Application and implementation of FAIR

In research contexts, ‘FAIR’ or ‘FAIR data’ should be understood as a shorthand for a concept that comprises a range of scholarly materials that surround and relate to research data. This includes the algorithms, tools, workflows, and analytical pipelines that lead to creation of the data and give it meaning. It also encompasses the technical specifications, standards, metadata, vocabularies, ontologies and identifiers that are needed to provide meaning, both to the data itself and any associated materials. Furthermore, it includes the legal and ethical specifications regarding the generation, processing, storage and sharing of research data, metadata and associated workflows and resources.

Rec. 16: Apply FAIR broadly

FAIR should be applied broadly to all objects (including metadata, identifiers, software and DMPs) that are essential to the practice of research, and should inform metrics relating directly to these objects.

Similarly, many different categories of data exist (e.g. raw, reduced or processed, and ‘science ready’ data products). There may be sound scientific, methodological, ethical or economic reasons in particular disciplines for prioritising the communication of different types or categories of data over others. Some major facilities necessarily discard huge volumes of raw data. However, these differences do not undermine the general case for adopting FAIR approaches to data. Implementation will vary by research community, and different decisions will be made as to which data should be FAIR and to what degree. It should be understood that FAIR is a scale and varying degrees of FAIRness may be applied to different data sets. It may not make sense, or even be feasible, to apply all of the FAIR principles to all outputs. A base level of FAIRness should be applied at a minimum (e.g.

discovery metadata, persistent identifiers and access to the data or metadata) to data that are retained.

The Expert Group is not in favour of expanding the successful FAIR acronym. The FAIR principles were intended as a minimal set of essential characteristics and are successful in that function. For implementation and to make FAIR data a reality, certain concepts, which it may be argued are implicit in the principles, need expansion and unpacking. Similarly, the implications for the wider data ecosystem need to be extrapolated and described.

2.4.1 Data appraisal and selection

Research communities often produce vast quantities of data, not all of which can or should be kept, and decisions about what has long-term value and should be shared and preserved will differ between domains.

The implementation of FAIR principles in specific domains should be accompanied with criteria for prioritisation, appraisal and selection. In cases where data are not to be retained for long-term stewarding, the corresponding metadata should by default remain FAIR and should reference these decisions.

Rec. 19: Select and prioritise FAIR Digital Objects

Research communities and data stewards should develop and implement processes to assist the appraisal and selection of outputs that will be retained for a significant period of time and made FAIR.

2.4.2 Long-term preservation and stewardship

The FAIR principles focus on access to the data and do not explicitly address the long-term preservation needed to ensure that this access endures. Data should be stored in a trusted and sustainable digital repository to provide reassurances about the standard of stewardship and the commitment to preserve.

2.4.3 Assessability

As noted in the Royal Society report, “data should be assessable so that judgments can be made about their reliability and the competence of those who created them”.¹⁴ The rich metadata and provenance information required to achieve Reusability should include details that address data assessability. It is important to provide information that allows potential (re)users to judge the accuracy, reliability and quality of the data, and to determine whether these data meet their needs.

14 Royal Society (2012) Science as an open enterprise, p. 7. https://royalsociety.org/topics-policy/projects/science-public-enterprise/

report

2.4.4 Legal interoperability

The FAIR principles state that data should be released with a clear and accessible data usage licence. This principle could be usefully enriched by the concept of legal interoperability as defined by the RDA-CODATA Legal Interoperability Group.¹⁵ The usage conditions should be readily determinable for each of the data sets, typically through automated means; they should allow for creation and use of combined or derivative products;

and users should be able to legally access and use each data set without seeking authorisation from data rights holders. The licence or waiver assigned should be well-defined and internationally recognised to ensure that the conditions on data access and reuse are comparable across jurisdictions. Data creators and owners should opt for a waiver or licence with minimum restrictions. This is particularly important in circumstances when researchers seek to combine data from many sources, as such integrated data products need to use the most restrictive licence from their components (a phenomenon sometimes called licence stacking)¹⁶.

2.4.5 Timeliness of sharing

Research data should be made available (and FAIR) as soon as possible. This is critical, for instance, in public health emergencies to ensure research communities and health authorities can collaborate effectively and advance the speed of the response and of further discovery. Where such urgency arguments do not apply, there is still great value in sharing research as it unfolds rather than after the fact. There is also a strong case that any embargo period standing in the way of sharing should be limited and expressed relative to the creation of the data in question. It is often argued that embargos are important in some research areas to allow the data creators a sufficient period to obtain benefits from their work - and there is some truth in this. However, the example of significant benefits obtained by research communities with rapid data sharing agreements and the increasing recognition for data sharing means that the case for embargos is limited. A dimension on the timeliness of sharing should be added to the notion of FAIR.

Rec. 1: Define FAIR for implementation

To make FAIR data a reality it is necessary to incorporate and emphasise concepts that are implicit in the FAIR principles, namely: data selection, long-term stewardship, assessability, legal interoperability and the timeliness of sharing.

15 https://www.rd-alliance.org/group/rdacodata-legal-interoperability-ig/outcomes/rda-codata-legal-interoperability-research-data 16 https://mozillascience.github.io/open-data-primers/5.3-license-stacking.html

Addressing public health emergencies with timely shared FAIR data

Disasters routinely create a wide range of data needs as decisions about response measures have to be made on short notice and with incomplete information. Making disaster-related data FAIR is crucial for preparedness and response, as is timely data sharing.

Addressing public health emergencies requires timely decisions. To support them with the best available evidence, relevant data need to be identified and combined across sources and integrated with new information on an ongoing basis. FAIR data facilitates this.

Some of the data-related needs can be foreseen based on past events, and infrastructure and workflows prepared accordingly. Other needs are specific to the event in question: at the beginning of the Zika virus outbreak, a link between maternal exposure to the virus and neurological abnormalities in the fetus was not known. Once it was suspected, dermatological data had to be combined with fetal brain imaging and with viral sequences obtained from pregnant women and their fetuses or sexual partners or from mosquitoes, whose distribution needed to be monitored, modelled and controlled, which involved climate data and satellite observations as well as Wolbachia infections. Additional variables like cross-reactivity between Zika and related viruses became important for diagnostic tools, while global traffic patterns, vacant properties in an affected area or general characteristics of national health systems had to be taken into account when considering travel warnings or preventive measures.

Such diverse kinds of data are currently hard to integrate due to the very limited degree to which they are FAIR.

Making disaster-related data FAIR means general-purpose open technologies can be leveraged to get machines to act on the data, which can dramatically improve the efficiency of disaster responses, while evading the need to build custom infrastructure.

However, even if all relevant data were fully FAIR to the extent possible at some point after an emergency, this may not be enough for an efficient response during the event, since a key aspect of emergencies is the temporal urgency, which the FAIR principles as such do not address. Measures to increase the FAIRness of disaster-related data should thus be included in preparedness efforts, as should be workflows for efficient data sharing, since “open data matters most when the stakes are high”.

Image sources: https://commons.wikimedia.org/wiki/

File:Zika_virus_cryo-EM_structure.png and https://

commons.wikimedia.org/wiki/File:Aedes_aegypti_

CDC08.tif (both public domain).

Figure 5: Zika case study: addressing public health emergencies with timely data sharing