Findable, Accessible, Interoperable And Reusable Research Articles

Features of a FAIR vocabulary

BackgroundThe Findable, Accessible, Interoperable and Reusable(FAIR) Principles explicitly require the use of FAIR vocabularies, but what precisely constitutes a FAIR vocabulary remains unclear. Being able to define FAIR vocabularies, identify features of FAIR vocabularies, and provide assessment approaches against the features can guide the development of vocabularies.ResultsWe differentiate data, data resources and vocabularies used for FAIR, examine the application of the FAIR Principles to vocabularies, align their requirements with the Open Biomedical Ontologies principles, and propose FAIR Vocabulary Features. We also design assessment approaches for FAIR vocabularies by mapping the FVFs with existing FAIR assessment indicators. Finally, we demonstrate how they can be used for evaluating and improving vocabularies using exemplary biomedical vocabularies.ConclusionsOur work proposes features of FAIR vocabularies and corresponding indicators for assessing the FAIR levels of different types of vocabularies, identifies use cases for vocabulary engineers, and guides the evolution of vocabularies.

FAIR in action - a flexible framework to guide FAIRification

The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

FAIR degree assessment in agriculture datasets using the F-UJI tool

For the agricultural scientific community, data sharing is crucial both for the advancement of the discipline and ability to meet global challenges, such as the target no. 2, i.e., “Zero Hunger,” of the Sustainable Development Goals (SDG 2030). In this context, FAIR (Findable, Accessible, Interoperable and Reusable) principles play an important role, as they guarantee the findability, accessibility, interoperability, and reusability of shared data. To improve the practice of data sharing, institutions, funders, and publishers are increasingly demanding data be shared as well as be of an acceptable level of quality, including compliance with FAIR principles. Therefore, the objective of this work is twofold: first, this research aims to determine the degree of compliance with the FAIR principles exhibited by a number of datasets; and second, it aims to explore useful and valid methodologies and procedures that can be used to perform this evaluation quickly, automatically, and effectively. For this purpose, the Data Citation Index (DCI) was used to obtain many datasets in the field of agriculture, which were further grouped by repositories and evaluated using the automated assessment tool F-UJI provided by the FAIRsFAIR project. The results indicated that the principle that exhibited the highest scores was “Findable”, while “Reusable” received the lowest scores, as none of the analysed repositories achieved a 50% compliance score in this respect. The datasets published in the Zenodo and Dryad repositories exhibited better overall results in terms of the FAIR principles, and the AG Commons repository was the third best rated repository, representing only one of the first three repositories belonging to the agricultural sector. Regarding the use of F-UJI as an automated assessment tool and DCI as a source for obtaining datasets, we conclude that this methodology is useful, and that although it can be improved, it is easy to use and implement by other scientific groups and agents of interest.

Cybersecurity in a Large-Scale Research Facility—One Institution’s Approach

A cybersecurity approach for a large-scale user facility is presented—utilizing the National High Magnetic Field Laboratory (NHMFL) at Florida State University (FSU) as an example. The NHMFL provides access to the highest magnetic fields for scientific research teams from a range of disciplines. The unique challenges of cybersecurity at a widely accessible user facility are showcased, and relevant cybersecurity frameworks for the complex needs of a user facility with industrial-style equipment and hazards are discussed, along with the approach for risk identification and management, which determine cybersecurity requirements and priorities. Essential differences between information technology and research technology are identified, along with unique requirements and constraints. The need to plan for the introduction of new technology and manage legacy technologies with long usage lifecycles is identified in the context of implementing cybersecurity controls rooted in pragmatic decisions to avoid hindering research activities while enabling secure practices, which includes FAIR (findable, accessible, interoperable, and reusable) and open data management principles. The NHMFL’s approach to FAIR data management is presented. Critical success factors include obtaining resources to implement and maintain necessary security protocols, interdisciplinary and diverse skill sets, phased implementation, and shared allocation of NHMFL and FSU responsibilities.

Hydrographic open data for society

Hydrography is defined as: “The branch of applied sciences which deals with the measurement and description of the physical features of oceans, seas, coastal areas, lakes and rivers, as well as with the prediction of their change over time, for the primary purpose of safety of navigation and in support of all other marine activities, including economic development, security and defence, scientific research, and environmental protection” (Publication S-32). Due to their broad concept hydrographic data and information provide the foundational bases for marine space management, fisheries, coastal environment, policy decisions, shipping, energy and oil industry, etc. Portuguese Hydrographic Institute (IH) manages geospatial datasets from several scientific and technical domains. Data management has been on daily agenda and always has an internal priority. Facing the digital transformation tsunami and rapid evolution of society data requirements is the main driven for developing an internal sustainable open data strategy aligned with findable, accessible, interoperable and reusable (FAIR) principles and as open as possible. The European Open Data and Open Science strategies combined with the need to fill the ocean knowledge gaps are changing the way how data producers deal with geospatial information. This article presents several IH projects to increase sharing and reuse of hydrographic data by society.

A status assessment of selected data synthesis products for ocean biogeochemistry

Ocean data synthesis products for specific biogeochemical essential ocean variables have the potential to facilitate today’s biogeochemical ocean data usage and comply with the Findable Accessible Interoperable and Reusable (FAIR) data principles. The products constitute key outputs from the Global Ocean Observation System, laying the observational foundation for information and services regarding climate and environmental status of the ocean. Using the Framework of Ocean Observing (FOO) readiness level concept, we present an evaluation framework for biogeochemical data synthesis products, which enables a systematic assessment of each product’s maturity. A new criteria catalog provides the foundation for assigning scores to the nine FOO readiness levels. As an example, we apply the assessment to four existing biogeochemical essential ocean variables data products. In descending readiness level order these are: The Surface Ocean CO2 Atlas (SOCAT); the Global Ocean Data Analysis Project (GLODAP); the MarinE MethanE and NiTrous Oxide (MEMENTO) data product and the Global Ocean Oxygen Database and ATlas (GO2DAT). Recognizing that the importance of adequate and comprehensive data from the essential ocean variables will grow, we recommend using this assessment framework to guide the biogeochemical data synthesis activities in their development. Moreover, we envision an overarching cross-platform FAIR biogeochemical data management system that sustainably supports the products individually and creates an integrated biogeochemical essential ocean variables data synthesis product; in short a system that provides truly comparable and FAIR data of the entire biogeochemical essential ocean variables spectrum.

Abstract 6576: Gabriella Miller Kids First Data Resource Center (KFDRC): Empowering discovery across germline and somatic variation in pediatric cancer

Abstract The Gabriella Miller Kids First Pediatric Research Program (Kids First) has enabled genomic and transcriptomic characterization across a multitude of pediatric diseases at an unprecedented scale. This includes both pediatric cancer cohorts and structure birth defect cohorts, as co-occurrences of these suggest a shared context of developmental biology alterations. Making this quantity of data findable, accessible, interoperable and reusable (FAIR) to researchers around the world has been enabled via the Gabriella Miller Kids First Data Resource Center (KFDRC). Twenty-six Kids First studies are released on the Kids First Data Resource Portal, representing more than 21,000 participants and more than 1.25 PB of data, with additional datasets being released yearly. Additionally, via a partnership between NCI Childhood Cancer Data Initiative and Kids First, large studies currently undergoing sequencing on childhood sarcomas and brain tumors will also be made available via the KFDRC. The KFDRC Portal (https://portal.kidsfirstdrc.org/) provides an interactive cohort building interface as well as powerful search capabilities across 61 billion germline variants in real-time. The somatic variants are made available on the open-access PedcBioPortal (https://pedcbioportal.kidsfirstdrc.org/). Search features include leveraging ontologies to enable different granularity across studies to be cross-querable. The use of these ontologies enable semantic interoperability, while also leveraging FHIR as an interoperable standard for exchange of clinical and other relevant metadata. GA4GH DRS and Passport services via Gen3 and NIH’s Researcher Authentication Service (RAS) enable streamlined access to controlled access data within researchers’ cloud platform of choice, including CAVATICA (https://cavatica.org) which is directly integrated with the KFDRC Portal. The KFDRC is part of the NIH Cloud-Based Platform Interoperability efforts, which includes the NCI Cancer Research Data Commons. This enables discovery and analysis across combined cancer datasets and data modalities and further potential for the cross analysis between cancers and structural birth defects to accelerate the discovery process to ultimately lead to improved knowledge and outcomes for childhood cancer and more generally pediatric disease from genetic causes. Citation Format: David Higgins, Jean-Philippe Thibert, Michele Mattioni, Jack DiGiovanna, Robert L. Grossman, Bailey K. Farrow, Eric Wenger, Samuel Volchenboum, Robert J. Carroll, Melissa A. Haendel, Deanne M. Taylor, Yuankun Zhu, Vincent Ferretti, Adam C. Resnick, Allison P. Heath. Gabriella Miller Kids First Data Resource Center (KFDRC): Empowering discovery across germline and somatic variation in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6576.

A FAIR-Decide framework for pharmaceutical R&D: FAIR data cost-benefit assessment.

The FAIR (findable, accessible, interoperable and reusable) principles are data management and stewardship guidelines aimed at increasing the effective use of scientific research data. Adherence to these principles in managing data assets in pharmaceutical research and development (R&D) offers pharmaceutical companies the potential to maximise the value of such assets, but the endeavour is costly and challenging. We describe the 'FAIR-Decide' framework, which aims to guide decision-making on the retrospective FAIRification of existing datasets by using business analysis techniques to estimate costs and expected benefits. This framework supports decision-making on FAIRification in the pharmaceutical R&D industry and can be integrated into a company's data management strategy.

Setting up a data management infrastructure for bioimaging.

While the FAIR (Findable, Accessible, Interoperable, and Re-usable) principles are well accepted in the scientific community, there are still many challenges in implementing them in the day-to-day scientific process. Datamanagement of microscopy images poses special challenges due to the volume, variety, and many proprietary formats. In particular, appropriate metadata collection, abasic requirement for FAIR data, is a real challenge for scientists due to the technical and content-related aspects. Researchers benefit here from interdisciplinary research network with centralized data management. The typically multimodal structure requires generalized data management and the corresponding acquisition of metadata. Here we report on the establishment of an appropriate infrastructure for the research network by a Core Facility and the development and integration of a software tool MDEmicthat allows easy and convenient processing of metadata of microscopy images while providing high flexibility in terms of customization of metadata sets. Since it is also in the interest of the core facility to apply standards regarding the scope and serialization formats to realize successful and sustainable data management for bioimaging, we report on our efforts within the community to define standards in metadata, interfaces, and to reduce thebarriers of daily data management.

Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain.

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.

Accelerating Food Allergy Research: Need for a Data Commons

Food allergy is a significant health problem affecting approximately 8% of children and 11% of adults in the United States. It exhibits all the characteristics of a "complex" genetic trait; therefore, it is necessary to look at very large numbers of patients, far more than exist at any single organization, to eliminate gaps in the current understanding of this complex chronic disorder. Advances may be achieved by bringing together food allergy data from large numbers of patients into a Data Commons, a secure and efficient platform for researchers, comprising standardized data, available in a common interface for download and/or analysis, in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Prior data commons initiatives indicate that research community consensus and support, formal food allergy ontology, data standards, an accepted platform and data management tools, an agreed upon infrastructure, and trusted governance are the foundation of any successful data commons. In this article, we will present the justification for the creation of a food allergy data commons and describe the core principles that can make it successful and sustainable.

EnzymeML: seamless data flow and modeling of enzymatic data.

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .

Characterizing the Vector Data Ecosystem.

A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct control and mitigation efforts. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, means that it is distributed across many formats and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data - the Vector Data Ecosystem - can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is 'out there', and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data).

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented.

Opportunities for improving data sharing and FAIR data practices to advance global mental health.

It is crucial to optimize global mental health research to address the high burden of mental health challenges and mental illness for individuals and societies. Data sharing and reuse have demonstrated value for advancing science and accelerating knowledge development. The FAIR (Findable, Accessible, Interoperable, and Reusable) Guiding Principles for scientific data provide a framework to improve the transparency, efficiency, and impact of research. In this review, we describe ethical and equity considerations in data sharing and reuse, delineate the FAIR principles as they apply to mental health research, and consider the current state of FAIR data practices in global mental health research, identifying challenges and opportunities. We describe noteworthy examples of collaborative efforts, often across disciplinary and national boundaries, to improve Findability and Accessibility of global mental health data, as well as efforts to create integrated data resources and tools that improve Interoperability and Reusability. Based on this review, we suggest a vision for the future of FAIR global mental health research and suggest practical steps for researchers with regard to study planning, data preservation and indexing, machine-actionable metadata, data reuse to advance science and improve equity, metrics and recognition.

Data Protection in Healthcare-Integrated Biobanking

ABSTRACTIntroductionDevelopment of personalized medicine depends on research using clinical biospecimens and data. This interface between clinical care and translational research is increasingly served by hospital-integrated biobanks; yet their implementation is hampered by complex data regulations.MethodsA generic data protection concept with a decision and application matrix was developed addressing five criteria: (1) organizational integration into university medicine, (2) biobank governance, (3) ethical and legal aspects, (4) specifications of the BSI (Bundesamt für Sicherheit in der Informationstechnik [Federal Office for Information Security]), and (5) FAIR (findable, accessible, interoperable, and reusable) principles for research data. Applicability was tested for the highest complexity level at Campus Lübeck.ResultsThe data protection concept was approved by the local ethics committee as well as local and national data protection authorities. The concept allows an automated research-guided patient recruitment and data protection-compliant information technology (IT) in connection to national and international research networks. It ensures university and hospital conformity with the EU Data Protection Regulation. Consent behavior of 277,766 patients over five years proved routine practicability (error rate 0.0013%; withdrawals 0.09%). Clinical staff obtained higher consent rates (85.6%) compared with consent rates for use of data only at central patient admission (56.1%); even though consents in central patient admission increased constantly during observation time.ConclusionThe generic data protection concept can legitimately enable personalized medicine through biobanking in the clinical context.

FAIR SHARING OF THE BLSA DATA ECOSYSTEM

Abstract The BLSA is an invaluable resource for the study of human aging with uniquely rich measures taken longitudinally on a continuous replenishment cohort since 1958. Tremendous interest has been expressed by the United States and international research communities in expanded access to and use of BLSA data to address emerging scientific questions. Here, we will describe recent study leadership initiatives into: (1) making the BLSA data more FAIR (Findable, Accessible, Interoperable, and Reusable), (2) growing a BLSA data ecosystem that enables scalable sharing, and (3) leveraging and developing platforms for accessing the ecosystem. Lastly, we will demonstrate current platform functions that empower researchers to find and access BLSA data, metadata, protocols, code and supporting materials to make new discoveries.

How FAIR are pharma publication data?

Sharing research data increases reusability, reduces waste, supports reproducibility and promotes innovation. In medical research, sharing data also promotes transparency and access to information relevant to patient care. While important advancements have been made in data sharing by regulators, the pharmaceutical industry and academic publishers, several barriers remain. Some of these barriers stem from concerns about data privacy and patient safety, but others are related to the need for confidence in sharing, which can be improved through agreed standards and systems for reuse of research data, including the application of FAIR (findable, accessible, interoperable, and reusable) principles and the overarching principle of “as open as possible, as closed as necessary”. Medical writers, who are key links between the pharmaceutical and publishing industries, can contribute to making pharma publication data FAIRer. They also have an important role in educating others about the path to more findable, accessible, interoperable, and reusable data.

Health-related quality of life and patient-reported outcome measurements in patients with cystinosis.

Nephropathic cystinosis is a rare autosomal recessive lysosomal storage disorder. With the availability of treatment and renal replacement therapy, nephropathic cystinosis has evolved from an early fatal disease to a chronic, progressive disorder with potentially high impairment. We aim to review the literature on the health-related quality of life and identify appropriate patient-reported outcome measurements to assess the health-related quality of life of patients with cystinosis. For this review, we conducted a literature search in PubMed and Web of Science in September 2021. Inclusion and exclusion criteria for the selection of articles were defined a priori. We identified 668 unique articles through the search and screened them based on title and abstract. The full texts of 27 articles were assessed. Finally, we included five articles (published between 2009 and 2020) describing the health-related quality of life in patients with cystinosis. All studies, apart from one, were conducted in the United States, and no condition-specific measurement was used. Patients with cystinosis reported a lower health-related quality of life (for certain dimensions) than healthy subjects. Few published studies address the health-related quality of life of patients with cystinosis. Such data must be collected standardized and follow the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. To gain a comprehensive understanding of the impact of this disorder on health-related quality of life, it is necessary to use generic and condition-specific instruments to measure this, preferably in large samples from longitudinal studies. A cystinosis-specific instrument for measuring health-related quality of life has yet to be developed.

Opening Research Data: What Does It Mean for Social Sciences?

Recent international trends demonstrate multilevel efforts to ‘open’ science across its whole ecosystem and lifecycle – from capturing research data through to publishing results. In social sciences, the publication process is already largely ‘open access’ or transitioning toward it. However, opening research data raises specific issues and concerns for the field. Here, we set out to understand what open research data mean for social sciences, and if, why, and how data should be made open. We argue that while the ecosystem of actors, infrastructures, standards, and principles is starting to take structure in France and abroad, there are several barriers to the process of opening data in social sciences: (1) a misperception of the motivations for opening data (i.e., focusing on risks of exercising control over researchers and their academic freedom and overlooking motivations like data patrimonialization, pooling and potential synergies, trust-building, and broader engagement), (2) a system based on competition and the dominant process of ‘starification’ in research, (3) a lack of resources and capabilities that might further exacerbate inequalities among genders, communities, institutions, and countries, and (4) the potential risks inherent to opening data and the specific constraints posed by social science data. Against this backdrop, we investigate several ways forward to operationalize not only FAIR (Findable, Accessible, Interoperable and Reusable) but also CARE (Collective benefit, Authority to control, Responsibility, Ethics) principles for open data in social sciences, before going on to present M@n@gement’s new open data policy.