Findable, Accessible, Interoperable And Reusable Principles Research Articles

Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept.

Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training. The focus is the development and presentation of a data management approach specifically tailored for clinical researchers involved in active patient care, especially in the neuroscientific environment of German university hospitals. Our design considers the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the secure handling of sensitive data in compliance with the General Data Protection Regulation. We introduce a streamlined database concept, featuring an intuitive graphical interface built on Hypertext Markup Language revision 5 (HTML5)/Cascading Style Sheets (CSS) technology. The system can be effortlessly deployed within local networks, that is, in Microsoft Windows 10 environments. Our design incorporates FAIR principles for effective data management. Moreover, we have streamlined data interchange through established standards like HL7 Clinical Document Architecture (CDA). To ensure data integrity, we have integrated real-time validation mechanisms that cover data type, plausibility, and Clinical Quality Language logic during data import and entry. We have developed and evaluated our concept with clinicians using a sample dataset of subjects who visited our memory clinic over a 3-year period and collected several multimodal clinical parameters. A notable advantage is the unified data matrix, which simplifies data aggregation, anonymization, and export. THIS STREAMLINES DATA EXCHANGE AND ENHANCES DATABASE INTEGRATION WITH PLATFORMS LIKE KONSTANZ INFORMATION MINER (KNIME): . Our approach offers a significant advancement for capturing and managing clinical research data, specifically tailored for small-scale initiatives operating within limited information technology (IT) infrastructures. It is designed for immediate, hassle-free deployment by clinicians and researchers.The database template and precompiled versions of the user interface are available at: https://github.com/stebro01/research_database_sqlite_i2b2.git.

Development of a maturity model to assess the FAIRness of architectural data in Switzerland

This research carries out an in-depth assessment of the application of the FAIR (Findable, Accessible, Interoperable and Reusable) principles by the Swiss scientific community specialized in architecture, and consequently its positioning in the context of open science. The FAIR maturity assessment of research data is based on the use of maturity models. They provide a structured framework for implementing and improving data management practices. Our methodology involved a careful comparison of six existing maturity models and their alignment with the FAIR principles. This ensured a relevant and appropriate selection of evaluation criteria for own model. Developing straightforward criteria that can be applied in real-world scenarios is a key aspect of our approach. Inspired by the FAIR principles, we formulated our matrix-based maturity model, the Architectural Maturity Model (AMM). The goal of the AMM is to improve the understanding of metrics using a question-answer approach. We then applied the AMM to evaluate selected datasets stored in the Zenodo and ETH Research Collection repositories to assess the FAIRness of architectural research data.
 The results show that architectural data are findable and accessible, but that they not very interoperable and can be reused only with limitations. To improve the FAIRness of architectural research data we recommend data producers to prioritize licences without restriction (e.g. CC0) and open formats. We also encourage researchers to discuss with their data stewards how to use controlled vocabularies to improve the visibility of their data on digital platforms.

How to Assess FAIRness of Your Data - A Summary of Testing Two FAIR Validators.

Decision-making in healthcare is heavily reliant on data that is findable, accessible, interoperable and reusable (FAIR). Evolving advancements in genomics also heavily rely on FAIR data to steer reliable research for the future. For practical purposes, ensuring FAIRness of a clinical data set can be challenging but could be aided by using FAIR validators. The study describes the test of two open-access web-tools in their demo versions to determine the FAIR levels of three submitted genomic data files with different formats (JSON, TXT, CSV). The F-UJI tool and FAIR-Checker tools provided similar FAIR scores for the three submitted files. However, the F-UJI tool assigned a total rating whereas the FAIR-Checker gave scores clustered by FAIR principles. Neither tool was suited to determine FAIR levels of a FHIR® JSON metadata file. Despite their early developmental status, FAIR validator tools have great potential to assist clinicians in the FAIRification of their research data.

A multi-omics data analysis workflow packaged as a FAIR Digital Object.

Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.

Using <em>OpenBIS</em> as Virtual Research Environment: An ELN-LIMS Open-Source Database Tool as a Framework within the <em>CRC 1411 Design of Particulate Products</em>

The digital transformation and consequent use of new digital technologies not only have a substantial impact on society and companies, but also on science. Analog documentation as we have known it for centuries will eventually be replaced by intelligent and FAIR (Findable, Accessible, Interoperable, and Reusable) systems. In addition to the actual research data and results, metadata now plays an important role not only for individual, independently existing projects, but for future scientific use and interdisciplinary research groups and disciplines as well. The solution presented here, consisting of an electronic laboratory notebook and laboratory information management system (ELN-LIMS) based on the openBIS (open Biology Information System) environment, offers interesting features and advantages, especially for interdisciplinary work. The Collaborative Research Centre (CRC) 1411 ‘Design of Particulate Products’ of the German Research Foundation is characterized by the cooperation of different working groups of synthesis, characterization, and simulation, and therefore serves as a model environment to present the implementation of openBIS. OpenBIS, as an open source ELN-LIMS solution following FAIR principles, provides a common set of general entries with the possibility of sharing and linking (meta-)data to improve the scientific exchange between all users.

Tailoring evidence into action: Using a co‐design approach for biodiversity information in the Tropical Andes

AbstractBiodiversity conservation is a complex and transdisciplinary problem that requires engagement and cooperation among scientific, societal, economic, and political institutions. However, historical approaches have often failed to bring together and address the needs of all relevant stakeholders in decision‐making processes. The Tropical Andes, a biodiversity hotspot where conservation efforts often conflict with socioeconomic issues and policies that prioritize economic development, provides an ideal model to develop and implement more effective approaches. In this study, we present a co‐design approach that mainstreams and improves the flow of biodiversity information in the Tropical Andes, while creating tailored outputs that meet the needs of economic and societal stakeholders. We employed a consultative process that brought together biodiversity information users and producers at the local, national, and regional levels through a combination of surveys and workshops. This approach identified priority needs and limitations of the flow of biodiversity information in the region, which led to the co‐design of user‐relevant biodiversity indicators. By leveraging the existing capacities of biodiversity information users and producers, we were able to co‐design multiple biodiversity indicators and prioritize two for full implementation ensuring that the data was findable, accessible, interoperable, and reusable based on the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This approach helped address limitations that were identified in the stakeholder engagement process, including gaps in data availability and the need for more accessible biodiversity information. Additionally, capacity‐building workshops were incorporated for all producers of biodiversity information involved, which aimed to not only improve the current flow of biodiversity information in the region but also facilitate its future sustainability. Our approach can serve as a valuable blueprint for mainstreaming biodiversity information and making it more inclusive in the future, especially considering the diverse worldviews, values, and knowledge systems between science, policy, and practice.

Towards a Distributed System for Essential Variables for the Southern Ocean

The Southern Ocean (SO), delinated to the north by the Antarctic convergence, is a unique environment that experiences rapid change in some areas while remaining relatively untouched by human activities. At the same time, these ecosystems are under severe threat from climate change and other stressors. While our understanding of SO biological processes (e.g., species distributions, feeding ecology, reproduction) has greatly improved in recent years, biological data for the region remains patchy, sparse, and unstandardised depending on the taxonomic group (Griffiths et al. 2014). Due to the scarcity of standardised observations and data, it is difficult to model and predict SO ecosystem responses to climate change, which is often accompanied by other anthropogenic pressures, such as fishing and tourism. Understanding the dynamics and change in the SO necessitates a comprehensive system of observations, data management, scientific analysis, and ensuing policy recommendations. It should be built as much as feasible from current platforms and standards, and it should be visible, verifiable and shared in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles (Van de Putte and Griffiths 2021). For this we need to identify the stakeholders' needs, sources of data, the algorithms for analysing the data and the infrastructure on which to run the algorithms (Benson and Brooks 2018). Existing synergistic methods for identifying selected variables for (life) monitoring include Essential Biodiversity Variables (EBVs; Pereira and Ferrier 2013), Essential Ocean Variables (EOVs; Miloslavich and Bax 2018), Essential Climate Variables (ECVs; Bojinski and Verstraete 2014), and ecosystem Essential Ocean Variables (eEOVs; Constable and Costa 2016). (For an overview see Muller-Karger and Miloslavich 2018.) These variables, can be integrated into the Southern Ocean Observation System (SOOS) and SOOSmap but also national or global systems (e.g., Group on Earth Observations-Biodiversty Observation Network (GEO-BON)). The resulting data products can in turn be used to inform policy makers. The use of Essential Variables (EVs) marks a significant step forward in the monitoring and assessment of SO ecosystems. However, these EVs will necessitate prioritising certain variables and data collection. Here we present the outcomes of a workshop organised in August 2023 that aimed to outline the set Essential Variables and workflows required for a distributed system that can translate biodiversity data (and environmental data) into policy-relevant data products. The goals of the workshop were: Create an inventory of EVs relevant for the Southern Ocean based on existing efforts by the GEO-BON and the Marine Biodiversity Observation Network (MBON). Identify data requirements and data gaps for calculating such EVs and prioritise EVs to work on. Identify existing workflows and tools. Develop a framework for developing the workflows required to turn public biodiversity data into relevant EVs. Create an inventory of EVs relevant for the Southern Ocean based on existing efforts by the GEO-BON and the Marine Biodiversity Observation Network (MBON). Identify data requirements and data gaps for calculating such EVs and prioritise EVs to work on. Identify existing workflows and tools. Develop a framework for developing the workflows required to turn public biodiversity data into relevant EVs.

Keep It Simple: Using README Files to Advance Standardization in Chronobiology.

Standardization plays a crucial role in ensuring the reliability, reproducibility, and interoperability of research data in the biomedical sciences. Metadata standards are one foundation for the FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data management. They facilitate data discovery, understanding, and reuse. However, the adoption of metadata standards in biological research lags in practice. Barriers such as complexity, lack of incentives, technical challenges, resource constraints, and resistance to change hinder widespread adoption. In the field of chronobiology, standardization is essential but faces particular challenges due to the longitudinal nature of experimental data, diverse model organisms, and varied measurement techniques. To address these challenges, we propose an approach that emphasizes simplicity and practicality: the development of README templates tailored for particular data types and species. Through this opinion article, our intention is to initiate a dialogue and commence a community-driven standardization process by engaging potential contributors and collaborators.

Establishing FAIR (Findable, Accessible, Interoperable and Reusable) principles for estuarine organisms exposed to engineered nanomaterials

Establishing FAIR (Findable, Accessible, Interoperable and Reusable) principles for estuarine organisms exposed to engineered nanomaterials

Improving Marine Megafauna Knowledge with eDNA: A French Story

In the current context of large-scale biodiversity loss (Díaz et al. 2019), it is urgent to carry out biodiversity inventories in order to investigate spatial and temporal variations. Among the available methods, environmental DNA (eDNA) (Taberlet et al. 2018) combined with molecular metabarcoding (Valentini et al. 2009) bring new insights in drafting inventories of taxa present in an environment. Approaches based on eDNA facilitate the study of remote ecosystems and the detection of rare, cryptic or invasive species in a non-invasive way (Bohmann et al. 2014). In the marine realm, the study of mobile fauna, including mammals particularly, can greatly benefit from these new techniques. Here, we present a case study carried out in the northeast and northwest Atlantic, within the boundaries of marine protected areas. The Iroise Natural Marine Park*1 was the first French marine natural park created in France in 2007 (Fig. 1). The first campaign in the Iroise Natural Marine Park successfully detected several species of cetacean (e.g., Lagenorhynchus albirostris, Lagenorhynchus obliquidens) and four species of elasmobranchs (e.g., Scyliorhinus canicula, Torpedo marmorata, Raja undulata and Mustelus asterias). This study was extended to the Martinique Natural Marine Park*2, where similar analyses identified three cetacean species (Stenella attenuata, Lagenodelphis hosei and Tursiops truncatus) and 193 teleost taxa (Riou 2022), representing more than half of the species expected in Martinique. The analyses carried out in Martinique highlighted several remarkable sites for fish biodiversity. In Guadeloupe, in the heart of the Agoa Marine Mammals Sanctuary*3 (corresponding to the French Antilles exclusive economic zone), the taxonomic composition of different eDNA samples made in 2021 and 2022 was compared. The samples were analyzed using different primer sets, specific to vertebrates, teleosts and mammals. Comparison of the results obtained with vertebrate-specific primers and those specific to teleosts showed complementary results. For cetaceans, the analyses distinguished ten distinct cetacean taxonomic units and at least 3 distinct species from four samples. In addition, the eDNA samples were used to discriminate between the two recently recognised bottlenose dolphin ecotypes, based on the observation of a fixed nucleotide difference on the barcode targeted by the mammal-specific primers. New primers are currently being developed to make it easier to distinguish species of Delphinidae, which are still complex to identify. Environmental DNA and metabarcoding offer new possibilities for the inventories of marine megafauna and support adequate conservation decision-making processes. It is a major step towards better monitoring of marine mammal populations of the Atlantic Ocean. Moreover, our datasets will be freely available through open platforms following the FAIR (Findable, Accessible, Interoperable, and Reusable) principles.

A Strategy for Building the WFO Plant List

A Strategy for Building the WFO Plant List

Data lake governance using IBM-Watson knowledge catalog

The strategic importance of data in decision-making is increasingly recognized, demanding efficient solutions such as data catalogs to ensure data governance and emphasize data interoperability, in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. However, the usage of FAIR-compliant data catalogs lacks empirical studies due to its novelty. This study aims to promote the practical adoption of data catalogs as a means to manage the expanding data landscape. We differentiate our contribution by providing an empirical evaluation and comparison of IBM Watson Knowledge Catalog (IBM-WKC), a leading data cataloging solution, with two other prominent alternatives, Open-Metadata and Data-Galaxy, for extracting relevant information from data lakes containing heterogeneous data sources in their native formats. Our proposed methodology utilizes an innovative tool built on IBM-WKC for annotating collected documents. To evaluate our approach, we conducted experiments on a dataset of 100 documents sourced from scientific databases. Moreover, to assess our proposal, we compare the retrieved text to the appropriate interventions that use the original checklist. The results demonstrate the superiority of IBM-WKC over its competitors, showcasing its enhanced performance in addressing data cataloging challenges. Notably, the tested queries achieved an impressive accuracy, precision, and recall value of 96%. These findings highlight the reliability and alignment of IBM-WKC with the FAIR principles.

Identifying and sharing per-and polyfluoroalkyl substances hot-spot areas and exposures in drinking water

Exposure to per- and polyfluoroalkyl substances (PFAS) in drinking water is widely recognized as a public health concern. Decision-makers who are responsible for managing PFAS drinking water risks lack the tools to acquire the information they need. In response to this need, we provide a detailed description of a Kentucky dataset that allows decision-makers to visualize potential hot-spot areas and evaluate drinking water systems that may be susceptible to PFAS contamination. The dataset includes information extracted from publicly available sources to create five different maps in ArcGIS Online and highlights potential sources of PFAS contamination in the environment in relation to drinking water systems. As datasets of PFAS drinking water sampling continue to grow as part of evolving regulatory requirements, we used this Kentucky dataset as an example to promote the reuse of this dataset and others like it. We incorporated the FAIR (Findable, Accessible, Interoperable, and Reusable) principles by creating a Figshare item that includes all data and associated metadata with these five ArcGIS maps.

A proposed FAIR approach for disseminating geospatial information system maps

We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets.

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 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.

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.