Guest post: FAIR data for next generation management of MS

Transforming the population based management of today into an individualized, personalized and precision level management is a major goal in research. I believe, the key to reach this next level is FAIR data.

Every day, young people are diagnosed with MS. People with MS struggle with a lot of insecurities about the future. For example, they were planning to buy a car, but will they be able to use it for a while? Or will they end up in a wheelchair? Which treatment will be most effective for them? Therefore, there is an urgent need for decision support systems to support diagnosis, prognosis and treatment. However, to reach insights on an individual level, a modelling approach combining clinical, biological and imaging measures with individualized information like immunological and genetic profiles is needed. In other words, an enormous amount of data is required.

In my recent paper in the Multiple Sclerosis Journal, a plan to revolutionize MS management into a personalized, individualized and precision level is outlined using a FOUR C- plan (=Collect, Connect, Complete and Construct, figure below).

In order for this plan to succeed, data should be FAIR. 

FAIR is a fairly recent concept that stands for Findable, Accessible, Interoperable and Reusable. Imagine any type of data being ‘Findable, Accessible, Interoperable and Reusable’ by both humans and machines. 

The possibilities to discover new insights multiplies manifold. To truly capture the potential of this “four C-plan”, please reflect on following question: “what would YOU investigate, if you had all the data in the world at your disposal and the analysis tools to optimally mine this data?” The day that data will achieve maximal impact, the answers to all these questions can be investigated.

My dream is that one day, this will be possible. I believe that many new insights are to be discovered using the data that is already there. Community efforts need to be combined and synchronized to overcome the challenges obstructing us to reach these insights. So, let’s DREAM… and TEAM UP. The paper is open access, read it here. For contact details and more information, please visit our website. Looking forward to reading your comments.

by Dr Liesbet Peeters
Project manager ​- ​MS DATACONNECT​

About the author

Rebecca Aldam


  • Hi, great project. So… the dream is to link the largest existing MS registries/databanks in a “big data” type manner? To make sure that existing data is used as efficiently as possible? I had experience of linking a CPRD dataset to an NCAPOP project and it was about as straight forward as boiling the ocean so I really hope you can make this feasible 😀
    What I’d like to see from it:
    1) Research waste is infuriating, and I would like to see more academic led clinical trials using repurposed generics (the think hand type ones). It would be great if we could have the intervention and it’s initiation being funded by NIHR, and then the follow-up data being collected by your (immaculate and perfectly formed) cohort dataset, the placebo group could even come from existing data. This would reduce the cost of the trial (less CTU and RN time) … it would be more likely to get funding.
    2) Anything on aetiology.
    3) The JLA priorities are quite reflective of my views otherwise

    • Dear J,
      Thank you very much for your comment.
      I am not really sure if I understand what you mean and whether you want me to respond :-). The idea is indeed that IF pooling and/or linking is desired, FAIR data will make it much mure easier.

    • I was being rhetorical in case I had completely understood (it wouldn't be the first time). It's great to hear that this work is happening and I wish you every success =-).



By Rebecca Aldam



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