Akudo McGee about making data FAIR
This week, Akudo McGee, PhD candidate in the LIMES project, tells us about how and why she made her data FAIR.
How did you find out about FAIR?
It is almost impossible not to hear about FAIR at UM. I have seen it frequently in staff communications. However, it was Maria Vivas Romero, the data steward for FASoS, who really explained it to me.
What does FAIR mean to you?
FAIR means to me that I make my research easy for other researchers to locate and use by collecting, storing, and sharing it in a way that is consistent with certain principles. I view FAIR as structured guidance, which prompts researchers to think about whether other researchers will be able to locate and access our data, whether the data we collect can be integrated with other data (including for use in mainstream applications), and whether our data is preserved in a form that is reusable. This all involves making conscious decisions in consideration of the future of our data throughout the data collection and storage processes.
Why did you decide to make your data FAIR?
I am just starting out on laying the groundwork for making the data I have been collecting and will eventually analyse for use in my thesis FAIR. However, the decision for me was quite an easy one. As a scientific community, our ultimate goal should be to present our findings in a way that can be accessed, understood, tested, and even challenged by others in the community. The first step in this is making the data accessible and usable by other researchers. It also signals a personal commitment to data integrity since it eliminates the covert avenues through which one may store or use data and encourages greater transparency.
How has the data steward (Maria Vivas Romero) helped you make your data FAIR?
The better question is how hasn’t she helped me! She has helped me every step of the way, from creating a Data Management Plan, to connecting me with other researchers about how I can analyse and store large data sets, to understanding what different terms mean and how they might influence the way I use or collect my data.
Was it a lot of work to make your data FAIR?
I have just started out collecting data but so far, it has not been difficult. It is a little more work than one may do otherwise but in the end, it prevents you from having to dig back through old data to make it more accessible, structured, or usable for others.
How do you think making data FAIR benefits you?
Being FAIR makes it more likely that I will keep track of how I am managing and storing my data and overall invites me to be more transparent with how I use and collect data. By sharing our data we not only make contributions to the rest of the scientific community but also invite others to challenge or try and replicate our data. Therefore, I feel that FAIR encourages me to make a greater commitment to data integrity.
How do you think making data FAIR helps other researchers?
FAIR makes it more likely that researchers can take advantage of existing data for the purposes of their own research. I think of it similar to coding. Instead of having to create code from scratch, generally other coders will leave some base code for you to work off of. In the same way, researchers do not have to reinvent the wheel but rather can access already collected data for the purposes of their own research. Their research may also be focused on testing existing hypotheses or conclusions, in which case making my data FAIR may aid them in proving or disproving some of their own hypotheses or the hypotheses I have arrived at in my own work. It also eliminates headaches that come from obscurely collected and stored data as well as pay walls that make it difficult to access much-needed data.
What do you think are the biggest advantages of FAIR?
I think the biggest benefits of FAIR are making research more accessible to researchers and therefore (hopefully) advancing that field of research. It may also encourage researchers to be more transparent with how they collect and use data and create a more collaborative climate where data does not live and die with the researcher who collected and analysed it originally but instead can be the start of another researchers’ work.
What do you think are the hurdles when trying to make data FAIR?
I think for some researchers a significant hurdle can be change, especially if FAIR was never a part of their vocabulary before. This will entail making different decisions along the data collection process. Another obstacle could be the sharing of sensitive data. This data may be sensitive due to the nature of the participants it was collected from, how the data was obtained, or maybe the rules of organisations that helped in the data collection process.