FAIRification

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.

Table of contents

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.

Need more reasons why not sharing data is bad?

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:

GO FAIR GO FAIR is a bottom-up, stakeholder-driven and self-governed initiative that aims to implement the FAIR data principles, making data Findable, Accessible, Interoperable and Reusable (FAIR). It offers an open and inclusive ecosystem for individuals, institutions and organisations working together through Implementation Networks (INs). The INs are active in three activity pillars: GO CHANGE, GO TRAIN and GO BUILD.

WorldFAIR In the WorldFAIR project, CODATA and the Research Data Alliance (RDA), work with a set of 11 disciplinary and cross-disciplinary case studies to advance implementation of the FAIR principles and, in particular, to improve interoperability and reusability of digital research objects, including data.

And specific to nanomaterials and nanosafety:

GO FAIR Implementation Network Advanced Nano This Implementation Network actively supports the implementation of the FAIR principles in the current nano-EHS databases, i.e. data on NM physicochemical characteristics, release and exposure, toxicity and functionality.

WorldFAIR’s Nanomaterials Use Case It will test the pilot operationalisation of the FAIR principles; run conference sessions and workshops with stakeholders (including the InChI-for-nano domain experts, and international ‘nano’ database managers and their users) to apply, refine, implement, improve the metrics for FAIR nanosafety datasets; and develop an inventory of FAIR nanoinformatics models and their domains of applicability, underpinning datasets and APIs to support interoperability, including guidelines to further improve the interoperability of nanoinformatics models.

Start becoming FAIR

Annotating Your Experimental Data 3 June 2020. Learn to enrich your (published) data to make it have more impact. Description of experimental data was aligned to community terminology (ontology terms). Experiences were gathered how you can make your research articles findable by other researchers. More info…

FAIR CookBook: How to Register a Dataset with Wikidata Guidance that describes how published data sets can be made more findable by sharing metadata in Wikidata.

Making Research Objects Citable Guidance from The Turing Way about how to make research objects citable and consequently more findable.

Internal FAIR resources

Why FAIR is necessary? Some data and experience why not sharing data is bad

Scientific FAIR principles The original principles are mainly focussed on how data should be shared, and not on what needs to be reported to make the data reusable with high confidence. Thus, a gap still exists when it comes to the scientific FAIR principles needed to ensure that high-quality data generation and collection, and metadata processing have sufficient "completeness" to facilitate the FAIRification process.

Ten simple actions for NSC Research Output

FAIRness, completeness, and data quality scores Documentation of ongoing activities and approaches to evaluate the FAIRness and quality of datasets.

FAIR Implementation Profiles FAIR Implementation Profiles, FAIR Enabling and Supporting Resources

(Meta)data documentation Data documentation - can we do more then data templates and annotation?

Terminology / ontology General and nano-specific resources for the development and usage of harmonised terminology and ontologies.

Nanomaterial identifiers and structural representations List of (unique) identifiers and structural representations used for nanomaterials

External FAIR resources

The FAIR Guiding Principles for scientific data management and stewardship Publication on the principles.

FAIR cookbook by the ELIXIR communities The FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. However, the FAIR Principles are aspirational and generic. The FAIR Cookbook guides researchers and data stewards of the Life Science domain in their FAIRification journey; and also provides policy makers and trainers with practical examples to recommend in their guidance and use in their educational material.

RDMkit The ELIXIR Research Data Management Kit (RDMkit) has been designed to guide life scientists in their efforts to better manage their research data following the FAIR Principles. It is based on the various steps of the data lifecycle, although not all the steps will be relevant to everyone. The contents are generated and maintained by the ELIXIR community coming together as part of the ELIXIR-CONVERGE project.

FAIR Connect This (upcoming) platform facilitates and supports the (citable) publication of FAIR descriptions, complemented with concise living review articles, limited to FAIR Supporting Resources (FSRs), making these themselves Findable, Accessible, Interoperable and ultimately Reusable, while at the same time preventing the needless re-invention of wheels.

FAIR Points The event series highlighting pragmatic measures developed by the community towards the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles.

Effective and Ethical Data Sharing at Scale A cookbook for data producers, donors, policymakers, and other development practitioners by the Global Partnership for Sustainable Development Data

FIP Wizard A FAIR Implementation Profile (FIP) is a list of declared technology choices, also referred to as FAIR Enabling Resources, that are intended to implement one or more of the FAIR Guiding Principles, made as a collective decision by the members of a particular community of practice.
See lessons learned from different communities: FAIR Implementation Profiles (FIPs) in WorldFAIR: What Have We Learnt?
Nanomaterials case study: Nanomaterials domain-specific FAIRification mapping

IUPAC FAIR Chemistry

One of the 11 WorldFAIR case studies is Chemistry. The goal is to align chemistry data standards with the FAIR data principles to enhance interoperability with other domains like nanomaterials (another WorldFAIR case, see above).