Research data experts back Commission simplification plan

The European Commission’s new data-simplification plan is being met with optimism among research leaders, who see a valuable opportunity to advance open science and expand access to high-quality datasets. At the same time, they emphasise that these benefits will depend on the final legislation preserving clear and robust safeguards.

On November 19, the Commission unveiled the Digital Simplification Package, an initiative that aims, among other objectives, to improve access to data as a driver for innovation and competitiveness. To achieve this, the proposal seeks to streamline and simplify EU data rules, reduce administrative burdens and expand access to datasets, including for AI training, which would benefit from “high-quality and fresh” data sources. 

“By cutting red tape, simplifying EU laws, opening access to data [. . .] we are giving space for innovation to happen and to be marketed in Europe,” Henna Virkkunen, the commissioner responsible for tech sovereignty, security and democracy, said in a statement.

The proposal comes as policymakers push for Europe to reduce its dependence on foreign-controlled databases, particularly those in the US. Such reliance would make European researchers and institutions vulnerable to geopolitical shifts and commercial decisions made elsewhere, stakeholders say.

However, there is also “a real risk” that elements of the package could dilute protections for sensitive datasets, according to Klaus Tochtermann, president of the European Open Science Cloud (EOSC) Association. The ultimate impact, he told Science|Business, will depend heavily on the final legal text and on how delegated and implementing acts are used.

From a research data perspective, the priority is to ensure that any simplification measures retain clear safeguards so that “easier does not mean less secure,” he said, referring to tools such as data protection impact assessments, provenance, access controls and oversight.

“[Greater access to research data] is essential for competitiveness,” said Hilary Hanahoe, secretary-general of the Research Data Alliance, a global organisation supporting open data sharing and reuse. “But it should be done in compliance with the Fair principles.” These are guidelines for making research data findable, accessible, interoperable and reusable.

According to Hanahoe, expanding access to data is a key step toward fully achieving open science. “There are many types of research that can be valuable to society, but you need to make it open and available for reuse and reproducibility,” she told Science|Business.

Hanahoe also sees the reforms as an opportunity for the EU and its member states to reinforce existing initiatives, from data spaces to the EOSC, that have already received significant public investment. Launched in 2015 and now uniting dozens of infrastructures, EOSC aims to give researchers seamless access to European data while ensuring providers retain ownership of their datasets.

Potential risks

Among the Commission’s proposals is a plan to harmonise, at EU level, the lists of processing activities that require, or are exempt from, a data-protection impact assessment. “While intended to simplify procedures, this may weaken the careful, case-by-case assessments that currently protect sensitive research datasets, potentially increasing exposure of personal data,” Tochtermann said.

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Hanahoe also pointed to challenges in the standardisation process. Developing data standards, she said, is often slow and complex, and rushing it risks imposing “one-size-fits-all” rules on disciplines that are at very different stages of maturity. In many cases, she said, shared community agreements that evolve into de facto standards can be more effective than prematurely formalised ones.

Another concern is the potential expansion of data reuse for AI training. Tochtermann said that the package could make it easier to process personal or sensitive data for AI, or delay the application of obligations in the EU AI Act. “This could allow large-scale reuse of research datasets without individual consent or safeguards, directly affecting the integrity, confidentiality and trustworthiness of sensitive research data,” he said.