Module 4 - Realizing FAIR data#

In this module, we will focus on the realisation of FAIR data. We will explore some best practices and tools that can facilitate the implementation of the FAIR principles.

At the end of this module you should be able to:

  • Understand the importance of good data organisation and be aware of best practices around data organisation

  • Recognise the relevance of documentation and be familiar with diverse tools to document data

  • Identify relevant tools to advance towards FAIR data

  • Discuss data publication, best practices and restrictions

There is also an activity in this module you should complete:

✅ Watch the 📽️ videos about ‘Data organisation’, ‘Data Documentation tools’ and ‘Data access and Data publication’ and complete the corresponding quizzes. ✅ Optional activity for those working with Research Software - If Research Software is one of the main outputs of your PhD project, we strongly encourage you to take some extra minutes to complete section 4.4: Additional Resource - FAIR and Reproducible Research Software in this module.

4.1 Data organisation#

In module 3, you learned that ‘Documentation’ is one of the key elements of the FAIR principles. Good data documentation starts with good organisation including file naming of the data of your project!

Good data organisation on your computer or your Project Data (U:) drive will allow you to make the data Findable both for yourself and for your collaborators who have access to the data. Making the data Findable also indirectly improves the re-usability of the data. After all, you can’t reuse data that you can’t find! To practise good data organisation to make the data/code Findable, the first step is to apply a clear folder structure and appropriate file naming convention.

In the next 📽️ video, we will go through some best practices to help you organise the data of your project efficiently. These best practices are using a good folder structure, tagging files (if possible) and an appropriate file naming convention to enhance the findability of the data in your directories.

Test your knowledge!#

4.2 Data documentation tools#

Now that you know more about how to properly organise the data/code of your project by applying a clear folder structure and appropriate file naming convention, we can talk about documentation. Documentation is vital to making your work understandable, which is, in turn, necessary for your work to be reusable.

Documentation is important not only for data, but for your projects in general, including the code (software) you write. Your documentation should provide context for your project and its data. Also, documentation should provide, for example, information about the data collection, structure, and ownership.

In the next 📽️ video we will talk about different methods and tools you can use to document projects, datasets, and code. We will also briefly talk about metadata.

Test your knowledge!#

4.3 Data access and Data publication#

In the next 📽️ video we will delve more into data access during and after a research project. You will also be introduced to the topic of data publication and data repositories.

Data access is one of the five key elements of the FAIR principles you learned in Module 3. Remember that it is important to reflect on data access at the beginning of your project to ensure that the right people have access to the right data.

One way to make the data and code of your project Findable and Accessible to a broad audience is to publish it in a data repository. Data repositories help you comply with the FAIR principles and make it easy to apply some of the five key elements, such as assigning Licences and Persistent identifiers.

Test your knowledge!#

How to upload data and software in a data repository such as 4TU.ResearchData?#

If you’re interested in learning more about creating a dataset within a repository, you can watch the video below on how to accomplish this in 4TU.ResearchData - TU Delft’s partner. The 4TU.ResearchData repository serves as an archive for ensuring the long-term access and curation of research datasets, with a specific emphasis on data related to science, engineering, and technology.

Researchers from TU Delft can deposit up to 1TB of data and software per year.

For more infomation visit: https://data.4tu.nl/info/en/use/publish-cite/upload-your-data-in-our-data-repository

4.4 Additional - FAIR and reproducible research software#

If Research Software is one of the main outputs of your PhD project, we strongly encourage you to take some extra minutes to complete this section!

Before you get started with writing your code, you might want to look for code and software that others have already written and shared. This could be work by a predecessor in your team, a code snippet you found online, or software that is published and archived. When reusing research software, there are some relevant factors to consider, such as, where to find and how to evaluate the reusability of the available software? Maurits walks you through those considerations in the following 📽️ video:

Video title: Finding and reusing code. Video recording from TU Delft MOOC Open Science: Sharing Your Research with the World. Presenter: Dr. Maurits Kok. Credits: TU Delft Extension School, TU Delft New Media Center, TU Delft Digital Competence Center. Licence: CC-BY-NC-SA.

Developing research software Once you get started writing your code or developing your software, other best practices and management tools will be needed. If you have collected code snippets from different sources, debugging the code might be challenging, and as the development process goes along, you might want to compare current versions to earlier versions, etc.

The same as with research data, you should always think ‘who might be reusing your code?’ At the very least, it is your past, present and future selves. Therefore, implementing best practices to make your software FAIR and reproducible since the beginning of the project is very relevant!

In the next 📽️ video, Maurits will provide an overview of good practices for developing reproducible and reusable code:

Video title: Developing research software. Video recording from TU Delft MOOC Open Science: Sharing Your Research with the World. Presenter: Dr. Maurits Kok. Credits: TU Delft Extension School, TU Delft New Media Center, TU Delft Digital Competence Center. Licence: CC-BY-NC-SA.

**Publishing research software ** Once you are ready to publish your research work, the data and the code also needs to be published as you have already learned in the ‘Data Access and Data Publication’ video. If you need some extra encouragement to publish your research software or piece of code, in the following 📽️ video Maurits will tell you why you should publish your code as open source and how to do so following the FAIR principles for research software:

Video title: Publishing Research Software. Video recording from TU Delft MOOC Open Science: Sharing Your Research with the World. Presenter: Dr. Maurits Kok. Credits: TU Delft Extension School, TU Delft New Media Center, TU Delft Digital Competence Center. Licence: CC-BY-NC-SA.