Include FAIRlinkung

data-organisation.qmd

@@ -8,26 +8,10 @@ link-external-newwindow: true
 
 Research data is valuable for researchers and forms the basis for their research. Therefore, it is advisable to structure the data well to save time and effort in the daily handling of research data. In this part of the workshop, we will look closer at organizational aspects of data management, mainly the folder structure, file and folder naming, and file formats. 
 
-A clear and consistent folder structure and folder and file naming convention are important for making your data [interoperable](https://www.go-fair.org/fair-principles/). You should think about it beforehand in order to avoid inconsistencies or the need to rename large amounts of data.
+A clear and consistent folder structure and folder and file naming convention are important for making your data [**f**indable and **i**nteroperable](https://www.go-fair.org/fair-principles/). You should think about it beforehand in order to avoid inconsistencies or the need to rename large amounts of data.
 
 Your structure and your naming conventions should be intuitive. However, we recommend to explicitly describe them (typically in a [README file](documentation.qmd#readme-file)) because they may not be that intuitive for others or your future self ("why did I do it like this?").
 
-
-
-::: {.callout-tip collapse="false" icon="true" title="(General) Task 1: (\~ 30 minutes in total)"}
-Check the file (and potentially folder) structure of the dataset.
-
-For the aspects mentioned in the sections below, think and discuss about:
-
-- Check the current situation: What is done, how, and why?
-- Discuss:
-  - What would you leave as it is, what would you change, or what are the alternatives? And why?
-  - How can you improve the dataset?
-
-The most important part of this task is the group discussion because there are several possible solutions for most aspects.
-:::
-
-
 In the following sections, you'll find some input on the organizational aspects you should consider. Note that not all of them may apply to each dataset. Besides the tasks, we'll provide some general hints and rules. Some rules only apply to some use cases, and sometimes, there are good arguments for not sticking to every rule. However, in such cases you should know (and potentially document) why you decide differently.
 
 

documentation.qmd

@@ -5,7 +5,7 @@ link-external-newwindow: true
 
 ## Overview
 
-Effective documentation is a crucial aspect of [FAIR](https://www.go-fair.org/fair-principles/) data management, ensuring that research data are not only well-organized but also easily discoverable, accessible, and reusable by others. In this section, we'll delve into the importance of documentation by exploring how to create best practice documentation that supports the entire research lifecycle. We'll cover the essential components like including metadata that provide context and description of the dataset, as well as README files that offer a concise introduction to the dataset. Additionally, we'll discuss the role of codebooks for making all components of the dataset self-explanatory.
+Effective documentation is a crucial aspect of [FAIR](https://www.go-fair.org/fair-principles/) data management, ensuring that research data are not only well-organized but also easily **F**indable, **A**ccessible, and **R**eusable by others. In this section, we'll delve into the importance of documentation by exploring how to create best practice documentation that supports the entire research lifecycle. We'll cover the essential components like including metadata that provide context and description of the dataset, as well as README files that offer a concise introduction to the dataset. Additionally, we'll discuss the role of codebooks for making all components of the dataset self-explanatory.
 
 ## Metadata
 

publication.qmd

@@ -5,7 +5,7 @@ link-external-newwindow: true
 
 ## Overview
 
-Data publication is the final step of FAIR data management, ensuring data **F**indability and **A**ccessibility (when done well). If you have taken care of the previous sections [Data organization](data-organisation.qmd) and [Documentation](documentation.qmd) and thereby made your data **I**nteroperable and **R**eusable, publishing those data will take minimal effort.  
+Data publication is the final step of [FAIR data management](https://www.go-fair.org/fair-principles/), ensuring data **F**indability and **A**ccessibility (when done well). If you have taken care of the previous sections [Data organization](data-organisation.qmd) and [Documentation](documentation.qmd) and thereby made your data **I**nteroperable and **R**eusable, publishing those data will take minimal effort.  
 Most commonly, data are published as supplements to journal articles and an increasing number of journals actually require that. Data sets can also be published in specialized data journals (e.g., Scientific data, Data in Brief), which means that the article itself is a rich and detailed description of the data set. As you may have guessed, this option is mainly chosen for rather large, sampling-intensive data sets. Last but not least, it is also possible to publish an independent (without connected publication) data set in a repository and this is often required by funders like the DFG or EU. Irrespective of the publication option, there are common good practices when publishing data, namely
 
 -   indicating in the article that data and/or code are available,