Nanotechnology is progressively being recognized as a key enabling technology that is helping to remarkably improve many industry sectors and applications like cancer diagnosis , drug delivery , food safety , energy, and environmental science , to name a few. Furthermore, the growing of its importance lead to increase the introduction of engineered nanomaterials (ENMs) to the market . These advances lead to questions about nanoparticle safety. Despite the significant efforts to understand the toxicity and safety issues associated with it, nanosafety, is still far from being addressed and that is partially due to the expensive and time-consuming traditional experimental testing procedures . Consequently, The European Commission (EC) have funded many nanosafety research projects to study and understand the potential hazard of ENMs exposure on human health and environment. These projects are coordinated under the umbrella of the NanoSafety Cluster (NSC) which integrates and synthesises the existing and produced nanosafety knowledge to provide a unified message to stakeholders including academics, regulators, industry and civil society . From nanomaterial toxicity to exposure monitoring and integrated risk assessment, large amounts of data has been generated for a wide range of nanomaterials. However, not all the data was organized in databases nor annotated with harmonized metadata standards . Moreover, a systemic problem across the field is the inconsistency of standards and experimental reporting. The variability in the published literature regarding the reported experimental and material characterization variables constitutes a significant barrier to progress in such an interdisciplinary field . In recent years, the concept of FAIR data has been on the rise . FAIR is a set of guiding principles to make the data Findable, Accessible, Interoperable and Reusable. The mindset of the FAIR initiative is of exceptional value for the Nanosafety community and could bring outstanding benefits regarding data standardization, sharing and reuse . Data FAIRness refers to the maturation process where digital resources are increasingly becoming self-descriptive to the machine, and hence, facilitating interoperability and reusability of the data among humans and machines at the same time. The FAIR framework is composed with four principles (F, A, I and R) and 14 subprinciples under the main ones (e.g. F1, A2, R1.3) each of describes an aspect of data FAIRness (see Box 2 in ). A limitation of the FAIR framework is the broad textual description of its principles without specifying any technical guidelines on the implementation and the interpretation of those principles which lead to a follow up work to address this limitation and another work aimed at defining a way to track and quantify the FAIR aspects of data which was called, maturity indicators . A FAIR maturity indicator (MI) is a measurement that can be used to determine if a digital resource fulfills a particular FAIR (sub)principle , and may give an indication how the resource can be made more FAIR .