Findable, Accessible, Interoperable, Reusable Principles Research Articles

The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science.

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening

BackgroundPatient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem.MethodsIn the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted.ResultsIn total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and “other” geographical scope were ranked at the bottom of the list with the lowest proportion. Responders’ willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed.ConclusionsThe most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.

A research data management (RDM) community for ELIXIR

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR’s vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

A One Health Platform for Future Epidemic Preparedness.

Here, we introduce the EpiConnect Intelligence Platform (ECIP), a platform facilitating rapid, transparent data sharing and analysis to support researchers and public health officials in Europe, with a focus on Italy. ECIP provides reliable, concise, machine-readable data to aid in epidemiological understanding, standardize case characteristics, and estimate key parameters. The platform adheres to FAIR (findable, accessible, interoperable, reusable) principles, offering easily accessible and downloadable datasets for researchers' endeavors. Future enhancements include involving national public health authorities, expanding data streams, and fostering collaboration between experts and users for improved epidemic risk monitoring. Shared standards among diverse surveillance systems are advocated to achieve common strategic goals, emphasizing the need for forward-looking policies to empower professionals to analyze disease dynamics in the context of evolving health crises. The recent emergencies underscore the importance of collective efforts towards shared strategic goals, highlighting the necessity for coordinated action to address mutual concerns affecting everyone's lives.

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology.

Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.

Making bioinformatics training FAIR: the EMBL-EBI training portal.

EMBL-EBI provides a broad range of training in data-driven life sciences. To improve awareness and access to training course listings and to make digital learning materials findable and simple to use, the EMBL-EBI Training website, www.ebi.ac.uk/training, was redesigned and restructured. To provide a framework for the redesign of the website, the FAIR (findable, accessible, interoperable, reusable) principles were applied to both the listings of live training courses and the presentation of on-demand training content. Each of the FAIR principles guided decisions on the choice of technology used to develop the website, including the details provided about training and the way in which training was presented. Since its release the openly accessible website has been accessed by an average of 58,492 users a month. There have also been over 12,000 unique users creating accounts since the functionality was added in March 2022, allowing these users to track their learning and record completion of training. Development of the website was completed using the Agile Scrum project management methodology and a focus on user experience. This framework continues to be used now that the website is live for the maintenance and improvement of the website, as feedback continues to be collected and further ways to make training FAIR are identified. Here, we describe the process of making EMBL-EBI's training FAIR through the development of a new website and our experience of implementing Agile Scrum.

OneFlowTraX: a user-friendly software for super-resolution analysis of single-molecule dynamics and nanoscale organization.

Super-resolution microscopy (SRM) approaches revolutionize cell biology by providing insights into the nanoscale organization and dynamics of macromolecular assemblies and single molecules in living cells. A major hurdle limiting SRM democratization is post-acquisition data analysis which is often complex and time-consuming. Here, we present OneFlowTraX, a user-friendly and open-source software dedicated to the analysis of single-molecule localization microscopy (SMLM) approaches such as single-particle tracking photoactivated localization microscopy (sptPALM). Through an intuitive graphical user interface, OneFlowTraX provides an automated all-in-one solution for single-molecule localization, tracking, as well as mobility and clustering analyses. OneFlowTraX allows the extraction of diffusion and clustering parameters of millions of molecules in a few minutes. Finally, OneFlowTraX greatly simplifies data management following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We provide a detailed step-by-step manual and guidelines to assess the quality of single-molecule analyses. Applying different fluorophores including mEos3.2, PA-GFP, and PATagRFP, we exemplarily used OneFlowTraX to analyze the dynamics of plant plasma membrane-localized proteins including an aquaporin, the brassinosteroid receptor Brassinosteroid Insensitive 1 (BRI1) and the Receptor-Like Protein 44 (RLP44).

Ten simple rules for starting FAIR discussions in your community

This work presents 10 rules that provide guidance and recommendations on how to start up discussions around the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and creation of standardised ways of working. These recommendations will be particularly relevant if you are unsure where to start, who to involve, what the benefits and barriers of standardisation are, and if little work has been done in your discipline to standardise research workflows. When applied, these rules will support a more effective way of engaging the community with discussions on standardisation and practical implementation of the FAIR principles.

Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders

An abnormal hormonal activity or exposure to endocrine-disrupting chemicals (EDCs) can cause endocrine system malfunction. Among the many interactions EDCs can affect is the disruption of estrogen signalling, which can lead to adverse health effects such as cancer, osteoporosis, neurodegenerative diseases, cardiovascular disease, insulin resistance, and obesity. Knowing which chemical can act as an EDC is a significant advantage and a practical necessity. New Approach Methodologies (NAM) computational models offer a quick and cost-effective solution for preliminary hazard assessment of chemicals without animal testing. Therefore, a machine learning approach was used to investigate the relationships between estrogen receptor (ER) activity and chemical structure to identify chemicals that can interact with ER. For this purpose, the consolidated in vitro assay data from ToxCast/Tox21 projects was used for developing Random Forest classification models for ER binding, agonists, and antagonists. The overall classification prediction accuracy reaches up to 82%, depending on whether the model predicted agonists, antagonists, or compounds that bind to the active site. Given the imbalance in endocrine disruption data, the derived models are good candidates for deprioritising chemicals and reducing animal testing. The interpretation of theoretical molecular descriptors of the models was consistent with the molecular interactions known in the ligand binding pocket. The estimated class probabilities enabled the analysis of the applicability domain of the developed models and the assessment of the predictions’ reliability, followed by the guidelines for interpreting prediction results. The models are openly accessible and useable at QsarDB.org (http://dx.doi.org/10.15152/QDB.259) according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Leveraging OGC API for cloud-based flood modeling campaigns

Cloud technology and services provide a convenient platform for managing large scale watershed modeling studies, such as probabilistic studies or modeling campaigns requiring thousands of simulations. As federal and state agencies undertake large scale projects that leverage commercial cloud platforms for computing and storage of model data, ensuring that the Findable, Accessible, Interoperable, Reusable (FAIR) principles are incorporated early in the design stage is crucial. This paper is the first attempt to explore the feasibility of implementing the OGC API - Processes specification for use as an interoperability layer in an environmental modeling study for performing large scale environmental studies consistent with the FAIR principles. Specifically, an evaluation of the suitability for implementing the OGC API - Processes specification in the commercial cloud for managing the fundamental processes that are involved in the setup, execution, and post-processing of hydraulic modeling in a large-scale watershed study is performed. The experimental results show that the implementation of the OGC API - Processes specification can play a valuable role in providing an interoperable service layer that can be used to request geoprocessing services and chain model development and simulation processes.

No Pain No Gain: Standards mapping in Latimer Core development

Latimer Core (LtC) is a new proposed Biodiversity Information Standards (TDWG) data standard that supports the representation and discovery of natural science collections by structuring data about the groups of objects that those collections and their subcomponents encompass (Woodburn et al. 2022). It is designed to be applicable to a range of use cases that include high level collection registries, rich textual narratives and semantic networks of collections, as well as more granular, quantitative breakdowns of collections to aid collection discovery and digitisation planning. As a standard that is (in this first version) focused on natural science collections, LtC has significant intersections with existing data standards and models (Fig. 1) that represent individual natural science objects and occurrences and their associated data (e.g., Darwin Core (DwC), Access to Biological Collection Data (ABCD), Conceptual Reference Model of the International Committee on Documentation (CIDOC-CRM)). LtC’s scope also overlaps with standards for more generic concepts like metadata, organisations, people and activities (i.e., Dublin Core, World Wide Web Consortium (W3C) ORG Ontology and PROV Ontology, Schema.org). LtC represents just an element of this extended network of data standards for the natural sciences and related concepts. Mapping between LtC and intersecting standards is therefore crucial for avoiding duplication of effort in the standard development process, and ensuring that data stored using the different standards are as interoperable as possible in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles. In particular, it is vital to make robust associations between records representing groups of objects in LtC and records (where available) that represent the objects within those groups. During LtC development, efforts were made to identify and align with relevant standards and vocabularies, and adopt existing terms from them where possible. During expert review, a more structured approach was proposed and implemented using the Simple Knowledge Organization System (SKOS) mappingRelation vocabulary. This exercise helped to better describe the nature of the mappings between new LtC terms and related terms in other standards, and to validate decisions around the borrowing of existing terms for LtC. A further exercise also used elements of the Simple Standard for Sharing Ontological Mappings (SSSOM) to start to develop a more comprehensive set of metadata around these mappings. At present, these mappings (Suppl. material 1 and Suppl. material 2) are provisional and not considered to be comprehensive, but should be further refined and expanded over time. Even with the support provided by the SKOS and SSSOM standards, the LtC experience has proven the mapping process to be far from straightforward. Different standards vary in how they are structured, for example, DwC is a ‘bag of terms’, with informal classes and no structural constraints, while more structured standards and ontologies like ABCD and PROV employ different approaches to how structure is defined and documented. The various standards use different metadata schemas and serialisations (e.g., Resource Description Framework (RDF), XML) for their documentation, and different approaches to providing persistent, resolvable identifiers for their terms. There are also many subtle nuances involved in assessing the alignment between the concepts that the source and target terms represent, particularly when assessing whether a match is exact enough to allow the existing term to be adopted. These factors make the mapping process quite manual and labour-intensive. Approaches and tools, such as developing decision trees (Fig. 2) to represent the logic involved and further exploration of the SSSOM standard, could help to streamline this process. In this presentation, we will discuss the LtC experience of the standard mapping process, the challenges faced and methods used, and the potential to contribute this experience to a collaborative standards mapping within the anticipated TDWG Standards Mapping Interest Group.

A framework for FAIR robotic datasets

It is essential to publish and make available environmental data gathered by emerging robotic platforms to contribute to the Global Ocean Observing System (GOOS), supported by the United Nations - Decade of Ocean Science for Sustainable Development (2021–2030). The transparency of these unique observational datasets needs to be supported by the corresponding robotic records. The data describing the observational platform behaviour and its performance are necessary to validate the environmental data and repeat consistently the in-situ robotic deployment. The Free and Open Source Software (FOSS), proposed in this manuscript, describes how, using the established approach in Earth Sciences, the data characterising marine robotic missions can be formatted and shared following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The manuscript is a step-by-step guide to render marine robotic telemetry FAIR and publishable. State-of-the-art protocols for metadata and data formatting are proposed, applied and integrated automatically using Jupyter Notebooks to maximise visibility and ease of use. The method outlined here aims to be a first fundamental step towards FAIR interdisciplinary observational science.

Making Schemas and Mappings Available and FAIR: A metadata and schema crosswalk registry from the FAIRCORE4EOSC project

Community standards like the Darwin Core (Darwin Core Task Group 2009) together with semantic artefacts (controlled vocabularies, ontologies, thesauri, and other knowledge organisation systems) are key building blocks for the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016), specifically as emphasized in the Interoperability principle I2 “(Meta)data use vocabularies that follow FAIR principles”. However, most of these artefacts are actually not FAIR themselves (Le Franc et al. 2020). To address this, the FAIRCORE4EOSC project (2022-25) is developing a Metadata Schema and Crosswalk Registry (MSCR) that will allow registered users and communities to create, register and version schemas and crosswalks that all have persistent identifiers (PIDs). The published content can then be searched, browsed and downloaded without restrictions. The MSCR will also provide an API to facilitate the transformation of data from one schema to another via registered crosswalks. It will provide projects and individual researchers with the possibility to manage their metadata schemas and/or relevant metadata schema crosswalks. The schema and crosswalks will be shared with the community for reuse and extension supported by a proper versioning mechanism. The registry tool will facilitate better interoperability between resource catalogues and information systems using different (metadata) schemas and encourage organisations and especially researchers to share their metadata interoperability by publishing the metadata crosswalks used in their workflows, which are currently not visible (FAIRification). By providing an easy-to-use graphical user interface (GUI) for creating crosswalks, the GUI will attract users currently relying on project-specific solutions.

Building Research Data Management (RDM) Expertise and Training Resources in ELIXIR Nodes

Research data Management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, the ELIXIR-CONVERGE project was launched in 2020 and included a work package on Training & Capacity Building in Data Stewardship, in which 21 Nodes participate. Since there is a clear need for dissemination of RDM know-how, practices and resources and a demand for RDM training material and courses, for researchers, trainers and RDM professionals, the aim of the work package was to build a comprehensive, interconnected and sustainable RDM Training Portfolio across all Nodes, and to increase the RDM training expertise and capacity in ELIXIR Nodes. 
 RDM professionals, training experts and trainers from ELIXIR and beyond identified gaps in the Nodes’ RDM training programmes and defined priority topics (DMP, Data Stewardship, FAIR/metadata and Reproducibility) (Cardona et al, 2021) for training development. Collaborative work from all the ELIXIR Nodes during ELIXIR-CONVERGE resulted in an extensive Data Management/Data Stewardship (DM/DS) course portfolio (Cardona et al, 2021) which aligns with the Nodes’ RDM training strategies. The portfolio includes both generic DM/DS resources and materials, as well as specific materials related to the priority topics mentioned above, and it also includes training materials for specific domains (e.g. the Plant Demonstrator). The course portfolio is available in TeSS, in which training materials are tagged with RDM terms to increase their findability, and links to relevant training materials were included in RDMkit. In collaboration with the ELIXIR Training Platform, the learning paths methodology has been applied to RDM for data stewards and researchers (e.g. with the Plants Sciences and the System Biology Communities), and work is ongoing to extend the ELIXIR-GOBLET Train-the-Trainer programme to tackle the challenges that exist in teaching RDM. Moreover, during ELIXIR-CONVERGE, a strong RDM trainers network was formed, establishing solid RDM training expertise within the Nodes. This emerging network raised great interest in the Nodes, showing their willingness to work together on developing training materials, sharing their knowledge, and establishing best practices and standards for RDM training.
 The main RDM training related actions in ELIXIR going forward are: (i) gathering the RDM courses in the portfolio into learning paths for specific audiences, thus shaping the ELIXIR RDM curriculum, (ii) increasing findability and reusability of the developed courses and modules by optimising annotation and linking in TeSS, RDMkit and FAIR Cookbook, (iii) encouraging (re)use of the RDM materials in ELIXIR Nodes, and (iv) increasing RDM training capacity in Nodes by finalising and launching the RDM Train the Trainer programme.In 2023, the ELIXIR RDM Community was initiated, and the network of RDM trainers will be an integral part of this Community.  This will enable the upskilling of RDM trainers and scaling up RDM training capacity in the Nodes, while bridging to RDM professionals outside ELIXIR as well. 
  

Towards FAIR Principles in Biodiversity Research: Enabling computable taxonomic descriptions and ecological data with Phenoscript

Taxonomic descriptions hold immense phenotypic data, but their natural language (NL) format poses challenges for computer analysis. In this talk, we will present Phenoscript, a user-friendly computer language enabling computer-readable species descriptions and automated phenotype comparisons, in accordance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Phenoscript facilitates the creation of semantic species descriptions that represent a knowledge graph composed of terms from predefined biological ontologies. A Phenoscript description resembles a NL description, but follows a specific language grammar. We have developed the Phenospy package: a Python-based Phenoscript toolkit. Phenospy converts Phenoscript descriptions into both NL format, facilitating scientific publication, and the Web Ontology Language (OWL) format, enabling downstream analysis and computable phenotypic comparisons. OWL is a standard for sharing semantic data on the Web. While initially designed for phenotypes, Phenoscript can be extended to create semantic ecological data, encompassing environmental traits, functional traits, and species interactions. We will discuss the integration of species and ecological traits encoded in Phenoscript into downstream analysis, highlighting its potential for phenomic-level research in biology.

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

Microbial taxonomy and nomenclature have been challenged by methodological advances in high-throughput sequencing and high-performance computing. While taxonomy appears to adapt rapidly and has benefited enormously from the availability of whole-genome sequences, nomenclature still struggles to embrace these changes. Here, we present two independent initiatives that have resulted from the transitions of taxonomic practices in microbiology from a phenotypic and single gene-driven framework to a genome-based driven framework. The first initiative, the Genome Taxonomy Database (GTDB), was developed to address the needs of microbial taxonomists to classify rapidly accumulating genome sequences from both cultured and uncultured microorganisms. Availability of growing numbers of metagenome-assembled genomes (MAGs) and single amplified genomes (SAGs), combined with the genomes from cultured species, created a perfect opportunity for building a consensus classification based on an evolutionary framework. This has been realised in the GTDB, a knowledgebase that provides phylogenetically consistent and rank-normalised taxonomies for bacterial and archaeal genomes. A distinctive feature of GTDB is a complete classification of genomes from species to domain using an automated approach combining average nucleotide identity (ANI) and relative evolutionary divergence (RED), followed by manual curation. GTDB has become an essential taxonomic resource for microbiologists worldwide, attracting ~3,500 users per month. GTDB mainly relies on two public databases, the National Center for Biotechnology Information (NCBI) Assembly database to which GTDB releases are indexed and the List of Prokaryotic names with Standing in Nomenclature (LPSN), as the primary nomenclatural reference. The database operates according to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and incorporates its own internal (e.g., standards for delineating taxa) as well as external standards. The latter are often directly adopted from the NCBI since it is used as a primary source of genomes as well as metadata. Examples of such standards include Darwin Core data standards from Biodiversity Information Standards (TDWG), Minimum Information (MI) about any (x) Sequence (MIxS) and MISAG and MIMAG standards (Bowers et al. 2017) from the Genomic Standards Consortium. GTDB is used by many third-party resources and provides direct links to external public resources used for curation and validation of taxonomies. Importantly, GTDB contributes to the further generation of knowledge by enabling users to classify their own genomes within the GTDB taxonomic framework using our open-source GTDB-Tk tool. To our knowledge, GTDB is the only database that provides a comprehensive systematic de novo taxonomy for prokaryotes, which serves a multitude of purposes to its global users. The second initiative, the Code of Nomenclature of Prokaryotes Described from Sequence Data or SeqCode, was developed in response to the need for formal naming of uncultured microbial diversity. This need has become even more evident with the establishment of the GTDB taxonomy, which highlighted many issues with nomenclature of uncultured taxa at scale. These include the absence of nomenclatural types, proposed higher taxon names without named children, and the lack of priority for Candidatus names (a prefix indicating a provisional status for the names of organisms falling outside the existing Prokaryotic Code). All these issues arise from one core issue: the absence of regulations for naming uncultured taxa because the International Code of Nomenclature of Prokaryotes (ICNP; Oren et al. 2023) only applies to microorganisms able to be obtained in pure culture. To solve this problem and ultimately to be able to express taxonomic affiliations of uncultured taxa in a regulated manner, genome sequences are proposed to serve as nomenclatural types under the SeqCode. This new code has many common aspects with the ICNP and recognises names that are validly published under the ICNP. It operates via an online Registry that allows registration and validation of names following one of two paths: new names are registered and reviewed prior to publication and validated upon the notification about effective publication, or existing names such as names of Candidatus taxa are registered and reviewed with a validation certificate granted upon the satisfaction of all checks. new names are registered and reviewed prior to publication and validated upon the notification about effective publication, or existing names such as names of Candidatus taxa are registered and reviewed with a validation certificate granted upon the satisfaction of all checks. To avoid naming ambiguity and ensure accurate species descriptions, SeqCode requires that genome sequences designated as types satisfy recommendations on minimal standards for DNA sequences, which are largely adopted from the MISAG and MIMAG standards. The SeqCode Registry also embraces FAIR principles, and was developed with interoperable data structures to facilitate the sharing of its names across global biodiversity resources including GTDB. Recently, we illustrated how SeqCode can be applied, along with the ICNP, by proposing new names for GTDB-defined higher taxonomic names under the two codes (Chuvochina et al. 2023). While it is not ideal to operate under two Prokaryotic codes, we believe that this development is a necessary step towards a unified nomenclatural system.

Post-Publication Linking

One of the main challenges in biodiversity data reusability is finding ways to transform what is provided in research publications into different and reusable formats, following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Agosti and Egloff 2009). Most often, data is restricted to text, figures and tables in the so-called “PDF prison” or other flat formats. Plazi's infrastructure and workflow (Guidoti et al. 2021) transform such data into reusable formats that can then be exported and linked across different platforms, such as the Global Biodiversity Information Facility (GBIF), Biodiversity Literature Repository, Zenodo, Synospecies, ChecklistBank, and OpenBiodiv among others. In order to liberate the many relevant pieces of information, such as taxonomic treatments (Catapano 2019), material citations (Darwin Core term MaterialCitation) or bibliographic references from the publication types mentioned above, one has to run a single document or a batch of documents through a series of extraction steps, which can be done manually or automatically, through the use of templates. The latter are a set of parameters that tell the Plazi-dedicated software (GoldenGATE suite) how to read and where to find key pieces of information; these parameters are established by examining publication standards and publisher-specific layouts, followed by a series of iterative tests, to ascertain the quality of the automation. However, even with a high number of tests to ensure a better extraction, human quality control is still needed (Simoes et al. 2021). To that end, Plazi has a quality control process, based on logical rules, which checks the components of the extracted document, flagging errors in four different levels of severity, which can then be checked and corrected (if needed) by a trained user. These errors are also used in a data transit control mechanism, internally dubbed “the gatekeeper”, which blocks certain data transits to create deposits or reuse of data in the presence of specific errors. In this presentation, we will go through the steps of the entire process, from publication to liberated data (and how it is presented in the linked platforms), highlighting the importance of accurate quality control, and explore some of the many challenges along the way.

Data-Sharing Across Otolaryngology: Comparing Journal Policies and Their Adherence to the FAIR Principles.

Data-sharing plays an essential role in advancing scientific understanding. Here, we aim to identify the commonalities and differences in data-sharing policies endorsed by otolaryngology journals and to assess their adherence to the FAIR (findable, accessible, interoperable, reusable) principles. Data-sharing policies were searched for among 111 otolaryngology journals, as listed by Scimago Journal & Country Rank. Policy extraction of the top biomedical journals as ranked by Google Scholar metrics were used as a comparison. The FAIR principles for scientific data management and stewardship were used for the extraction framework. This occurred in a blind, masked, and independent fashion. Of the 111 ranked otolaryngology journals, 100 met inclusion criteria. Of those 100 journals, 79 provided data-sharing policies. There was a clear lack of standardization across policies, along with specific gaps in accessibility and reusability which need to be addressed. Seventy-two policies (of 79; 91%) designated that metadata should have globally unique and persistent identifiers. Seventy-one (of 79; 90%) policies specified that metadata should clearly include the identifier of the data they describe. Fifty-six policies (of 79; 71%) outlined that metadata should be richly described with a plurality of accurate and relevant attributes. Otolaryngology journals have varying data-sharing policies, and adherence to the FAIR principles appears to be moderate. This calls for increased data transparency, allowing for results to be reproduced, confirmed, and debated.

FAIR environmental and health registry (FAIREHR)- supporting the science to policy interface and life science research, development and innovation.

The environmental impact on health is an inevitable by-product of human activity. Environmental health sciences is a multidisciplinary field addressing complex issues on how people are exposed to hazardous chemicals that can potentially affect adversely the health of present and future generations. Exposure sciences and environmental epidemiology are becoming increasingly data-driven and their efficiency and effectiveness can significantly improve by implementing the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship. This will enable data integration, interoperability and (re)use while also facilitating the use of new and powerful analytical tools such as artificial intelligence and machine learning in the benefit of public health policy, and research, development and innovation (RDI). Early research planning is critical to ensuring data is FAIR at the outset. This entails a well-informed and planned strategy concerning the identification of appropriate data and metadata to be gathered, along with established procedures for their collection, documentation, and management. Furthermore, suitable approaches must be implemented to evaluate and ensure the quality of the data. Therefore, the 'Europe Regional Chapter of the International Society of Exposure Science' (ISES Europe) human biomonitoring working group (ISES Europe HBM WG) proposes the development of a FAIR Environment and health registry (FAIREHR) (hereafter FAIREHR). FAIR Environment and health registry offers preregistration of studies on exposure sciences and environmental epidemiology using HBM (as a starting point) across all areas of environmental and occupational health globally. The registry is proposed to receive a dedicated web-based interface, to be electronically searchable and to be available to all relevant data providers, users and stakeholders. Planned Human biomonitoring studies would ideally be registered before formal recruitment of study participants. The resulting FAIREHR would contain public records of metadata such as study design, data management, an audit trail of major changes to planned methods, details of when the study will be completed, and links to resulting publications and data repositories when provided by the authors. The FAIREHR would function as an integrated platform designed to cater to the needs of scientists, companies, publishers, and policymakers by providing user-friendly features. The implementation of FAIREHR is expected to yield significant benefits in terms of enabling more effective utilization of human biomonitoring (HBM) data.

BIOREALM—An ontology of comparative biogeography: New insights into the semantics of biodiversity conservation

AbstractAimWe aimed to apply ontological techniques to address semantic ambiguities in protected area and conservation informatics. By doing so, we aimed to create a coherent, machine‐actionable semantic representation of the biogeographic areas (which often overlap protected areas) to support more efficient and standardized informatics, supporting research and decision‐making. We present BIOREALM, the first informatic ontology for comparative biogeography.LocationGlobal.TaxonAny taxon can be integrated in BIOREALM.MethodsWe convert a cladogram of biogeographic areas—generated by a process known as bioregionalization—into a series of ontological classes. Areas of endemism are treated as formal objects related by hierarchical relationships and constrained by a condition of monophyly. We use semantic web approaches to extend the Environment Ontology (ENVO) with classes for (often semantically confounded) biogeographic entities, including biogeographic areas, areas of endemism and endemic areas. We applied this approach to a bioregionalization of Australia as a case study. In all, 20 subregions which are part of the Austral Bioregionalisation Atlas have been selected for the study and integrated in BIOREALM.ResultsWe have created an ontology—formatted in the Web Ontology Language and adhering to the practices of the Open Biomedical and Biological Ontology Foundry—which provides a rigorous, extensible and machine‐actionable framework that can improve biogeographic analyses and interoperability between systems. One main class and 20 individuals per class were implemented.Main ConclusionsBIOREALM encodes a model‐theoretic view of endemism using semantic web approaches, offering new avenues to express and analyse biogeographic units. This approach offers a means to identify monophyletic biogeographic areas for conservation, based on specific combinations of monophyletic endemic taxa. Such an ontology provides knowledge representation solutions which supports interoperability along the FAIR (Findable, Accessible, Interoperable, Reusable) principles, thus fostering more consistent ecological informatics.