This is the material for a workshop I gave at the University of Maribor Open Science Summer School 2023. The lecture was meant for a very diverse class of students (from Bachelor to PhD degrees), and is a broad introduction to FAIR data, with a series of hands-on exercises on the FAIR principles. The Dealing with FAIR data PDF file is the backbone of the lecture it starts with an introduction: a broad recap of research data, the definition of open data, the research data lifecycle, a list of terminologies useful to follow along, and a brief mention of the FAIR principles (things the students had seen the day before) the second part is about the FAIR principles in action with hands-on exercises for each of the four letters: persistent identifiers, APIs, machine-readable formats, licenses, etc. the third part is about the process of FAIRification of a dataset: we look at tabular data, in particular into tidy formats vs messy formats, and we use OpenRefine to tidy up some datasets. Towards the last part of the workshop, we also look at the Frictionless Data Package format, we create one with the Data Package Creator, and we finally upload our FAIR toy dataset to the sandbox environment of Zenodo, getting a DOI. The other csv files are the datasets used during the workshop: untidy1.csv and untidy2.csv are used for the exercises in OpenRefine scientific-publications-per-million.csv is instead used for the data package creation, which produces the datapackage.json file The HTML document of this workshop is published on the web at this link.