Horizon Europe Data Management Plan from an Ethics Perspective

Introduction

Innovative, interdisciplinary R&D projects produce large amounts of research information. Depending on the nature of the project, its scope and research objectives, the information used or created during the project lifecycle may include personal data. While the management of research information in general pertains to the Open Research Data Guidance policies and usually means the obligation to adhere to the so-called FAIR principles, the focus of this text is on the Data Management Plan from an ethics perspective – that is – as a privacy and data protection requirement that is all about how personal data is processed for research purposes.

Horizon Europe Data Management Plan – describing the lifecycle of personal data

The data processed during the project realization might affect many natural persons. Also, there are various categories of data that might be processed including special categories of personal data. To satisfy legislative and ethics requirements, innovative research projects must be realized in accordance with national, international laws and the specific sources of regulation applicable to the project including the rules concerning data processing. While some projects like CYRENE, IOT-NGIN and MARVEL do not deal directly with special categories of personal data and only process personal data in terms of contact information for event organisation, other projects use personal data to deliver research results in a more direct way. These are usually health-related, cybercrime-related or projects involving anthropological or longitudinal studies. In Privanova’s current portfolio examples are DigiCare4You, AI4HealthSec, FACILITATE, CC-DRIVER or TRACE.

All these research initiatives are funded under different programmes by the EU and thus must be compliant with specific legal and ethical requirements concerning data processing. Especially with the updated Ethics Appraisal Process in Horizon Europe, these requirements may impose the need for the development of a project Data Management Plan (DMP).

All project partners should align their data processing activities with the DMP. Relevant guidelines about the DMP specify the structure of the plan and content – what data a project will generate, what data will be publicly available, what data should stay confidential, whether and how it will be exploited or made accessible for verification and re-use, and how it will be curated and preserved. Of importance is that open access to research data does not necessarily mean opening all research data. Thus, the underlying principle for DMP in the projects is ‘as open as possible, as closed as necessary’. The focus is on encouraging sound data management as an essential part of research best practices.

Horizon Europe Data Management Plan – purpose and importance

The DMP has a significant role in the compliant realization of project activities and deployment of the results. Also, there are many other benefits of good data management, i.e., it ensures that research data are of high quality, well organized, documented, preserved, accessible and valid. This should result in efficient and excelling research. Well-managed data are easily shared and can thus be used for new research or to duplicate and validate existing research.

The DMP is a living document, and it is expected to be developed during the whole lifecycle of the project development. Researchers should consider data processing perspectives from the very beginning of the research cycle. This requirement exactly creates the need for and explains the purpose of the DMP.

Planning research includes designing research, planning data management, planning consent, planning data collection, processing protocols and templates, and exploring existing data sources. Collecting data includes not only data gathering but capturing data with metadata and acquiring existing third-party data. Processing and analyzing data include any of the following actions: entering, digitizing, transcribing and translating data, checking, validating, cleaning or anonymizing data, deriving data, describing and documenting data, managing and storing data, analyzing and interpreting data, producing research outputs and citing data. Publishing and sharing data include establishing copyright, creating user documentation, creating discovery metadata, selecting appropriate access to data, publishing or sharing data, and promoting data. Preserving data involves migrating data to the best format and media, storing and backing up data, creating preservation documentation, and preserving the data. Re-using the data involves conducting secondary analysis, undertaking follow-up research, conducting research reviews, scrutinizing findings, and using data for teaching and learning.

The Horizon Europe Data Management Plan provides principles and rules on how all these steps should be conducted properly in order to satisfy data processing and protection requirements. Thus, it provides insights on what data will be generated, how it will be exploited, and how it will be made accessible, reusable, curated, and preserved during and after the realization of this project. Also, it provides a strategy and relevant tactics for managing data. Therefore, the DMP is a source of formal self-regulation that outlines how data will be handled during the course of the research project and after the project completion. For all these reasons, any innovative research project is guided by DMP steps in its data management process.

Horizon Europe Data Management Plan and other project deliverables

The DMP is perceived not only as a complementary but supplementary source of regulation for projects supported by the Horizon Europe. The DMP has transversal nature and concerns the processing of data and personal data in all work packages and across all project research activities. It sets up the concrete measures that should enable appropriate processing and protection of data. Therefore, the application of the DMP should prevail in all project activities that include data processing.

Considering that research could include processing sensitive categories of data (such as health-related data, children’s data, or data related to criminal convictions) the DMP is critically important for the proper realization of the technical work packages that include processing of special categories of data. The DMP supplements the legal framework that will be developed within different work packages. Also, all dissemination activities must consider rules and principles proclaimed by the DMP when publishing certain datasets. All activities related to the development of any methodology, tools, and technology that will serve for data processing must be based on principles laid down by the DMP. Therefore, it would not be wrong to claim that the DMP grounds ethical standards relevant to all research activities as well as accompanying activities included in a project. Therefore, the DMP provides a set of rules that supplement the ethical dimension of a project realization.

Conclusion

The Horizon Europe Data Management Plan is the means of proving that the project consortium is aware of the potential ethical implications of the project and confirms its commitment to respect the ethical standards and rules. Thus, the ethical standards and guidelines of Horizon Europe including those that regulate data processing/protection must be rigorously applied, regardless of the country in which the research takes place. For this reason, it is often requested that the project-specific Ethics Advisory Board reviews the DMP as part of the external supervisory role it fulfils. The DMP confirms that all project partners will conduct research in accordance with the fundamental principles of research integrity, such as reliability, honesty, respect, and accountability. It relates to a number of different areas addressed by the Ethics Appraisal Process, creates benefits for exploitation of project results and must ensure the safety and dignity of individuals whose data will be processed within a project, as well as the integration of the highest standards of research integrity in research activities and outcomes.