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European Alliance for Research Excellence

EARE

The European Alliance for Research Excellence (EARE) is a coalition of companies and research organisations committed to fostering excellence in research and innovation in Europe.

Lobbying Activity

Response to Digital package – digital omnibus

13 Oct 2025

Researchers, startups, and innovators across the EU need a legal framework that enables easy and open access to high-quality data. Such access is essential to reduce research bias, accelerate scientific discovery, and drive innovation across sectors. To fully unlock this potential, the EU should prioritise a simplified and harmonised approach to data and AI legislation that minimises reporting obligations for researchers and innovators and ensures consistency across Member States. This would foster cross-border collaboration, support emerging discoveries and strengthen Europes research and innovation ecosystem. Strengthened text and data mining (TDM) exceptions and open data policies are essential to accelerate innovation in Europe. The current TDM exceptions established under Art 3 and 4 of the Copyright Directive already provide a balanced framework that supports both innovation and rightsholders. However, the current lack of harmonisation and legal uncertainty around Art 3 and 4 add confusion for researchers, startups and innovators, limiting the use of data and increasing data access costs, particularly compared to countries such as Japan, Singapore, and the U.S., where copyright laws facilitate innovation. The EU should therefore focus on ensuring consistent and effective implementation of the current provisions across Member States. Similarly, a modern data strategy must reflect the cross-sector nature of todays research and innovation ecosystem. Limiting TDM exceptions to non-commercial use creates legal uncertainty, especially in public-private partnerships or when non-commercial research leads to commercial outcomes. This limitation restricts data sharing, impacting startups and SMEs. Clarifying the application of the current legal framework, for example through guidelines, can help ensure that TDM exceptions and open data policies apply to research with commercial potential, reducing ambiguity and encouraging data sharing. In sum: i) Art 3 does not really allow public-private partnerships. ii) Most Member States are silent on sharing copyright protected works, and the Directive contains no cross-border provision. iii) The circumvention measure where TPMs are blocking machine learning are complex, unclear, and unworkable. iv) By reducing what a model can train on Art 4 promotes bias and poor model production. Researchers and innovators also face administrative burdens which should be addressed, particularly regarding the predictability and effective application of the AI Act. As noted by the European Commissions study on improving access to reuse and research results for scientific purposes, a detailed summary of the data used for training GPAI models as part of the AI Act can add a layer of compliance costs for research organisations. This is especially relevant when research organisations are involved in public-private partnerships. Similarly, SMEs, and startups, struggle to produce this document, as it is nearly impossible to determining when commercial intent begins, as required by the AI Act. For this reason, the European Commission should focus on working with AI providers, research organisations, SMEs, startups, and other relevant stakeholders to monitor the implementation of the template, simplify and automate reporting, provide training for researchers and startups, and consider offering extended compliance timelines for entities that may be unaware of these provisions or lack the resources to comply. Besides ensuring the optimal application of the recently adopted rules, simplifying legislation and reducing compliance burdens for researchers, startups, and innovators is essential to building a dynamic and competitive EUs innovation ecosystem. By simplifying and clarifying data and AI frameworks, the EU can empower its researchers and innovators to focus on breakthrough discoveries, technological development, and cross-sector collaboration, ensuring Europe remains at the forefront of global innovation.
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Meeting with Sergey Lagodinsky (Member of the European Parliament)

30 Jul 2025 · Exchange of Views

Response to A European Strategy for AI in science – paving the way for a European AI research council

4 Jun 2025

EAREwelcomes the opportunity to contribute to the development of the European Strategy for AI in Science. We commend the European Commissions recognition of AIs strategic role in advancing science and research. The proposed strategy rightly aims to accelerate responsible AI adoption and facilitate its use by researchers across the EU. However, several persistent challenges must be addressed to realize this vision: 1) Limited access to data: Mechanisms used by content aggregators and rightsholders such as restrictive terms of use, robots.txt, paywalls, and technological protection measures (TPMs) hinder access to data, even for paid or legally accessible materials, jeopardizing modern research and AI training. This scarcity increases the risk of bias in research outcomes and AI systems, ultimately undermining the quality, reliability, and inclusiveness of European innovation. 2) Inconsistent implementation of text and data mining (TDM) exceptions and legal fragmentation: The inconsistent implementation of the TDM exceptions across the EU and increasing court decisions interpreting the scope of aspects of TDM, is leading to divisions across the Union on how TDM applies and causes confusion and deters researchers from using data which is key for research development and AI innovation. 3) Ambiguity regarding commercial and non-commercial research: The artificial divide in the EU Copyright Directive (Articles 3 and 4) between commercial and non-commercial research does not reflect todays research reality, where commercial involvement is common. The legal divide creates uncertainty, especially when researchers are involved in public-private partnerships or when a research project intends to develop commercial products. This ambiguity disproportionately affects startups and SMEs, which often rely on such collaborations to bring innovation to the market. In today's research ecosystem, where collaboration across sectors is common, this distinction is increasingly unjustified and limits the ability to turn research into real-world solutions and market-ready innovations. EARE Recommendations for the future AI in science strategy: 1) Ensure open access to data: To avoid limited access to data and biases in research and AI, the future strategy should focus on granting and ensuring access to large and diverse datasets, creating an environment where researchers, startups, and innovators can have access to high quality datasets. 2) Strengthen and harmonize TDM exceptions: To ensure that researchers can access high quality datasets, the future strategy should emphasize the importance of TDM exceptions within the EU Copyright Directive (Article 3 and 4), prioritizing the implementation of these exceptions in the Single Market to foster the use of AI in science. The uniform implementation of these exceptions across Europe is key for researchers developing AI models where collaboration on scientific projects can often span multiple jurisdictions. 3) Align legal frameworks with the realities of research today: The strategy should reflect the principle that modern research is collaborative andcross-sectorial, often involving public-private partnerships between researchers and AI providers. To maximize the impact of European research, the strategy should facilitate the translation of research into real-time solutions and support the use of scientific outcomes in startups and innovation ecosystems. Aligning regulation with research practice will ensure a stronger return on Europes scientific investments. Conclusion: To succeed with AI in science, Europe must empower researchers, drive innovation, and reduce legal uncertainty. Becoming a global leader in scientific AI requires a legal framework that reflects how research is actually conducted. Researchers need clear, supportive regulations that encourage AI use and foster collaboration with AI providers and innovators, rather than creating hesitation through ambiguity.
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Response to Apply AI Strategy

4 Jun 2025

EARE welcomes the opportunity to contribute to the upcoming Apply AI Strategy. We commend the Commissions commitment to making Europe a global AI leader and its efforts to reduce regulatory burdens to support researchers, startups and innovators. Yet challenges remain. The global race for AI leadership is intensifying: the US, China, Japan and Singapore are advancing rapidly through innovation-friendly policies. To avoid falling behind, Europe must ensure it can compete on an equal footing, including by addressing the challenges facing innovators and researchers: 1. Limited access to data hinders innovation: Access to large, diverse datasets is essential for effective AI training and development. Yet, researchers face barriers due to paywalls, copyright restrictions, and technological protection measures (TPMs), which can introduce bias and limit research quality. Introducing Secondary publishing rights (SPRs) at the EU level could also expand data access, improving Europes competitiveness and innovation capacity. Today, a substantial portion of publicly funded research remains behind paywalls, despite being funded by taxpayers. SPRs would allow researchers to republish their work in open repositories after a short embargo, improving transparency, reproducibility, and the quality of datasets used in AI development. Our recommendation: Any future strategy should prioritize access to large, diverse, and high-quality datasets, particularly for researchers, startups and innovators.EARE calls for data to be as open as possible, as closed as necessary. 2. Strong TDM rights support AI development: TDM is vital for AI and scientific research. Articles 3 and 4 of the DSM Directive provide TDM exceptions that balance innovation with rightsholder interests. This creates a good balance between fostering innovation and protecting rightsholders' interests - even if it is not ideal from every perspective. The increasing push toward licensing schemes, such as mandatory or extended collective licensing, could reduce the availability of data by turning freely usable content into licensed material, creating new barriers for access and reuse. This would disproportionately affect research, SMEs and startups, compared to peers in other countries, where copyright laws are flexible enough to protect innovation and expression. Our recommendation: Strengthen the TDM exceptions in the DSM Directive to support a competitive AI ecosystem. EARE supports the introduction of a broad and harmonized TDM exception that applies to non-commercial and commercial uses. The EU should avoid restrictive licensing frameworks and instead explore alternative models for creator remuneration that do not impede research and innovation. 3.Outdated commercial vs. non-commercial distinction hinders AI development: Limiting TDM exceptions in AI training to non-commercial use creates significant legal uncertainty, notably in public-private partnerships or when non-commercial research leads to commercial outcomes (e.g. medical innovations). This disproportionately affects startups and SMEs, which rely on such collaborations to bring innovation to the market. It restricts AI training, hinders innovation, and delays the translation of research into real-world applications. Our recommendation: Move beyond the outdated distinction between commercial and non-commercial research. Countries like Japan, Singapore and the US, which maintain a thriving creative industry, have already implemented this shift. Conclusion: AI is a transformative opportunity for Europe, not a threat. To stay competitive, the EU must align its policies with todays reality, where collaboration between academia, startups and industry is essential. A modern legal framework can and should support scientific advancements and innovation in ways that drive concrete benefits across industries and sectors, empowering European startups, without being parasitic to creative works or a rich creative commons.
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Meeting with Alin Mituța (Member of the European Parliament, Shadow rapporteur) and Insurance Europe and Lufthansa Group

17 Jan 2023 · Data Act