Towards FAIRification of Sensitive and Fragmented Rare Disease Patient Data: Challenges and Solutions in European Reference Network Registries

Vieira, Bruna dos Santos; Bernabé, César H.; Zhang, Shuxin; Abaza, Haitham; Benis, Nirupama; Cámara, Alberto; Cornet, Ronald; Cornec, Clémence M. A. Le; Hoen, Peter A.C. ‘t; Schaefer, Franz; Velde, K. Joeri van der; Swertz, Morris A.; Wilkinson, Mark D.; Jacobsen, Annika; Roos, Marco

doi:10.21203/rs.3.rs-1572508/v1

Cited by 4 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While other FAIRi cation work ows may also de ne a step for identifying FAIRi cation objectives [23], to the best of our knowledge, none of them has provided detailed guidance on de ning FAIRi cation objectives or other FAIRi cation planning related aspects, such as distinguishing between the different types of stakeholders involved in FAIRi cation projects.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A goal-oriented method for FAIRification planning

Bernabé

Sales

Schultes³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The FAIR Principles provide guidance on how to improve the findability, accessibility, interoperability, and reusability of digital resources. Since the publication of the principles in 2016, several workflows have been proposed to support the process of making data FAIR (FAIRification). However, to respect the uniqueness of different communities, both the principles and the available workflows have been deliberately designed to remain agnostic in terms of standards, tools, and related implementation choices. Consequently, FAIRification needs to be properly planned in advance, and implementation details must be discussed with stakeholders and aligned with FAIRification objectives. To support this, we describe GO-Plan, a method for identifying and refining FAIRification objectives. Leveraging on best practices and techniques from requirements and ontology engineering, the method aims at incrementally elaborating the most obvious aspects of the domain (e.g. the initial set of elements to be collected) into complex and comprehensive objectives. Experience has demonstrated that the definition of clear objectives enables stakeholders to communicate effectively and make informed implementation decisions, such as defining achievement criteria for distinct principles and identifying relevant metadata to be collected. This paper describes the GO-Plan method and reports on a real-world application in the development of a FAIR ontology catalogue.

show abstract

Section: Related Workmentioning

confidence: 99%

“…However, this exibility requires careful guidance throughout the FAIRi cation process to ensure that the implementation decisions (e.g., standards, metadata) align with the FAIRi cation objectives. In fact, the identi cation of FAIRi cation objectives is the initial and crucial step of several FAIRi cation work ows [23,9,14].…”

Section: Introductionmentioning

confidence: 99%

A goal-oriented method for FAIRification planning

Bernabé

Sales

Schultes³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…FAIR stewards facilitate communication between data owners and FAIR experts, collecting and curating the FAIRification challenges passed from owner to expert and curating and interpreting the suggested solution. A FAIR steward would also be responsible for advising on developing, implementing, and monitoring an RDM policy, including support of FAIR data and Open Science principles [8].…”

Section: Fair Support Stewards and Trainersmentioning

confidence: 99%

Community-driven governance of FAIRness assessment: an open issue, an open discussion

Wilkinson¹,

Sansone²,

Méndez³

et al. 2022

Open Res Europe

Self Cite

View full text Add to dashboard Cite

Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. A range of FAIR assessment metrics and tools have been designed that measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation in an open Google Group fair-assessment-governance@googlegroups.com

show abstract

“…As a result, improving the alignment between ERNs is one of the objectives of the European Joint Programme on Rare Diseases (EJP RD), a project with over 130 institutions from 35 countries, including representatives of all 24 ERNs, designed to establish a self-sustaining infrastructure for rare disease research and care (Inserm 2022). The EJP RD has been supporting patient registries, managed by ERNs, in making informed choices about their data management and in harmonizing choices among registries (Dos Santos Vieira, Bernabé, and Zhang et al 2022). Wilkinson et al (2016) introduced the findable, accessible, interoperable, and reusable (FAIR) principles, a set of high-level guidelines for research data management, stating that data should be FAIR for humans and computers.…”

Section: Introductionmentioning

confidence: 99%

“…Wilkinson et al (2016) introduced the findable, accessible, interoperable, and reusable (FAIR) principles, a set of high-level guidelines for research data management, stating that data should be FAIR for humans and computers. Dos Santos Vieira et al (2022) provided insight into common challenges ERNs encountered when making their patient registry data FAIR and put forward a list of solutions that may help solve those challenges. To obtain these insights, a team of data stewards specialized in FAIR data have been working closely with ERN patient registries.…”

Section: Introductionmentioning

confidence: 99%

A Resource for Guiding Data Stewards to Make European Rare Disease Patient Registries FAIR

Damme¹,

Alarcon²,

Bernabé³

et al. 2023

Data Science Journal

View full text Add to dashboard Cite

Objective: This paper reports on the development of a dynamic data management planning questionnaire to guide data stewards of the European Reference Network (ERN) rare disease patient registries to make their data findable, accessible, interoperable, and reusable (FAIR). As part of this work, the questionnaire was validated through expert review and aligned with existing resources on rare diseases and FAIR data management. Materials and Methods:The questionnaire was developed for the Data Stewardship Wizard, a tool for data management planning. Knowledge sources on FAIR data, ERN patient registries, and data management were used to compose questions. Ten domain experts validated the questionnaire. The topics in the questionnaire were aligned with existing knowledge bases.Results: A total of 57 questions were included in the questionnaire. Twenty-three references to the FAIR Cookbook and Research Data Management toolkit for Life Sciences were added. Expert validation provided a total of 166 comments on content, structure, and software-related issues. A public instance of the Data Stewardship Wizard was deployed for use by data stewards of ERN patient registries. Discussion:The questionnaire addresses issues that ERNs encounter when making their registries FAIR and follows the implementation choices made by the European rare disease community. A challenging task for future research is to extend the questionnaire to other types of registries and to validate with users. Conclusion:This smart questionnaire is the first model created for the Data Stewardship Wizard that helps ERN patient registries with making their data FAIR. It will assist data stewards in aligning their efforts and providing guidance on FAIR data. 2 van Damme et al.

show abstract

Towards FAIRification of Sensitive and Fragmented Rare Disease Patient Data: Challenges and Solutions in European Reference Network Registries

Cited by 4 publications

References 11 publications

A goal-oriented method for FAIRification planning

A goal-oriented method for FAIRification planning

Community-driven governance of FAIRness assessment: an open issue, an open discussion

A Resource for Guiding Data Stewards to Make European Rare Disease Patient Registries FAIR

Contact Info

Product

Resources

About