Biodiversity Information Science and Standards最新文献

{"title":"OpenObs: Living Atlases platform for French biodiversity data","authors":"Alice Ainsa, Sophie Pamerlon, Anne-Sophie Archambeau, Solène Robert, Rémi Beauvieux","doi":"10.3897/biss.7.112179","DOIUrl":"https://doi.org/10.3897/biss.7.112179","url":null,"abstract":"The OpenObs project, led by Patrinat, was launched in September 2017, and the first version of this tool was released in October 2020. OpenObs is based on the Atlas of Living Australia platform, supported by the Global Biodiversity Information Facility (GBIF) community, particularly the Living Atlases (LA) collective.\u0000 OpenObs enables the visualization and downloading of observation data on species available in the National Inventory of Natural Heritage (INPN), the national platform of SINP (Information System for the Inventory of Natural Heritage). It provides open access to non-sensitive public data and includes all available observations, whether they are occurrence or synthesis data.\u0000 As of July 2023, OpenObs has 134,922,015 observation records, and new data is reguarly added (at least twice a year). Furthermore, the project is constantly evolving with new developments planned, such as a user validation interface and new cartographic tools.\u0000 We will present the architecture of this LA-based national biodiversity portal (Fig. 1), as well as its planned new functionality and development roadmap.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"16 15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87077943","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":"An Australian Model of Cooperative Data Publishing to OBIS and GBIF","authors":"Katherine Tattersall, P. Newman, Sachit Rajbhandari, Dave Watts, Mahmoud Sadeghi","doi":"10.3897/biss.7.112228","DOIUrl":"https://doi.org/10.3897/biss.7.112228","url":null,"abstract":"The Australian Commonwealth Science and Industrial Research Organisation (CSIRO) hosts both the Australian Ocean Biodiversity Information System (OBIS) and Global Biodiversity Information Facility (GBIF) nodes within the National Collections and Marine Infrastructure (NCMI) business unit. OBIS-AU is led by the NCMI Information and Data Centre and publishes marine biodiversity data in the Darwin Core (DwC) standard via an Integrated Publishing Toolkit (IPT), with over 450 marine datasets at present. The Australian GBIF node is hosted by a separate team at the Atlas of Living Australia (ALA), a national-scale biodiversity analytical and knowledge delivery portal. The ALA aggregates and publishes over 800 terrestrial and marine datasets from a wide variety of research institutes, museums and collections, governments and citizen science agencies, including OBIS-AU. Many OBIS-AU published datasets are harvested and republished by ALA and vice-versa.\u0000 OBIS-AU identifies, performs Quality Control and formats marine biodiversity and observation data, then publishes directly to the OBIS international data repository and portal, using GBIF IPT technology. The ALA data processing pipeline harvests, aggregates and enhances datasets from many sources with authoritative taxonomic and spatial reference data before passing the data on to GBIF. OBIS-AU and ALA are working together to ensure that the publication pathways for any datasets managed by both (with potential for duplication of records and incomplete metadata harvests) are rationalised and that a single collaborative workflow across both units is followed for publication to GBIF. Recently, the data management groups have established an agreement to cooperatively publish marine data and eDNA data. OBIS-AU have commenced publishing datasets directly to GBIF with ALA endorsement.\u0000 We present the convergent evolution of OBIS and GBIF data publishing in Australia, adaptive data workflows to maintain data and metadata integrity, challenges encountered, how domain expertise ensures data quality and the benefits of sharing data skills and code, especially in publishing eDNA data types in DwC (using the DNA-derived data extension) and exploring the new CamTrap Data Package using Frictionless data. We also present the work that both data groups are doing toward adopting the GBIF new Unified Data model for publishing data. This Australian case study demonstrates the strengths of collaborative data publishing and offers a model that minimises replication of data in global aggregators through the development of regional integrated data publishing pipelines.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79790555","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":"Making Schemas and Mappings Available and FAIR: A metadata and schema crosswalk registry from the FAIRCORE4EOSC project","authors":"T. Suominen, Joonas Kesäniemi, Hanna Koivula","doi":"10.3897/biss.7.112223","DOIUrl":"https://doi.org/10.3897/biss.7.112223","url":null,"abstract":"Community standards like the Darwin Core (Darwin Core Task Group 2009) together with semantic artefacts (controlled vocabularies, ontologies, thesauri, and other knowledge organisation systems) are key building blocks for the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016), specifically as emphasized in the Interoperability principle I2 “(Meta)data use vocabularies that follow FAIR principles”. However, most of these artefacts are actually not FAIR themselves (Le Franc et al. 2020). \u0000 To address this, the FAIRCORE4EOSC project (2022-25) is developing a Metadata Schema and Crosswalk Registry (MSCR) that will allow registered users and communities to create, register and version schemas and crosswalks that all have persistent identifiers (PIDs). The published content can then be searched, browsed and downloaded without restrictions. The MSCR will also provide an API to facilitate the transformation of data from one schema to another via registered crosswalks. It will provide projects and individual researchers with the possibility to manage their metadata schemas and/or relevant metadata schema crosswalks. The schema and crosswalks will be shared with the community for reuse and extension supported by a proper versioning mechanism.\u0000 The registry tool will facilitate better interoperability between resource catalogues and information systems using different (metadata) schemas and encourage organisations and especially researchers to share their metadata interoperability by publishing the metadata crosswalks used in their workflows, which are currently not visible (FAIRification). By providing an easy-to-use graphical user interface (GUI) for creating crosswalks, the GUI will attract users currently relying on project-specific solutions.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"200 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83798084","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 Impossible Museum: A national infrastructure to digitise the UK’s natural science collections","authors":"Vincent Smith, Helen Hardy, Laurence Livermore, Lisa French, Tara Wainwright, Josh Humphries","doi":"10.3897/biss.7.112294","DOIUrl":"https://doi.org/10.3897/biss.7.112294","url":null,"abstract":"The Distributed System of Scientific Collections UK (DiSSCo United Kingdom, Smith et al. 2022) is a proposal to the UK Research and Innovation (UKRI) Infrastructure Programme to revolutionise how we manage, share and use the UK’s natural science collections, creating a distributed network that provides a step change in research infrastructure for the UK. While the physical integration of such a collection would be almost inconceivable, its digital integration is within reach. Building on the UK Natural History Museum’s (NHM) digitisation programme and in partnership with more than 90 collection-holding institutions across the length and breadth of the UK, DiSSCo UK seeks to unlock the full scientific, economic and social benefits of the UK’s natural science collections, which are presently constrained by the limits of physical access. With just 8% of the UK’s 137 million specimens currently available digitally, their role in the emerging biodiversity data revolution is diminished. Through nationally coordinated action, DiSSCo UK seeks to massively accelerate the digitisation of these collections and the impact of these data. \u0000 Five options to digitise UK collections are presently under consideration. These options iterate across the collection groups, number and type of institution, technical infrastructure level and \"catalysis\" to capitalise on the benefits of unlocking data and accelerating data production. Subject to UKRI approval, the full business cases for a preferred option will go through an 18–24 month approval process starting November 2023, unlocking tens to hundreds of millions of pounds of investment in UK collections. We will outline the strategic case, options and operational model for DISSCo UK, updating on our coordination, digitisation and catalysis activities.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"129 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78158316","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":"Mobilising Long-Term Natural Environment and Biodiversity Data and Exposing it for Federated, Semantic Queries","authors":"Hanna Koivula, Christoph Wohner, Barbara Magagna, Paolo Tagliolato Acquaviva d’Aragona, A. Oggioni","doi":"10.3897/biss.7.112221","DOIUrl":"https://doi.org/10.3897/biss.7.112221","url":null,"abstract":"\u0000 Biodiversity and ecosystems cannot be studied without assessing the impacts of changing environmental conditions. Since the 1980s, the U.S. National Science Foundation's Long Term Ecological Research (LTER) Network has been a major force in the field of ecology to better understand ecosystems. In Europe, the LTER developments are led by the the Integrated European Long-Term Ecosystem, critical zone and socio-ecological system Research Infrastructure (eLTER RI), a currently project-based infrastructure initiative with the aim to facilitate high impact research and catalyse new insights about the compounded impacts of climate change, biodiversity loss, soil degradation, pollution, and unsustainable resource use on a range of European ecosystems and socio-ecological systems. The European LTER network, which forms the basis for the up-coming eLTER RI, is active in 26 countries and has 500 registered sites that provide legacy data e.g., historical time-series data about the environment (not only biodiversity). Its site information and dataset metadata with the measured variables are available to be searched at the Dynamic Ecological Information Management System - Site and dataset registry (DEIMS-SDR, \u0000 Wohner et al. 2019). While DEIMS-SDR data models utilize parts of the Ecological Metadata Language (EML) schema 2.0.0, location information follows the European INSPIRE specification.\u0000 \u0000 The future eLTER data is planned to consist of site-based, long-term time-series of ecological data. The eLTER projects have defined eLTER Standard Observations (SO), which will include the minimum set of variables as well as the associated method protocols that can characterise adequately the state and future trends of the Earth's systems. (Masó et al. 2020, Reyers et al. 2017).\u0000 The current eLTER network consists of sites that differ in terms of infrastructure maturity or environment type and may focus on one or several of the future SOs or they are not yet executing any holistic monitoring scheme. The main objective is to convert the eLTER site network into a distributed research infrastructure that incorporates a clearly outlined mandatory monitoring program. Essential to this effort are the suggested variables for eLTER SOs and the corresponding methods and protocols for relevant habitat types according to the European Nature Information System (EUNIS) in each domain. eLTER variables are described by using the eLTER thesaurus \"EnvThes\". These descriptions are currently enhanced by the use of the InteroperAble Descriptions of Observable Property Terminology (I-ADOPT, Magagna et al. 2022) framework to provide the necessary level of detail required for seamless data discovery and integration. Variables and their associated methods and protocols will be formalised to enable automatic site classifications, by building on existing observation representations such as the Extensible Observation Ontology (OBOE), Open Geospatial Consortium's Observation and Measurement, a","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72934532","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":"Towards a Distributed System for Essential Variables for the Southern Ocean","authors":"A. P. van de Putte, Yi-Ming Gan, Alyce Hancock, Ben Raymond","doi":"10.3897/biss.7.112289","DOIUrl":"https://doi.org/10.3897/biss.7.112289","url":null,"abstract":"The Southern Ocean (SO), delinated to the north by the Antarctic convergence, is a unique environment that experiences rapid change in some areas while remaining relatively untouched by human activities. At the same time, these ecosystems are under severe threat from climate change and other stressors. While our understanding of SO biological processes (e.g., species distributions, feeding ecology, reproduction) has greatly improved in recent years, biological data for the region remains patchy, sparse, and unstandardised depending on the taxonomic group (Griffiths et al. 2014).\u0000 Due to the scarcity of standardised observations and data, it is difficult to model and predict SO ecosystem responses to climate change, which is often accompanied by other anthropogenic pressures, such as fishing and tourism. Understanding the dynamics and change in the SO necessitates a comprehensive system of observations, data management, scientific analysis, and ensuing policy recommendations. It should be built as much as feasible from current platforms and standards, and it should be visible, verifiable and shared in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles (Van de Putte and Griffiths 2021). For this we need to identify the stakeholders' needs, sources of data, the algorithms for analysing the data and the infrastructure on which to run the algorithms (Benson and Brooks 2018). Existing synergistic methods for identifying selected variables for (life) monitoring include Essential Biodiversity Variables (EBVs; Pereira and Ferrier 2013), Essential Ocean Variables (EOVs; Miloslavich and Bax 2018), Essential Climate Variables (ECVs; Bojinski and Verstraete 2014), and ecosystem Essential Ocean Variables (eEOVs; Constable and Costa 2016). (For an overview see Muller-Karger and Miloslavich 2018.) These variables, can be integrated into the Southern Ocean Observation System (SOOS) and SOOSmap but also national or global systems (e.g., Group on Earth Observations-Biodiversty Observation Network (GEO-BON)). The resulting data products can in turn be used to inform policy makers.\u0000 The use of Essential Variables (EVs) marks a significant step forward in the monitoring and assessment of SO ecosystems. However, these EVs will necessitate prioritising certain variables and data collection. Here we present the outcomes of a workshop organised in August 2023 that aimed to outline the set Essential Variables and workflows required for a distributed system that can translate biodiversity data (and environmental data) into policy-relevant data products.\u0000 The goals of the workshop were:\u0000 \u0000 \u0000 \u0000 Create an inventory of EVs relevant for the Southern Ocean based on existing efforts by the GEO-BON and the Marine Biodiversity Observation Network (MBON).\u0000 \u0000 \u0000 Identify data requirements and data gaps for calculating such EVs and prioritise EVs to work on.\u0000 \u0000 \u0000 Identify existing workflows and tools.\u0000 \u0000 \u0000 Develop a framework for developing the workf","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73126878","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":"On Image Quality Metadata, FAIR in ML, AI-Readiness and Reproducibility: Fish-AIR example","authors":"Y. Bakiş, Xiaojun Wang, B. Altıntaş, Dom Jebbia, Henry Bart Jr.","doi":"10.3897/biss.7.112178","DOIUrl":"https://doi.org/10.3897/biss.7.112178","url":null,"abstract":"A new science discipline has emerged within the last decade at the intersection of informatics, computer science and biology: Imageomics. Like most other -omics fields, Imageomics also uses emerging technologies to analyze biological data but from the images. One of the most applied data analysis methods for image datasets is Machine Learning (ML). In 2019, we started working on a United States National Science Foundation (NSF) funded project, known as Biology Guided Neural Networks (BGNN) with the purpose of extracting information about biology by using neural networks and biological guidance such as species descriptions, identifications, phylogenetic trees and morphological annotations (Bart et al. 2021). Even though the variety and abundance of biological data is satisfactory for some ML analysis and the data are openly accessible, researchers still spend up to 80% of their time preparing data into a usable, AI-ready format, leaving only 20% for exploration and modeling (Long and Romanoff 2023). For this reason, we have built a dataset composed of digitized fish specimens, taken either directly from collections or from specialized repositories. The range of digital representations we cover is broad and growing, from photographs and radiographs, to CT scans, and even illustrations. We have added new groups of vocabularies to the dataset management system including image quality metadata, extended image metadata and batch metadata. With the image quality metadata and extended image metadata, we aimed to extract information from the digital objects that can possibly help ML scientists in their research with filtering, image processing and object recognition routines. Image quality metadata provides information about objects contained in the image, features and condition of the specimen, and some basic visual properties of the image, while extended image metadata provides information about technical properties of the digital file and the digital multimedia object (Bakış et al. 2021, Karnani et al. 2022, Leipzig et al. 2021, Pepper et al. 2021, Wang et al. 2021) (see details on Fish-AIR vocabulary web page). Batch metadata is used for separating different datasets and facilitates downloading and uploading data in batches with additional batch information and supplementary files.\u0000 Additional flexibility, built into the database infrastructure using an RDF framework, will enable the system to host different taxonomic groups, which might require new metadata features (Jebbia et al. 2023). By the combination of these features, along with FAIR (Findable, Accessable, Interoperable, Reusable) principles, and reproducibility, we provide Artificial Intelligence Readiness (AIR; Long and Romanoff 2023) to the dataset.\u0000 Fish-AIR provides an easy-to-access, filtered, annotated and cleaned biological dataset for researchers from different backgrounds and facilitates the integration of biological knowledge based on digitized preserved specimens into ML pipelines.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90968505","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 Australian Reference Genome Atlas (ARGA): Finding, sharing and reusing Australian genomics data in an occurrence-driven context","authors":"Kathryn Hall, Matt Andrews, Keeva Connolly, Yasima Kankanamge, Christopher Mangion, Winnie Mok, Lars Nauheimer, Goran Sterjov, Nigel Ward, Peter Brenton","doi":"10.3897/biss.7.112129","DOIUrl":"https://doi.org/10.3897/biss.7.112129","url":null,"abstract":"Fundamental to the capacity of Australia’s 15,000 biosciences researchers to answer questions in taxonomy, phylogeny, evolution, conservation, and applied fields like crop improvement and biosecurity, is access to trusted genomics (and genetics) datasets. Historically, researchers turned to single points of origin, like GenBank (part of the United States' National Center for Biotechnology Information), to find the reference or comparative data they needed, but the rapidity of data generation using next-gen methods, and the enormous size and diversity of datasets derived from next-gen sequencing methods, mean that single databases no longer contain all data of a specific class, which may be attributable to individual taxa, nor the full breadth of data types relevant for that taxon. Comprehensively searching for taxonomically relevant data, and indeed, data of types germane to the research question, is a significant challenge for researchers. Data are openly available online, but the data may be stored under synonyms or indexed via unconventional taxonomies. Data repositories are largely disconnected and researchers must visit multiple sites to have confidence that their searches have been exhaustive. Databases may focus on single data types and not store or reference other data assets, though they may be relevant for the taxon of interest. Additionally, our survey of the genomics community indicated that researchers are less likely to trust data with inadequately evidenced provenance metadata. This means that genomics data are hard to find and are often untrusted. Moreover, even once found, the data are in formats that do not interoperate with occurrence and ecological datasets, such as those housed in the Atlas of Living Australia. \u0000 We built the Australian Reference Genome Atlas (ARGA) to overcome the barriers faced by researchers in finding and collating genomics data for Australia’s species, and we have built it so that researchers can search for data within taxonomically accepted contexts and defined intersections and conjunctions with verified and expert ecological datasets. Using a series of ingestion scripts, the ARGA data team has implemented new and customised data mappings that effectively integrate genomics data, ecological traits, and occurrence data within an extended Darwin Core Event framework (GBIF 2018). Here, we will demonstrate how the architecture we derived for ARGA application works, and how it can be extended as new data sources emerge. We then demonstrate how our flexible model can be used to:\u0000 \u0000 \u0000 \u0000 locate genomics data for taxa of interest;\u0000 \u0000 \u0000 explore data within an ecological context; and\u0000 \u0000 \u0000 calculate metrics for data availability for provincial bioregions.\u0000 \u0000 \u0000 \u0000 locate genomics data for taxa of interest;\u0000 explore data within an ecological context; and\u0000 calculate metrics for data availability for provincial bioregions.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"1a 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88128719","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":"Can Biodiversity Data Scientists Document Volunteer and Professional Collaborations and Contributions in the Biodiversity Data Enterprise?","authors":"Robert Stevenson, Elizabeth R. Ellwood, Peter Brenton, P. Flemons, Jeff Gerbracht, Wesley Hochachka, Scott Loarie, Carrie Seltzer","doi":"10.3897/biss.7.112126","DOIUrl":"https://doi.org/10.3897/biss.7.112126","url":null,"abstract":"The collection, archiving and use of biodiversity data depend on a network of pipelines herein called the Biodiversity Data Enterprise (BDE) and best understood globally through the work of the Global Biodiversity Information Facility (GBIF). Efforts to sustain and grow the BDE require information about the data pipeline and the infrastructure that supports it. A host of metrics from GBIF, including institutional participation (member countries, institutional contributors, data publishers), biodiversity coverage (occurrence records, species, geographic extent, data sets) and data usage (records downloaded, published papers using the data) (Miller 2021), document the rapid growth and successes of the BDE (GBIF Secretariat 2022). Heberling et al. (2021) make a convincing case that the data integration process is working.\u0000 The Biodiversity Information Standards' (TDWG) Basis of Record term provides information about the underlying infrastructure. It categorizes the kinds of processes*1 that teams undertake to capture biodiversity information and GBIF quantifies their contributions*2 (Table 1). Currently 83.4% of observations come from human observations, of which 63% are of birds. Museum preserved specimens account for 9.5% of records. In both cases, a combination of volunteers (who make observations, collect specimens, digitize specimens, transcribe specimen labels) and professionals work together to make records available.\u0000 To better understand how the BDE is working, we suggest that it would be of value to know the number of contributions and contributors and their hours of engagement for each data set. This can help the community address questions such as, \"How many volunteers do we need to document birds in a given area?\" or \"How much professional support is required to run a camera trap network?\" For example, millions of observations were made by tens of thousands of observers in two recent BioBlitz events, one called Big Day, focusing on birds, sponsored by the Cornell Laboratory of Ornithology and the other called the City Nature Challenge, addressing all taxa, sponsored jointly by the California Academy of Sciences and the Natural History Musuems of Los Angeles County (Table 2). In our presentation we will suggest approaches to deriving metrics that could be used to document the collaborations and contribution of volunteers and staff using examples from both Human Observation (eBird, iNaturalist) and Preserved Specimen (DigiVol, Notes from Nature) record types. The goal of the exercise is to start a conversation about how such metrics can further the development of the BDE.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81336615","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":"Harmonised Data is Actionable Data: DiSSCo’s solution to data mapping","authors":"Sam Leeflang, W. Addink","doi":"10.3897/biss.7.112137","DOIUrl":"https://doi.org/10.3897/biss.7.112137","url":null,"abstract":"Predictability is one of the core requirements for creating machine actionable data. The better predictable the data, the more generic the service acting on the data can be. The more generic the service, the easier we can exchange ideas, collaborate on initiatives and leverage machines to do the work. It is essential for implementing the FAIR Principles (Findable, Accessible, Interoperable, Reproducible), as it provides the “I” for Interoperability (Jacobsen et al. 2020). The FAIR principles emphasise machine actionability because the amount of data generated is far too large for humans to handle. \u0000 While Biodiversity Information Standards (TDWG) standards have massively improved the standardisation of biodiversity data, there is still room for improvement. Within the Distributed System of Scientific Collections (DiSSCo), we aim to harmonise all scientific data derived from European specimen collections, including geological specimens, into a single data specification. We call this data specification the open Digital Specimen (openDS). It is being built on top of existing and developing biodiversity information standards such as Darwin Core (DwC), Minimal Information Digital Specimen (MIDS), Latimer Core, Access to Biological Collection Data (ABCD) Schema, Extension for Geosciences (EFG) and also on the new Global Biodiversity Information Facility (GBIF) Unified Model. In openDS we leverage the existing standards within the TDWG community but combine these with stricter constraints and controlled vocabularies, with the aim to improve the FAIRness of the data. This will not only make the data easier to use, but will also increase its quality and machine actionability.\u0000 As the first step towards this the harmonisation of terms, we make sure that similar values use the same term in a standard as key. This enables the next step in which we harmonise the values. We can transform free-text values into standardised or controlled vocabularies. For example: instead of using the names J. Doe, John Doe and J. Doe sr. for a collector, we aim to standardise these to J. Doe, with a person identifier that connects this name with more information about the collector.\u0000 Biodiversity information standards such as DwC were developed to lower the bar for data sharing. The downside of including minimal restraints and flexibility is that they provide room for ambiguity, leading to multiple ways of interpretation. This limits interoperability and hampers machine actionability. In DiSSCo, data will come from different sources that use different biodiversity information standards. To cover this, we need to harmonise terms between these standards. To complicate things further, different serialisation methods are used for data exchange. Darwin Core Archives (DwC-A; GBIF 2021) use Comma-separated values (CSV) files. ABCD(EFG) exposed through Biological Collection Access Service (BioCASe) uses XML. And most custom formats use JavaScript Object Notation (JSON).\u0000 In this lightn","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74282797","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}