Data Science is at the heart of a lot of research activities, also for pig research. Consequently, the role of data and “taking care of data” is becoming increasingly important, as it has become an indispensable asset. The amounts of available data are growing exponentially, and to make that data most valuable for the research community, it is key to ensure that relevant data is available, can be found, and be reused. This is also becoming an important factor in research, as many funders emphasise the importance of high-quality data and data management, and making data reusable for the wider community. To ensure the reusability of the data, they are often checked against the FAIR (Findable, Accessible, Interoperable and Reusable) criteria (see chapter 3). The PIGWEB project has performed surveys to explore the current landscape of pig research when it comes to data and data management. Some relevant outcomes of the landscape survey in pig research were: • Collaboration is key to pig researchers’ work. • Many researchers are involved in tasks related to data processing. • In general, researchers are relatively comfortable with sharing data and data being reused. They are mostly positive about data sharing and see the benefits of data reuse. • Some researchers never reuse data, and most of them only reuse their own data. • Successful data reuse is achieved in about half of the attempts. • Researchers are not very familiar with the FAIR principles and FAIR policies and think they generally do not deliver FAIR data. They feel they need help with (FAIR) data sharing. • In general, researchers see many barriers for data sharing, like lack of time, lack of budget, lack of knowledge, and lack of rewards for data sharing. • Researchers feel they get too little credit for data, where citation and co-authorship would be good incentives. This reflects a couple of aspects around (FAIR) data sharing and reuse. First, researchers seem to see the value of sharing and reusing data but are practically hindered by a lack of knowledge and resources. Secondly, the incentives to share data seem to be insufficient. These might be the main causes of the currently low data sharing and reuse adoption. At the same time, there might be some misunderstanding regarding the current opportunities and incentives. The FAIR data guidelines for pig research in this Deliverable introduce the FAIR principles and the requirements for delivering FAIR data, and the various aspects regarding data management and curation that are relevant for efficient data sharing and reuse. The objective is to provide knowledge and introduce good practices and tools that can support the adoption of FAIR data practices by the broader community’s adoption of FAIR data practices. Moreover, it attempts to lower some of the barriers to data sharing and reuse by discussing some observed misunderstandings and interpretations and clarifying some often less well-known opportunities and incentives. This Deliverable starts with an introduction to Open Science and the FAIR principles, explaining the motivation behind the FAIR data movement and how it relates to the broader process of working with data. The various steps of data curation, the handling of data, from data collection to data publication and reuse, are presented. Some key aspects in this process are discussed in more detail, specifically how data can be harmonized using common standards, formats, semantics etc., and how data can (should) be published so they can be easily reused. A separate section focuses on data management plans (see chapter 8). A data management plan describing how data can be handled in a project, which is a mandatory deliverable for more and more research projects. In the various chapters, several use cases from the pig research domain are used to illustrate how FAIR data and data management aspects can be applied practically in research.