Culture. https://doi.org/10.2766/ 95340. Nakos, Basil, and Lazaros Tsoulos. 2022. “Web-Based Nautical Charts Automated Compilation from Open Hydrospatial Data.” Journal of Navigation 75 (6): 1–19. https: //doi.org/10.1017/S0373463322000489. Open Music Europe. 2023. Open Music Europe (OpenMusE) – An Open, Scalable, Data-toPolicy Pipeline for European Music Ecosystems.https://doi.org/10.3030/101095295. Open Music Europe Consortium. 2023. Open Music Europe User Stories: OPACompliant User Stories for System Competences and Semantic Architecture. Https://github.com/dataobservatory-eu/open-music-europe-user-stories/.https: //github.com/dataobservatory-eu/open-music-europe-user-stories/. 98 ———. 2025. Policy Brief: An Open, Scalable Data-to-Policy Pipeline for European Music Ecosystems. EU Horizon Europe Deliverable D5.7. Open Music Europe Consortium. https://openmuse.eu/. OpenRefine Community. 2021. OpenRefine Reconciliation API Standard.https: //reconciliation-api.github.io/specs/latest/. Partanen, Niko, Philippe Rixhon, Karīna Bandere, Jānis Ziediņš, Pawan Kumar Dutt, Matīss Bolšteins, Matias Frosterus, Mona Lehtinen, Inta Miklūna-Žukeviča, Deniss Ozerskis, Päivi Maria Pihlaja, Jogita Sauka, Katerina Sornova, and Aija Uzula. 2025. Interoperable, Trustworthy, and Machine-Readable Copyright Data in the AI Era: Report of the CITF First Project. Publications of the Ministry of Education and Culture, Finland 2025:23. Helsinki: Ministry of Education; Culture, Finland; National Library of Finland; National Library of Latvia; Culture Information Systems Centre (Latvia); Tallinn University of Technology (Estonia); Valunode OÜ. https: //julkaisut.valtioneuvosto.fi/. Partanen, Niko, Philippe Rixhon, Karīna Bandere, Jānis Ziediņš, Pawan Kumar Dutt, Matīss Bolšteins, Matias Frosterus, Mona Lehtinen, Inta Miklūna-Žukeviča, Deniss Ozerskis, Päivi Maria Pihlaja, Jogita Sauka, Katerina Sornova, and Aija Uzu. 2025. Interoperable, Trustworthy, and Machine-Readable Copyright Data in the AI Era: Report of the CITF First Project. Research report. Helsinki: Ministry of Education; Culture. https://urn.fi/URN:ISBN:978-952-415-143-6. Paskin, Norman. 2006. “Identifier Interoperability: A Report on Two Recent ISO Activities.” D-Lib Magazine 12 (4): 1–20. https://doi.org/10.1045/april2006-paskin. Pomerantz, Jeffrey. 2015. Metadata. The MIT Press Essential Knowledge Series. Cambridge, MA, USA: MIT Press. Project, CEDAR. 2023. “A Hitchhiker’s Guide to High Value Datasets.” https://cedarheu-project.eu/articles/hitchikers-guide-high-value-datasets. PRS for Music. 2023. “PRS for Music Expands Pioneering Nexus Programme.” September 6. https://www.prsformusic.com/press/2023/prs-for-music-expands-pioneeringnexus-programme. PwC. 2023. Digital IQ 2023: Driving ROI on Digital Investments. PricewaterhouseCoopers International Limited. https://www.pwc.com/gx/en/industries/technology/ digital-iq-survey.html. ———. 2024. 27th Annual Global CEO Survey. PricewaterhouseCoopers International Limited. https://www.pwc.com/gx/en/ceo-agenda/ceosurvey/2024.html. Quine, Willard Van Orman. 1968. “Ontological Relativity.” The Journal of Philosophy 65 (7): 185–212. https://doi.org/10.2307/2024305. 99 Sardo, Lucia, and Carlo Bianchini. 2022. “Wikidata: A New Perspective Towards Universal Bibliographic Control.” JLIS.it : Italian Journal of Library and Information Science 13 (1): 291–311. https://doi.org/10.4403/jlis.it-12725. Schnurr, Daniel. 2021. Open Government Data in Digital Markets: Effects on Innovation, Competition and Societal Benefits. SSRN Working Paper. https://papers.ssrn.com/ sol3/Delivery.cfm?abstractid=3743648. SEMIC Support Centre. 2023. Wikidata and Wikibase — SEMIC Support Centre. https://interoperable-europe.ec.europa.eu/collection/semic-support-centre/wikidataand-wikibase. Senftleben, Martin, Thomas Margoni, Joost Poort, Kacper Szkalej, and Etienne Valk. 2024. Policy Brief 1: Music Metadata Mainstreaming and EU Law. EU Horizon Europe Deliverable D5.6. OpenMusE Consortium. https://www.openmuse.eu/. Simoni, Marco U., Kristin A. Aasly, and Frode Schjøth. 2021. MINERAL Intelligence for Europe (Mintell4EU) – Case Study Overview. GeoERA. https://geoera.eu/wpcontent/uploads/2021/10/D4.1-Mintell4EU-Case-Study-Overview.pdf. Stallmann, Claudia, Koen Deneckere, Ruben Verborgh, et al. 2023. “MetaBelgica Project: A Linked Data Infrastructure Between Federal Scientific Institutes in Belgium.” Proceedings of the 19th Extended Semantic Web Conference (ESWC 2023) (Cham), 2023. https://doi.org/10.1007/978-3-031-33455-9_24. Teosto. 2024. “New ISNI Identifier Creates Better Opportunities for International Author Identification.” March 12. https://www.teosto.fi/en/new-isni-identifier-creates-betteropportunities-for-international-author-identification/. Varghese, Jacob. 2024. “Beyond the Metadata: How Can We Solve the Black Box Royalty Mystery?” Noctil, August 14. https://independentmusicinsider.com/editorial-articles/ 4820/. Virág, Barnabás. 2024. “Our Library’s Music Collection in the Era of Streaming Services.” Canadian Journal of Information and Library Science / La Revue Canadienne Des Sciences de l’information Et de Bibliothéconomie 47 (2): 175–87. https://doi.org/10. 5206/cjils-rcsib.v47i2.17436. W3C. 2013a. PROV-o: The PROV Ontology. Edited by Satya AND McGuinness Lebo Timothy AND Sahoo. W3C. https://www.w3.org/TR/prov-o/. ———. 2013b. PROV-Overview: An Overview of the PROV Family of Documents. Edited by Paolo Moreau Luc AND Missier. W3C. https://www.w3.org/TR/prov-overview/. World Intellectual Property Organization (WIPO). 2023. “Project Nexus: A Data Matching Project of PRS for Music.” April 19. https://www.wipo.int/edocs/mdocs/mdocs/ 100 en/wipo_webinar_cr_2023_6/wipo_webinar_cr_2023_6_presentation.pdf. 101