Findable Accessible Interoperable Reusable Data Research Articles

Enhancing machine learning classification of microstructures: A workflow study on joining image data and metadata in CNN

AbstractIn view of the paradigm shift toward data-driven research in materials science and engineering, handling large amounts of data becomes increasingly important. The application of FAIR (findable, accessible, interoperable, reusable) data principles emphasizes the importance of metadata describing datasets. We propose a novel data processing and machine learning (ML) pipeline to extract metadata from micrograph image files, then combine image data and their metadata for microstructure classification with a deep learning approach compared to a classic ML approach. The ML model attained excellent performances with and without metadata and bears potential for performance improvement of further use cases within the community. Graphical abstract

Nomenclature of functional crystals in glass-ceramics: A recommendation based on aluminosilicate solid solutions

The need for a homogeneous nomenclature has become particularly acute due to the increasing digitization and open accessibility of research data provided by peer-reviewed scientific publishers and other online repositories and databases, and the resulting big-data mining approaches. Therefore, basic guidelines for a general nomenclature system based on the crystallographic structure are presented to enable scientists, engineers and technologists to name the crystalline phases detected in glass-ceramics as uniformly and unambiguously as possible and exemplified by quartz, keatite and indialite solid solutions. The adoption of a standardized nomenclature is essential to ensure effective use of the vast scientific knowledge available today in accordance with the FAIR (findable, accessible, interoperable, reusable) data management systems.

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.

Abstract 7432: Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal

Abstract The Computational Resources for Cancer Research website is a public portal supported by NCI that democratizes access to available computational resources for the cancer research community. In supporting an emerging FAIR (Findable, Accessible, Interoperable, Reusable) data science ecosystem inclusive of software, models and data, the goals of the portal are to serve cancer data scientists by increasing understanding, access, and adoption of computational resources for cancer research, and to foster new collaborative research projects. Initially populated with resources developed in NCI supported collaborations with the US Department of Energy, the portal is serving as a unified reference and resource for computational resources developed broadly by the cancer community. These collaborative projects include CANDLE (the Cancer Distributed Learning Environment), IMPROVE (Innovative Methods and data for PRedictive Oncology model Validation and Evaluation), ADMIRRAL (AI Driven Multi-scale Investigation of Ras-Raf Activation Lifecycle), MOSSAIC (Model Outcomes using Surveillance Data and Scalable AI for Cancer) and ATOM (Accelerating Therapeutics for Opportunities in Medicine). The resources include software, models, and datasets as well as descriptive use cases and references to tutorials associated with many of the described resources. The portal also integrates content from the Predictive Oncology Model and Data Clearinghouse (modac.cancer.gov) which can be accessed through REST APIs. Ongoing development of the portal is driven by community insights and contributions including input from an interdisciplinary advisory committee, working groups, early users, and preliminary feedback received at AACR 2023. The portal will also support community contributions of computational resources. In addition, the portal highlights computational approaches in emerging and challenging areas, such as digital twins for cancer. The portal will enable the cancer research community to learn about new computational research methods through use cases and educational material, access cancer related software, datasets, and AI models, engage with peers and cross-disciplinary researchers at different career levels through an interactive forum, explore emerging areas, such as computational imaging, community-based standards for data management, predictive radiation oncology, and digital twin technologies, learn about in industry and community events, conferences, and workshops, and create new collaborative research projects. The presentation will deliver insights into key resources offered through the Computational Resources for Cancer Research Portal, how the portal will help the cancer research community identify and adopt computational resources of interest, and how researchers can join and participate in this growing community. Citation Format: Eric Stahlberg, Lynn Borkon, Ruth Frost, Sara Jones, Amar Khalsa, Lauren Lewis, Emily Greenspan, Sunita Menon. Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7432.

Enrichment of lung cancer computed tomography collections with AI-derived annotations

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections containing computed tomography images of the chest, NSCLC-Radiomics, and a subset of the National Lung Screening Trial. Using publicly available AI algorithms, we derived volumetric annotations of thoracic organs-at-risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR (Findable, Accessible, Interoperable, Reusable) data principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can aid in cancer imaging.

Harmonizing marine zooplankton trait data toward a mechanistic understanding of ecosystem functioning

AbstractCompiling trait information promotes discovery and innovation in using trait‐based approaches in ecology. Various zooplankton trait datasets are stored in unlinked data repositories, in diverse data structures, and have varying levels of complexity. These require standardization and harmonization to allow interoperability and to limit the duplication of efforts in the time‐consuming and error‐prone task of trait compilation. This study aggregated and harmonized 33 zooplankton traits datasets and supplemented these with more than 150 references into a single zooplankton trait database with an initial set of 56 traits for 3535 marine zooplankton species. The database has a long data table structure using the entity‐attribute‐value format and includes taxonomic and ancillary metadata, and data source provenance preserving how the data were originally recorded. The database is stored both at the individual level (Level 1) and as species level means (Level 2). The Level 1 database has 57,615 rows of trait records and the Level 2 database has 14,977 unique trait‐taxon records. We evaluated the coverage of trait data, taxonomic representation, and strategies in filling‐in data gaps. Comparison of trait value estimation approaches identified allometric scaling to be more accurate than taxon‐level generalization and imputation. This centralized and harmonized marine zooplankton trait database aims to be extendable and future‐proof and to promote trait data sharing, FAIR (findable, accessible, interoperable, reusable) data practices, and reproducibility.

The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset

Abstract. The Greenland Climate Network (GC-Net) consists of 31 automatic weather stations (AWSs) at 30 sites across the Greenland Ice Sheet. The first site was initiated in 1990, and the project has operated almost continuously since 1995 under the leadership of the late Konrad Steffen. The GC-Net AWS measured air temperature, relative humidity, wind speed, atmospheric pressure, downward and reflected shortwave irradiance, net radiation, and ice and firn temperatures. The majority of the GC-Net sites were located in the ice sheet accumulation area (17 AWSs), while 11 AWSs were located in the ablation area, and two sites (three AWSs) were located close to the equilibrium line altitude. Additionally, three AWSs of similar design to the GC-Net AWS were installed by Konrad Steffen's team on the Larsen C ice shelf, Antarctica. After more than 3 decades of operation, the GC-Net AWSs are being decommissioned and replaced by new AWSs operated by the Geological Survey of Denmark and Greenland (GEUS). Therefore, making a reassessment of the historical GC-Net AWS data is necessary. We present a full reprocessing of the historical GC-Net AWS dataset with increased attention to the filtering of erroneous measurements, data correction and derivation of additional variables: continuous surface height, instrument heights, surface albedo, turbulent heat fluxes, and 10 m ice and firn temperatures. This new augmented GC-Net level-1 (L1) AWS dataset is now available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2023) and will continue to be refined. The processing scripts, latest data and a data user forum are available at https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing (last access: 30 November 2023). In addition to the AWS data, a comprehensive compilation of valuable metadata is provided: maintenance reports, yearly pictures of the stations and the station positions through time. This unique dataset provides more than 320 station years of high-quality atmospheric data and is available following FAIR (findable, accessible, interoperable, reusable) data and code practices.

Cyberinformatics tool for in-season crop-specific land cover monitoring: Design, implementation, and applications of iCrop

Cyberinformatics tools have supported decision makings in agriculture through cutting-edge big data, artificial intelligence/machine learning (AI/ML), and high-performance computing technologies. An open and easy-to-use agricultural cyberinformatics tool based on the findable, accessible, interoperable, reusable (FAIR) data principle is essential for the efficient distribution of crop-specific land cover information. This paper introduces iCrop, a new cyberinformatics tool to enable in-season crop type monitoring for the Conterminous United States (CONUS). As a web-based geographic information system (GIS), iCrop not only delivers three sets of new ML-based field-level crop-specific land cover geospatial data, including pre-season crop cover maps, in-season crop cover maps, and Refined Cropland Data Layer (R-CDL), but also provides a suite of mapping and geoprocessing functionalities through the FAIR geospatial data standards, such as Web Map Service (WMS), Web Coverage Service (WCS), and Web Processing Service (WPS). Meanwhile, we outline several use cases to highlight iCrop's applications under various agricultural operation scenarios, its functionality for land use change analysis, and its interoperability with generic web-based and desktop GIS software (e.g., GeoPlatform and QGIS). Our experimental results show that the new cyberinformatics tool can provide timely and unique crop-specific land cover information through the geoprocessing functionalities to facilitate U.S. agricultural information management and decision support. Moreover, this paper can be used as a systematic guidance for the design and implementation of the cyberinformatics tool to disseminate agro-geoinformation based on the FAIR data principle.

Unearthing the Past for a Sustainable Future: Extracting and transforming data in the Biodiversity Heritage Library for climate action

As the urgency to address the climate crisis intensifies, the availability of accurate and comprehensive biodiversity data has become crucial for informing climate change studies, tracking key environmental indicators, and building global biodiversity monitoring platforms. The Biodiversity Heritage Library (BHL) plays a vital role in the core biodiversity infrastructure, housing over 60 million pages of digitized literature about life on Earth. Recognizing the value of over 500 years of data in BHL, a global network of BHL staff is working to establish a scalable data pipeline to provide actionable occurrence data from BHL’s vast and diverse collections. However, transforming textual content into FAIR (findable, accessible, interoperable, reusable) data poses challenges due to missing descriptive metadata and error-ridden unstructured outputs from commercial text engines. (Fig. 1) Despite the wealth of knowledge in BHL now available to global audiences, the underutilization of biodiversity and climate data contained in BHL's textual corpus hinders scientific research, hampers informed decision-making for conservation efforts, and limits our understanding of biodiversity patterns crucial for addressing the climate crisis. By leveraging recent advancements in text recognition engines, along with cutting-edge AI (Artificial Intelligence) models like OpenAI’s CLIP (Contrastive Language-Image Pre-Training) and nascent features in transcription platforms, BHL staff are beginning to process vast amounts of textual and image data and transform centuries worth of data from BHL collections into computationally usable formats. Recent technological breakthroughs now offer a transformative opportunity to empower the global biodiversity community with prescient insights from our shared past and facilitate the integration of historical knowledge into climate action initiatives. To bridge gaps in the historical record and unlock the potential of the Biodiversity Heritage Library (BHL), a multi-pronged effort utilizing innovative cross-disciplinary approaches is being piloted. These technical approaches were selected for their efficiency and ability to generate rapid results that could be applied across the diverse range of materials in BHL. (Fig. 2) Piloting a data pipeline that is scalable to 60 million pages requires considerable investigation, experimentation, and resources but will have an appreciable impact on global conservation efforts by informing and establishing historic baselines deeper into time. This presentation will focus on the identification, extraction, and transformation of OCR into structured data outputs in BHL. Approaches include: Upgrading legacy OCR text using Tesseract OCR engine to improve data quality by 20% and openly publish 40 GBs of textual data as FAIR data; Evaluating handwritten text recognition (HTR) engines (Microsoft Azure Computer Vision, Google Cloud Vision API (Application Programming Interface), and Amazon Textract) to improve scientific name-finding in BHL’s handwritten archival materials using algorithms developed by Global Names Architecture; Extracting data from collecting events using HTR coordinate outputs with Python library Pandas DataFrame to create structured data; Classifying BHL page-level images with OpenAI's CLIP, a neural network model to accurately identify the handwritten sub-corpus of primary source materials in BHL; Running an A/B test to evaluate the efficiency and accuracy of human-keyed transcription data extraction to provide high-quality, human-vetted datasets that can be deposited with data aggregators. Upgrading legacy OCR text using Tesseract OCR engine to improve data quality by 20% and openly publish 40 GBs of textual data as FAIR data; Evaluating handwritten text recognition (HTR) engines (Microsoft Azure Computer Vision, Google Cloud Vision API (Application Programming Interface), and Amazon Textract) to improve scientific name-finding in BHL’s handwritten archival materials using algorithms developed by Global Names Architecture; Extracting data from collecting events using HTR coordinate outputs with Python library Pandas DataFrame to create structured data; Classifying BHL page-level images with OpenAI's CLIP, a neural network model to accurately identify the handwritten sub-corpus of primary source materials in BHL; Running an A/B test to evaluate the efficiency and accuracy of human-keyed transcription data extraction to provide high-quality, human-vetted datasets that can be deposited with data aggregators. The ongoing development of a scalable data pipeline of BHL’s relevant biodiversity and climate-related datasets requires sustained support and partnership with the biodiversity community. Initial results demonstrate that liberating data from archival and handwritten field notes is arduous but feasible. Extending these methodologies to the broader scientific literature presents new research opportunities. Extracting and normalizing data from unstructured textual sources can significantly advance biodiversity research and inform environmental policy. The Biodiversity Heritage Library staff are committed to building multiple scalable data pipelines with the ultimate goal of erecting a global biodiversity knowledge graph, rich in interconnected data and semantic meaning, enabling informed decisions for the preservation and sustainable management of Earth's biodiversity.

EMODnet Biology: Unlocking European marine biodiversity data

EMODnet Biology (hosted and coordinated by the Flanders Marine Institute (VLIZ)) is one of the seven themes within the European Marine Observation and Data network (EMODnet). The EMODnet Biology consortium aims to facilitate the accessibility and usage of marine biodiversity data. With the principle of "collect once, use many times" at its core, EMODnet Biology fosters collaboration across various sectors, including research, policy-making, industry, and individual citizens, to enhance knowledge sharing and inform decision-making. EMODnet Biology focuses on providing free and open access to comprehensive historical and recent data on the occurrence of marine species and their traits in all European regional seas. It achieves this through partnerships and collaboration with diverse international initiatives, such as the World Register of Marine Species (WoRMS), Marine Regions and the European node of the Ocean Biodiversity Information System (EurOBIS) among others. By promoting the usage of the Darwin Core Standard (Wieczorek et al. 2012), EMODnet Biology fosters data interoperability and ensures seamless integration with wider networks such as the Global Biodiversity Information Facility (GBIF) and the Ocean Biodiversity Information System (OBIS), serving as a significant data provider of the latter, as it is responsible for most of its data generated in Europe. Since its inception, EMODnet Biology has undertaken actions covering various areas, including providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. Furthermore, EMODnet Biology contributes to the overall EMODnet initiative, which covers multidisciplinary data and products. Thanks to the use of standard protocols and tools across disciplines, EMODnet Biology products can contribute to multidisciplinary analysis of pressures and impacts on key marine species and habitats, and, lastly, support a better management and planning of the maritime space. In conclusion, EMODnet Biology plays a pivotal role in biodiversity informatics by providing users with a wealth of accessible and reusable marine biodiversity data and products. Its collaborative approach, extensive partnerships, and adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles (Wilkinson et al. 2016) as well as to the Infrastructure for Spatial Information in Europe (INSPIRE) metadata technical guidelines (European Commission Joint Research Centre 2013) and the Open Geospatial Consortium (OGC) standards make it a valuable resource for advancing knowledge, informing policies, and supporting sustainable management of marine ecosystems.

The HMC Information Portal for Enhanced Metadata Collaboration in the Helmholtz FAIR Data Space

The Helmholtz Metadata Collaboration (HMC) platform was launched in late 2019 to turn FAIR (Findable, Accessible, Interoperable, Reusable) research data into reality within the Helmholtz Association and beyond. The Information Portal was initiated to enable the structured cartography of metadata and FAIR landscape of Helmholtz, providing information for multi-level decision-making and creating a curated knowledge base for research data managers, scientists and other stakeholders.
 Developed through a top-down approach, 18 categories, and associated metadata schemas were defined and aligned by an HMC taskforce. Data curation followed, with resources collected from different domains based on the aligned metadata schema. The Information Portal is a web application for capturing FAIR data practices across all Helmholtz domains, offering a unified user interface for collecting and exploring results.
 Built using state-of-the-art technologies, including Python and Docker, the Information Portal leverages GitLab as a database. It offers a public / central read-only version for stakeholders and a personal instance for curation - synchronized to a GitLab repository. Git-based systems offer advantages, such as raw data accessibility, flexible data curation, easy synchronization, and customizable repositories.
 The single-page web application is user-friendly and developed in multiple iterations for an intuitive and flexible interface. The Information Portal is crucial for creating a sustainable, distributed, semantically enriched Helmholtz data space, promoting seamless data sharing and reuse.

Sharing FAIR monitoring program data improves discoverability and reuse.

Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.

Reaching Further with Earth Science Data

Earth Sciences cover a broad spectrum of research fields such as petrology, sedimentology, structural geology, seismology, and geomorphology, to name a few, which aim to understand interrelated processes on the surface and in the interior of our planet. Many of the research questions studied in the Earth sciences, such as, understanding past climates, the human impact on the Critical Zone*1, or the co-evolution of the geo- and biosphere, require interdisciplinary approaches, employing methodologies and integrating observations from diverse subdisciplines such as geochemistry, mineralogy, geophysics, and paleontology, but also increasingly from other disciplines, specifically biology and genomics. The use of data and samples in multi- and interdisciplinary research is substantially facilitated by consistent metadata including schemas and vocabularies, data formats, and data exchange protocols. The association of Biodiversity Information Standards (TDWG) with the Earth Science part of the Natural Sciences has, until recently, been largely limited to palaeontology, primarily due to its close affinity with biology. Standards have been developed that support the taxonomy of fossils and include terms for "deep time" intervals (chrono- and bio-stratigraphy) and "paleo-surface environment" (sedimentology). Other important and highly relevant fields such as mineralogy, geochemistry, meteoritics, and soil sciences have not been included or coordinated with so far. Although some progress is being made with the ongoing development of the Mineral Extension for Darwin Core and the inclusion of some geological terms in Latimer Core (Woodburn et al. 2022), more effort should be made to link and align with existing and emerging data standards in these Earth Science communities. These Earth Science communities are very active, building their own domain-specific FAIR (Findable, Accessible, Interoperable, Reusable) data standards and infrastructures with data systems such as EarthChem, the Astromaterials Data System, Mindat, and WoSIS. We will introduce the users, data systems, and community standards and best practices, including metadata, vocabularies, and persistent identifiers for data and samples being used and developed in the subdisciplines of the Earth Sciences mentioned above. We advocate for collaboration and coordination with these fields to make TDWG more inclusive, expand accessible resources, and provide an opportunity for TDWG to link to these other communities. Broader networking within an interdisciplinary research framework has the potential to address more fundamental and over-arching issues of both scientific and public relevance.

The Biodiversity Knowledge Hub (BKH): A Crosspoint and Knowledge Broker for FAIR and Linked Biodiversity Data

The Biodiversity Knowledge Hub (BKH) is a web platform acting as an integration point and broker of an open, FAIR (Findable, Accessible, Interoperable, Reusable) and interlinked corpora of biodiversity data, services and knowledge. It serves the entire biodiversity research cycle, from specimens and observations to sequences, taxon names and finally to scientific publications. The strategic aim of the BKH is to support a functional and integrated biodiversity knowledge graph and an emerging new community of users. The BKH is aimed at biodiversity researchers in the widest sense, research infrastructures and publishers (Fig. 1). The BKH is the key product of the EU-funded Biodiversity Community Integrated Knowledge Library (BiCIKL) project (Penev et al. 2022). The four goals of BiCIKL and the BKH are: Improved access to open and FAIR biodiversity data; Establishing of bi-directional data linkages between infrastructures; Development of new methods and workflows for semantic publishing, harvesting, liberating, linking, accessing and re-using of data in literature (specimens, material citations, samples, sequences, taxonomic names, taxonomic treatments, figures, tables); Testing and implementation of services through use cases and open call projects for researchers outside the project. Improved access to open and FAIR biodiversity data; Establishing of bi-directional data linkages between infrastructures; Development of new methods and workflows for semantic publishing, harvesting, liberating, linking, accessing and re-using of data in literature (specimens, material citations, samples, sequences, taxonomic names, taxonomic treatments, figures, tables); Testing and implementation of services through use cases and open call projects for researchers outside the project. The BKH consists of several modules, such as the Home page that presents the main user groups and the benefits that the BKH provides to them. It has guidelines and protocols, such as various documents on the policies, functions, and recommendations for the users. And it has relevant projects, that use linked FAIR biodiversity data. In the core of the BKH is the FAIR Data Place (FDP), which presents novel services and tools developed over the course of BiCIKL. In the future, the FDP will also accept services for linked data provided by new contributors. The FDP consists of three sub-modules: Infrastructures and organisations: Lists the contributing organisations and research infrastructures with short descriptions and links to their data, tools and services. Research infrastructures are sorted by the main type of biodiversity data they aggregate and serve: specimens, sequences, taxon names and literature. Linked data services: A catalogue of novel services that deliver FAIR data linked between the participating research infrastructures. Examples of such services are: ChecklistBank, LifeBlock, OpenBiodiv, TreatmentBank, SIBiLS “BiodiversityPMC”, eBioDiv, SynoSpecies, PlutoF Curation Tool and others. Become a contributor application form: A formal questionnaire which serves as a basis to check the suitability of an organisation or research infrastructure to join the BKH. Part of the application form is a FAIR data checklist. Infrastructures and organisations: Lists the contributing organisations and research infrastructures with short descriptions and links to their data, tools and services. Research infrastructures are sorted by the main type of biodiversity data they aggregate and serve: specimens, sequences, taxon names and literature. Linked data services: A catalogue of novel services that deliver FAIR data linked between the participating research infrastructures. Examples of such services are: ChecklistBank, LifeBlock, OpenBiodiv, TreatmentBank, SIBiLS “BiodiversityPMC”, eBioDiv, SynoSpecies, PlutoF Curation Tool and others. Become a contributor application form: A formal questionnaire which serves as a basis to check the suitability of an organisation or research infrastructure to join the BKH. Part of the application form is a FAIR data checklist. The BKH serves as a navigation system in a universe of interconnected biodiversity research infrastructures and is open to new contributors and collaborators in accessing open data and knowledge by anybody, anywhere, at any time.

A Community-Driven Strategy for Addressing Fossil Taxonomy Challenges

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

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

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

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

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

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

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

How data governance principles influence participation in biodiversity science

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

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

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