In a series of blogs, we compare our mission of UNLOCK (to unlock microbial potential) with the quest for a dream team, like in sports. Our research platforms have different ways to identify new talent, but they are complementary and have one thing in common: They generate data. These data are taken care of by the FAIR Data platform.

By the UNLOCK team / June 20, 2022


Technical difficulty
Sports is all about performance – and so is microbial team play. In our previous blogs on the Biodiscovery, Parallel Cultivation, and Modular Bioreactor Platform, we explained different ways of monitoring the performance of a microbial team: How fast do the bacteria grow, and what do they produce? How stable is the team? Obviously, we are highly interested in these numbers, since they allow us to compare microbial members and teams. Similarly, they give athletes and teams an indication of how good they are – or whether they need extra training.

The complexity of team play

However, these numbers can become quite complex when measuring performance in parallel (an intensive training day under different conditions) or in a modular set-up (a relay training or race). That’s why UNLOCK has a data platform fully dedicated to analyzing, processing and storing data: The FAIR data platform. It ensures that all data generated within an UNLOCK project is noted, analyzed, and safely stored according to the FAIR guiding principles.

Data according to the FAIR principles

FAIR stands for Findable, Accessible, Interoperable, and Reusable. Imagine a sports team working with a new coach who wants to know all about the performance of the team and its members so far. Think about the number of victories, strengths, and weaknesses, health-related data.

  • It would be most efficient if this data is easily findable and accessible in a (digital) logbook that is kept and made available by the previous coach(es).
  • Furthermore, the metrics used in this logbook should be interoperable – understandable – for every (new) coach. In this case, it would help to work with units everyone understands: weight in kg, heartbeat per minute, and power in watts.
  • Finally, the data needs to be reusable: Perhaps the coach wants to reanalyze the previous data, for instance, by not looking at the average heart rates in rest but fluctuations of the heart rate over time. To this end, the data should be handed over in a format that allows the coach to redo this analysis.
Our research platforms generate a lot of data, which need to be noted down, analyzed and safely stored according to the FAIR guiding pricnciples. Photo by Anna Nekrashevich via Pexels.

More than just numbers

A critical feature of FAIR data is to not only store the actual numbers, but also the context in which data is recorded. As you may know, team performance in sports depend heavily on the conditions: The victory of the Tour de France in 2021, cannot be compared to the one in 2020, although both resulted in the yellow jersey. When (re-)analyzing data from these two events, it might be informative to have data on – a. o. – the weather conditions, opponents, and stage profiles: This is what we call metadata. Without metadata, data is meaningless.

How to ensure FAIR play

Our FAIR Data Platform ensures that the data is made FAIR right from the start. To this end, we have developed a so-called FAIR Data Station, which ensures that all metadata is appropriately recorded when starting to design the intended experiment. Going back to the coach, instead of looking at how conditions were different between two races; it should be clear before the race what conditions might affect performance and need to be properly recorded. Eventually, this will  also help to optimize team performance in future races.

Team work makes the dream work?

In this blog series, we aim to illustrate the importance of team play when studying microbial communities. In our next and final blog, we will explain where microbial teams can be used for and how UNLOCK can actually contribute to unlocking microbial potential.

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