Findable Accessible Interoperable Reusable Data Research Articles

A Community-Driven Strategy for Addressing Fossil Taxonomy Challenges

Taxonomy has long been a cornerstone issue in the paleontological collections community, especially as digitization has enabled us to share data at a global scale. Communities of practice such as the Paleo Data Working Group (PDWG, Krimmel et al. 2021) have made concerted efforts to understand and evaluate challenges with the taxonomic information published via fossil occurrence records (Little et al. 2021, Little et al. 2022). Since 2020, PDWG has been leading discussions to define the nuances of challenges associated with fossil taxonomy, to look at the landscape of cyberinfrastructure tools available to tackle these challenges, and to develop community guidelines. This work has led to a deeper understanding of the full range of challenges surrounding the use of fossil taxonomy, from local practices and systems to representation at the data aggregator level. Here we share our initial strategy for tackling challenges with fossil data, particularly taxonomy, centered on developing a reusable framework for assessing and addressing issues so that data can be made more discoverable and usable for transformative and translational research. An essential element of this approach is the coordination of efforts across stakeholder communities. Through these collaborations, we ultimately seek to improve systems for handling and curating taxonomic data, increase the quality and discoverability of taxonomic data presented by aggregators, and better integrate data provided by fossil occurrence records into the research lifecycle. The required coordination will be driven through the development of a partner network from across the global bio- and geo- science data ecosystem. This network is composed of representatives with a variety of perspectives and roles, including participation from Catalogue of Life, Global Biodiversity Information Facility, and Biodiversity Information Standards (TDWG), as well as paleontology collections professionals (led by PDWG), informaticians, researchers, and other end-users. Integrating the existing PDWG community of practice into this larger network allows us to learn from work already underway in the landscape. In addition, recognizing that the paleo community is not alone in these challenges, we believe that a successful strategy for this network is to create processes and envision solutions that can be adapted by other disciplines with similar needs. Our strategy underscores the need for the combined efforts of both community engagement and evaluation of the existing technical landscape. Through this work we aim to establish an enhanced network of FAIR (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. 2016) and research-ready fossil data, improving the support for and quality of fossil taxonomy data along the way.

Genome Taxonomy Database and SeqCode: Microbial Taxonomy and Nomenclature in the Age of Big Sequence Data

Genome Taxonomy Database and SeqCode: Microbial Taxonomy and Nomenclature in the Age of Big Sequence Data

Building School Data Equity, Infrastructure, and Capacity Through FAIR Data Standards: Findable, Accessible, Interoperable, and Reusable

Despite increasing calls to build equitable data infrastructures, the education field has yet to have a shared guideline around equitable education data management and stewardship. To address this gap, we propose one framework from the data governance literature: the FAIR (Findable, Accessible, Interoperable, Reusable) data management principles complemented by the CARE (Collective benefits, Authority to control, Responsibility, Ethics) principles. We argue that making education data Findable, Accessible, Interoperable, and Reusable (FAIR) is a matter of equity and central to equity-focused data reuse. We illustrate the importance of FAIR education data by synthesizing our research experience and literature at the intersection of data governance and equity-focused data use.

A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

How data governance principles influence participation in biodiversity science

ABSTRACT Biodiversity science is in a pivotal period when diverse groups of actors – including researchers, businesses, national governments, and Indigenous Peoples – are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. The management of these repositories, often called biodiversity data portals, can serve either to redress or to perpetuate the colonial history of biodiversity science and current inequities. Both researchers and Indigenous Peoples are implementing new strategies to influence whom biodiversity data portals recognise as salient participants in data management and use. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Data Principles. Actors use these principles to influence the governance of biodiversity data portals. ‘Fit-for-use’ data is a social status provided by groups of actors who approve whether the data meets specific purposes. Advocates for the FAIR and CARE Principles use them in a similar way to institutionalise the authority of different groups of actors. However, the FAIR Principles prioritise the ability of machine agents to understand the meanings of data, while the CARE Principles prioritise Indigenous Peoples and their data sovereignty. Together, FAIR and CARE illustrate a broader emerging strategy for institutionalising international norms for digital repositories about who they should recognise as having a formal role in determinations of the fitness-for-use of data.

Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence

BackgroundThe clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. MethodsA conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure compliance with FAIR principles. ResultsThe framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion, and frame levels. ConclusionOur study shows that the proposed framework facilitates the storage of visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

FAIR data station for lightweight metadata management and validation of omics studies.

The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive-compatible XML metadata file. Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.

Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

Connecting the Dots: Joint development of best practices between infrastructures in support of bidirectional data linking

Working together is key in terms of knowledge exchange and in the Biodiversity Community Integrated Knowledge Library project (BiCIKL), infrastructures involved in the biodiversity data landscape are working together to connect data from their different but related data domains. This will be key to connecting the dots towards the development of global collections and global specimen networks in line with e.g., the Extended Specimens Network (Lendemer et al. 2019), which describes a strategy to enhance US biodiversity collections and promote research and education by enabling seamless data integration, attribution and use tracking. It will enable and improve linkages in the fragmented landscape of literature, specimens, collections, species and sequence data. It is required to reliably connect collection level data and specimen level data with derived and related data served through these infrastructures. BiCIKL is a project funded by the European Commission that will develop bidirectional data linkages through persistant identifiers between the data so that they become part of a big FAIR (Findable, Accessible, Interoperable, Reusable) data pool available to researchers, public authorities and business to foster innovation in the digital economy and to contribute in solving global environmental challenges. This will enable end-users to seamlessly access a larger pool of enriched data and knowledge, thereby bypassing the traditional barrier of having to search multiple databases and then link up different data types, or to manually extract data from literature. Two elements are key for development of the data linkages: 1) the global adoption of agreed upon persistent identifiers for digital specimen objects, taxonomic treatments, collections, persons, sequences and taxa across stakeholders, and 2) best practices for the generation, management and curation of the linked data. We will present the best practices that have been jointly developed by the infrastructures in the BiCIKL project for findability, re-use and accessibility. The presentation will focus on the best practices that are essential for reliably linking specimen- and collection-level data with other types of data. Implementing these practices will be an important contribution towards connecting the dots in the biodiversity data landscape and will enrich the information at the data provider level.

Zen and the Art of Persistent Identifier Service Development for Digital Specimen

One of the most desired (and still missing) elements to enable the concept of Digital Extended (Webster et al. 2021) Specimens is a persistent identifier (PID) for the new digital specimen object. Digital Specimens are created to act as a digital surrogate of the physical objects. Digital Specimens contain all data relevant to the specimens as well as derived data like genetic sequences, trait information, and references to publications, species and environmental information. A PID for the Digital Specimen is thus essential to link it to the extended information. Furthermore, the extended information also needs PIDs to create unique and resolvable references to enable bidirectional linking. DiSSCo, Distributed System of Scientific Collections, has done extensive work to select the most appropriate PID scheme (Hardisty et al. 2021) and design a PID infrastructure for the pan-European specimen collections. The design was created in the Biodiversity Community Integrated Knowledge Library project (BiCIKL) with funding from the European Commission. The draft design has been discussed with technical specialists in the joint DiSSCo and Consortium of European Taxonomic Facilities (CETAF) community, with international stakeholders like GBIF and iDigBio. The draft was also presented at the 2022 conference of SPNHC (the Society for the Preservation of Natural History Collections) for further feedback, after which it was finalised. This talk will provide a short overview of the key elements in the design of the Persistent Identifier system for Digital Specimens such as the metadata schema, a human friendly string format, and PID lifecycle with support for state changes of the physical object such as splitting and merging. We will illustrate this by showing the current status of development. The Digital Specimen forms the backbone of the FAIR (Findable, Accessible, Interoperable, Reusable) services (Addink et al. 2021) in development by the DiSSCo Prepare project, a project to prepare for the new research infrastructure which currently involves 170 institutions in 23 countries holding an estimated 1,5 billion specimens. The Digital Specimen is a FAIR Digital Object with a PID and machine actionable metadata to make it FAIR. The PID service ensures the identifiers’ integrity and preserves these linkages, improving the FAIR quality of all specimen data. The talk will describe the system design and current test deployment setup, as well as delve into a few technical details of the Handle Server architecture, PID Kernel records, and a roadmap for the service development. The design and test work are done under the auspices of the BiCIKL project that aims to foster collaboration between infrastructures and develop bidirectional connections between the data so that they become part of a big FAIR data pool seamlessly available to researchers (Penev et al. 2022).

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

BackgroundIn times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.ResultsWe argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.ConclusionsWe conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.

Background Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusions The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph

Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite—demonstrated here in the domain of polymer nanocomposite materials science—offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

The Pre-Analytical CEN/TS Standard for Microbiome Diagnostics-How Can Research and Development Benefit?

Recently, CEN/TS 17626:2021, the European pre-analytical standard for human specimens intended for microbiome DNA analysis, was published. Although this standard relates to diagnostic procedures for microbiome analysis and is relevant for in vitro diagnostic (IVD) manufacturers and diagnostic laboratories, it also has implications for research and development (R&D). We present here why standards are needed in biomedical research, what pre-analytical standards can accomplish, and which elements of the pre-analytical workflow they cover. The benefits of standardization for the generation of FAIR (findable, accessible, interoperable, reusable) data and to support innovation are briefly discussed.

Emacs as a Tool for Modern Science

It is human nature to prefer additive problem solving even if removal may be the more efficient solution. This heuristic has wide ranging implications when dealing with science, innovation and complex problem solving. This is compounded when dealing with these issues at an institutional level. Additive solutions to workflows with extra software tools and proprietary digital solutions can impede work without offering any advantages in terms of Findable, Accessible, Interoperable, Reusable (FAIR) data principles or productivity. This viewpoint highlights one possible workflow and the mentality underpinning it with an aim to incorporate FAIR data, improved productivity and longevity of written documents while improving workloads within industrial research and development (R&D).

Paths towards greater consensus building in experimental biology.

In a recent editorial, the Editors-in-Chief of Journal of Experimental Biology argued that consensus building, data sharing, and better integration across disciplines are needed to address the urgent scientific challenges posed by climate change. We agree and expand on the importance of cross-disciplinary integration and transparency to improve consensus building and advance climate change research in experimental biology. We investigated reproducible research practices in experimental biology through a review of open data and analysis code associated with empirical studies on three debated paradigms and for unrelated studies published in leading journals in comparative physiology and behavioural ecology over the last 10 years. Nineteen per cent of studies on the three paradigms had open data, and 3.2% had open code. Similarly, 12.1% of studies in the journals we examined had open data, and 3.1% had open code. Previous research indicates that only 50% of shared datasets are complete and re-usable, suggesting that fewer than 10% of studies in experimental biology have usable open data. Encouragingly, our results indicate that reproducible research practices are increasing over time, with data sharing rates in some journals reaching 75% in recent years. Rigorous empirical research in experimental biology is key to understanding the mechanisms by which climate change affects organisms, and ultimately promotes evidence-based conservation policy and practice. We argue that a greater adoption of open science practices, with a particular focus on FAIR (Findable, Accessible, Interoperable, Re-usable) data and code, represents a much-needed paradigm shift towards improved transparency, cross-disciplinary integration, and consensus building to maximize the contributions of experimental biologists in addressing the impacts of environmental change on living organisms.

AusGeochem: An Open Platform for Geochemical Data Preservation, Dissemination and Synthesis

To promote a more efficient and transparent geochemistry data ecosystem, a consortium of Australian university research laboratories called the AuScope Geochemistry Network assembled to build a collaborative platform for the express purpose of preserving, disseminating and collating geochronology and isotopic data. In partnership with geoscience‐data‐solutions company Lithodat Pty Ltd, the open, cloud‐based AusGeochem platform (https://ausgeochem.auscope.org.au) was developed to simultaneously serve as a geosample registry, a geochemical data repository and a data analysis tool. Informed by method‐specific groups of geochemistry experts and established international data reporting practices, community‐agreed database schemas were developed for rock and mineral geosample metadata and secondary ion mass spectrometry U‐Pb analysis, with additional models for laser ablation‐inductively coupled‐mass spectrometry U‐Pb and Lu‐Hf, Ar‐Ar, fission‐track and (U‐Th‐Sm)/He under development. Collectively, the AusGeochem platform provides the geochemistry community with a new, dynamic resource to help facilitate FAIR (Findable, Accessible, Interoperable, Reusable) data management, streamline data dissemination and advanced quantitative investigations of Earth system processes. By systematically archiving detailed geochemical (meta‐)data in structured schemas, intractably large datasets comprising thousands of analyses produced by numerous laboratories can be readily interrogated in novel and powerful ways. These include rapid derivation of inter‐data relationships, facilitating on‐the‐fly data compilation, analysis and visualisation.

Tyto: A Python Tool Enabling Better Annotation Practices for Synthetic Biology Data-Sharing.

As synthetic biology becomes increasingly automated and data-driven, tools that help researchers implement FAIR (findable-accessible-interoperable-reusable) data management practices are needed. Crucially, in order to support machine processing and reusability of data, it is important that data artifacts are appropriately annotated with metadata drawn from controlled vocabularies. Unfortunately, adopting standardized annotation practices is difficult for many research groups to adopt, given the set of specialized database science skills usually required to interface with ontologies. In response to this need, Take Your Terms from Ontologies (Tyto) is a lightweight Python tool that supports the use of controlled vocabularies in everyday scripting practice. While Tyto has been developed for synthetic biology applications, its utility may extend to users working in other areas of bioinformatics research as well. Tyto is available as a Python package distribution or available as source at https://github.com/SynBioDex/tyto.

Дослідження принципів, моделей та методів парадигми менеджменту наукових даних FAIR для аналізу метаданих BIG DATA

In this research work we considere the basic principles, models and methods of the FAIR (Findable, Accessible, Interoperable, Reusable) scientific data management paradigm as a separate case of Big Data. This paradigm is focused on the reuse of scientific research results.Basic principles of FAIR apply to three types of entities: data (or any digital object), metadata (information about this digital object) and infrastructure. Information on various projects, initiatives and communities working on solving the problems of scientific data and their metadata integration is examined. We analyze how the properties of FAIR data contribute to the unification and integration of the scientific infrastructure in the paradigm of open science, which is based on free access to research results and open data.

Grapevine and Wine Metabolomics-Based Guidelines for FAIR Data and Metadata Management.

In the era of big and omics data, good organization, management, and description of experimental data are crucial for achieving high-quality datasets. This, in turn, is essential for the export of robust results, to publish reliable papers, make data more easily available, and unlock the huge potential of data reuse. Lately, more and more journals now require authors to share data and metadata according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This work aims to provide a step-by-step guideline for the FAIR data and metadata management specific to grapevine and wine science. In detail, the guidelines include recommendations for the organization of data and metadata regarding (i) meaningful information on experimental design and phenotyping, (ii) sample collection, (iii) sample preparation, (iv) chemotype analysis, (v) data analysis (vi) metabolite annotation, and (vii) basic ontologies. We hope that these guidelines will be helpful for the grapevine and wine metabolomics community and that it will benefit from the true potential of data usage in creating new knowledge being revealed.