Biodiversity Information Science and Standards最新文献

{"title":"Working across Scales: Shared Building-Blocks of a Sustainable Infrastructure Environment for Bio/Geodiversity Data","authors":"Jutta Buschbom, James Macklin, Deborah Paul, Elizabeth R. Ellwood, Alina Freire‐Fierro, Wouter Addink, Teresa J. Mayfield-Meyer, Breda M. Zimkus, Gil Nelson, Kerstin Lehnert, Katelin Pearson, Mariko Kageyama","doi":"10.3897/biss.9.183171","DOIUrl":"https://doi.org/10.3897/biss.9.183171","url":null,"abstract":"The bio- and geodiversity communities aim to build digital infrastructures capable of delivering high-quality, well-structured, and richly interlinked data at a scale that enables powerful analyses to support the conservation and management of Earth’s diversity. Informaticians, scientists, and resource managers face the challenge of globally accelerating and upscaling open data availability. Achieving this requires sustainable, resilient, and bi-directional socio-technical connections that promote interactions and feedback both ways across the data landscape. Such interactions will connect global and national repositories to local providers, data stewards, and data users in ways that are context-sensitive and adapted to partners' location, time and communities. Although communities ranging from individual local experts to the governing bodies of global platform consortia are highly motivated and productive, they struggle to manage the rapidly growing volume of data, keep pace with technical innovation, and adhere to shared standards and best practices. Persistent challenges include insufficient recognition and visibility for contributors; functional and operational limitations in infrastructure; and inadequate long-term funding. Although the importance of the life cycles of data, infrastructures, and services is well-recognized, insufficient expertise in assessing the value that they represent and generate, and integrating those benefits into global, whole-community finance strategies presents an additional challenge. However, methodologies developed by global initiatives such as the United Nations System of Environmental-Economic Accounting (SEEA; United Nations et al. 2025) and UN Biodiversity Finance (BIOFIN; Cruz-Trinidad et al. 2024) provide innovative examples for addressing these. Without mechanisms that return generated value to data providers, stewards, and infrastructure developers, the community cannot initiate the self-reinforcing cycle of social and technical capacity growth needed for global upscaling. Instead, the current resource-limited landscape threatens the robustness of community networks and the long-term sustainability of existing infrastructures. The insights from comprehensive community outreach and infrastructure reviews conducted by the U.S.-based BIOFAIR (Building an Integrated, Open, Findable, Accessible, Interoperable, and Reusable) Data Network form the foundation for a living roadmap designed to support effective solutions and sustainable models for global biodiversity data infrastructures (Kunkel et al. 2025). Informed by these findings, the International Partners for the Digital Extended Specimen (IPDES) network are discussing shared and unique strengths and gaps across partners at different levels in this ecosystem. Adding theory of change (Rice et al. 2020), as well as socioeconomic and biodiversity finance considerations to the BIOFAIR roadmap, members of IPDES developed an extended model of infrastructure sus","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"9 1","pages":"e183171"},"PeriodicalIF":0.0,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147882446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking Natural Science Objects and Their Physical and Digital Derivatives in DINA","authors":"Jonas Grieb, James Macklin, David Peter Shorthouse, Christian Bölling, Volker Lohrmann, Michaela Grein, Etta Grotrian, Satpal Bilkhu, Claus Weiland","doi":"10.3897/biss.9.180398","DOIUrl":"https://doi.org/10.3897/biss.9.180398","url":null,"abstract":"Provenance plays an important role in natural history collections, but capturing this information accurately, proved challenging for legacy digital collection management systems. DINA*1 is being developed to address these limitations through a process-oriented data model that more effectively captures the complexity and context of provenance information (Bölling et al. 2022). DINA is an open-source, robust sample- and specimen-based collection management system in production and developed by an unincorporated international consortium of technologists and practitioners of the natural sciences (Glöckler et al. 2020). Its governance and data models help to foster the adoption of FAIR principles (Findable, Accessible, Interopable, Reusable) and to integrate objects across science domains. DINA's innovative \"samplistic\" data model records metadata on stepwise, hierarchical processes that generate physical and digital derivatives from parent material samples, accommodating complex real-life sample trajectories, for example: A fossil specimen is acquired by a museum, which contains the remains of several organisms in hardened resin. Later, it is sawed into smaller pieces; some pieces are stored under new catalog numbers, a tiny piece is sent for a destructive C14 (radioactive carbon) analysis (Fig. 1 a). A naturally deceased individual of a mammal species is collected under a material transfer agreement (MTA). Later, subsamples like teeth, bones, and tissues undergo different preparation and preservation processes and are finally stored in specialized collections, each with its own identifier (scheme); a subsample might even be retrieved for sequencing. Strong provenance tracking is required to ensure that regulatory constraints like the MTA are consistently passed down to any derivatives. A soil core is collected from a sampling site and registered together with observational metadata according to the MIxS (Minimum Information about Any Sequence) soil extension standard. Several subsamples are extracted before the remaining core is preserved. The subsamples are subjected to DNA extraction and sequencing to analyze environmental DNA (eDNA). The resulting sequences are then processed computationally to identify the species present (Fig. 1 b). A fossil specimen is acquired by a museum, which contains the remains of several organisms in hardened resin. Later, it is sawed into smaller pieces; some pieces are stored under new catalog numbers, a tiny piece is sent for a destructive C14 (radioactive carbon) analysis (Fig. 1 a). A naturally deceased individual of a mammal species is collected under a material transfer agreement (MTA). Later, subsamples like teeth, bones, and tissues undergo different preparation and preservation processes and are finally stored in specialized collections, each with its own identifier (scheme); a subsample might even be retrieved for sequencing. Strong provenance tracking is required to ensure that regulatory constraints like th","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://biss.pensoft.net/article/180398/download/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147905494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Canadian Genomic Adaptation and Resilience to Climate Change (GenARCC) Project","authors":"James Macklin, Tony Kess, Satpal Bilkhu, Ian Bradbury","doi":"10.3897/biss.9.180293","DOIUrl":"https://doi.org/10.3897/biss.9.180293","url":null,"abstract":"Genomic technologies provide the highest resolution molecular information on species biology, and can help us understand risks and potential for adaptation among species in a changing environment. The Genomic Adaptation and Resilience to Climate Change (GenARCC) project uses molecular tools to identify ecosystem composition, pathogen and pest prevalence, and adaptive capacity within species. This Government of Canada project purposefully takes a multi-department/agency approach, drawing on complementary expertise and centralized infrastructure to address the complexity of climate change spanning multiple levels of trophic and taxonomic diversity. The GenARCC project’s goal is to develop capacity to use genomics to assess, predict, and adapt to climate change for the protection of Canada's biodiversity, ecosystem resilience, food security, and health. Together, the data and expertise generated in this project represent the largest single national effort and combined dataset to address climate change impacts across species and ecosystems with molecular data. GenARCC comprises research across a diversity of environments and species, covering forest and tundra, arctic, marine, and agricultural ecosystems. Genomic, climate and phenomic datasets have been generated and centralized to better understand impacts to biodiversity across biological levels of organisation. To facilitate storage and analysis of this data, we have deployed a common high performance compute environment allowing participants across multiple Canadian departments and agencies to leverage shared infrastructure, datasets and workflows. Project outputs and inputs are managed through a dedicated sample data research management platform, DINA (Fig. 1). DINA utilizes a highly flexible data model with a core set of fields that can be extended using domain-based standards as field-extensions and/or managed attributes that are user defined, with both able to leverage controlled vocabularies. Strong process-based provenance is maintained for samples and their derivatives. Samples managed, for example, range from individual specimens of bacteria and viruses, plants, insects, fish, and mammals to environmental samples of soil, water, and external and internal microbiomes. A robust API facilitates data migration and customized export supporting data analysis and publication (GitHub Repo). The project has produced several standardized pipelines using Snakemake and Nextflow for efficiency and ease of comparison. An effort was also made to standardize Canadian climatic data for modelling allowing for cross-species comparisons (Marquis 2024). Additionally, project participants benefit from training on scientific computing, as well as genomic methods in discussion forums for data analysis and integration best practices. This access to computing resources and training has supported publication of more than 30 studies that can be found on the GenARCC publication website. Results from this project will","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://biss.pensoft.net/article/180293/download/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147891541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Symbiota Portals to Integrate Biodiversity Information in Latin America","authors":"K. Samanta Orellana, Edward Gilbert, Katelin D. Pearson, Greg Post, Mark A. Fisher, Nico M. Franz","doi":"10.3897/biss.9.177303","DOIUrl":"https://doi.org/10.3897/biss.9.177303","url":null,"abstract":"Symbiota is an open-source software for the management and mobilization of biodiversity data (Gries et al. 2014). Online Symbiota portals, often with regional or taxonomic scope, are used by more than 1,200 natural history collections to directly digitize and manage their specimen records. A simplified form with all relevant terms from the Darwin Core standard (Wieczorek et al. 2012), allow users to easily transcribe the information about the specimens (Fig. 1). At the same time, Symbiota portals facilitate the integration of images and other linked resources to each record, increasing the digital information available for each specimen (Hardisty et al. 2022). Symbiota portals can also incorporate snapshots of external datasets, expanding the accessibility of biodiversity information for the community of users. Data integration and specialized tools in Symbiota portals enable the generation of interactive resources to advance biodiversity research (e.g. checklists and taxonomic keys). Most Symbiota portals have been funded by projects in the United States, and are managed by the Symbiota Support Hub (SSH), based at the University of Kansas. However, easy access to existing portals has encouraged their use by collections worldwide. Translations to Spanish, French and Portuguese also increase the participation of international users in Symbiota communities. Moreover, by following biodiversity data standards (e.g., Darwin Core) and accessibility principles (Wilkinson et al. 2016), collections digitized in Symbiota portals can be further mobilized to regional and global aggregators (e.g., Integrated Digitized Biocollections, Global Biodiversity Integration Facility), increasing the reach of the data. In Latin America, at least 140 collections in 12 countries are actively using Symbiota portals to manage and mobilize their specimen data. Portals like Neotropical Flora, Mexican Herbaria Network, and InvertEBase are currently hosting more than 500,000 botanical, zoological and paleontological specimen records. Communities in portals like Consortium of Lichen Herbaria and Guatemala Biodiversity are also developing interactive resources for the study of local biodiversity, including checklists for specific taxa (Yánez-Ayabaca et al. 2023) or regions (Castillo-Cabrera et al. 2025). Furthermore, collections from Guatemala, the Dominican Republic, and Costa Rica are sharing more than 64,000 records to GBIF directly from Symbiota portals. Data mobilization to this global aggregator has increased data access, generating over one thousand citations in scientific literature for these natural history collections. Additional perspectives about the use of Symbiota portals in Latin America, and their potential for biodiversity data integration and use were shared during the presentation. Original slides in Spanish are available as supplementary material Suppl. material 1.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://biss.pensoft.net/article/177303/download/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147881346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DiSSCover the Potential of FAIR Digital Object Annotations and How You Can Use Them!","authors":"Tom Dijkema, Sam Leeflang","doi":"10.3897/biss.8.133172","DOIUrl":"https://doi.org/10.3897/biss.8.133172","url":null,"abstract":"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 t","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://biss.pensoft.net/article/133172/download/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147909427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meeting Report for the Phenoscape TraitFest 2023 with Comments on Organising Interdisciplinary Meetings","authors":"Jennifer C. Girón Duque, Meghan Balk, W. Dahdul, H. Lapp, István Mikó, Elie Alhajjar, Brenen M. Wynd, Sergei Tarasov, Christopher Lawrence, Basanta Khakurel, Arthur Porto, Lin Yan, Isadora E Fluck, D. Porto, Joseph Keating, I. Borokini, Katja Seltmann, G. Montanaro, Paula M. Mabee","doi":"10.3897/biss.8.115232","DOIUrl":"https://doi.org/10.3897/biss.8.115232","url":null,"abstract":"The Phenoscape project has developed ontology-based tools and a knowledge base that enables the integration and discovery of phenotypes across species from the scientific literature. The Phenoscape TraitFest 2023 event aimed to promote innovative applications that adopt the capabilities supported by the data in the Phenoscape Knowledgebase and its corresponding semantics-enabled tools, algorithms and infrastructure. The event brought together 26 participants, including domain experts in biodiversity informatics, taxonomy and phylogenetics and software developers from various life-sciences programming toolkits and phylogenetic software projects, for an intense four-day collaborative software coding event. The event was designed as a hands-on workshop, based on the Open Space Technology methodology, in which participants self-organise into subgroups to collaboratively plan and work on their shared research interests. We describe how the workshop was organised, the projects developed and outcomes resulting from the workshop, as well as the challenges in bringing together a diverse group of participants to engage productively in a collaborative environment.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"15 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation Experience Report for the Developing Latimer Core Standard: The DiSSCo Flanders use-case","authors":"Lissa Breugelmans, Maarten Trekels","doi":"10.3897/biss.7.113766","DOIUrl":"https://doi.org/10.3897/biss.7.113766","url":null,"abstract":"<jats:p />","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"605 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139213641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Future of Natural History Transcription: Navigating AI advancements with VoucherVision and the Specimen Label Transcription Project (SLTP)","authors":"William Weaver, Kyle Lough, Stephen Smith, Brad Ruhfel","doi":"10.3897/biss.7.113067","DOIUrl":"https://doi.org/10.3897/biss.7.113067","url":null,"abstract":"Natural history collections are critical reservoirs of biodiversity information but collections staff are constantly grappling with substantial backlogs and limited resources. The task of transcribing specimen label text into searchable databases requires a significant amount of time, manual labor, and funding. To address this challenge, we introduce VoucherVision, a tool harnessing the capabilities of several Large Language Models (LLMs; Naveed et al. 2023) to augment specimen label transcription. The VoucherVision tool automates laborious components of the transcription process, leveraging an Optical Character Recognition (OCR) system and LLMs to convert unstructured label text into appropriate data formats compatible with database ingestion. VoucherVision uses a combination of structured output parsers and recursive re-prompting strategies to ensure consistency and quality of the LLM-formatted text, significantly reducing errors.\u0000 \u0000 Integration of VoucherVision with the University of Michigan Herbarium’s transcription workflow resulted in a significant reduction in per-image transcription time, suggesting significant potential advantages for collections workflows. VoucherVision offers promising strides towards efficient digitization, with curatorial staff playing critical roles in data quality assurance and process oversight. Emphasizing the importance of knowledge sharing, the University of Michigan Herbarium is backing the Specimen Label Transcription Project (SLTP), which will provide open access to benchmarking datasets, fine-tuned models, and validation tools to rank the performance of different methodologies, LLMs, and prompting strategies. In the rapidly evolving landscape of Artificial Intelligence (AI) development, we recognize the profound potential of diverse contributions and innovative methodologies to redefine and advance the transformation of curatorial practices, catalyzing an era of accelerated digitization in natural history collections.\u0000 An early, public version of VoucherVision is available to try here: https://vouchervision.azurewebsites.net/","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"No Pain No Gain: Standards mapping in Latimer Core development","authors":"Matt Woodburn, Jutta Buschbom, Sharon Grant, Janeen Jones, Ben Norton, Maarten Trekels, Sarah Vincent, Kate Webbink","doi":"10.3897/biss.7.113053","DOIUrl":"https://doi.org/10.3897/biss.7.113053","url":null,"abstract":"Latimer Core (LtC) is a new proposed Biodiversity Information Standards (TDWG) data standard that supports the representation and discovery of natural science collections by structuring data about the groups of objects that those collections and their subcomponents encompass (Woodburn et al. 2022). It is designed to be applicable to a range of use cases that include high level collection registries, rich textual narratives and semantic networks of collections, as well as more granular, quantitative breakdowns of collections to aid collection discovery and digitisation planning. As a standard that is (in this first version) focused on natural science collections, LtC has significant intersections with existing data standards and models (Fig. 1) that represent individual natural science objects and occurrences and their associated data (e.g., Darwin Core (DwC), Access to Biological Collection Data (ABCD), Conceptual Reference Model of the International Committee on Documentation (CIDOC-CRM)). LtC’s scope also overlaps with standards for more generic concepts like metadata, organisations, people and activities (i.e., Dublin Core, World Wide Web Consortium (W3C) ORG Ontology and PROV Ontology, Schema.org). LtC represents just an element of this extended network of data standards for the natural sciences and related concepts. Mapping between LtC and intersecting standards is therefore crucial for avoiding duplication of effort in the standard development process, and ensuring that data stored using the different standards are as interoperable as possible in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles. In particular, it is vital to make robust associations between records representing groups of objects in LtC and records (where available) that represent the objects within those groups. During LtC development, efforts were made to identify and align with relevant standards and vocabularies, and adopt existing terms from them where possible. During expert review, a more structured approach was proposed and implemented using the Simple Knowledge Organization System (SKOS) mappingRelation vocabulary. This exercise helped to better describe the nature of the mappings between new LtC terms and related terms in other standards, and to validate decisions around the borrowing of existing terms for LtC. A further exercise also used elements of the Simple Standard for Sharing Ontological Mappings (SSSOM) to start to develop a more comprehensive set of metadata around these mappings. At present, these mappings (Suppl. material 1 and Suppl. material 2) are provisional and not considered to be comprehensive, but should be further refined and expanded over time. Even with the support provided by the SKOS and SSSOM standards, the LtC experience has proven the mapping process to be far from straightforward. Different standards vary in how they are structured, for example, DwC is a ‘bag of terms’, with informal classes and no structura","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136236194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structuring Information from Plant Morphological Descriptions using Open Information Extraction","authors":"Maria Mora-Cross, William Ulate, Brandon Retana Chacón, María Biarreta Portillo, Josué David Castro Ramírez, Jose Chavarria Madriz","doi":"10.3897/biss.7.113055","DOIUrl":"https://doi.org/10.3897/biss.7.113055","url":null,"abstract":"Taxonomic literature keeps records of the planet's biodiversity and gives access to the knowledge needed for research and sustainable management. The number of publications generated is quite large: the corpus of biodiversity literature includes tens of millions of figures and taxonomic treatments. Unfortunately, most of the taxonomic descriptions are from scientific publications in text format. With more than 61 million digitized pages in the Biodiversity Heritage Library (BHL), only 467,265 taxonomic treatments are available in the Biodiversity Literature Repository. To obtain highly structured texts from digitized text has been shown to be complex and very expensive (Cui et al. 2021). The scientific community has described over 1.2 million species, but studies suggest that 86% of existing species on Earth and 91% of species in the ocean still await description (Mora et al. 2011). The published descriptions synthesize observations made by taxonomists over centuries of research and include detailed morphological aspects (i.e., shape and structure) of species useful to identify specimens, to improve information search mechanisms, to perform data analysis of species having particular characteristics, and to compare species descriptions. To take full advantage of this information and to work towards integrating it with repositories of biodiversity knowledge, the biodiversity informatics community first needs to convert plain text into a machine-processable format. More precisely, there is a need to identify structures and substructure names and the characters that describe them (Fig. 1). Open information extraction (OIE) is a research area of Natural Language Processing (NLP), which aims to automatically extract structured, machine-readable representations of data available in unstructured text; usually the result is handled as n-ary propositions, for instance, triples of the form &amp;lt;noun phrase, relation phrase, noun phrase&amp;gt; (Shen et al. 2022). OIE is continuously evolving with advancements in NLP and machine learning techniques. The state of the art in OIE involves the use of neural approaches, pre-trained language models, and integration of dependency parsing and semantic role labeling. Neural solutions mainly formulate OIE as a sequence tagging problem or a sequence generation problem. Ongoing research focuses on improving extraction accuracy; handling complex linguistic phenomena, for instance, addressing challenges like coreference resolution; and more open information extraction, because most existing neural solutions work in English texts (Zhou et al. 2022). The main objective of this project is to evaluate and compare the results of automatic data extraction from plant morphological descriptions using pre-trained language models (PLM) and a language model trained on data from plant morphological descriptions written in Spanish. The research data for this study were sourced from the species records database of the National Biodi","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136155252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}