The European Commission and a long list of International Funders made a clear choice towards Open Science, as it means enabling broader access to publicly funded research results and therefore helps to build on previous research results, encourage collaboration and avoid duplication of effort, speed up innovation, and involve citizens and society. Open science is defined as an "umbrella term", comprising different elements: from open access to research results (literature, data, software, etc.), to open peer review, open methodologies, protocols, workflows, from open education to citizen science. These elements need to be embedded in a system where research infrastructures and a new evaluation model go hands in hands with research integrity.

One important aspect of embedding open science in the everyday life of researchers is research data management, which implies a structured way of completing the research data lifecycle with the main objective of delivering re-usable research data that can be shared with others. A good research data management needs to follow the FAIR principles, a set of good practices to help making data Findable, Accessible, Interoperable and Reusable [6].

Openness and FAIRness are therefore the means to make science more transparent and reproducible, repeatable, replicable, reusable. In this view, research data is just one of the resources involved. Open Science is about each element of research: data, software, publications, services, etc. There is a general need to identify Open Science resources and how these are related, to ensure their openness and FAIRness. In this context, the definition and development of standards, tools and research infrastructure eliminating the barriers and facilitating the work of scientists by embedding open science good practices in their daily work is key. Several tools and infrastructures are already in place, other needs to be developed.

In this context, one of the latest initiatives of the European Commission is the launch of the European Open Science Cloud, that aims at creating a virtual research environment to access and interoperate research data and other research outputs in Europe across the different disciplines [7][8][9].

Open Science in the Database Systems sector deals with sharing software in a way that makes it reproducible, preservable, and citable, but also with new and challenging research opportunities linked to establishing infrastructures that can support best practices for research transparency and collaboration.

The "R* of Science" deal with actions that should be the basis of scientific method [10,11] . Repeating science deals with defending the thesis (repeat the same experiment with the same setup in the same lab). The method researchers claim should also be Replicable in order to be certified by others (same experiment and setup, independent lab). Reproducibility of science introduces variations in some of the aspects of research methods (experiment setup or lab). Finally, Reusing research results deals with the transfer of knowledge to enable different experiments, also by others.

Best practices in the ICT domains already exist, ranging from software collaborative development and publication, software and data papers drafting, preprint and postprint selfarchiving, dataset FAIR management and sharing and interlinking of results.

However, work still needs to be done on reproducibility. Research in this sector includes challenging aspects as the definitions of standards and templates for reporting methods, provenance and tracking, the workflow/script automation, design and development of tools and platforms for capturing, tracking, structuring, organising assets throughout the whole project research cycle.

The tutorial presented at SEBD2020 by the authors is available in open access in Zenodo [12].