This is a website for an H2020 project which concluded in 2019 and established the core elements of EOSC. The project's results now live further in and

Planning early, following through: Data Management Planning in the EOSC

Tuesday, October 9, 2018 - 12:15 to 15:45

The Open Science paradigm strongly contributes towards lifting the barriers that restrict access and re-use of research data. Aligned with the paradigm, funders and agencies, at European and national level, increasingly promote the adoption of strategies of FAIR and open research data. Such strategies, covering all datasets utilised or generated in the course of a research project, are embodied in the Data Management Plan (DMP).

Why are DMPs important ?
 Data Management Plans are important for individual researchers, community or institutional data managers and fundholders. Most H2020 proposals now require a DMP as specified in Article 29.3 of the Grant Agreement, and many countries also require this for nationally funded research. The aim of a DMP is to: - engage researchers to plan sustainable, result-oriented and cost-effective research strategies during and beyond the project lifetime, - enable research communities to discover and utilise invaluable, trustworthy data and, - allow funders assess their strategy and actions in a multitude of directions. When applicable, open access to the data, complemented with effective citation mechanism, guarantees visibility of the scientific results, for the benefit of the researcher, of the scientific community and of the society in its whole.

Learning goal: 

The training is aimed at people who support research projects or research infrastructures. Specifically you will be guided through real use case scenarios and the use of two emerging DMP tools to learn: - Essential background information on the data lifecycle and the rationale of a DMP; - Procedures and policies to ensure high availability and discoverability of data used/generated (e.g., FAIR, GDPR); - How to effectively implement the FAIR principles to ensure open and reproducible science; - Comply with the H2020 grant requirements and community best practices concerning research data management; - How to relate to existing infrastructures, to ultimately ensure interoperability, reproducibility and long-term preservation of all artefacts, be it data, publications or software. Moreover you will gain an overview of EOSC services engaged in the open research data management lifecycle (storage, access, retrieval, archival etc) and learn how to interoperate with them at an early stage.


ISCTE, University of Lisbon