High-quality data management and sharing needs to follow the FAIR principles, a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable.

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Although the human/animal health and ecotoxicological effects, environmental fate and risk assessment of nanomaterials (NMs) are in the centre of attention of European and international scientific research, nanosafety research is still suffering from the lack of sufficient reusable and verifiable data. This is the result of data fragmentation and inaccessibility, caused by the lack of infrastructure and knowledge in some nanosafety projects to make their data FAIR or even better publicly available, the incompatibility of the chosen solutions and the lack of coordination with international activities and consortia.

To change this, training resources are collected here, which not only describe the general concepts but provide hands-on solutions for everybody to be implemented into their daily work according to their specific roles in the data management life cycle.

Benefits of FAIR and Open data

  1. Knowledge discovery: FAIR data in combination with semantic annotation allow data to be queried and discovered by both machines and humans, while ensuring that the queried data are similar and can be brought together for the creation of bigger datasets.
  2. Data integration, harmonisation and reusability: FAIR data can be brought together and integrated in a harmonised way, thus allowing data reusability under different formats and applications.
  3. Maximum data exploitation and impact: Data reusability, in combination with other similar datasets, allows maximum exploitation through the application of different analytical and modelling tools and can lead to the identification of otherwise hidden patterns and novel insights.
  4. Improved research quality: FAIR data, implemented with the necessary metadata, will significantly improve data quality. The integration of similar datasets, and the subsequent increase of data information, will allow better data gap identification and will help refine data plausibility and/or the identification of outliers.
  5. Discipline/field independent: FAIR data does not follow a “model fits all approach”, but is highly adaptable on the specific requirements of each field.
  6. Promotion of data innovation: the effort towards a full implementation of FAIR principles is directly related to data innovation and entrepreneurship as it implies overcoming the following common obstacles for successful development of digital services::
    1. Lack of systematic processes, coordination and collaboration.
    2. Insufficient competence and training.
    3. Lack of relevant infrastructure and tools.
    4. Problems with data access across different fields.
    5. Data is not used as a strategic resource for further exploitation and growth.
  7. Positive socio-economic value: a recent study commissioned by the Danish Agency for Science and Higher Education concluded that by making FAIR 50% of all data produced in Denmark, the socio-economic net present value will be about DKK 2 billion (€268mln) over a 40-year period. This estimate is based on the most conservative calculation assumptions found in literature, while disregarding the benefits that would fall to other countries if the FAIR data concept is introduced in Denmark; nor have any benefits gained from a FAIR data collaboration with other countries been included in the calculations.

What are you using to be FAIR?

The implementation of the FAIR principles is a very active area and it is hard to keep up-to-date. Therefore, please help us to identify important developments by editing the mural below. We will then transfer this information into additional Handbook pages.

FAIR communities

Besides the tools, staying connected and active knowledge sharing within the community but also across (neighbouring) disciplines is key. Initiatives to do so include:

And specific to nanomaterials and nanosafety:

Start becoming FAIR

Internal FAIR resources

External FAIR resources