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The critical window for becoming a FAIR researcher

19 April 2021

Heidi Kleven and Ingvild E. Bjerke,
Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Norway

They say old habits die hard. If FAIR data sharing1 is going to be a fundamental part of the research process in the future, we - the young researchers - need to establish these habits sooner rather than later.

In our work with FAIR data sharing through the EBRAINS infrastructure2, we often talk to other researchers who could potentially share their data. This has resulted in a manifold of conversations, often with very different results: some people have been positive and willing to share so much data that it has fostered year-long collaborative projects, others have reacted with indifference or concern for the work involved or how their data may be used. While the awareness of data sharing has increased across the neuroscience community, as a natural consequence of new requirements for open data from publishers3 and funding bodies4, many still see this as a tedious formality causing additional work. Some conversations about data sharing and documentation practices with other researchers have been very enjoyable, and some not. However, in our experience, it has rarely been difficult to engage young researchers in data sharing. 

This has made us wonder - speaking in terms of developmental neurobiology - is there a critical window for making young researchers FAIR? Certainly, there is a period early in our scientific career, where we are especially impressionable. We learn not only laboratory methods and use of various software, but we also pick up working habits, communication styles, and - to some degree - viewpoints, from our senior investigators. In the earliest stages of our careers, our everyday decisions about how to work and what topics to pursue is influenced by what our superiors deem important and fruitful. When we are taught, directly or indirectly, that a journal article is the ultimate goal of any project, our choices will reflect this prioritization. When a researcher becomes independent, he or she will build a career on the foundation built together with earlier supervisors. Through our conversation with researchers over the years, our impression is that by this time, the window of opportunity is closing.

Is this an attempt to remove any responsibility from young researchers? No. For those of us who aspire to climb the carreer ladder and continue to work in academia, we eventually need to take full responsibility for our data management habits and the FAIRness of our research products. So how do we create a generation of researchers that see FAIR sharing of all their research products as an opportunity and a natural part of any project, rather than a cumbersome formality?

There are some simple guidelines senior investigators can offer a young researcher during his or her formative years:

  • Plan for sharing data and metadata when planning an experiment or an analysis. In practice, a standardized spreadsheet to collect metadata and a description of how data are organized can go a long way. The work should be organized so that any qualified colleague or collaborator can pick it up. Not only does a data management plan reduce the dependency on individual lab members, it is also now often required when applying for funding. For this purpose, it is also advisable to decide on where (i.e. through which repository) the data will be disseminated. Many of the data sharing platforms also provide help and advice on how to manage and organize data.
  • Collect metadata systematically during data acquisition. When a plan for how data and metadata should be managed is in place, collecting metadata and organizing data consistently from the beginning of the project will be easy. In our experience, this also saves time and effort in the long run, making it easier to come back to the data after breaks or pauses in the project, or to re-do parts of it if needed. 
  • Use public data repositories to collect, compare and combine data. The growing trend of publishing data has led to several repositories containing valuable data, tools and workflows that individual researchers can use to enrich their own projects. This gives any researcher instant access to information which earlier would only be available at the grace of local collaborators, information through the grapevine or costly exchanges through the postal system. 
  • Share research products through public repositories. One of the great benefits of sharing data and other research products, apart from making it available for further analysis, is to have the information written up in a standardized way. Additionally, having the data from a project organized and stored on a digital platform, with detailed information about procedures, methods and analysis, removes the need for storing all of this information locally.    

But speaking of old habits dying hard, many senior investigators might find it daunting to invest time and effort in novel principles, methods and routines for FAIR research. However, introducing the young researchers to the FAIR principles is easy as there are several papers5, initiatives2,6, and videos7,8 that explain, guide and encourage researchers to be FAIR. The theory of FAIR science is clear, and a young researcher can read up on this on his or her own, and put it into local practice under guidance. 

So what are the benefits for researchers implementing FAIR practices? Using the FAIR principles in our scientific work opens up a whole new set of opportunities for the young researcher. Publishing high quality data with persistent identifiers, such as DOIs and RRIDs9, will help build a portfolio of citable material for the early career researcher. International and interdisciplinary collaborations are also both easier and less expensive when performed online, compared to travelling and sending data back and forth over several years. Open FAIR data does not only enable new combinations of findings, but it also builds the foundation for a new set of metrics through which researchers can be measured. In addition to traditional citations of publications, future researcher may be measured on how often their data are reused or the number of new discoveries their data have contributed to. Last, but perhaps not least, when the young and FAIR researchers unfold their wings and fly off to new horizons, the senior investigator is no longer left with a shelf of protocols and a disc with data, but rather with organized and documented collections of data and metadata suitable for re-use.

Future researchers will need to master not only the traditional skills of clear communication of scientific theories through publications, but also the great craftsmanship and data management needed to produce high-quality data for public reuse. Senior investigators and young researchers should together establish good habits that will stand the test of time.


  1. Stall, S. et al. Make scientific data FAIR. Nature vol. 570 27–29 (2019).
  2. EBRAINS. [Internet]
  3. Bloom, T., Ganley, E. & Winker, M. Data Access for the Open Access Literature: PLOS’s Data Policy. PLoS Med. 11, e1001607 (2014).
  4. The Research Council of Norway [Internet] (2019).
  5. Wilkinson, M. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 1–9 (2016).
  6. INCF training space. [Internet]
  7. FAIR data in neuroscience and life sciences: EBRAINS solutions for data publishing. [Internet]
  8. The Use of Ontologies for FAIR Neuroscience. [Internet]
  9. Bandrowski, A. & Martone, M. RRIDs: A simple step toward improving reproducibility through rigor and transparency of experimental methods. Neuron 90, 434–436 (2016).