Chapter 1 Introduction
1.1 What is FAIR data?
To understand the spread of azole resistance traits within the Aspergillus fumigatus population, a lot of data must be combined: where is an isolate found? How well can it deal with azoles? What is it’s genotype? etc. While a lot of data about these subjects is collected, a clear overview of all data is not easy to obtain. Many researchers use different protocols, data formats, and do not share their data in a easy-to-find manner.
This method of (not) sharing research data is limiting the understanding of azole resistance in A. fumigatus. The solution is to engage in data sharing that adheres to the FAIR principles (“FAIR Principles” n.d.):
- Findable
- The metadata is machine readable, making sure that the dataset can be found by a computer using relevant search terms.
- Accessable
- The dataset is stored in a public database, where anyone can access it.
- Interoperable
- Metadata should be formal, and it should be possible to combine it with other FAIR datasets.
- Reusable
- The dataset is described in a way that it can be reused by another user for a different analysis. Licencing should be set up to make the reuse legal.
In this book, ASPAR_KR is introduced, a system for managing A. fumigatus observations in a FAIR manner. The book consists of a few chapters.
- Introduction
This chapter. - How can you play FAIR?
An example of how you can start to use ASPAR_KR for your own needs, with worked code examples. - Other chapters.
- Appendix
1.2 Why would FAIR data be better data?
Making data more FAIR–FAIRification–is in the interest of the researcher and the wider research community. For the researcher, it is beneficial as FAIR data is becoming more important for grand organisations, with funding becoming conditional on FAIR data management (NWO n.d.). Additionally, the research whose data is published in a FAIR format, is more likely to get cited, since the first aspect of FAIR data is being easily findable in a public database. For the research community at large, FAIR data is important since FAIR data can be reused more easily, this means that less money is wasted to collect data that already exist. Furthermore, it is easier to replicate FAIR studies, as the meta-data is easier to understand.
Throughout this book, we’ll follow the story of Marie and Peter–both are mycologist interested in how azole resistance spreads throughout the A. fumigatus population. Marie is using ASPAR_KR to make her new experimental data FAIR while Peter is using ASPAR_KR for FAIRification of his existing datasets, as his publisher now requires FAIR data management. As we go along, we’ll find that Marie has a way easier time in her FAIRification journey than Peter.
Besides the direct benefits of FAIR data, you’ll also learn about interesting technologies, such as SPARQL, that can make your analysis pipeline more easy.