FAIR data background

This page provides information about data management and FAIR data background, and examples thereof in ZonMw’s programmes.

Why does ZonMw devote attention to data management and FAIR data?

Data are the raw material for knowledge development in ZonMw’s projects. As such, data – in addition to the publications on the scientific results of a project – are the form in which knowledge is recorded (safeguarded) and distributed. Making data reusable means that they can be used to verify research results, conduct new research and justify policy and practice. In the end, the objective is to ensure that the data files thereby contribute to the quality of research as well as knowledge development and innovation in the field of health and healthcare.

Data management that adheres to the FAIR principles as much as possible is necessary to make data reusable. FAIR stands for Findable, Accessible, Interoperable and Reusable, and means that data can be found, understood and used by both people and computers. The methods for data management and FAIR data are in full development in the Netherlands and abroad. Where possible, ZonMw implements new insights in the instructions for the grant procedure.

The text below focuses on FAIR data management, which means data management in line with the FAIR principles.

FAIR principles

Data management that adheres to the FAIR-principles  as much as possible is necessary to make data reusable. FAIR stands for Findable, Accessible, Interoperable and Reusable, and means that data can be found, understood and used by both people and computers.

The text below focuses on FAIR data management, which means data management in line with the FAIR principles.

What is FAIR data management?

All data files and other resources for research that are generated and/or used, can be found as quickly as possible during a project (through FAIR metadata) and are available immediately after the project in accordance with the following principle: ‘as open as possible, as restricted as necessary’. The resources are delivered in a form that is as reusable as possible for man and machine (FAIR): newly generated data files are ‘FAIR by design’ (i.e. in line with FAIR principles from the outset); existing data files and physical resources are, at the very least, adequately described with FAIR metadata.

Data and other scientific output

Where data files are involved, there are also other sources used to conduct the research. These include quantitative and qualitative data sources, the software to use data, collections of physical resources like biological materials, audio and video recordings, etc. At the end of any project, parties must at the very least deliver the data and metadata on which the project results and publications are based. These must be reusable and accessible (possibly subject to specific conditions, such as privacy directives).

Other (scientific) output includes a variety of publications, reports and protocols. ZonMw makes scientific publications available via Open Access magazines and journals. Other output, including products from (practical) projects, are reported in the final report and published via the website, for instance.

Metadata

FAIR data are primarily about metadata. Metadata describe the dataset, the context within which it was produced, where it can be found, the conditions to gain access to it, and the information that is needed to be able to use the data. Metadata must be FAIR, i.e. ‘machine-readable’ or ‘machine-actionable’. Without FAIR metadata, FAIR data cannot exist. Using FAIR metadata, other resources (such as biological materials) can also be made FAIR.

In principle, metadata do not contain sensitive information and so they can be available on an open basis.

How do you implement FAIR data management?

It starts with good data management. The best way to imagine how to plan and implement good data management is by means of the concrete activities in the ‘data life cycle’. Read more about the practical approach in Data life cycle | RDMkit (elixir-europe.org).

Making data FAIR is part of data management. The activities are of a generic or domain-specific nature, technical, institutional, and social. ZonMw may set different requirements according to the type of project involved. You should always check the programme text and call for grant applications. The ZonMw policy will be adjusted as the field expands and grows and the steps for FAIRification are more clearly defined.

Read more about the instructions for grant applicants and recipients in FAIR-metadata and data in research projects.

Data FAIRification

The FAIR principles you follow in the activities of data management/stewardship describe how to make data FAIR (‘FAIRification’). However, the formulation of the FAIR principles is fairly abstract and does not provide a concrete description of what to do. You can find an explanation of each of the FAIR principles in Interpreting FAIR.

There are different approaches, tools and services for data FAIRification:

  • The ‘three-point FAIRification framework‘ (3-PFF) formulated by the GO FAIR Foundation is a coherent implementation of the various types of FAIRification activities. Its main components are: (1) M4M, i.e. ‘metadata for machines’, (2) FIP, the FAIR Implementation Profile, and (3) FDP, the FAIR data point through which (meta)data are available on the internet.
  • The FAIR service desk of Health-RI provides an overview of tools, services and infrastructure for data FAIRification, primarily targeted at the health and life sciences domain in the Netherlands.
  • The FAIR Cookbook is an European platform with ‘recipes’ for data FAIRification, and references to services, infrastructures, and examples of implementation in the life sciences.

The methods for data management and FAIR data are in full development in the Netherlands and abroad. Where possible, ZonMw implements new insights in the instructions for the grant procedure.

Domain-specific

The domain-specific approach is vital to FAIR data (as well as good data management). ZonMw encourages researchers in the same research domain and/or consortium to adhere to standards (e.g. terminologies, data models or an infrastructure for data collection) that are commonly used in the field in question as much as possible. This facilitates mutual comparison and linking of data (also known as interoperability).

Criteria

GO FAIR has formulated criteria to be able to speak of FAIR data at the end of the process. These criteria cover:

  • The minimum standard that makes (meta)data machine-actionable (‘centre of the hourglass’). This means that a computer can find, read, understand and analyse the (meta)data.
  • Data openness. FAIR data are as open as possible unless this is not possible with an eye to privacy or specific interests. Such limited access can be defined with the aid of FAIR principle A (Accessible). Data for which accessibility is limited, must still be F, I and R.
  • Data distribution. Where possible, FAIR data must remain with the source that generated them (‘distributed’). This enhances safety, privacy and efficiency and prevents errors, costs and dependency on a vendor or system.
  • Data are not transferred to a central storage location or only to the most limited possible extent.
  • Freedom for all parties to use the data as opposed to vendor lock-in. This means that data access must not be dependent on a vendor or specific system.

Examples of FAIR data in ZonMw programmes

Read the articles below that explain how to implement FAIR data.