Findable, Accessible, Interoperable And Reusable Research Articles

Instance maps as an organising concept for complex experimental workflows as demonstrated for (nano)material safety research.

Nanosafety assessment, which seeks to evaluate the risks from exposure to nanoscale materials, spans materials synthesis and characterisation, exposure science, toxicology, and computational approaches, resulting in complex experimental workflows and diverse data types. Managing the data flows, with a focus on provenance (who generated the data and for what purpose) and quality (how was the data generated, using which protocol with which controls), as part of good research output management, is necessary to maximise the reuse potential and value of the data. Instance maps have been developed and evolved to visualise experimental nanosafety workflows and to bridge the gap between the theoretical principles of FAIR (Findable, Accessible, Interoperable and Re-usable) data and the everyday practice of experimental researchers. Instance maps are most effective when applied at the study design stage to associate the workflow with the nanomaterials, environmental conditions, method descriptions, protocols, biological and computational models to be used, and the data flows arising from study execution. Application of the InstanceMaps tool (described herein) to research workflows of increasing complexity is presented to demonstrate its utility, starting from (i) documentation of a nanomaterial's synthesis, functionalisation, and characterisation, over (ii) assessment of a nanomaterial's transformations in complex media, (iii) description of the culturing of ecotoxicity model organisms Daphnia magna and their use in standardised tests for nanomaterials ecotoxicity assessment, and (iv) visualisation of complex workflows in human immunotoxicity assessment using cell lines and primary cellular models, to (v) the use of the instance map approach for the coordination of materials and data flows in complex multipartner collaborative projects and for the demonstration of case studies. Finally, areas for future development of the instance map approach and the tool are highlighted.

A new multi-grid bathymetric dataset of the Gulf of Naples (Italy) from complementary multi-beam echo sounders

Abstract. High-resolution bathymetry provides critical information to marine geoscientists. Bathymetric big data help characterise the seafloor and its benthic habitats, understand sedimentary records, and support the development of offshore engineering infrastructures. From 27 September to 20 October 2022, the new CNR research vessel Gaia Blu explored the seafloor of the Naples and Pozzuoli gulfs and the Amalfi coastal area (Tyrrhenian Sea, Italy) from 50 to more than 2000 m water depth, acquiring about 5000 km2 of multi-beam echo sounder data. This area is particularly vulnerable to abrupt changes driven by the dynamics of several volcanic complexes, active in the area, and by human-induced impacts reflecting the proximity to the highly populated and touristic coastal area of Naples and nearby famous islands. For these reasons, the seafloor of the area needs to be known and constantly monitored. The digital bathymetric data previously available are restricted to the shallow highly dynamic area of the Gulf of Naples and appear fragmented as they were acquired in successive years, with different goals thereby using a variety of devices, with markedly different spatial resolutions. In this paper, we present bathymetric maps of the Gulf of Naples and adjacent slope basins at unprecedented resolution using three state-of-the-art multi-beam echo sounders. These high-resolution data highlight the technological advances of geophysical surveys achieved over the last 20 years and contribute to assessing the most dynamic areas where changes in the seafloor over time can be quantified. The new digital multi-resolution bathymetric products are openly accessible via Marine Geosciences Data System MGDS (refer to “Data availability” section, Table 8, for datasets and product DOIs), perfectly matching the FAIR (findable, accessible, interoperable, and reusable) and open science principles.

Guidelines for Gene and Genome Assembly Nomenclature.

The rapid increase in the number of reference-quality genome assemblies presents significant new opportunities for genomic research. However, the absence of standardized naming conventions for genome assemblies and annotations across datasets creates substantial challenges. Inconsistent naming hinders the identification of correct assemblies, complicates the integration of bioinformatics pipelines, and makes it difficult to link assemblies across multiple resources. To address this, we developed a specification for standardizing the naming of reference genome assemblies, to improve consistency across datasets and facilitate interoperability. This specification was created with FAIR (Findable, Accessible, Interoperable, and Reusable) practices in mind, ensuring that reference assemblies are easier to locate, access, and reuse across research communities. Additionally, it has been designed to comply with primary genomic data repositories, including members of the International Nucleotide Sequence Database Collaboration (INSDC) consortium, ensuring compatibility with widely used databases. While initially tailored to the agricultural genomics community, the specification is adaptable for use across different taxa. Widespread adoption of this standardized nomenclature would streamline assembly management, better enable cross-species analyses, and improve the reproducibility of research. It would also enhance natural language processing applications that depend on consistent reference assembly names in genomic literature, promoting greater integration and automated analysis of genomic data. This is a good time to consider more consistent genomic data nomenclature as many research communities and data resources are now finding themselves juggling multiple datasets from multiple data providers.

Ice thickness and bed topography of Jostedalsbreen ice cap, Norway

Abstract. We present an extensive dataset of ice thickness measurements from Jostedalsbreen ice cap, mainland Europe's largest glacier. The dataset consists of more than 351 000 point values of ice thickness distributed along ∼ 1100 km profile segments that cover most of the ice cap. Ice thickness was measured during field campaigns in 2018, 2021, 2022 and 2023 using various ground-penetrating radar (GPR) systems with frequencies ranging between 2.5 and 500 MHz. A large majority of the ice thickness observations were collected in spring using either snowmobiles (90 %) or a helicopter-based radar system (8 %), while summer measurements were carried out on foot (2 %). To ensure accessibility and ease of use, metadata were attributed following the GlaThiDa dataset (GlaThiDa Consortium, 2020) and follow the FAIR (findable, accessible, interoperable and reusable) guiding principles. Our findings show that glacier ice of more than 400 m thickness is found in the upper regions of large outlet glaciers, with a maximum ice thickness of ∼ 630 m in the accumulation area of Tunsbergdalsbreen. Thin ice of less than 50 m covers narrow regions joining the central part of Jostedalsbreen with its northern and southern parts, making the ice cap vulnerable to break-up with future climate warming. Using the point values of ice thickness as input to an ice thickness model, we computed 10 m grids of ice thickness and bed topography that cover the entire ice cap. From these distributed datasets, we find that Jostedalsbreen (458 km2 in 2019) has a present (∼ 2020) mean ice thickness of 154 ± 22 m and an ice volume of 70.6 ± 10.2 km3. Locations of depressions in the map of bed topography are used to delineate potential future lakes, consequently providing a glimpse of the landscape if the entire Jostedalsbreen melts away. Together, the comprehensive ice thickness point values and ice-cap-wide grids serve as a baseline for future climate change impact studies at Jostedalsbreen. All data are available for download at https://doi.org/10.58059/yhwr-rx55 (Gillespie et al., 2024).

Modular approach to near-time data management for multi-city atmospheric environmental observation campaigns

Abstract. Urban observation networks are becoming denser, more diverse, and more mobile, while being required to provide results in near time. The Synergy Grant “urbisphere” funded by the European Research Council (ERC) has multiple simultaneous field campaigns in cities of different sizes, collecting data to improve weather and climate models and services, including assessing the impact of cities on the atmosphere (e.g., heat, moisture, pollutant, and aerosol emissions) and people's exposure to extremes (e.g., heat waves, heavy precipitation, air pollution episodes). Here, a solution to this challenge for facilitating diverse data streams from multiple sources, scales (e.g., indoors, regional-scale atmospheric boundary layer), and cities is presented. For model development and evaluation in heterogeneous urban environments, we need meshed networks of in situ observations with ground-based and airborne (remote) sensing platforms. In this contribution we describe challenges, approaches, and solutions for data management, data infrastructure, and data governance to handle the variety of data streams from primarily novel modular observation networks deployed in multiple cities, in combination with existing data collected by partners, ranging in scale from indoor sensor deployments to regional-scale boundary layer observations. A metadata system documents (1) sensors and instruments, (2) the location and configuration of deployed components, and (3) maintenance and events. This metadata system provides the backbone for converting instrument records to calibrated, location-aware, convention-aligned, and quality-assured data products, according to FAIR (findable, accessible, interoperable, and reusable) principles. The data management infrastructure provides services (via, e.g., Application Programming Interface – APIs, apps, integrated computing interfaces – ICEs) for data inspection and subsequent calculations by campaign participants. Some near-real-time distributions are made to international networks (e.g., AERONET, PhenoCam) or local agencies (e.g., GovDATA) with appropriate attribution. The data documentation conventions, used to ensure structured datasets, in this case are used to improve the delivery of integrated urban services, such as to research and operational agencies, across many cities.

A general strategy for generating expert-guided, simplified views of ontologies.

Annotation with widely used, well-structured ontologies, combined with the use of ontology-aware software tools, ensures data and analyses are Findable, Accessible, Interoperable and Reusable (FAIR). Standardized terms with synonyms support lexical search. Ontology structure supports biologically meaningful grouping of annotations (typically by location and type). However, there are significant barriers to the adoption and use of ontologies by researchers and resource developers. One barrier is complexity. Ontologies serving diverse communities are often more complex than needed for individual applications. It is common for atlases to attempt their own simplifications by manually constructing hierarchies of terms linked to ontologies, but these typically include relationship types that are not suitable for grouping annotations. Here, we present a suite of tools for validating user hierarchies against ontology structure, using them to generate graphical reports for discussion and ontology views tailored to the needs of the HuBMAP Human Reference Atlas, and the Human Developmental Cell Atlas. In both cases, validation is a source of corrections and content for both ontologies and user hierarchies.

Experts Speak Forum: Implementation of the FAIR Principles in Biobanking Needs Fair Incentives.

While the FAIR (Findable, Accessible, Interoperable, and Reusable) principles are primarily concerned with data, samples can also be considered a distinct category of data. In light of these considerations, the FAIR principles represent a major challenge for biobanks, as discussed in detail in two recently published studies. We invited seven experts with diverse backgrounds to share their views on these studies and the FAIR principles in general. The contributions are written from different perspectives, including those from human biobanks operating globally, located in low- or middle-income countries or in high-income countries, as well as those from industrial or environmental biobanks. The last two contributions focused on technical feasibility and the necessary incentives. All authors agreed that while the FAIR principles present a challenge for biobanks, they also offer opportunities. Various useful instruments already exist, and more will follow. The key is to provide meaningful incentives.

Reprocessing of eXpendable BathyThermograph (XBT) profiles from the Ligurian and Tyrrhenian seas over the time period 1999–2019 with a full metadata upgrade

Abstract. The advent of open science and the United Nations Decade of Ocean Science for Sustainable Development are revolutionizing the ocean-data-sharing landscape for an efficient and transparent ocean information and knowledge generation. This blue revolution raised awareness on the importance of metadata and community standards to activate interoperability of the digital assets (data and services) and guarantee that data-driven science preserves provenance, lineage and quality information for its replicability. Historical data are frequently not compliant with these criteria, lacking metadata information that was not retained, crucial at the time of data generation and further ingestion into marine data infrastructures. The present data review is an example attempt to fill this gap through a thorough data reprocessing starting from the original raw data and operational log sheets. The data gathered using XBT (eXpendable BathyThermograph) probes during several monitoring activities in the Tyrrhenian and Ligurian seas between 1999 and 2019 have first been formatted and standardized according to the latest community best practices and all available metadata have been inserted, including calibration information never applied, uncertainty specification and bias correction from Cheng et al. (2014). Secondly, a new automatic quality control (QC) procedure has been developed and a new interpolation scheme applied. The reprocessed (REP) dataset has been compared to the data version, presently available from the SeaDataNet (SDN) data access portal, processed according to the pioneering work of Manzella et al. (2003) conducted in the framework of the European Union Mediterranean Forecasting System Pilot Project (Pinardi et al., 2003). The comparison between REP and SDN datasets has the objective to highlight the main differences derived from the new data processing process. The maximum discrepancy among the REP and SDN data versions always resides within the surface layer (REP profiles are warmer than SDN ones) until 150 m depth generally when the thermocline settles (from June to November). The overall bias and root mean square difference are equal to 0.002 and 0.041 °C, respectively. Such differences are mainly due to the new interpolation technique (Barker and McDougall, 2020) and the application of the calibration correction in the REP dataset. The REP dataset (Reseghetti et al., 2024; https://doi.org/10.13127/rep_xbt_1999_2019.2) is available and accessible through the INGV (Istituto Nazionale di Geofisica e Vulcanologia, Bologna) ERDDAP (Environmental Research Division's Data Access Program) server, which allows for machine-to-machine data access in compliance with the FAIR (findable, accessible, interoperable and reusable) principles (Wilkinson et al., 2016).

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.

A global database of butterfly species native distributions.

Butterflies represent a diverse group of insects, playing key ecosystem roles such as pollination and their larval form engage in herbivory. Despite their importance, comprehensive global distribution data for butterfly species are lacking. This lack of comprehensive global data has hindered many large-scale questions in ecology, evolutionary biology, and conservation at the regional and global scales. Here, I use an integrative workflow that combines occurrence records, alpha hull polygons, species' dispersal capacity, and natural habitat and environmental variables within a framework of species distribution models to generate species-level native distributions for butterflies at a global scale in the contemporary period. The database releases native range maps for 10,372 extant species of butterflies at a spatial grain resolution of 5 arcmin (~10 km). This database has the potential to allow unprecedented large-scale analyses in ecology, biogeography, and conservation of butterflies. The maps are available in the WGS84 coordinate reference system (EPSG:4326 code) and stored as vector polygons in the GEOPACKAGE format for maximum compression, allowing easy data manipulation using a standard computer. I additionally provide each species' spatial raster. All maps and R scripts are open access and available for download in Dryad and Zenodo, respectively, and are guided by FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. By making these data available to the scientific community, I aim to advance the sharing of biological data to stimulate more comprehensive research in ecology, biogeography, and conservation of butterflies.

AutoGeoFAIR: A framework for evaluating the sharing degree of open earth surface system data based on extended FAIR principles

Expert participation and collaboration are an efficient, transparent, credible and innovative way to implement open scientific data management, evaluation and analytical mining. An unprecedented surge in openly accessible earth surface system (ESS) data across web platforms has emerged. This data is characterized by dispersion, multi-sourcing, heterogeneity, and multi-modality, and predominantly manifests through thematic sharing websites, data services, metadata, and journal publications, particularly data papers. Addressing the pressing need to swiftly evaluate the sharing degree of these openly shared ESS data sets constitutes a new frontier in this domain. Current frameworks and tools designed for open scientific data sharing lack direct applicability to the intricacies of earth surface systems, thereby impeding users’ access to data of high sharing degree. This study introduces an ESS-oriented evaluation tool tailored to assess the sharing degree of open ESS data. Central to this tool are the components of an evaluation metric system and an evaluation model. Leveraging the principles of FAIR (Findable, Accessible, Interoperable, and Reusable), a comprehensive sharing degree assessment index comprising 19 secondary metrics categorized under 5 primary metrics is formulated. Furthermore, a novel evaluation model for quantifying the sharing degree of open scientific data is proposed, integrating hierarchical analysis and the entropy method. This integration effectively captures the nuanced uncertainties and variability inherent in the data, enhancing the accuracy and reliability of evaluation outcomes. The resulting software tool facilitates rapid and quantitative assessment of open scientific data sharing degree within the ESS domain, thereby enabling the recommendation of highly-shared datasets to researchers and modelers alike.

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 2014 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).