FIP Check: A Rubric-Based Tool for Assessing FAIR Implementation Profiles and Enabling Resources

Increasing attention is being paid to the operation of biomedical data repositories in light of efforts to improve how scientific data is handled and made available for the long term. Multiple groups have produced recommendations for functions that biomedical repositories should support, with many using requirements of the FAIR data principles as guidelines. However, FAIR is but one set of principles that has arisen out of the open science community. They are joined by principles governing open science, data citation and trustworthiness, all of which are important aspects for biomedical data repositories to support. Together, these define a framework for data repositories that we call OFCT: Open, FAIR, Citable and Trustworthy. Here we developed an instrument using the open source PolicyModels toolkit that attempts to operationalize key aspects of OFCT principles and piloted the instrument by evaluating eight biomedical community repositories listed by the NIDDK Information Network (dkNET.org). Repositories included both specialist repositories that focused on a particular data type or domain, in this case diabetes and metabolomics, and generalist repositories that accept all data types and domains. The goal of this work was both to obtain a sense of how much the design of current biomedical data repositories align with these principles and to augment the dkNET listing with additional information that may be important to investigators trying to choose a repository, e.g., does the repository fully support data citation? The evaluation was performed from March to November 2020 through inspection of documentation and interaction with the sites by the authors. Overall, although there was little explicit acknowledgement of any of the OFCT principles in our sample, the majority of repositories provided at least some support for their tenets.

In 2023, a team from a local grant-funded medical data repository requested guidance from Penn Libraries on evaluating the extent to which their repository was FAIR-enabling. After a consultation with the repository team, our research data experts discovered that many of the current self-assessments of the FAIR guidelines were for data creators rather than data repository managers. In addition, we wanted a self-assessment tool similar to the process and guidance created by CoreTrustSeal but focusing explicitly on the FAIR Principles. In answer to their request, the Penn Libraries Research Data Engineer conducted a literature review and coalesced current guidance and assessment tools on the principles. After this review of the existing documentation, a small team developed a FAIR Principles self-assessment tool for repository teams. In addition to several iterations of the tool, we also met with the repository team for feedback on making the tool more understandable. Our conversation provided insights into the challenges of explaining the FAIR Principles to those without information or data science backgrounds. The discussion and creation of this self-assessment tool helped develop a more transparent and trustworthy repository. This paper will discuss our process for developing the assessment, the goals for utilizing the tool, and the lessons learned. Reporting our findings as they currently stand will prompt the research data management field to ruminate on the adoption of FAIR Principles for data repositories. We also intend to encourage conversation on the usability of the FAIR Principles for professionals without an information or data science background.

Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.

The FAIR principles (Findability, Accessibility, Interoperability, and Reusability) constitute a guide whose aim is to improve the management of digital scholarly resources. Nevertheless, the literature regarding data services other than data repositories is still scarce.OpenEdition is a digital infrastructure for open scholarly communication in the Social Sciences and Humanities (SSH) that carried out an internal full review to assess the degree of FAIRness of its activities. The objective of this paper is to present the methodology employed by OpenEdition’s team and the recommendations for the FAIRification of a publishing system, and hence, the elements for the FAIR Publishing Toolkit. The FAIR review was conducted in three main phases: preparation, assessment, and result phase, which listed the recommendations for the FAIR principles implementation. The preparation phase gathered the available information to define the perimeter of the FAIR review. It comprised two steps: the landscape study and the exam of actual use cases. The assessment phase contextualized the FAIR principles according to the scholarly publishing context, defined the datasets to be analyzed, carried outa FAIR maturity review per dataset, and analyzed the state of the art of some important FAIR-related elements. The result phase produced the recommendations, organized as priorities and extended objectives. The priority recommendations regard persistent identifiers and licensing policies. The extended objectives focus on authors' information management, controlled vocabularies, machine-actionability, and Digital Management Plans.

PurposeThis study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.Design/methodology/approachThe study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.FindingsThe study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.Practical implicationsThe study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.Originality/valueTo the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

Addressing global environmental and societal challenges requires robust, interdisciplinary data ecosystems that support collaboration across geographic, cultural, and disciplinary boundaries. AuScope, Australia’s National Research Infrastructure (NRI) provider for the geoscience community, collaboratively tackles interdisciplinary grand challenges such as climate change, natural resource security, and natural hazards. AuScope is funded by the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS) and integrates tools, data, analytics, and services across Australian research and government agencies, in particular, partnering with organisations at the forefront of research data systems and infrastructure.Through collaborations with CSIRO, Geoscience Australia, state and territory geological surveys, universities, and other NCRIS facilities, including the National Computational Infrastructure (NCI), the Terrestrial Ecosystem Research Network (TERN), and the Australian Research Data Commons (ARDC), AuScope is addressing the complexities of modern FAIR data management at scales ranging from small scale local installations to co-located High Performance Compute and Data (HPCD) Platforms. Key AuScope initiatives such as Geophysics 2030 Collections (https://ardc.edu.au/project/2030-geophysics-collections/), AusGeochem (https://ausgeochem.auscope.org.au/), the Modelling Atlas of The Earth (M@TE; https://mate.science), and the AuScope Data Repository (https://repository.data.auscope.org.au/) exemplify how the FAIR principles can be operationalised to support impactful research both within and beyond the geosciences and at multiple scales.Nationally, AuScope collaborates with other Earth and Environmental Research Infrastructure providers, working to transform Australia’s research capabilities through, for example, Coastal Research Infrastructure (CoastRI) and implementing the National Digital Research Infrastructure Strategy (NDRI). Globally, AuScope contributes to initiatives like OneGeochemistry, the CODATA-led WorldFAIR Plus project, EarthScope (US), EPOS, Geo-INQUIRE, and ChEESE (Europe), ensuring compatibility with international research infrastructures, data standards, and best practices while at the same time, aligning with Australia’s geoscience priorities. This presentation will highlight how AuScope is progressively operationalising the FAIR and TRUST principles across its investments by focusing on place-based research to foster interoperability, strategic collaboration, and Open Science practices. By aligning with the CARE principles as well as advancing collaborative data infrastructure, AuScope creates trusted, interoperable data ecosystems that empower researchers to effectively and efficiently address pressing interdisciplinary societal challenges at both a national and international scale.

Due to the increasing number of available biomedical data repositories, providing a comprehensive and intuitive access to information is still a demanding task for Information Retrieval systems. In this work we present an interactive data exploration system that retrieves relevant information by propagating the user's interest within a network. The developed techniques have been applied to two different retrieval tasks useful for biomedical research: the prioritization of proteins related to a disease of interest and the search of publications in the literature. The method relies on a network of biomedical entities, scoring of entities of interest by the user, and score propagation. The assessment of the relevance of the retrieved information confirmed a high accuracy of the presented algorithms for both the domains considered.

The adaptive immune receptor repertoire community as a model for FAIR stewardship of big immunology data

BackgroundBiobanks and biomedical research data repositories collect their samples and associated data from volunteer participants. Their aims are to facilitate biomedical research and improve health, and they are framed in terms of contributing to the public good. Biobank resources may be accessible to researchers with commercial motivations, for example, researchers in pharmaceutical companies who may utilise the data to develop new clinical therapeutics and pharmaceutical drugs. Studies exploring citizen perceptions of public/private interactions associated with large health data repositories/biobanks indicate that there are sensitivities around public/private and/or non-profit/profit relationships and international sample and data sharing. Less work has explored how biobanks communicate their public/private partnerships to the public or to their potential research participants.MethodsWe explored how a biobank’s aims, benefits and risks, and private/public relationships have been framed in public facing recruitment documents (consent forms and participant information sheets).ResultsBiobank documents often communicate their commercial access arrangements but not the detail about what these interactions would entail, and how risks and benefits would be distributed to the public.ConclusionWe argue that this leads to a polarised discourse between public and private entities and/or activities, and fails to attend to the blurred lines between them. This results in a lack of attention to more important issues such as how risks and benefits in general are distributed to the public. We call for a nuanced approach that can contribute to the much-needed dialogue in this space.

Data repositories are an essential component of data infrastructure. This article explores their functionalities with accompanying examples with a focus on open, quantitative data sets. Based on researcher needs, the principle of data friendliness is presented, which involves data context (data characteristics), research context (individual circumstances and research settings) and the context of reuse paths (conditions allowing access to, understanding of and use of data). An examination of data context suggests that metadata and data enrichment are required to provide successful data context. Enrichment is also required for orientation of the research context to provide greater insights across data sets. Finally the article considers the reuse paths, and the say that data paths intersect human interaction and how these are being addressed by different repositories. The review concludes that data repositories need to focus on how to present data that is fully interoperable and allows for reuse as well as access.

In clinical research, many potentially useful variables are available via the routine activity of cancer center-based clinical registries (CCCR). We present the experience of the breast cancer clinical registry at Fondazione IRCCS "Istituto Nazionale dei Tumori" to give an example of how a CCCR can be planned, implemented, and used. Five criteria were taken into consideration while planning our CCCR: (a) available clinical and administrative databases ought to be exploited to the maximum extent; (b) open source software should be used; (c) a Web-based interface must be designed; (d) CCCR data must be compatible with population-based cancer registry data; (e) CCCR must be an open system, able to be connected with other data repositories. The amount of work needed for the implementation of a CCCR is inversely linked with the amount of available coded data: the fewer data are available in the input databases as coded variables, the more work will be necessary, for information technology staff, text mining analysis, and registrars (for collecting data from clinical records). A cancer registry in a comprehensive cancer center can be used for several research aspects, such as estimate of the number of cases needed for clinical studies, assessment of biobank specimens with specific characteristics, evaluation of clinical practice and adhesion to clinical guidelines, comparative studies between clinical and population sets of patients, studies on cancer prognosis, and studies on cancer survivorship.

Despite the increasing availability of data used to inform educational policies and practices, concerns persist regarding its quality and accessibility. This study surveys quality education data from Brazil, Colombia, and Peru and evaluates their alignment with the FAIR principles and availability to support academic analytics (AA) and learning analytics (LA). We identified and analyzed 112 data sources, from which 93\% of the data sets originate from government repositories and open data platforms, with coverage of macro-level data relevant for AA but lack of granularity for LA. The FAIR assessment showed 50\% of compliance with findability (F), 33\% for accessibility (A), and less than 50\% for both interoperability (I) and reusability (R), which limits broader utility. Moreover, these diverse data sources present limitations in quality assurance metrics such as ``institutional development" and ``quality management." We conclude by offering recommendations, emphasizing the need for enhanced data frameworks that bridge macro- and micro-level data for AA and LA to enable data-driven decisions for improving educational quality in Latin America.

Patients with neurodegenerative movement disorders can develop cognitive impairment during the disease. Cognitive symptoms have been associated with decreased quality of life, higher caregiver burden, and earlier institutionalization, and are therefore critical for physicians to understand and address. The evaluation of cognitive performance of patients with neurodegenerative movement disorders is important for providing adequate diagnosis, management, prognosis, and support patients and their caregivers. In this review, we discuss the features of the cognitive impairment profile of commonly encountered movement disorders: Parkinson's disease, dementia with Lewy bodies, multiple system atrophy, progressive supranuclear palsy, corticobasal syndrome, and Huntington's disease. In addition, we provide neurologists with practical guidance and evaluation tools for the assessment and management of these challenging patients.

Purpose. Episciences is a platform for publishing overlay diamond open access scientific journals. It hosts and supports 43 overlay journals from several disciplines—mainly mathematics, informatics, applied mathematics, social sciences, and humanities—and, for this purpose, utilises open science infrastructures. Approach. The content of the publications is hosted on open repositories (such as HAL, arXiv, Zenodo, bioRxiv, and medRxiv), data repositories for datasets and Software Heritage for software. This allows Episciences to benefit from the FAIR principles supported by these infrastructures while also adding value and services. The platform offers a complete publishing solution for researchers, enabling them to peer-review and publish articles, datasets, and software. All these components may also be linked to each other to promote the FAIR principles and the reproducibility of science. Findings. Relying on existing infrastructures enables cost-efficient publishing. Led by academics, the platform is supported by French academic funding from the Ministry of Higher Education and Research, CNRS, Inria, and INRAE. Originality. The platform engages with scientific communities, through scientific committees. It provides solutions for copy-editing, communication, and advice on the best practices in open science publishing. Episciences is also open to new projects and collaborations at both European and international levels. The platform is available on the OpenAIRE catalogue of services and the EOSC marketplace. Relying on open infrastructures, the overlay model, and a dedicated support and editorial team, Episciences increases the transparency of the editorial workflow and enables researchers to regain control of their publishing methods and processes.

This document is the second report from Task 2.2 of the FAIRsFAIR project. It demonstrates how the features for FAIR enabling repositories (Behnke et al., 2020) are put into practice by building a FAIR Data Point (FDP) prototype 1 based on the DCAT2 data model, which is an RDF vocabulary designed to facilitate interoperability between data catalogues published on the Web. The root of the FAIRsFAIR reference implementation is a FAIR Data Point. In general, it serves three goals: It allows a repository or any other holder or editor2 of data to expose metadata in a FAIR manner, with a strong focus on the F, A, and R as described in the FAIR Principles (Wilkinson et al., 2016) A consumer or viewer of the data can discover information that is stored in it. It is optimised to interact with humans and machines. The first chapter gives a recap on the motivation and maps requirements from Behnke et al. (2020) to the prototype. The second chapter explains the reference implementation’s technical details, while the third describes the DCAT2 data model. In the fourth chapter, the authors analyse the community uptake, and in the fifth and last chapter, an outlook is presented.