Are our data ready for the future?
Meeting about making data FAIR
On 22 June 2022, ZonMw held a meeting about FAIR data. The meeting was intended for project leaders, researchers and data managers involved in antimicrobial resistance and infectious diseases research. The aim of this meeting was to inform and share experiences with making the data reusable and the process applying the FAIR metadata schemes (M4M, metadata-for-machines) that were developed for these themes. Here you can read that report on this meeting and watch the video in which researchers talk about their experiences with FAIR data.
Data are crucial for the effective tackling of infectious diseases
The COVID-19 pandemic has shown how important it is to rapidly have access to sufficient data, amongst other things, for the development of vaccines, insight into the spread of the virus and which measures against this are required. For other infections, good data are also crucial for an effective approach. For instance, a lot of genetic information from different strains of bacteria is needed to understand how bacteria become insensitive to antibiotics. So there are good reasons why ZonMw has started to pay extra attention to FAIR data in the area of infectious diseases. In the third round of the ZonMw programme Antibiotic Resistance (ABR) researchers were challenged to set up new projects that make use of existing data. In the COVID-19 programme, FAIR data were worked with right from the outset.
FAIR means that the computer can read it
The acronym FAIR stands for findable, accessible, interoperable and reusable. With these words, also called FAIR principles, we tend to think of people who must find something, access it, use it to make calculations, etc. However, in the everyday practice of 21st-century science, it is mainly the computer that must be able to find that data and do something with it. Therefore another explanation of FAIR is 'Fully AI Ready'.
Computers usually need extra information to be able to genuinely 'understand' the data. This concerns, for instance, how the data was produced and in which context. For data about a bladder inflammation, it is relevant to know, for example, whether it concerns children, pregnant women or older persons in a nursing home. Such background information is often stored in so-called metadata. Making existing data FAIR means adding such metadata to the data collection. Which metadata are needed depends on the discipline, the 'domain'. Researchers within that domain must make agreements about the metadata.
'Initially, researchers do not always easy to understand exactly what FAIR means and why it is important', says Margreet Bloemers, project leader FAIR data and data management at ZonMw. 'Researchers are often focused on a single specific research question and are therefore less busy with the value of the data for future research. However, once they realise the importance, it becomes clear what the added value is for them and other researchers. In the video presented by Bloemers, researchers talk about their experiences with FAIR data. They often stated that they increasingly value their data manager. The data manager can help to store the data in a FAIR manner right from the start of the project.
Watch the video about FAIRification of data within Antibioc Resistance programm below (Dutch):
For health data, in particular, it is definitely advantageous if the actual data does not need to be shared. Data that are stored in a FAIR manner can be made accessible for analysis by external parties. The data are then visited by computer program (algorithm). That is better for patient privacy and also more convenient.
For example, imagine a researcher who wants to know how many hospital patients with pneumonia have received an antibiotic and how quickly they recovered. The researcher can then send a computer algorithm that can count, for example, in the data from a large number of hospitals who received that antibiotic and how long it took before the body temperature was normal again. The patient data remains confidential: a few numbers are returned to the researcher, but the research question can be answered.
From the presentations during the entire meeting, it was clear what FAIR means in practice. Thanks to projects from Leiden University, data about infections and a large number of African countries have now been stored in a FAIR manner and are kept on location. In the past, the data often disappeared to research institutions in other countries, as a result of which local care providers could no longer access it. Thanks to the Virus Outbreak Data Network (VODAN), the opposite is now true: researchers send a computer algorithm past the data in various African hospitals and health centres. Now the local infrastructure is being invested in, and research can be done throughout the world using this data. Of course, local laws and agreements in the area of data and the ethics for such research are respected.
ZonMw supports researchers and institutions
Since 2013, ZonMw has encouraged making data within Dutch health research reusable. Everyone who receives a grant from ZonMw for research must submit a data management plan. That data management plan has become even more important due to the FAIR principles that were published in 2016. Good data management also aligns with the increasing attention for Open Science. The researchers' stories reveal that it helps to think clearly in advance about the storage of data, but that is not yet enough. The plan must also be carried out. Fortunately, a growing number of research institutions are also investing in data management. Early career researchers are taught about FAIR and what that means for their research.
A growing number of data managers and more FAIR data
The number of data managers is gradually growing. The national Health-RI network and internationally-oriented GO FAIR play an active role in this together with ZonMw. Experiences such as the COVID-19 programme and the ABR programme also contribute to smart ways in which researchers can be supported. Ultimately, it is in their interests and in the interest of prevention and healthcare that data are (re)usable for research. And now, thanks to the growing possibilities provided by artificial intelligence, ZonMw wants to do everything possible to ensure that all data are Fully AI Ready. In other words, they are ready for the future.