To apply data management in the research process, a firm grasp of the concepts, requirements and advantages that can be gained from RDM is paramount. Module 1 covers all basic definitions and concepts of RDM, such as the FAIR principles (findable, accessible, interoperable, reusable) and provide a common ground for the general understanding. Especially the significance of metadata for reusable data in catalysis is discussed in detail. Important factors and aspects are summarized in basic guidelines to take the first steps into FAIR RDM.
Publication of research results remains a key factor in science. However, a large share of data from catalysis is not published in a FAIR way, especially in catalysis and the related science. While not all data can be made available openly, FAIR data treatment is crucial to ensure data will not lose its value in the future. In module 2, the multiple option to publish data are examined and evaluated, from the classic publication as supplementary information in a journal up to separate data sets. The advantages and disadvantages of different options are considered, including open access, open data and preprints. For data that cannot be broadly published, options for archiving and storage within an institution or a project are discussed. Additionally, the module sheds light on what is allowed to be done with data once it is published, along with the possibilities of sharing research online and in social media.
In recent years, the legal landscape of RDM has been rapidly changing. Despite or even due to this fact, the consideration of relevant laws and policies is essential for planning and implementation of RDM during a project. Module 3 provides an overview of the legal aspects of RDM and measures that can be taken to ensure proper handling of data. As FAIR data is not necessarily open data, approaches to licensing of data are explored. Special attention is paid to funding agency, publisher and institutional policies relevant in catalysis as well as the peculiarities of projects within or in cooperation with industry.
Planning is a major factor for the effective application of RDM and may be even required for proposals for new projects. Module 4 considers the measures that need to be taken to make sure stored data will remain safe and available. The knowledge and skills gained in the previous modules are than applied to dive into the creation of data management plans (DMP). Relevant policies, especially from funding agencies are examined to provide the means and get started with individual DMPs for research projects.
One of the major factors to gain the most from RDM is selecting the most suitable tools for the right purpose. Especially in larger projects, the coordination and compatibility of those tools is vital for effective workflows. Apart from DMPs, Module 4 aims to contemplate tools that can be applied for general aspects of RDM, e.g., versioning, collaborative working or data treatment. Furthermore, more specialized tools for RDM in catalysis like electronic lab books, storage methods and tools for data analysis are explored in greater detail.
Storing data in a safe way is important, but to make data FAIR, it must also be stored in an accessible and interoperable way, at least within an institution, to make the data useful during a project and beyond. The methods and systems of this storage will greatly affect the reusability of data and the possibilities to gain further insights. Module 5 covers the basics of modelling of information and the functions and advantages of databases at the example of use cases from catalysis.