Data management

The introduction will provide a framework for the course and present the key concepts relevant to managing data and data policy within RIs, including definitions of data and typologies of RIs, the role of metadata, and operational considerations for managers.

After completing this Session, the participant will be able to: 

• understand the key issues and concepts around the management of data and data policy within an operational RI; 
• situate his or her own RI within a typology of research infrastructures, and appreciate how this influences the treatment of data management and policy;
• determine how different drivers affect how data management and data policy are implemented in an RI; and
• assess the different relevant factors in play when implementing data management and policy in real-time settings, such as resources, technologies, and staff competencies.

This Session will cover the role of data policy and data management in RIs, including how data policy for different data management domains should be crafted and formulated for different audiences and purposes, and aligned with internal and external drivers and implemented in RI workflows. It will also address how policy can be converted into practical guidelines for different user groups.

After completing this Session, the participant will be able to:

• identify and develop relevant data management policies for research infrastructure throughout the data lifecycle;
• define appropriate workflows, roles, and responsibilities among research infrastructure staff for data management and policy implementation;
• understand, catalogue, and manage different types of data (datasets, software, workflows, etc.) handled and generated in the research infrastructure and by its users;
• decide what is needed to make data and metadata accessible for long-term preservation;
• understand data sharing and access (open data vs closed data) and restrictions/control in an Open Science context; and
• assess certification mechanisms available for data management in different research infrastructures/scientific communities and determine whether they are needed for the research infrastructure.

This Session focuses on deepening the participants’ understanding of the FAIR principles and empowering them to adapt and extend the principles of findability, accessibility, interoperability, and reusability in their RI. By the end of this Session, the participants will be able to set up the guidelines for FAIRness in their Research Infrastructure, and plan the development and/or integration of the protocols, techniques and tools required to achieve such FAIRness.

After completing this Session , the participants will be able to:

• understand the FAIR principles in general and the main concepts and vocabulary used;
• make data findable, understanding the most common metadata schemas and the importance of generating persistent identifiers;
• make data accessible, describing the different protocols to access data, both for humans and for machines;
• make data interoperable, using ontologies and other semantic artifacts;
• make data reusable, selecting the appropriate license and providing rich metadata to facilitate its reuse and provenance; and
• implement FAIR principles in the setting of a real research infrastructure.

Developing a reliable and specific data management plan is a core business of each research infrastructure. A data management plan ensures that the RI’s data will be adequately described and made available to a broader public. This enables a more thorough perspective for the re-use of data and the formulation of innovative data-centric research questions. This Session turns the theoretical knowledge of data management policies and plans into practice.

After completing this Session , the participant will be able to:

• understand the importance of dedicated data management plans for increased data quality of the research infrastructures’ main assets;
• support compliance with the research Infrastructure’s data management policies;
• contribute to knowledge exchange with regard to data management by the research infrastructure’s gateway function;
• increase the quality of service in their RI by improving the essential cornerstones of research data management plans; 

• develop an individual and personalised template for a data management plan for the participants’ research infrastructure; and 
• ensure improved quality of the draft template by expert-level feedback after assessment of the provided data management plan template.