Findable, Accessible, Interoperable And Reusable Research Articles

CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.

Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science. This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process. We employed the "Best fit" framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework. The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices. The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration. The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment.

Enhancing data standards to advance translation in spinal cord injury

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2015 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have been advanced. Implementation of these frameworks require multi-modal data and a shift towards open science and principles such as requiring data to be FAIR (Findable, Accessible, Interoperable and Reusable). Advanced analytics such as artificial intelligence require data to be interoperable so data can be exchanged among different technology systems and software applications. The TBI field has multiple ongoing initiatives to promote sharing and data reuse for both pre-clinical and clinical studies, which is an opportunity for the SCI field given these injuries can often occur concomitantly. The adoption of interoperable standards, data sharing, open science, and the use of advanced analytics in SCI is needed to facilitate translation in research and care. It is critical that people with lived experience are engaged to ensure data are relevant and enhances quality of life.

EarthCODE – ESA’s Earth Science Collaborative Open Development Environment

Abstract. Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

From complex histories to cohesive data, a long-term agricultural dataset from the Morrow Plots

Long-term agricultural experiments are essential to measure the impacts of farming practices on crop yields, soil fertility and biogeochemical processes. However, these impacts often only manifest at decadal timescales, requiring committed and consistent data collection that exceeds the timelines for most experiments. The second oldest agricultural experiment in the world, the Morrow Plots at University of Illinois Urbana-Champaign (USA) has examined the impact of crop rotation and fertility treatments on maize (Zea mays L.) yields since 1876. While results have been widely reported since 1888, the publicly available longitudinal dataset described here now allows for validation of those past results, as well as new analyses and investigations. A multi-disciplinary team identified, collected, and aggregated multiple historical data sources into one comprehensive and FAIR (Findable, Accessible, Interoperable and Reusable) dataset that synthesizes yield data and management practices from 1888–2021. Updated versions of the dataset will continue to be published as additional data from this ongoing experiment are made available and as new historical data sources are uncovered.

Towards FAIRification of learning resources and catalogues—lessons learnt from research communities

Since the introduction of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles in 2016, discussions have evolved beyond the original focus on research data to include learning resources. In 2020, a set of simple rules to FAIRify learning resources was proposed, building on existing expertise within the training community. Disciplinary communities have played an important role in advancing FAIR principles for learning resources, although they have approached FAIRification activities in different ways. These communities range from volunteer-led to funded and independent organisations, however commonly include activities such as organising training and capacity building, and coordinated discussions on disciplinary-focused FAIR best practises and standards. Eight disciplinary community case studies are presented and analysed in this paper to examine the motivations, challenges and opportunities towards FAIRification of learning resources, reflecting on how community structure leads to differing responsibilities. The case studies are based on reflections formulated in 2022, the aim is to pull together the experiences of these different communities, focusing on the processes and challenges they encountered, in order to structure this knowledge across different learning platforms, draw attention to the question of sustainability for learning resources and anticipate improvements in future policies and governance.

Planetary Geologic Maps: Essential Tools for Scientific Inquiry and Space Exploration

AbstractPlanetary geologic maps are crucial tools for understanding the geological features and processes of solid bodies in the Solar System. Over the past six decades, best practices in planetary geologic mapping have emphasized clear and objective observation, geological interpretation, multi‐sensor fusion, and iterative revision of maps based on new data. We summarize here four ways in which maps serve as indispensable instruments for scientific investigation, from enhancing observations to interrogating surface processes. With respect to space exploration, we underscore the role of planetary geologic maps as tools to link testable, hypothesis‐driven science to exploration goals and provide actionable information for hazard identification, resource evaluation, sample collection, and potential infrastructure development. To further advance the field of planetary geologic mapping, international collaboration is essential. This includes sharing data and maps through FAIR (findable, accessible, interoperable, and reusable) platforms, establishing standardized mapping practices, promoting diverse nomenclature, and fostering continued cooperation in space exploration.

Interactive Checklists to Increase Access to Urban Biodiversity Information in Guatemala

The Guatemala Biodiversity Portal (GBP) is a customized platform developed with Symbiota (Gries et al. 2014) for the digitization of local natural history collections and research projects (Orellana et al. 2023). Currently, the portal includes 42 virtual profiles for the live management of biodiversity data and already hosts more than 50,000 specimen and observation records. Additionally, over 400,000 records of Guatemalan specimens in international collections have been imported from other Symbiota portals and the Global Biodiversity Information Facility (GBIF), increasing accessibility to data from the country. A bilingual interface (English-Spanish), and integrated tools to generate interactive checklists (Pearson and Walker 2021) have also encouraged use of the portal to develop online resources to explore and share knowledge of Guatemalan biological diversity. In particular, urban green spaces in Guatemala City have been a recent focus of attention due to their significance in biodiversity conservation (Castillo-Cabrera et al. 2021). Efforts to study and preserve these natural spaces include biological surveys to compile an updated inventory of species and assess their conservation status. To increase access to the information obtained, observations and specimens (e.g., fauna, flora) are being digitized and managed in the GBP, granting instant availability of the records and images derived from the surveys. At the same time, data from protected or threatened species remains redacted from the general public. Complementary to the surveys in Guatemala City, a more extensive inventory of species is being generated with selected specimens and observations available in the GBP and GBIF. A series of interactive checklists in the GBP (Fig. 1) facilitate the management of the scientific names, digital vouchers, and annotations about known conservation status (CONAP 2022, IUCN 2024), establishment means, and references in the literature. Information gathered for each species is later added to the taxon pages in the GBP, to expand its accessibility. The integration of urban biodiversity data in the GBP allowed the generation of digital records that follow international standards (e.g., Darwin Core, Wieczorek et al. 2012) and FAIR (findable, accessible, interoperable, and reusable) principles (Wilkinson et al. 2016), facilitating their mobilization to global aggregators (GBIF.org 2024). Data cleaning tools (e.g., geographic and taxonomic cleaning, Pearson 2021) in the GBP also improved the information management, increasing the accuracy of each observation and specimen record digitized. Furthermore, the resulting interactive checklists provide a way to synthesize and review the data for the different taxa, having the opportunity to remain constantly updated as sources of information become available. Therefore, curated species checklists for Guatemala City will become an important contribution and reference for related studies across the country. Similar to other regions (Yánez-Ayabaca et al. 2023), accessibility to edit online biodiversity resources has opened the door for collaboration among Guatemalan biologists, engaging a broad community of researchers and students from national institutions. The presentation will address more about the functionality and effectiveness of interactive checklists, and the impact of online biodiversity resources in Guatemala.

Implementation of a common data model in Health Data Research Network Canada: Lessons learned

BackgroundThe Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is a standardized structure for observational data that enables collaborative health research. Health Data Research Network (HDRN Canada) is conducting a pilot study to implement this standard at partner data linkage centers. ObjectiveOur objective is to describe the process used to implement the OMOP CDM, methodological and technical challenges, and the approach to evaluate CDM implementation at multiple sites. ApproachCDM working group members include technical staff from data linkage centers in four provinces and a national data coordinating center, HDRN Canada operational and executive leads, and OMOP CDM consultants. The evaluation aims to collect quantitative and qualitative information on Extract, Transform, and Load (ETL) processes, performance of the CDM in a multi-site observational case study, and data quality. ResultsInitial steps have included collaborative learning about the CDM structure, importing open-source tools and licensed coding standards (i.e., Systematized Nomenclature of Medicine-Clinical Terms [SNOMED-CT], RxNorm) at data linkage centres, and exploring the technical requirements for developing ETL processes and hosting OMOP data. Creation of select OMOP tables has been accompanied by discussions about models for mapping clinical and prescription drug coding standards. A scoping review of evaluation strategies is underway. Conclusions and ImplicationsThe benefits of standardizing health data for multi-site research and supporting the production of findable, accessible, interoperable and reusable (FAIR) national data underscores the importance of HDRN Canada embarking on this OMOP CDM implementation project. The lessons learned will benefit other multi-site data standardization initiatives.

Mapping UK Pain Datasets to the OMOP Common Data Model: Lessons Learned

ObjectiveChronic Pain is defined as pain lasting for three months or longer and a report by the British Pain Society in 2016 found that 28 million adults live with this condition. Chronic pain research is problematic as it is poorly represented in health data and often in silos. Alleviate is the Advanced Pain Discovery Platform (APDP) Data Hub which is removing barriers to access to data. ApproachThe aim for Alleviate is to transform pain data sets within the UK to be Findable, Accessible, Interoperable and Reusable (FAIR) and be more discoverable for researchers via Health Data Research UK’s Cohort Discovery Tool. The tool enables identification of potential cohorts in real-time from datasets Alleviate provides. The onboarding requires datasets to be transformed to the open and widely used Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). ResultsWe collaboratively developed CaRROT tools and used our mapping expertise to transform datasets to OMOP-CDM. The tools have improved efficient mapping of clinical and research data, covering ~10 million individuals’ records. Subsequently we learned valuable lessons on how to accurately and consistently map across data sets to support federated discovery. We also identified a lack of standard vocabulary representations for pain specific data. ConclusionsMapping data to OMOP is not straightforward. Attention to detail is required to ensure that consistent mapping decisions are made to maximise data reusability and interoperability. ImplicationsIn sharing the lessons learned of mapping datasets to OMOP we aim to help improve interoperability of disparate data sources.

SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information.

The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).

BIOFAIR Data Network’s Listening Sessions for Engagement and Data Integration

During the last two decades, a wealth of data on biodiversity and associated environments has been mobilized in digital form. Collectively, these data provide a powerful resource that when curated and integrated with intention, can provide critical information to address emerging complex global biological, environmental, and public health challenges. Tapping into the vast potential of specimen, observation, and environmental data requires us to integrate diverse and multifaceted datasets, connect domain-specific communities, and bridge discipline-specific social norms and data infrastructures. Linking data and their respective communities is a critical next step to creating the accessible and enriched data source needed to empower broad integrative biological research and education. To initiate cross-domain collaborations, the Building an Integrated, Open, Findable, Accessible, Interoperable, and Reusable (*1) Data Network project, led by the Biodiversity Collections Network (BCoN) and funded by the United States National Science Foundation, convened stakeholders through six listening sessions over the summer of 2024. The sessions were aimed at building connections between disparate data communities—highlighting an iterative process of building a larger, interdisciplinary community from within. These listening sessions brought together representatives from federal agencies, the genetic data community, the ecology data community, the climate and environmental data community, the One Health community, and the biodiversity informatics community to initiate a collaborative and accessible partnership toward an integrative and expanded data network. Discussions focused on advancing data culture and infrastructure that meets emerging needs in research, education, conservation, biosecurity, and the bioeconomy. Participants discussed building on and bridging the Extended Specimen Network (ESN) vision with other existing conceptual frameworks for data integration and application (Lendemer et al. 2019, Thiers et al. 2019). Stakeholder groups will be brought together at an interdisciplinary workshop in early 2025, to develop a roadmap to augment existing initiatives with the aim of producing a FAIR (Findable, Accessible, Interoperable, and Reusable), open, integrated data network.

An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology

Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL https://zenodo.org/records/7886998.

Linking Between Molecular and Biodiversity Data: A BiCIKL Perspective

Molecular sequencing data generation is being driven by global and regional efforts to discover, understand and monitor biodiversity. To fully explore this data in biodiversity research we need a network of connected data resources, linking sequence data with natural history collections, taxonomy and literature. The BiCIKL project (Biodiversity Community Integrated Knowledge Library, Penev et al. 2022) has set the groundwork towards creating this network of linked data and fostering FAIR (Findable, Accessible, Interoperable and Reusable) practices in the biodiversity domain. Connecting biodiversity and molecular data along the biodiversity research cycle requires a foundation of well-structured and rich metadata in the molecular sequence databases. Referencing the physical specimens is important as this provides context about the source of the material that was used for generating the molecular sequence data, including information about origin and species identification. To connect biodiversity and molecular data, we developed tools and workflows for improving and standardising metadata, federated searches and validations for specimen reference in sequence data, such as the SpASe tool, which enables the discovery of links between natural history collections and sequences, and the European Nucleotide Archive Source Attribute Helper API, which facilitates the construction of specimen attributes in a structured format. This work was done in close collaboration with DiSSCo (Distributed System of Scientific Collections) and some biodiversity genomics projects (e.g. Biodiversity Genomics Europe, BGE). Furthermore, we enabled community curation of biological source annotations such as specimen references in sequence data through the PlutoF platform and the ELIXIR Contextual Data Clearinghouse (Abarenkov et al. 2021, Balavenkataraman Kadhirvelu et al. 2022) and increased bidirectional linking from sequences in the European Nucleotide Archive (ENA) to collections, taxonomy and literature services (e.g., Plazi TreatmentBank, OpenBioDiv). We also worked closely with the community to enable the structured publication of environmental DNA data, promoting and engaging in the definition of standards and developing tools to facilitate data deposition and retrieval. Overall, the project has contributed significantly to strengthen the connections between the biodiversity and genomics communities towards higher data integration and interoperability. Structured, enriched, accessible and linked sequence data will provide a strong foundation for the application of biodiversity knowledge in the response to global challenges, such as biodiversity loss, ecosystem change and food security. Beyond BiCIKL, we will continue our work as a community to promote a culture of FAIR linked molecular data, towards a fully integrated biodiversity knowledge ecosystem.

Metadata recommendations for light logging and dosimetry datasets

BackgroundLight exposure significantly impacts human health, regulating our circadian clock, sleep–wake cycle and other physiological processes. With the emergence of wearable light loggers and dosimeters, research on real-world light exposure effects is growing. There is a critical need to standardize data collection and documentation across studies.ResultsThis article proposes a new metadata descriptor designed to capture crucial information within personalized light exposure datasets collected with wearable light loggers and dosimeters. The descriptor, developed collaboratively by international experts, has a modular structure for future expansion and customization. It covers four key domains: study design, participant characteristics, dataset details, and device specifications. Each domain includes specific metadata fields for comprehensive documentation. The user-friendly descriptor is available in JSON format. A web interface simplifies generating compliant JSON files for broad accessibility. Version control allows for future improvements.ConclusionsOur metadata descriptor empowers researchers to enhance the quality and value of their light dosimetry datasets by making them FAIR (findable, accessible, interoperable and reusable). Ultimately, its adoption will advance our understanding of how light exposure affects human physiology and behaviour in real-world settings.

EDNAqua-Plan—Standardisation Overview for eDNA Sequencing of Aquatic Organisms and the Downstream Data Ecosystem

The eDNAqua-Plan project stands as a beacon of innovation in the biomonitoring of marine and freshwater ecosystems, propelled by the urgent need to integrate DNA-based approaches in aquatic bioassessment and monitoring frameworks. The broad utilisation of cutting-edge environmental DNA (eDNA) and DNA barcoding methodologies is dependent on complete, reliable, and accessible reference DNA sequence data (Rimet et al. 2021). Complete and interoperable metadata is crucial to allow a broad reuse of (e)DNA data and analysis outputs, and for a broader uptake of results by end users. The eDNAqua-Plan project aims to address key limitations to the routine implementation of eDNA-based monitoring methods in Europe by developing plans for federated DNA barcode reference libraries and eDNA data repositories to support DNA-based environmental monitoring. This will ensure a sustainable and reliable infrastructure to underpin its broad use, thereby paving the way for more effective conservation and management strategies. The project is working towards creating a comprehensive overview of standardisation efforts and data workflows, through collaborations with other projects, initiatives and infrastructures for aquatic monitoring across the European Union (EU) and associated countries. We are analysing existing archives (e.g., International Nucleotide Sequence Database Collaboration (INSDC), Barcode of Life Data System (BOLD), Global Biodiversity Information Facility (GBIF), Ocean Biodiversity Information System (OBIS)), portals, and papers to determine current and best practices through the use of questionnaires, manual evaluation of repositories, and machine learning methods (LLMs). This includes an overview of the usage of existing metadata and data standards (e.g., Minimum Information about any (X) Sequence Specifications from the Genomics Standards Consortium (GSC), Darwin Core standard). The results are being integrated by a team of experts in marine and freshwater biomonitoring. With a diverse consortium comprising 18 partner institutions from 11 countries and one international institute, eDNAqua-Plan brings together experts in marine and freshwater monitoring, eDNA analysis, and data science. The collective effort by this consortium will lay the groundwork for the creation of a digital ecosystem of eDNA repositories and an integrated reference library of marine and freshwater species, adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles.

Graphical User Interface for Biodiversity Digital Twins: Data Challenges

Digital Twins are a new concept in the field of biodiversity (de Koning et al. 2023), one aspect is the user interface for interacting with digital twins. In the BioDT (Biodiversity Digital Twin) (BioDT 2022) project we are creating a graphical web interface (Martinovič et al. 2024) using the R Shiny framework, which allows small-scale data analysis to be done directly on the server running the web interface, while making it possible to offload a large-scale analysis to a supercomputer such as LUMI (Large Unified Modern Infrastructure). Additionally, we foresee that multiple prototype Digital Twins (pDTs) will be available as part of one web application. This brings multiple challenges in optimization of data flows for the computation and interaction with users, especially since data used by the pDTs, developed in the BioDT, are usually stored across multiple data sources. In the BioDT web application, we want to enable users to access different pDTs concerned with questions such as ecosystem services, biodiversity dynamics, DNA-related biodiversity tasks, pollinators, invasive species, disease outbreaks, and more. While some of these pDTs compute results at a remote server and provide only the newest results to the application, others aim to allow users to execute their own pDT runs with their own data and settings. This leads to many different user scenarios and impacts authentication and authorization flows, as well as data flows. In addition to this, we want to make the whole system comply with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles as much as possible. For this, we need to support traceability of data, models, and pDT executions. This means that we advocate for data to be hosted at data providers with APIs for machine-to-machine interaction and extensive metadata support; models to be as open as possible and versioned; and to have a workflow execution orchestrator that can keep track of the models' execution and their related inputs and outputs. In terms of data, we are using research infrastructures (RIs) such as GBIF (Global Biodiversity Information Facility) and eLTER (Integrated European Long-Term Ecosystem, critical zone and socio-ecological Research) for the input data streams and when it is not possible to use established RIs, we are using self-hosted services. This work is still in progress and some of the self-hosted datasets are still being used, but in the future, we are looking into the possibility of having a dynamic system that will support data formats that cannot be currently stored at the established RIs. Our colleagues at BioDT consortium are preparing a special data server (El-Gabbas et al. 2023) that can handle complex data processing and serve data through an API without the need to download data for multiple commonly used formats. Some of the BioDT pDTs leverage the computational strength of High-Performance Computing (HPC) clusters and in such cases, classical cloud workflow orchestrators are not an option due to the specific security policies of such centers. To solve this, we turned to the LEXIS Platform, which can execute predefined workflows on a combination of cloud and HPC resources, and track the executions and related execution metadata. We are looking into exporting the descriptions of the workflow executions to Research Object Crates (RO-Crates) and then uploading this information to a remote server, where users could check their execution settings later. A current main development focus is the question of how to tackle the challenge of multiple authentication systems. This is specifically of concern in the case of sensitive data, which need to remain secure and available only to selected people. Due to interactions with several systems, we are encountering authentication issues of multiple different identities that should be recognized as one. The simplest solution here is to use one platform for data storage, workflow execution and web application. However, in the future we hope to find a more general solution that would not require data transfer to a single platform, since this could lower the usage of the BioDT platform due to legal restriction on some data. Another challenge in optimization of the dataflows is to avoid downloading the same data repeatedly and to be able to provide users with relevant data in the web application as fast as possible, since long waiting time would result in people not using the web application. For these issues, we are considering a smart-caching mechanism, however such functionality is not yet defined.

Dataspace Integration for Agrobiodiversity Digital Twins with RO-Crate

A severe impact of human-induced climate change is the global decrease in crop yields, threatening the United Nations sustainable development goal (SDG) to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture (SDG 2: “Zero Hunger Goal”). Decision-making on appropriate mitigation strategies requires data integration from heterogeneous data-rich contexts including Earth observation (EO), biodiversity monitoring, gene banks as well as socio-economic data. Accordingly, large-scale initiatives for thematic data spaces, such as the European projects Destination Earth (DestinE) and the Green Deal Data Space, have been launched. Underutilized crops and crop wild relatives (CWR) can contribute to mitigating the impacts of climate change since their greater genetic diversity allows adaptation of agro-food sources to extreme weather events such as flooding and drought or emerging crop pests. Within the framework of Biodiversity Digital Twin (BioDT), a prototype digital twin for crop genetic resources modeling is under development. This prototype combines the scanning of genetic resources of CWRs and traditional cultivars for promising pest resistance and abiotic tolerance traits with advanced species distribution modeling and actual climate scenarios from EO. The aim is to generate habitat suitability maps and identify potential areas where relevant populations are potentially growing (Chala et al. 2024). The talk gives an overview of data models used to facilitate the required interoperability of both Earth models and environmental data within and across different data spaces such as the DestinE Data Lake (Duatis Juarez et al. 2023). Based on our findings from the use case of agrobiodiversity digital twins, we will discuss approaches for processing machine-actionable digital objects (Fig. 1) containing workflow information (i.e., in the Common Workflow Language format, Crusoe et al. 2022), and for sharing resulting research data in a FAIR (Findable, Accessible, Interoperable, and Reusable) manner by leveraging on so-called webby FAIR Digital Objects involving Research Object Crate (RO-Crate, Soiland-Reyes et al. 2022), FAIR Signposting (Soiland-Reyes et al. 2024) and schema.org and its Bioschemas extension respectively (Castro et al. 2020),

Bridging Data Standards and FAIR Principles in Biodiversity Digital Twinning: Prototyping, Challenges, Lessons Learned, and Future Plans

Digital twins combine modelling, domain knowledge, computing power, and multiple datasets to offer the potential to unlock new insights into biodiversity (de Koning et al. 2023). The Biodiversity Digital Twin (BioDT) project pioneers this approach to aid in understanding biodiversity through prototyping digital twins (Golivets et al. 2024). However, working with biodiversity data presents challenges due to their dynamic and diverse nature as well as the need for having to deal with incompleteness, uncertainties (Rocchini et al. 2011), disproportionate representation patterns in global studies from wealthier economies (Hughes et al. 2024), and issues with data aggregation and integration (Wüest et al. 2020). Similar to BioDT, there are also plans for creating a Digital Twin of the Ocean (DTO). DTO-Bioflow project addresses these data challenges in the marine domain, where studies show that although European seas host 48,000 marine species (75% described), the data are not yet FAIR (Findable, Accessible, Interoperable, and Reusable) (Ramírez et al. 2022). These challenges hamper the modelling capabilities needed for effective predictions and conservation prioritisation. The adaptability of digital twins across temporal and spatial scales and their ability to model dynamic ecosystems make them ideal for biodiversity research and real-time conservation efforts. However, their success hinges on the consistent integration and alignment of data from disparate sources (Trantas et al. 2023). This integration involves standardising terms used to describe datasets, such as temporal coverage or controlled vocabularies like "Forest" for targetHabitatScope. Thus, adopting data standards is essential. Additionally, challenges such as model bias (Lewers 2023), research context, and data provenance must be considered, adding complexity to metadata capture and alignment. BioDT addresses these challenges with modular building blocks for data integration, model deployment, and workflow management. This approach facilitates the gradual adoption of data standards and FAIR principles, which need to encompass not just data, but also models and software. As automation, ease of reproducibility, and deployability are critical for digital twinning success, data integration and interoperability issues may arise due to missing or insufficient parameter descriptions in the model, incomplete information on data selection, and the unavailability of required software package details. Thus, data standardisation provides a pathway for a consistent approach that can be adopted for different use cases. Common data sources in BioDT, like species occurrences and environmental variables, benefit from standards such as Darwin Core (Wieczorek et al. 2012) and the Ecological Metadata Language (Jones et al. 2019). While valuable, these standards may not fully encompass the complexity needed for comprehensive biodiversity digital twins. Additionally, differing familiarity with these standards and FAIR principles among communities pose challenges. Continuous adoption of data standards, alongside exploring complementary approaches like schema.org or bioschemas.org for capturing diverse (meta)data, is essential. Collaboration with data providers, modellers, and various research infrastructures is also crucial (Andrew et al. 2024). We share our experience using Research Object Crate (RO-Crate), leveraging common JavaScript Object Notation for Linked Data (JSON-LD) representation for metadata profiles and workflow representation, to connect with different infrastructures. In the BioDT project, we are working with various use cases to create prototype digital twins that can serve as valuable resources for other projects. The evolving landscape of digital twin concepts, along with other European Union-funded initiatives like DTO-Bioflow and Destination Earth (DestinE), emphasises the importance of alignment within the digital twin ecosystem. BioDT is committed to aligning with and contributing to this broader context, highlighting the critical role of data standardisation and FAIR implementation.

DiSSCover the Potential of FAIR Digital Object Annotations and How You Can Use Them!

The infrastructure for the Distributed System of Scientific Collections (DiSSCo) is in full development. Work within the DiSSCo Transition Project has been focused on building infrastructure, creating data models, and setting up Application Programming Interfaces (APIs) (Koureas et al. 2024). In the past years, DiSSCo has presented this work at different Biodiversity Information Standards (TDWG) conferences (Leeflang and Addink 2023, Leeflang et al. 2022, Addink et al. 2021). In this year’s session, we would like to focus on the human-facing application: DiSSCover. DiSSCover is the graphical user interface through which users can interact with Findable, Accessible, Interoperable and Reusable (FAIR) Digital Objects (FDOs), facilitating the curation and enhancement of specimen data (Islam 2024). Development started in 2022 and is ongoing. The interface acts as a gateway into the DiSSCo infrastructure, providing access to digital specimens and media. Extracted from the core DiSSCo API, the data is converted into an easily readable format and made discoverable through a diverse set of filters. DiSSCover’s main focus is to allow users to make annotations upon the data. Through the concept of annotations, we connect expert and machine-generated information, to create extended digital specimens (Hardisty et al. 2022), e.g, by creating linkages to other infrastructures, correcting or adding new information, or by triggering machine annotation services. Machine annotation services are automated scalable tools that run in the background and automatically curate and extend the specimen (Addink et al. 2023). Human users will remain important, as all annotations made by machine annotation services can be reviewed by a trusted person. Annotations are Fair Digital Objects and target a specific part of a specimen, be it a data fragment or an associated media file. At the heart of DiSSCover lies the Open Digital Specimen data specification (Leeflang and Addink 2023). It tries to harmonise multiple data standards into one generic specification based on the new Global Biodiversity Information Facility (GBIF) Unified Model (Robertson et al. 2022). The data is stored as JavaScript Object Notation (JSON) based on JSON Schemas (Anonymous 2024). Annotations are linked to specific data attributes using a JSON-path as the identifier. Data attributes can be individual terms, collections of terms called classes, or the whole object. This creates a flexible but complex data structure, the basis for which we used the World Wide Web Consortium (W3C) web annotation data model (Sanderson et al. 2017). The W3C Web annotation data model contains two main components: the target and body. The target specifies which data attribute the annotation is made on, for example, the term: ‘ods:specimenName’. This is a local term within the open Digital Specimen namespace (ods), which holds the accepted name of the digital specimen. The annotation body holds the value(s) that are appended to the digital specimen, and differ based upon the annotation motivation. DiSSCo recognises five different annotation motivations: addition, modification, comment, assessment and deletion, each of which has its own unique function. This creates a flexible structure that should be able to handle any information the user wants to add to the object. The challenge of DiSSCover is to preserve the complex structure of annotations, whilst making it convenient for users to work with. The session will provide a look at the different kinds of annotations and their use from a practical perspective. A demonstration of DiSSCover will show how users can create annotations, providing knowledge about the process that will give shape to DiSSCo’s main goal of enriching natural history data.

The Role of the European Reference Network for Rare Bone Diseases (ERN BOND) and European Registries for Rare Bone and Mineral Conditions (EuRR-Bone) in the Governance of the Management of Rare Bone and Mineral Diseases

Rare diseases (RDs) bear a significant challenge to individuals, healthcare systems, and societies. The European reference network on Rare BONe diseases (ERN BOND) is committed to improving multidisciplinary, patient-centred care for individuals with rare bone and mineral diseases (RBMDs). Its affiliated project, the European registries for rare bone and mineral conditions (EuRR-Bone) collects data using two different platforms, an electronic surveillance system (e-REC) that captures the occurrence of RBMDs and the Core Registry, a platform with the infrastructure for collecting Core data fields and longitudinal generic and condition-specific information. With emerging registries and the overlap with other ERNs, it is key to maintain the capability of the platforms to adapt to the needs of the network and the community whilst adhering to quality and FAIR (findable, accessible, interoperable, and reusable) principles. This binomial ensures long-term sustainability and potential advances in the care pathway of RBMDs whilst promoting good practice standards within Europe and beyond.