Biodiversity Information Science and Standards最新文献

{"title":"How Reproducible are the Results Gained with the Help of Deep Learning Methods in Biodiversity Research?","authors":"Waqas Ahmed, Vamsi Krishna Kommineni, Birgitta Koenig-ries, Sheeba Samuel","doi":"10.3897/biss.7.112698","DOIUrl":"https://doi.org/10.3897/biss.7.112698","url":null,"abstract":"In recent years, deep learning methods in the biodiversity domain have gained significant attention due to their ability to handle the complexity of biological data and to make processing of large volumes of data feasible. However, these methods are not easy to interpret, so the opacity of new scientific research and discoveries makes them somewhat untrustworthy. Reproducibility is a fundamental aspect of scientific research, which enables validation and advancement of methods and results. If results obtained with the help of deep learning methods were reproducible, this would increase their trustworthiness. In this study, we investigate the state of reproducibility of deep learning methods in biodiversity research. We propose a pipeline to investigate the reproducibility of deep learning methods in the biodiversity domain. In our preliminary work, we systematically mined the existing literature from Google Scholar to identify publications that employ deep-learning techniques for biodiversity research. By carefully curating a dataset of relevant publications, we extracted reproducibility-related variables for 61 publications using a manual approach, such as the availability of datasets and code that serve as fundamental criteria for reproducibility assessment. Moreover, we extended our analysis to include advanced reproducibility variables, such as the specific deep learning methods, models, hyperparameters, etc., employed in the studies. To facilitate the automatic extraction of information from publications, we plan to leverage the capabilities of large language models (LLMs). By using the latest natural language processing (NLP) techniques, we aim to identify and extract relevant information pertaining to the reproducibility of deep learning methods in the biodiversity domain. This study seeks to contribute to the establishment of robust and reliable research practices. The findings will not only aid in validating existing methods but also guide the development of future approaches, ultimately fostering transparency and trust in the application of deep learning techniques in biodiversity research.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912496","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":"A Simple Recipe for Cooking your AI-assisted Dish to Serve it in the International Digital Specimen Architecture","authors":"Wouter Addink, Sam Leeflang, Sharif Islam","doi":"10.3897/biss.7.112678","DOIUrl":"https://doi.org/10.3897/biss.7.112678","url":null,"abstract":"With the rise of Artificial Intelligence (AI), a large set of new tools and services is emerging that supports specimen data mapping, standards alignment, quality enhancement and enrichment of the data. These tools currently operate in isolation, targeted to individual collections, collection management systems and institutional datasets. To address this challenge, DiSSCo, the Distributed System of Scientific Collections, is developing a new infrastructure for digital specimens, transforming them into actionable information objects. This infrastructure incorporates a framework for annotation and curation that allows the objects to be enriched or enhanced by both experts and machines. This creates the unique possibility to plug-in AI-assisted services that can then leverage digital specimens through this infrastructure, which serves as a harmonised Findable, Accessible, Interoperable and Reusable (FAIR) abstraction layer on top of individual institutional systems or datasets. An early example of such services are the ones developed in the Specimen Data Refinery workflow (Hardisty et al. 2022). The new architecture, DS Arch or Digital Specimen Architecture, is built on the concept of FAIR Digital Objects (FDO) (Islam et al. 2020). All digital specimens and related objects are served with persistent identifiers and machine-readable FDO records with information for machines about the object together with a pointer to its machine-readable type description. The type describes the structure of the object, its attributes and describes allowed operations. The digital specimen type and specimen media type are based on existing Biodiversity Information Standards (TDWG) such as Darwin Core, Access to Biological Collection Data (ABCD) Schema and Audiovisual Core Multimedia Resources Metadata Schema, and include support for annotation operations based on the World Wide Web Consortium (W3C) Annotations Data Model. This enables AI-assisted services registered with DS Arch to autonomously discover digital specimen objects and determine the actions they are authorised to perform. AI-assisted services can facilitate various tasks such as digitisation, extract new information from specimen images, create relations with other objects or standardise data. These operations can be done autonomously, upon user request, or in tandem with expert validation. AI-assisted services registered with DS Arch, can interact in the same way with all digital specimens worldwide when served through DS Arch with their uniform FDO representation, even if the content richness, level of standardisation and scope of the specimen is different. DS Arch has been designed to serve digital specimens for living and preserved specimens, and preserved environmental, earth system and astrogeology samples. With the AI-assisted services, data can be annotated with new data, alternative values, corrections, and with new entity relationships. As a result, the digital specimens become Digital Extended S","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912500","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 Ecological and Socio-Environmental Data and Networks to Achieve Sustainability","authors":"Laure Berti-Equille, Rafael L. G. Raimundo","doi":"10.3897/biss.7.112703","DOIUrl":"https://doi.org/10.3897/biss.7.112703","url":null,"abstract":"Environmental degradation in Brazil has been recently amplified by the expansion of agribusiness, livestock and mining activities with dramatic repercussions on ecosystem functions and services. The anthropogenic degradation of landscapes has substantial impacts on indigenous peoples and small organic farmers whose lifestyles are intimately linked to diverse and functional ecosystems. Understanding how we can apply science and technology to benefit from biodiversity and promote socio-ecological transitions ensuring equitable and sustainable use of common natural resources is a critical challenge brought on by the Anthropocene. We present our approach to combine biodiversity and environmental data, supported by two funded research projects: DATAPB (Data of Paraíba) to develop tools for FAIR (Findable, Accessible, Interoperable and Reusable) data sharing for governance and educational projects and the International Joint Laboratory IDEAL (artificial Intelligence, Data analytics, and Earth observation applied to sustAinability Lab) launched in 2023 by the French Institute for Sustainable Development (IRD, Institut de Recherche pour le Développement) and co-coordinated by the authors, with 50 researchers in 11 Brazilian and French institutions working on Artificial Intelligence and socio-ecological research in four Brazilian Northeast states: Paraíba, Rio Grande do Norte, Pernambuco, and Ceará (Berti-Equille and Raimundo 2023). As the keystone of these transdisciplinary projects, the concept-paradigm of socio-ecological coviability (Barrière et al. 2019) proposes that we should explore multiple ways by which relationships between humans and nonhumans (fauna, flora, natural resources) can reach functional and persistent states. Transdisciplinary approaches to agroecological transitions are urgently needed to address questions such as: How can researchers, local communities, and policymakers co-produce participatory diagnoses that depict the coviability of a territory? How can we conserve biodiversity and ecosystem functions, promote social inclusion, value traditional knowledge, and strengthen bioeconomies at local and regional scales? How can biodiversity, social and environmental data, and networks help local communities in shaping adaptation pathways towards sustainable agroecological practices? How can researchers, local communities, and policymakers co-produce participatory diagnoses that depict the coviability of a territory? How can we conserve biodiversity and ecosystem functions, promote social inclusion, value traditional knowledge, and strengthen bioeconomies at local and regional scales? How can biodiversity, social and environmental data, and networks help local communities in shaping adaptation pathways towards sustainable agroecological practices? These questions require transdisciplinary approaches and effective collaboration among environmental, social, and computer scientists, with the involvement of local stakeholders (Biggs et al. ","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912056","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":"Leveraging AI in Biodiversity Informatics: Ethics, privacy, and broader impacts","authors":"Kristen \"Kit\" Lewers","doi":"10.3897/biss.7.112701","DOIUrl":"https://doi.org/10.3897/biss.7.112701","url":null,"abstract":"Artificial Intelligence (AI) has been heralded as a hero by some and rejected as a harbinger of destruction by others. While many in the community are excited about the functionality and promise AI brings to the field of biodiversity informatics, others have reservations regarding its widespread use. This talk will specifically address Large Language Models (LLMs) highlighting both the pros and cons of using LLMs. Like any tool, LLMs are neither good nor bad in and of themselves, but AI does need to be used within the appropriate scope of its ability and properly. Topics to be covered include model opacity (Franzoni 2023), privacy concerns (Wu et al. 2023), potential for algorithmic harm (Marjanovic et al. 2021) and model bias (Wang et al. 2020) in the context of generative AI along with how these topics differ from similar concerns when using traditional ML (Machine Learning) applications. Potential for implementation and training to ensure the most fair environment when leveraging AI and keeping FAIR (Findability, Accessibility, Interoperability, and Reproducibility) principles in mind, will also be discussed. The topics covered will be mainly framed through the Biodiversity Information Standards (TDWG) community, focusing on sociotechnical aspects and implications of implementing LLMs and generative AI. Finally, this talk will explore the potential applicability of TDWG standards pertaining to uniform prompting vocabulary when using generative AI and employing it as a tool for biodiversity informatics.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912302","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":"Mapping across Standards to Calculate the MIDS Level of Digitisation of Natural Science Collections","authors":"Elspeth Haston, Mathias Dillen, Sam Leeflang, Wouter Addink, Claus Weiland, Dagmar Triebel, Eirik Rindal, Anke Penzlin, Rachel Walcott, Josh Humphries, Caitlin Chapman","doi":"10.3897/biss.7.112672","DOIUrl":"https://doi.org/10.3897/biss.7.112672","url":null,"abstract":"The Minimum Information about a Digital Specimen (MIDS) standard is being developed within Biodiversity Information Standards (TDWG) to provide a framework for organisations, communities and infrastructures to define, measure, monitor and prioritise the digitisation of specimen data to achieve increased accessibility and scientific use. MIDS levels indicate different levels of completeness in digitisation and range from Level 0: not yet meeting minimal required information needs for scientific use to Level 3: fulfilling the requirements for Digital Extended Specimens (Hardisty et al. 2022) by inclusion of persistent identifiers (PIDs) that connect the specimen with derived and related data. MIDS Levels 0–2 are generic for all specimens. From MIDS Level 2 onwards we make a distinction between biological, geological and palaeontological specimens. While MIDS represents a minimum specification, defining and publishing more extensive sets of information elements (extensions) is readily feasible and explicitly recommended. The MIDS level of a digital specimen can be calculated based on the availability of certain information elements. The MIDS standard applies to published data. The ability to map from, to and between TDWG standards is key to being able to measure the MIDS level of the digitised specimen(s). Each MIDS term is being mapped across TDWG standards involving Darwin Core (DwC), the Access to Biological Collections Data (ABCD) Schema and Latimer Core (LtC, Woodburn et al. 2022), using mapping properties provided by the Simple Knowledge Organization System (SKOS) ontology. In this presentation, we will show selected case studies that demonstrate the implementation of the MIDS standard supplemented by MIDS mappings to ABCD, to LtC, and to the Distributed System of Scientific Collections' (DISSCo) Open Digital Specimen specification. The studies show the mapping exercise in practice, with the aim of enabling fully automated and accurate calculations. To provide a reliable indicator for the level of digitisation completeness, it is important that calculations are done consistently in all implementations.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912310","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":"Progress with Repository-based Annotation Infrastructure for Biodiversity Applications","authors":"Peter Cornwell","doi":"10.3897/biss.7.112707","DOIUrl":"https://doi.org/10.3897/biss.7.112707","url":null,"abstract":"Rapid development since the 1980s of technologies for analysing texts, has led not only to widespread employment of text 'mining', but also to now-pervasive large language model artificial intelligence (AI) applications. However, building new, concise, data resources from historic, as well as contemporary scientific literature, which can be employed efficiently at scale by automation and which have long-term value for the research community, has proved more elusive. Efforts at codifying analyses, such as the Text Encoding Initiative (TEI), date from the early 1990s and were initially driven by the social sciences and humanities (SSH) and linguistics communities, and extended with multiple XML-based tagging schemes, including in biodiversity (Miller et al. 2012). In 2010, the Bio-Ontologies Special Interest Group (of the International Society for Computational Biology) presented its Annotation Ontology (AO), incorporating JavaScript Object Notation and broadening previous XML-based approaches (Ciccarese et al. 2011). From 2011, the Open Annotation Data Model (OADM) (Sanderson et al. 2013) focused on cross-domain standards with utility for Web 3.0, leading to the W3C Web Annotation Data Model (WADM) Recommendation in February 2017*1 and the potential for unifying the multiplicity of already-in-use tagging approaches. This continual evolution has made the preservation of investment using annotation methods, and in particular of the connections between annotations and their context in source literature, particularly challenging. Infrastructure that entered service during the intervening years does not yet support WADM, and has only recently started to address the parallel emergence of page imagery-based standards such as the International Image Interoperability Framework (IIIF). Notably, IIIF instruments such as Mirador-2, which has been employed widely for manual creation and editing of annotations in SSH, continue to employ the now-deprecated OADM. Although multiple efforts now address combining IIIF and TEI text coordinate systems, they are currently fundamentally incompatible. However, emerging repository technologies enable preservation of annotation investment to be accomplished comprehensively for the first time. Native IIIF support enables interactive previewing of annotations within repository graphical user interfaces and dynamic serialisation technologies provide compatibility with existing XML-based infrastructures. Repository access controls can permit experts to trace annotation sources in original texts even if the literature is not publicly accessible, e.g., due to copyright restriction. This is of paramount importance, not only because surrounding context can be crucial to qualify formal terms that have been annotated, such as collecting country. Also, contemporary automated text mining—essential for operation at the scale of known biodiversity literature—is not 100% accurate and manual checking of uncertainties is currently essentia","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911890","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":"Application of Fuzzy Measures to Move Towards Cyber-Taxonomy","authors":"Richardson Ciguene, Aurélien Miralles, Francis Clément","doi":"10.3897/biss.7.112677","DOIUrl":"https://doi.org/10.3897/biss.7.112677","url":null,"abstract":"The species inventory of global biodiversity is constantly revised and refined by taxonomic research, through the addition of newly discovered species and the reclassification of known species. This almost three-century-old project provides essential knowledge for humankind. In particular, knowledge of biodiversity establishes a foundation for developing appropriate conservation strategies. An accurate global inventory of species relies on the study of millions of specimens housed all around the world in natural history collections. For the last two decades, biological taxonomy has generated an increasing amount of data every year, and notably through the digitization of collection specimens, has gradually been transformed into a big data science. In recognition of this trend, the French National Museum of Natural History has embarked on a major research and engineering challenge within its information system: the adoption of cyber-taxonomic practices that require easy access to data on specimens housed in natural history collections all over the world. To this end, an important step is to automatically complete and reconcile the heterogeneous classification data usually associated with specimens managed in different collection databases. We describe here a new fuzzy approach to reconciling the classifications in multiple databases, enabling more accurate taxonomic retrieval of specimen data across databases.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"2021 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912505","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":"Migration of the Catalogue of Afrotropical Bees into TaxonWorks","authors":"Dmitry Dmitriev, Connal Eardley, Willem Coetzer","doi":"10.3897/biss.7.112702","DOIUrl":"https://doi.org/10.3897/biss.7.112702","url":null,"abstract":"The Catalogue of Afrotropical Bees provides a comprehensive checklist of species of bees known from Sub-Saharan Africa and the western Indian Ocean islands, excluding the honey bee ( Apis mellifera Linnaeus) (Eardley and Urban 2010). The checklist has a detailed bibliography of the group, distribution records, and biological associations (visited flowers, host plants, plants used as nests, as well as parasitoids). The database, which was originally built in Microsoft Access, and later managed using Specify Software, was recently migrated to TaxonWorks. TaxonWorks is an integrated, web-based platform designed specifically for the needs of practicing taxonomists and biodiversity scientists, and maintained by the SpeciesFile Group. TaxonWorks has a variety of tools that were designed to help import, manage, validate, and package data for future exports (e.g., in the Darwin Core-Archives (DwC-A; GBIF 2021) or Catalogue of Life's COL-DP formats). Although TaxonWorks has batch upload functionality (e.g., in Darwin Core-Archive, BibTeX format), the complexity of the original dataset (Fig. 1) required special handling, and a custom migration was built to transfer the data from the original format. TaxonWorks could now be used to produce a paper-style catalogue or share the data via the TaxonWorks public interface, TaxonPages.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912817","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":"I Know Something You Don’t Know: The annotation saga continues…","authors":"James Macklin, David Shorthouse, Falko Glöckler","doi":"10.3897/biss.7.112715","DOIUrl":"https://doi.org/10.3897/biss.7.112715","url":null,"abstract":"Over the past 20 years, the biodiversity informatics community has pursued components of the digital annotation landscape with varying degrees of success. We will provide an historical overview of the theory, the advancements made through a few key projects, and will identify some of the ongoing challenges and opportunities. The fundamental principles remain unchanged since annotations were first proposed. Someone (or something): (1) has an enhancement to make elsewhere from the source where original data or information are generated or transcribed; (2) wishes to broadcast these statements to the originator and to others who may benefit; and (3) expects persistence, discoverability, and attribution for their contributions alongside the source. The Filtered Push project (Morris et al. 2013) considered several use cases and pioneered development of services based on the technology of the day. The exchange of data between parties in a universally consistent way necessitated the development of a novel draft standard for data annotations via an extension of the World Wide Web Consortium’s Web Annotation Working Group standard (Sanderson et al. 2013) to be sufficiently informative for a data curator to confidently make a decision. Figure 2 from Morris et al. (2013), reproduced here as Fig. 1, outlines the composition of an annotation data package for a taxonomic identification. The package contains the data object(s) associated with an occurrence, an expression of the motivation(s) for updating, some evidence for an assertion, and a stated expectation for how the receiving entity should take action. The Filtered Push and Annosys (Tschöpe et al. 2013) projects also considered implementation strategies involving collection management systems (e.g., Symbiota) and portals (e.g., European Distributed Institute of Taxonomy, EDIT). However, there remain technological barriers for these systems to operate at scale, the least of which is the absence of globally unique, persistent, resolvable identifiers for shared objects and concepts. Major aggregation infrastructures like the Global Biodiversity Information Facility (GBIF) and the Distributed System of Scientific Collections (DiSSCo) rely on data enhancement to improve the quality of their resources and have annotation services in their work plans. More recently, the Digital Extended Specimen (DES) concept (Hardisty et al. 2022) will rely on annotation services as key components of the proposed infrastructure. Recent work on annotation services more generally has considered various new forms of packaging and delivery such as Frictionless Data (Fowler et al. 2018), Journal Article Tag Suite XML (Agosti et al. 2022), or nanopublications (Kuhn et al. 2018). There is risk in fragmentation of this landscape and disenfranchisement of both biological collections and the wider research community if we fail to align the purpose, content, and structure of these packages or if these fail to remain aligned with FAIR prin","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913130","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":"Amplifying the Power of eDNA by Making it FAIR","authors":"Miwa Takahashi, Oliver Berry","doi":"10.3897/biss.7.112553","DOIUrl":"https://doi.org/10.3897/biss.7.112553","url":null,"abstract":"Environmental DNA (eDNA) is a fast-growing biomonitoring approach to detect species and map their distributions, with the number of eDNA publications exponentially increasing in the past decade. While millions of DNA sequences are often generated and assigned to taxa in each publication, these records are stored in numerous locations (e.g., supplementary materials at journals’ servers, open data publishing platforms such as Dryad) and in various formats, which makes it difficult to find, access, re-use and integrate datasets. Making eDNA data FAIR (findable, accessible, interoperable, re-usable) has vast potential to improve how the biological environment is measured and how change is detected and understood. For instance, it would allow biomonitoring and species distribution modelling studies across extended space and time scales, which is logistically difficult or impossible for individual projects. It would also shed light on “dark” (unassigned) DNA sequences by facilitating their storage and re-analyses with updated ever-growing DNA reference databases. Several challenges are associated with making eDNA FAIR, including how to standardise data formats and bioinformatics workflows, and simplifying the process of post-publication data archiving so that it is acceptable for eDNA practitioners to adopt. Over the next three years, we plan to work closely with biodiversity data platforms such as the Global Biodiversity Information Facility (GBIF) and Atlas of Living Austrlia (ALA), eDNA science journals, and eDNA practitioners, to solve these challenges and enable eDNA to achieve its revolutionary potential as a unified source of information that supports environmental management.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689431","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}