From 565257e7e973ce17092aa2ce1f01bab3a11be033 Mon Sep 17 00:00:00 2001 From: khaled196 Date: Fri, 15 Mar 2024 16:48:26 +0000 Subject: [PATCH] fix schema --- topics/fair/tutorials/fair-origin/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/fair/tutorials/fair-origin/tutorial.md b/topics/fair/tutorials/fair-origin/tutorial.md index 7af5a2191597b4..1c36b2f0c33fc8 100644 --- a/topics/fair/tutorials/fair-origin/tutorial.md +++ b/topics/fair/tutorials/fair-origin/tutorial.md @@ -86,7 +86,7 @@ Table 1.1: The FAIR guiding principles as described in Wilkinson, M., Dumontier, > > -> Look at the wording of the FAIR principles in [Table 1.1](#table). Which terms are used more than once? Which terms are you seeing for the first time? +> Look at the wording of the FAIR principles in **Table 1.1**. Which terms are used more than once? Which terms are you seeing for the first time? > > > > > @@ -121,7 +121,7 @@ The [Open Data handbook](https://opendatahandbook.org/) defines Open data as ** # What is meant by FAIRification and FAIRness of data? -**FAIRification** is the process of making your data FAIR compliant by applying the 15 Guiding Principles shown in [Table 1.1](#table). The extent to which you apply these principles defines the **FAIRness** of your data. In other words, FAIRness refers to the extent by which your data is FAIR and implies some implicit means of measuring its compliance. +**FAIRification** is the process of making your data FAIR compliant by applying the 15 Guiding Principles shown in **Table 1.1**. The extent to which you apply these principles defines the **FAIRness** of your data. In other words, FAIRness refers to the extent by which your data is FAIR and implies some implicit means of measuring its compliance. # FAIR’s origins