Biodiversity Information Science and Standards最新文献

{"title":"High Throughput Information Extraction of Printed Specimen Labels from Large-Scale Digitization of Entomological Collections using a Semi-Automated Pipeline","authors":"Margot Belot, Leonardo Preuss, Joël Tuberosa, Magdalena Claessen, Olha Svezhentseva, Franziska Schuster, Christian Bölling, Théo Léger","doi":"10.3897/biss.7.112466","DOIUrl":"https://doi.org/10.3897/biss.7.112466","url":null,"abstract":"Insects account for half of the total described living organisms on Earth, with a vast number of species awaiting description. Insects play a major role in ecosystems but are yet threatened by habitat destruction, intensive farming, and climate change. Museum collections around the world house millions of insect specimens and large-scale digitization initiatives, such as the digitization street digitize! at the Museum für Naturkunde, have been undertaken recently to unlock this data. Accurate and efficient extraction of insect specimen label information is vital for building comprehensive databases and facilitating scientific investigations, sustainability of the collected data, and efficient knowledge transfer. Despite the advancements in high-throughput imaging techniques for specimens and their labels, the process of transcribing label information remains mostly manual and lags behind the pace of digitization efforts. In order to address this issue, we propose a three step semi-automated pipeline that focuses on extracting and processing information from individual insect labels. Our solution is primarily designed for printed insect labels, as the OCR (optical character recognition) technology performs well for printed text while handwritten texts still yield mixed results. The pipeline incorporates computer vision (CV) techniques, OCR, and a clustering algorithm. The initial stage of our pipeline involves image analysis using a convolutional neural network (CNN) model. The model was trained using 2100 images from three distinct insect label datasets, namely AntWeb (ant specimen labels from various collections), Bees & Bytes (bee specimen labels from the Museum für Naturkunde), and LEP_PHIL (Lepidoptera specimen labels from the Museum für Naturkunde). The first model enables the identification and isolation of single labels within an image, effectively segmenting the label region from the rest of the image, and crops them into multiple new, single-label image files. It also assigns the labels to different classes, i.e., printed text or handwritten, with handwritten labels sorted out from the printed ones. In the second step, labels classified as “printed” are then parsed by an OCR engine to extract the text information from the labels. Tesseract and Google Vision OCRs were both tested to assess their performance. While Google Vision OCR is a cloud-based service with limited configurability, Tesseract provides the flexibility to fine-tune settings and enhance its performance for our specific use cases. In the third step, the OCR outputs are aggregated by similarity using a clustering algorithm. This step allows for the identification and formation of clusters that consist of labels sharing identical or highly similar content. Ultimately, these clusters are compared against a curated database of labels and are assigned to a known label or highlighted as new and manually added to the database. In order to assess the efficiency of our pipeline","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135982444","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":"Improving Biological Collections Data through Human-AI Collaboration","authors":"Alan Stenhouse, Nicole Fisher, Brendan Lepschi, Alexander Schmidt-Lebuhn, Juanita Rodriguez, Federica Turco, Emma Toms, Andrew Reeson, Cécile Paris, Pete Thrall","doi":"10.3897/biss.7.112488","DOIUrl":"https://doi.org/10.3897/biss.7.112488","url":null,"abstract":"Biological collections play a crucial role in our understanding of biodiversity and inform research in areas such as biosecurity, conservation, human health and climate change. In recent years, the digitisation of biological specimen collections has emerged as a vital mechanism for preserving and facilitating access to these invaluable scientific datasets. However, the growing volume of specimens and associated data presents significant challenges for curation and data management. By leveraging human-Artificial Intelligence (AI) collaborations, we aim to transform the way biological collections are curated and managed, unlocking their full potential in addressing global challenges. We present our initial contribution to this field through the development of a software prototype to improve metadata extraction from digital specimen images in biological collections. The prototype provides an easy-to-use platform for collaborating with web-based AI services, such as Google Vision and OpenAI Generative Pre-trained Transformer (GPT) Large Language Models (LLM). We demonstrate its effectiveness when applied to herbarium and insect specimen images. Machine-human collaboration may occur at various points within the workflows and can significantly affect outcomes. Initial trials suggest that the visual display of AI model uncertainty could be useful during expert data curation. While much work remains to be done, our results indicate that collaboration between humans and AI models can significantly improve the digitisation rate of biological specimens and thereby enable faster global access to this vital data. Finally, we introduce our broader vision for improving biological collection curation and management using human-AI collaborative methods. We explore the rationale behind this approach and the potential benefits of adding AI-based assistants to collection teams. We also examine future possibilities and the concept of creating 'digital colleagues' for seamless collaboration between human and digital curators. This ‘collaborative intelligence’ will enable us to make better use of both human and machine capabilities to achieve the goal of unlocking and improving our use of these vital biodiversity data to tackle real-world problems.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980919","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":"Swedish Biodiversity Data Infrastructure (SBDI): Insights from the Swedish ALA installation","authors":"Margret Steinthorsdottir, Veronika Johansson, Manash Shah","doi":"10.3897/biss.7.112429","DOIUrl":"https://doi.org/10.3897/biss.7.112429","url":null,"abstract":"The Swedish Biodiversity Data Infrastructure (SBDI) is a biodiversity informatics infrastructure and is the key national resource for data-driven biodiversity and ecosystems research. SBDI rests on three pillars: mobilisation and access to biodiversity data; development and operation of tools for analysing these data; and user support. SBDI is funded by the Swedish Research Council (VR) and eleven of Sweden’s major universities and research government authorities (Fig. 1). mobilisation and access to biodiversity data; development and operation of tools for analysing these data; and user support. SBDI is funded by the Swedish Research Council (VR) and eleven of Sweden’s major universities and research government authorities (Fig. 1). SBDI was formed in early 2021 and represents the final step in an amalgamation of national infrastructures for biodiversity and ecosystems research. SBDI includes the Swedish node of the Global Biodiversity Information Facility (GBIF), the key international infrastructure for sharing biodiversity data. SBDI's predecessor Biodiversity Atlas Sweden (BAS) was an early adopter of the Atlas of Living Australia (ALA) platform. SBDI pioneered the container-based deployment of the platform using Docker and Docker Swarm. This container-based approach helps simplify deployment of the platform, which is characterised by a microservice architecture with loosely coupled services. This enables scalability, modularity, integration of services, and new technology insertions. SBDI has customised the BioCollect module to remove region-specific constraints so that it can be more readily improved for environmental monitoring in Sweden. To further support this, there are plans to develop services for the distribution of terrestrial map layers, which will provide important habitat information for artificial intelligence and machine learning research projects. The Amplicon Sequence Variants (ASVs) portal, an interface to sequence-based observations, is an example of integration and new technology insertion. The portal developed in SBDI and seamlessly integrated with the ALA platform provides basic functionalities for searching ASVs and occurrence records using the Basic Local Alignment Search Tool (BLAST) or filters on sequencing details and taxonomy and for submitting metabarcoding dataset Fig. 2. Future developments for SBDI include a continued focus on eDNA and monitoring data as well as the implementation of procedures for handling sensitive data.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135982102","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":"Practice, Pathways and Lessons Learned from Building a Digital Data Flow with Tools: Focusing on alien invasive species, from occurrence via measures to documentation","authors":"Mora Aronsson, Malin Strand, Holger Dettki, Hanna Illander, Johan Olsson","doi":"10.3897/biss.7.112337","DOIUrl":"https://doi.org/10.3897/biss.7.112337","url":null,"abstract":"The SLU Swedish Species Information Centre (SSIC, SLU Artdatabanken) accumulates, analyses and disseminates information concerning species and habitats occurring in Sweden. The SSIC provides an open access biodiversity reporting and analysis infrastructure including the Swedish Species Observation System, the Swedish taxonomic backbone Dyntaxa, and tools for species information including traits, terminology, quality assurance and species identification.*1 The content is available to scientists, conservationists and the public. All systems, databases, APIs and web applications, rely on recognized standards to ensure interoperability. The SSIC is a leading partner within the Swedish Biodiversity Data Infrastructure (SBDI). Here we present a data flow (Fig. 1) that exemplifies the strengthening of the cooperation and transfer of experiences between research, community, non-governmental organizations (NGOs), citizen science and governmental agencies, and also presents solutions to current data challenges (e.g., data fragmentation, taxonomic issues or platform relations). This data flow aimed to facilitate the process for evaluating and understanding the distribution and spread of species (e.g., invasive alien species). It provides Findable, Accessible, Interoperable and Reusable (FAIR) data and links related information between different parties such as universities, NGOs, county administrative boards (CABs) and environmental protection agencies (EPAs). The digital structure is built on the national Swedish taxonomic backbone Dyntaxa, which prevents data fragmentation due to taxonomic issues and acts as a common standard for all users. The chain of information contains systems, tools and a linked data flow for reporting observations, verification procedures, and it can work as an early warning system for surveillance regarding certain species. After an observation is reported, an alert can be activated, field checks can be carried out, and if necessary, eradication measures can be activated. The verification tool that traditionally has been focused on the quality of species identification has been improved, providing verification of geographic precision. This is equally important for eradication actions as is species accuracy. A digital catalogue of eradication methods is in use by the CABs but there are also recommendations on methods for ‘public’ use, and collaboration between Invasive Alien Species (IAS) coordinators in regional CABs is currently being developed. The CABs have a separate tool for documentation of eradication measures and, if/when measures are carried out (by CABs), this information can be fed back from the CAB-tool into the database in SSIC where it is possible to search for, and visualize, this information. Taxonomic integrity over time should be intact and related to the taxon identifier (ID) provided by Dyntaxa. However, metadata, such as geographic position, date, verification status, mitigation results, etc., will be fully us","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981960","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":"Data Standards and Interoperability Challenges for Biodiversity Digital Twin: A novel and transformative approach to biodiversity research and application","authors":"Sharif Islam, Hanna Koivula, Dag Endresen, Erik Kusch, Dmitry Schigel, Wouter Addink","doi":"10.3897/biss.7.112373","DOIUrl":"https://doi.org/10.3897/biss.7.112373","url":null,"abstract":"The Biodiversity Digital Twin (BioDT) project (2022-2025) aims to create prototypes that integrate various data sets, models, and expert domain knowledge enabling prediction capabilities and decision-making support for critical issues in biodiversity dynamics. While digital twin concepts have been applied in industries for continuous monitoring of physical phenomena, their application in biodiversity and environmental sciences presents novel challenges (Bauer et al. 2021, de Koning et al. 2023). In addition, successfully developing digital twins for biodiversity requires addressing interoperability challenges in data standards. BioDT is developing prototype digital twins based on use cases that span various data complexities, from point occurrence data to bioacoustics, covering nationwide forest states to specific communities and individual species. The project relies on FAIR principles (Findable, Accessible, Interoperable, and Reusable) and FAIR enabling resources like standards and vocabularies (Schultes et al. 2020) to enable the exchange, sharing, and reuse of biodiversity information, fostering collaboration among participating research infrastructures (DiSSCo, eLTER, GBIF, and LifeWatch) and data providers. It also involves creating a harmonised abstraction layer using Persistent Identifiers (PID) and FAIR Digital Object (FDO) records, alongside semantic mapping and crosswalk techniques to provide machine-actionable metadata (Schultes and Wittenburg 2019, Schwardmann 2020). Governance and engagement with research infrastructure stakeholders play crucial roles in this regard, with a focus on aligning technical and data standards discussions. In addition to data, models and workflows are key elements in BioDT. Models in the BioDT context are formal representations of problems or processes, implemented through equations, algorithms, or a combination of both, which can be executed by machine entities. The current twin prototypes are considering both statistical and mechanistic models, introducing significant variations in (1) data requirements, (2) modelling approaches and philosophy, and (3) model output. The BioDT consortium will develop guidelines and protocols for how to describe these models, what metadata to include, and how they will interact with the diverse datasets. While discussions on this topic exist within the broader context of biodiversity and ecological sciences (Jeltsch et al. 2013, Fer et al. 2020), the BioDT project is strongly committed to finding a solution within its scope. In the twinning context, data and models need to be executed within a computing infrastructure and also need to adhere to FAIR principles. Software within BioDT includes a suite of tools that facilitate data acquisition, storage, processing, and analysis. While some of these tools already exist, the challenge lies in integrating them within the digital twinning framework. One approach to achieving integration is through workflow representation, encompa","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981858","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":"Documenting Biodiversity in Underrepresented Languages using Crowdsourcing","authors":"Mohammed Kamal-Deen Fuseini, Agnes Abah, Andra Waagmeester","doi":"10.3897/biss.7.112431","DOIUrl":"https://doi.org/10.3897/biss.7.112431","url":null,"abstract":"Biodiversity is the variety of life on Earth, and it is essential for our planet's health and well-being. Language is also a powerful medium for documenting and preserving cultural heritage, including knowledge about biodiversity. However, many indigenous and underrepresented languages are at risk of disappearing, taking with them valuable information about local ecosystems. Also, many species are at risk of extinction, and much of our knowledge about biodiversity is in underrepresented languages. (Cardoso et al. 2019). This can make it challenging to document and protect biodiversity, as well as to share this knowledge with others. Crowdsourcing is a way to collect information from a large number of people, and it can be a valuable tool for documenting biodiversity in underrepresented languages. By crowdsourcing, leveraging the iNaturalist platform, and volunteer contributors in the open movement including the Dagbani*1 and Igbo*2 Wikimedian communities, we can reach people who have knowledge about local biodiversity, but who may not have been able to share this knowledge before. For instance, the Dagbani and Igbo Wikimedia contributors did not have enough content on biodiversity data until they received education about the need. This can help us to fill in the gaps in our knowledge about biodiversity, and to protect species that are at risk of extinction. In this presentation, we will discuss the use of crowdsourcing to document biodiversity in underrepresented languages, the challenges and opportunities of using crowdsourcing for this purpose, and some examples of successful projects. We will also discuss the importance of sharing knowledge about biodiversity with others and share some ideas on how to do this. We believe that crowdsourcing has the potential to be a powerful tool for documenting biodiversity in underrepresented languages. By working together, we can help protect our planet's biodiversity and ensure that this knowledge is available to future generations.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980578","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":"Combining Camera Trap Data and Environmental Data to Estimate the Effects of Environmental Gradients on Abundance of the Asian Elephant Elephas maximus in Cambodia","authors":"Ret Thaung, Jackson Frechette, Matthew Luskin, Zachary Amir","doi":"10.3897/biss.7.112100","DOIUrl":"https://doi.org/10.3897/biss.7.112100","url":null,"abstract":"Asian elephant ( Elephas maximus ) populations in Cambodia are currently declining, and the effect of environmental degradation on the abundance and health of elephants is poorly understood. We used camera trap data from 42 locations between 2016 to 2020 in the southern Cardamom Mountains to investigate the impact of environmental degradation on the abundance and condition of Asian elephants. Camera trap data were organized using CameraSweet software to retrieve both number of individuals and their condition. For a number of individuals, we defined independent captures spatially and temporally. To assess condition, we created a visual scoring system based on past research (Wemmer et al. 2006, Fernando et al. 2009, Morfeld et al. 2014, Wijeyamohan et al. 2014, Morfeld et al. 2016, Schiffmann et al. 2020). This scoring system relies on visual assessment of the muscle and fat in relation to the pelvis, ribs, and back bone. To validate this subjective scoring system, two scorers reviewed elephant captures by using 10 reference photos and then reviewing each other’s assessment in the first five images showing the elephant's body condition. This method minimizes subjective assessment from two scorers. Environmental variables (Suppl. material 1) such as distance to forest edge, forest integrity index, elevation, global human settlements, distance to road, distance to river, night light and forest cover were obtained, then reclassified in ArcGIS to a common 1 km grid. We implemented hierarchical N-mixture models to investigate the impacts of environmental variables on abundance and used cumulative link models to investigate the impact of the same environmental variables on condition. We found that Asian elephant abundance exhibited a significant positive relationship with distance to forest edges, where abundance was greater further away from a forest edge. We found that body condition score exhibited the relationship with forest cover and Forest Landscape Integrity Index, which suggested that grassland and less dense forest support better condition. Moreover, males exhibited significantly higher scores for body condition than females, while babies, juveniles, and subadults all exhibited lower body condition scores compared to adults. The significantly lower body condition of young elephants is concerning and suggests that conservation managers in the region should prioritize environmental conditions that support young elephant health. Our results identify key environmental variables that appear to promote Asian elephant abundance and health in the Cardamom Mountains, thus informing relevant conservation actions to support this endangered species in Cambodia and beyond.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980426","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":"Unearthing the Past for a Sustainable Future: Extracting and transforming data in the Biodiversity Heritage Library for climate action","authors":"JJ Dearborn, Mike Lichtenberg, Joel Richard, Joseph deVeer, Michael Trizna, Katie Mika","doi":"10.3897/biss.7.112436","DOIUrl":"https://doi.org/10.3897/biss.7.112436","url":null,"abstract":"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 im","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981675","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":"\"Publish First\": A Rapid, GPT-4 Based Digitisation System for Small Institutes with Minimal Resources","authors":"Rukaya Johaadien, Michal Torma","doi":"10.3897/biss.7.112428","DOIUrl":"https://doi.org/10.3897/biss.7.112428","url":null,"abstract":"We present a streamlined technical solution (\"Publish First\") designed to assist smaller, resource-constrained herbaria in rapidly publishing their specimens to the Global Biodiversity Information Facility (GBIF). Specimen data from smaller herbaria, particularly those in biodiversity-rich regions of the world, provide a valuable and often unique contribution to the global pool of biodiversity knowledge (Marsico et al. 2020). However, these institutions often face challenges not applicable to larger herbaria, including a lack of staff with technical skills, limited staff hours for digitization work, inadequate financial resources for specialized scanning equipment, cameras, lights, and imaging stands, limited (or no) access to computers and collection management software, and unreliable internet connections. Data-scarce and biodiversity rich countries are also often linguistically diverse (Gorenflo et al. 2012), and staff may not have English skills, which means pre-existing online data publication resources and guides are of limited use. The \"Publish First\" method we are trialing, addresses several of these issues: it drastically simplifies the publication process so technical skills are not necessary; it minimizes administrative tasks saving time; it uses simple, cheap and easily available hardware; it does not require any specialized software; and the process is so simple that there is little to no need for any written instructions. \"Publish first\" requires staff to attach QR code labels containing identifiers to herbarium specimen sheets, scan these sheets using a document scanner costing around €300, then drag and drop these files to an S3 bucket (a cloud container that specialises in storing files). Subsequently, these images are automatically processed through an Optical Character Recognition (OCR) service to extract text, which is then passed on to OpenAI's Generative Pre-Transformer 4 (GPT-4) Application Programming Interface (API), for standardization. The standardized data is integrated into a Darwin Core Archive file that is automatically published through GBIF's Integrated Publishing Toolkit (IPT) (GBIF 2021). The most technically challenging aspect of this project has been the standardization of OCR data to Darwin Core using the GPT-4 API, particularly in crafting precise prompts to address the inherent inconsistency and lack of reliability in these Large Language Models (LLMs). Despite this, GPT-4 outperformed our manual scraping efforts. Our choice of GPT-4 as a model was a naive one: we implemented the workflow on some pre-digitized specimens from previously published Norwegian collections, compared the published data on GBIF with GPT-4's Darwin Core standardized output, and found the results satisfactory. Moving forward, we plan to undertake more rigorous additional research to compare the effectiveness and cost-efficiency of different LLMs as Darwin Core standardization engines. We are also particularly interested in exploring ","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981805","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":"Safeguarding Access to 500 Years of Biodiversity Data: Sustainability planning for the Biodiversity Heritage Library","authors":"Martin Kalfatovic, Bianca Crowley, JJ Dearborn, Colleen Funkhouser, David Iggulden, Kelli Trei, Elisa Herrmann, Kevin Merriman","doi":"10.3897/biss.7.112430","DOIUrl":"https://doi.org/10.3897/biss.7.112430","url":null,"abstract":"The Biodiversity Heritage Library (BHL) is the world’s largest open access digital library for biodiversity literature and archives. Headquartered at Smithsonian Libraries and Archives (SLA), BHL is a global consortium of research institutions working together to build and maintain a critical piece of biodiversity data infrastructure. BHL provides free access to over 60 million pages of biodiversity content from the 15th–21st centuries. BHL works with the biodiversity community to develop tools and services to facilitate greater access, interoperability, and reuse of content and data. Through taxonomic intelligence tools developed by Global Names Architecture, BHL has indexed more than 230 million instances of taxonomic names throughout its collection, allowing researchers to locate publications about specific taxa. BHL also works to bring historical literature into the modern network of scholarly research by retroactively assigning DOIs (digital object identifiers) and making this historical content more discoverable and trackable. Biodiversity databases such as the Catalogue of Life, International Plant Names Index, Tropicos, World Register of Marine Species, and iNaturalist, rely on literature housed in BHL. Locked within its 60 million pages are valuable species occurrence data and observations from expeditions. To make this data FAIR (findable, accessible, interoperable, and reusable), BHL and its partners are working on a data pipeline to transform textual content into actionable data that can be deposited into data aggregators such as the Global Biodiversity Information Facility (GBIF). BHL’s shared vision began in 2006 among a small community of passionate librarians, technologists, and biodiversity researchers. Uniting as a consortium, BHL received grant funding to build and launch its digital library. BHL partners received additional grant funding for further technical development and targeted digitization projects. When initial grant funding ended in 2012, BHL established an annual dues model for its Members and Affiliates to help support central BHL operating expenses and technical development. This dues model continues today, along with in-kind contributions of staff time from Members and Affiliates. Significant funding is also provided by the Smithsonian in the form of an annual U.S. federal allocation, endowment funds, and SLA cost subvention, to host the technical infrastructure and Secretariat staff. BHL also relies on user donations to support its program. Though BHL has diversified funding streams over the years, it relies heavily on a few key institutions to cover operating costs. Though these institutions have overarching open access, research, and sustainability goals, priorities and resources to achieve these goals shift over time. Without long-term commitments, institutions may choose to prioritize new projects over established programs. Many BHL contributors have experienced funding loss for digitization projects, reducin","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980587","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}