The role of data science in pig research is growing, making effective data management essential. With the exponential increase in available data, ensuring its accessibility, usability, and reusability is key. Many research funders emphasize high-quality data management, often evaluating it against the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Sharing data also helps to reduce animal experimentation by enabling researchers to build on existing data, which can minimize the need for repetitive studies. The PIGWEB project conducted surveys to assess the current state of pig research in terms of data management. Key findings include the importance of collaboration, researchers’ involvement in data processing, and a general willingness to share and reuse data. However, many researchers only reuse their own data, and successful reuse occurs in just half of the cases. Additionally, knowledge of FAIR principles is relatively low. The FAIR data guidelines introduced in this document aim to improve data sharing by providing knowledge, offering best practices, and clarifying misconceptions. It covers the open science and the FAIR principles, data curation, standardization, and data management plans. By addressing these different topics and challenges, the guidelines seek to foster broader adoption of FAIR data practices in pig research.