**Title:** **FAIR Data in the Scholarly Communications Life Cycle** **Instructors:** - Natasha Simons, Associate Director, Skilled Workforce, Australian Research Data Commons; http://orcid.org/0000-0003-0635-1998 - Christopher Erdmann, User Engagement, Support and Training Expert (RENCI), University of North Carolina; https://orcid.org/0000-0003-2554-180X - Daniel Bangert, Scientific Manager, Göttingen State and University Library; https://orcid.org/0000-0003-4981-2870 **Description:** This course will focus on FAIR research data management and stewardship practices. It will provide an understanding of FAIR (findable, accessible, interoperable, and reusable) data and how it fits into Scholarly Communication workflows. Participants will learn about the FAIR Data Principles and how they can be applied in practice. Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. In research libraries, the principles can be used as a framework for fostering and extending research data services. This course will provide an overview of the FAIR Data Principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Along the way, we will get hands-on with data and tools through self-paced exercises. There will be opportunities for participants to learn from each other and to develop skills in data management and expertise in making data FAIR. **Level:** Beginner to intermediate **Intended audience:** TThe course is aimed at individuals working with or expecting to work with data as researchers, publishers, librarians, or in research support, especially those seeking to develop their skills in managing FAIR data in practice and to understand the tools that can support them in doing this. Requirements: There are no special requirements for the course. **Course Learning Objectives** By the end of this course, a participant will be able to: - Articulate the value of FAIR data as well as drivers, barriers, and challenges for enabling FAIR - Understand how FAIR data fits into the scholarly communications life cycle - Refer to hands-on experience with techniques and tools for making data FAIR - Identify best-practice examples of FAIR data management **Course Topics** This course will be presented as 3 x 1 hour zoom sessions repeated once to cater for different time zones. The following topics will be covered: Course overview - What is FAIR data - Why FAIR data - Drivers, barriers and challenges for enabling FAIR - How does FAIR work - Repositories, metadata, licensing and persistent identifiers - How can I apply FAIR - What kind of support and services do institutions need to have in place to help FAIR data? What to communicate to researchers and how to do it? How FAIR can be applied to other research outputs? - Global initiatives and networks around FAIR data and how to get involved **Course Schedule** **Session 1: What is FAIR Data?** - Welcome and introduction - What is FAIR Data? - Why FAIR Data? **Session 2: How does FAIR Data work?** - Repositories and data - Metadata including licensing - Persistent identifiers **Session 3: How can I apply FAIR?** - FAIR support and services - FAIR beyond data - Global initiatives and networks on FAIR Data - Wrap up **Course Materials and Supplies Required** - Before the course begins, students should: - Bring a laptop (tablets don’t work as well for this course) - Read: [FAIR Guiding Principles for scientific data management and stewardship][1] - Read: [Three camps, one destination: the intersections of research data management, FAIR and Open][2] - Browse: [Top 10 FAIR Data & Software Things][3] [1]: https://www.nature.com/articles/sdata201618 [2]: https://insights.uksg.org/articles/10.1629/uksg.468/ [3]: https://librarycarpentry.org/blog/2019/02/top-10-fair-published/