null Palaute Euroopan komission etenemissuunnitelmasta koskien eurooppalaisten data-avaruuksien sääntelyä

CSC on antanut palautteensa Euroopan komission etenemissuunnitelmasta koskien eurooppalaisten data-avaruuksien sääntelyä. Palautteessa kannatetaan horisontaalisen hallintomallin luomista kattavaan yhteentoimivuusviitekehykseen nojautuen sekä peräänkuulutetaan kestäviä liiketoimintamalleja, joilla taataan julkisin varoin kustannetun datan maksuton saatavuus. Lisäksi komissiota kehotetaan kiinnittämään huomiota datan laadun varmistamiseen ja siihen liittyvän osaamisen vahvistamiseen.

Palaute kokonaisuudessaan:

CSC supports the idea of a horizontal governance model for the common European data spaces, based on a comprehensive interoperability framework, where different levels of interoperability are developed in coherence. The European Interoperability Framework, developed by the European Commission, serves as a good basis.

Legal interoperability: In development of data infrastructures, hard law options are to be considered only as a last resort. Europe must systematically avoid building new barriers for data movement, and the need for new regulation must be critically assessed. Instead, soft law and common practices approaches must be developed, and active attempts to reduce and harmonise legislation must be made across the Member States. It is of utmost importance to evaluate existing regulation carefully. For example, the scope of the text and data mining (TDM) exception of the DSM directive on copyright must be widened also to cover commercial use.

Organisational interoperability: It is crucial to agree on common policies and practices at Member State and institutional levels. Solutions must be developed in collaboration with all stakeholders. The data spaces should be built on top of the existing infrastructures, initiatives and policies, such as European Open Science Cloud. The idea of federated data infrastructures is functional for Europe: connecting existing infrastructures and making them interoperable, while also making efforts to identify and fill the gaps they may have.

Semantic interoperability: Data must be made understandable for the end-user regardless of where it is re-used. Existing semantic tools such as vocabularies, ontologies and enterprise architecture models must be leveraged. In developing tools and policies for interoperability, it is important to take into account the global aspect, ensuring that all policies comply with global standards and practices. For example, the tools developed for data management in the global Research Data Alliance must be systematically integrated into developing data infrastructures wherever possible.

Technical interoperability: Making the infrastructures compatible is an elementary building block for sustainable data flows, exchange and re-use between different IT systems and software applications. This must be systematically promoted in parallel and consistently with all other layers of interoperability. Joint services and easily accessible and usable tools and support to end-users are essential as well as the processes for authentication and authorization of usage. Technological solutions and planning must be based on end-users’ perspective, e.g. by using co-design model in service development.

Sustainable business models, as explored e.g. in a 2017 OECD report, are critical to ensure the availability of publicly funded data. European data spaces must be seen as a joint, strategic European investment by all member states, to strengthen European competence and capacity. The baseline for a business model for European data spaces must be, that publicly funded data must in no circumstances become something that is put behind a paywall. Thus, the role and the business model of novel data intermediaries mentioned in the roadmap must strictly comply with the principle of making all publicly funded data (including research data) as well as the associated services, such as search and programmatic access, available for everyone free of charge. Furthermore, the overall necessity and added value of the data intermediaries must be clarified and evaluated before introducing such a new structure into the data landscape.

Finally, in order to ensure quality of data, attention must be paid to validation of data and openness of methods, as well as data management practices. Skills in data processing and management need to be systematically developed on all educational levels, across all fields in education and research, including pedagogical education.