Update To FTR Metric Description In OSTrails FAIR Assessment Output Specification

Introduction

In this article, we will discuss the proposed update to the description of the ftr Metric within the OSTrails FAIR assessment output specification. The current definition includes the term "domain-agnostic," which some stakeholders believe is not entirely accurate. This article will delve into the discussion surrounding this issue, exploring the reasons for the proposed change and its potential impact on the overall specification. We aim to provide a comprehensive overview of the topic, ensuring that readers understand the nuances of the discussion and the rationale behind the suggested update. The focus is on clarifying the role of metrics in the FAIR assessment process and ensuring that the specification accurately reflects the community's understanding and intended usage of metrics.

Background

The discussion stems from a recent hackathon where participants debated whether metrics should be inherently domain-agnostic. While there was a consensus that metrics can be domain-agnostic, it was not universally agreed that they must be. This led to a review of the current definition of a metric within the ftr metric specification, which states: "Narrative domain-agnostic description that a Test must wholly implement." This definition, found in the ftr metric spec, has sparked a conversation about the necessity of the "domain-agnostic" requirement. The main point of contention is whether the current definition accurately reflects the intended flexibility and applicability of metrics across different domains. The concern is that the strict adherence to domain-agnostic metrics might limit the scope and effectiveness of FAIR assessments in specific contexts where domain-specific metrics may be more relevant and informative. Therefore, a re-evaluation of this aspect of the definition is crucial to ensure the specification remains practical and adaptable.

The Issue with "Domain-Agnostic"

The core issue lies in the interpretation of "domain-agnostic." While some metrics are universally applicable across various fields, others may be more relevant or specific to a particular domain. Forcing all metrics to be domain-agnostic could potentially limit the granularity and relevance of assessments. For instance, a metric evaluating the quality of metadata might have different considerations in the context of genomic data compared to social science data. The nuances within these domains necessitate flexibility in metric design. The term "domain-agnostic" implies a one-size-fits-all approach, which may not always be appropriate or effective in the diverse landscape of research data. This rigidity could hinder the development and implementation of metrics that are tailored to specific needs and contexts, ultimately affecting the accuracy and usefulness of FAIR assessments. Therefore, the proposed removal of the term aims to foster a more inclusive and adaptable framework for metric development.

Examples of Domain-Specific Metrics

To illustrate the point, consider metrics related to data provenance. In the field of genomics, provenance might involve tracking the specific instruments and reagents used in a sequencing experiment. This level of detail might not be necessary or relevant in other domains, such as social sciences, where provenance might focus more on the data collection methodology and ethical considerations. Similarly, metrics related to data interoperability might vary significantly depending on the data types and standards prevalent in different domains. For example, the interoperability of genomic data often relies on adherence to specific controlled vocabularies and data formats unique to the field. These examples highlight the need for domain-specific metrics that can capture the unique characteristics and requirements of different research areas, leading to more accurate and meaningful FAIR assessments. The flexibility to incorporate such metrics is essential for the practical application of the FAIR principles across a wide range of disciplines.

Proposed Solution

The proposed solution is to remove the phrase "domain-agnostic" from the definition of a metric within the ftr metric specification. This change would allow for the inclusion of both domain-agnostic and domain-specific metrics, providing a more flexible and comprehensive framework for FAIR assessments. The revised definition would focus on the core function of a metric as a narrative description that a Test must wholly implement, without imposing the restriction of domain-agnosticism. This adjustment is expected to broaden the scope of applicable metrics and encourage the development of metrics that are tailored to specific domain needs. By removing this constraint, the specification can better accommodate the diverse requirements of different research communities and data types, ultimately leading to more effective and relevant FAIR assessments.

Benefits of Removing "Domain-Agnostic"

Removing the "domain-agnostic" requirement offers several key benefits. First, it allows for the creation of more precise and relevant metrics that address the specific challenges and nuances within different domains. This means that assessments can be more tailored and provide a more accurate reflection of how FAIR a dataset or resource is within its particular context. Second, it encourages broader participation from domain experts who can contribute their specialized knowledge to the development of metrics. This collaborative approach ensures that metrics are not only technically sound but also practically useful and aligned with the needs of the research community. Finally, it enhances the overall adaptability of the FAIR assessment framework, making it more applicable across a wider range of disciplines and data types. This flexibility is crucial for the long-term success and adoption of the FAIR principles in the evolving landscape of research data management.

Alternative improvements to the Metric Definition

Beyond removing "domain-agnostic," there is also an opportunity to enhance the overall description of a metric to make it more informative and clear. The current definition, while concise, could benefit from additional details that clarify the purpose and function of a metric in the context of FAIR assessments. For example, elaborating on the role of metrics in evaluating specific aspects of Findability, Accessibility, Interoperability, and Reusability could provide valuable context for users of the specification. Additionally, including guidance on how to develop and implement effective metrics could further improve the usability of the specification. This might involve outlining best practices for defining clear and measurable criteria, as well as providing examples of well-defined metrics across different domains. By expanding the description, the specification can offer a more comprehensive understanding of metrics and their importance in promoting FAIR data practices.

Suggestions for a More Descriptive Definition

To create a more descriptive definition, we can consider incorporating elements that highlight the purpose, scope, and implementation of a metric. One possible revision could be: "A Metric is a clear, narrative description of a specific criterion or standard that a Test must wholly implement to evaluate an aspect of Findability, Accessibility, Interoperability, or Reusability." This revised definition emphasizes the evaluative nature of metrics and their direct link to the FAIR principles. It also clarifies that a metric should be clear and specific, making it easier for implementers to understand and apply. Furthermore, the definition could be supplemented with additional guidance on how to formulate effective metrics, such as ensuring they are measurable, relevant, and aligned with the overall goals of FAIR data management. By providing a more detailed and informative definition, the specification can better support the development and application of high-quality metrics across various domains and contexts.

Next Steps

The next step involves formally proposing the removal of "domain-agnostic" and potentially incorporating a more descriptive definition of a metric within the ftr metric specification. This proposal will likely undergo further discussion and review by the OSTrails community to ensure that the changes align with the overall goals and principles of the project. Feedback from stakeholders, including researchers, data managers, and developers, will be crucial in refining the proposed changes and ensuring they meet the diverse needs of the community. Following the review process, the updated specification will be released, providing a clearer and more flexible framework for FAIR assessments. This iterative approach to specification development ensures that the final product is robust, practical, and widely accepted by the community.

Community Involvement and Feedback

Community involvement is a critical component of this process. Open discussions, feedback sessions, and collaborative drafting are essential to ensure that the updated specification reflects the collective understanding and needs of the FAIR data community. Stakeholders are encouraged to actively participate in these discussions, sharing their perspectives and insights to help shape the final outcome. This collaborative approach not only enhances the quality of the specification but also fosters a sense of ownership and commitment among community members. By working together, the OSTrails community can develop a robust and adaptable framework for FAIR assessments that supports the widespread adoption of FAIR data principles.

Conclusion

The proposed update to the ftr metric description, specifically the removal of "domain-agnostic," represents a significant step towards a more flexible and practical FAIR assessment framework. This change acknowledges the importance of domain-specific considerations in metric design and allows for the development of more relevant and accurate assessments. By embracing this flexibility, the OSTrails community can better support the diverse needs of researchers and data managers across various disciplines. Furthermore, the ongoing efforts to refine the metric definition highlight the commitment to continuous improvement and community collaboration, ensuring that the FAIR assessment output specification remains a valuable resource for promoting FAIR data practices. The outcome of these discussions will have a lasting impact on how FAIRness is assessed and implemented in the research community.

By fostering a more inclusive and adaptable approach to metric development, the community can drive broader adoption of the FAIR principles and enhance the overall quality and accessibility of research data. This, in turn, will contribute to more efficient and impactful scientific discovery. The emphasis on community involvement and feedback ensures that the specification remains aligned with the evolving needs of the research landscape, solidifying its role as a key enabler of FAIR data practices. The dedication to clarity and precision in the definition of metrics underscores the importance of a shared understanding and consistent application of the FAIR principles across different domains and contexts.