Common European Data Spaces (CEDS) are aimed at creating a single market for data across the EU that will power AI innovation. CEDS cover 14 sectors/domains and will allow secure, trustworthy data/AI models exchange between companies, public administrations etc. The Common European Language Data Space (LDS) is part of CEDS and is already made available in beta phase. The paper presents its technical design and implementation, its governance framework as well as use cases that demonstrate its value. LDS aspires to become part of the future European Language Technology ecosystem.

To strengthen competitiveness and digital sovereignty, the European Union has promoted the development of Common European Data Spaces to enable secure and interoperable data sharing between participants for various sectors. Data spaces combine technical infrastructure with governance mechanisms to ensure trust, transparency, data sovereignty and interoperability. Their operation must comply with the evolving European regulatory framework as well as contractual law. This paper presents the strategy adopted in the Language Data Space (LDS) to operationalise these requirements, focusing on its contractual framework and supporting instruments. It outlines the governing principles designed to ensure lawful, transparent, and fair data transactions while safeguarding the rights and obligations of data providers and consumers alike. It further describes the actual framework, and the recommended data sharing licences, with a particular emphasis on the LDS standard licence. Finally, it presents the automation tools designed and developed to support the relevant workflows while serving a wide range of users that have little or no knowledge of technical and legal complexities.

This paper examines the legal implications of using synthetic data to develop and fine-tune general-purpose AI models in the European Union, using the LLMs4EU project as a case study. It situates synthetic data within the Union’s broader data policy and highlights it as a candidate tool for reconciling data availability with regulatory constraints. From a data-protection perspective, it analyses whether and when synthetic data should be classified as “personal data” under the GDPR. From a copyright and contractual standpoint, the paper assesses the risks that synthetic datasets may embed infringing content or derive from unlawfully trained models, in light of the GEMA v. OpenAI ruling on memorised works and emerging analyses of liability for AI-generated outputs, and considers the constraints imposed by model licensing and acceptable-use policies on using models to generate training data for other models. The paper concludes that synthetic data can play a valuable role in mitigating legal risks and enabling compliant AI development in LLMs4EU, but only if its generation and use are embedded in robust governance frameworks that address data protection, copyright and contractual obligations across the entire data value chain.

Creating novel ways of sharing data to boost the digital economy has been one of the growing priorities of the European Union. In order to realise a set of data-sharing modalities, the European Union funds several projects that aim to put in place Common Data Spaces. These infrastructures are set to be a catalyser for the data economy. However, many hurdles face their implementation. Legal compliance is still one of the major ambiguities of European Common Data Spaces and many initiatives intend to proactively integrate legal compliance schemes in the architecture of sectoral Data Spaces. The various initiatives must navigate a complex web of cross-cutting legal frameworks, including contract law, data protection, intellectual property, protection of trade secrets, competition law, European sovereignty, and cybersecurity obligations. As the conceptualisation of Data Spaces evolves and shows signs of differentiation from one sector to another, it is important to showcase the legal repercussions of the options of centralisation and decentralisation that can be observed in different Data Spaces. This paper will thus delve into their legal requirements and attempt to sketch out a stepping stone for understanding legal governance in data spaces.