Biodiversity Information Science and Standards最新文献

{"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}

{"title":"Extracting Masks from Herbarium Specimen Images Based on Object Detection and Image Segmentation Techniques","authors":"Hanane Ariouat, Youcef Sklab, M. Pignal, Régine Vignes Lebbe, Jean-Daniel Zucker, Edi Prifti, E. Chenin","doi":"10.3897/biss.7.112161","DOIUrl":"https://doi.org/10.3897/biss.7.112161","url":null,"abstract":"Herbarium specimen scans constitute a valuable source of raw data. Herbarium collections are gaining interest in the scientific community as their exploration can lead to understanding serious threats to biodiversity. Data derived from scanned specimen images can be analyzed to answer important questions such as how plants respond to climate change, how different species respond to biotic and abiotic influences, or what role a species plays within an ecosystem. However, exploiting such large collections is challenging and requires automatic processing. A promising solution lies in the use of computer-based processing techniques, such as Deep Learning (DL). But herbarium specimens can be difficult to process and analyze as they contain several kinds of visual noise, including information labels, scale bars, color palettes, envelopes containing seeds or other organs, collection-specific barcodes, stamps, and other notes that are placed on the mounting sheet. Moreover, the paper on which the specimens are mounted can degrade over time for multiple reasons, and often the paper's color darkens and, in some cases, approaches the color of the plants.\u0000 Neural network models are well-suited to the analysis of herbarium specimens, while making abstraction of the presence of such visual noise. However, in some cases the model can focus on these elements, which eventually can lead to a bad generalization when analyzing new data on which these visual elements are not present (White et al. 2020). It is important to remove the noise from specimen scans before using them in model training and testing to improve its performance. Studies have used basic cropping techniques (Younis et al. 2018), but they do not guarantee that the visual noise is removed from the cropped image. For instance, the labels are frequently put at random positions into the scans, resulting in cropped images that still contain noise. White et al. (2020) used the Otsu binarization method followed by a manual post-processing and a blurring step to adjust the pixels that should have been assigned to black during segmentation. Hussein et al. (2020) used an image labeler application, followed by a median filtering method to reduce the noise. However, both White et al. (2020) and Hussein et al. (2020) consider only two organs: stems and leaves. Triki et al. (2022) used a polygon-based deep learning object detection algorithm. But in addition to being laborious and difficult, this approach does not give good results when it comes to fully identifying specimens. \u0000 In this work, we aim to create clean high-resolution mask extractions with the same resolution as the original images. These masks can be used by other models for a variety of purposes, for instance to distinguish the different plant organs. Here, we proceed by combining object detection and image segmentation techniques, using a dataset of scanned herbarium specimens. We propose an algorithm that identifies and retains the pixels belongi","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73621649","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 Journey to a TDWG Mappings Task Group and its Plans for the Future","authors":"David Fichtmueller","doi":"10.3897/biss.7.112148","DOIUrl":"https://doi.org/10.3897/biss.7.112148","url":null,"abstract":"Some Biodiversity Information Standards (TDWG) standards have had mappings to other standards for years or even decades. However each standard is using its own approach to documenting those mappings, some are incomplete and often hard to find. There is no TDWG recommended approach for how mappings should be documented, like the Standards Documentation Standard (SDS) does for the standards themselves. \u0000 During TDWG 2022 in Sofia, Bulgaria, the topic of mapping between standards was mentioned several times throughout the conference, which led to an impromptu discussion about standards mappings at the Unconference slot on the last conference day. Afterwards a dedicated Slack channel within the TDWG Slack workspace was added to continue the conversation (#mappings-between-standards). During further discussions, both within the Technical Architecture Group (TAG) of TDWG and during separate video conferences on the topic, it was decided to form a dedicated task group under the umbrella of the TAG. This task group is still in the process of formation. The goal of the group is to review the current state of mappings for TDWG standards, align approaches by the different standards to foster interoperability and give recommendations for current and future standards on how to specify mappings. Further work to define the strategy and scope for achieving these goals is needed, particularly to gain community input and acceptance. Consideration has been given to a range of possible types of mappings, which serve the different use cases and expectations for mappings such as machine actionability and improved documentation of the TDWG standards landscape to aid user understanding and implementation. In this talk we will show the work that has already been done, outline our planned steps and invite the community to give input on our process.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91524354","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 the Atlas of Living Flanders, a Challenging Path","authors":"Dimitri Brosens, Sten Migerode, Aaike De Wever","doi":"10.3897/biss.7.112155","DOIUrl":"https://doi.org/10.3897/biss.7.112155","url":null,"abstract":"In Belgium, a federal country in the heart of Europe, the competencies for nature conservation and nature policy lie within the regions. The Research Institute for Nature and Forest (INBO) is an independent research institute, funded by the Flemish regional government, which underpins and evaluates biodiversity policy and management by means of applied scientific research, and sharing of data and knowledge.\u0000 One of the 12 strategic goals in the 2009-2015 INBO strategic planning was that: 'INBO manages data and makes them accessible. It looks into appropriate data gathering methods and means by which to disseminate data and make them readily available'. Since 2009, the INBO has steadily evolved into a research institute with a strong emphasis on open data and open science. In 2010 INBO became a data publisher for the Global Biodiversity Information Facility (GBIF), adopted an open data and open access policy and is known for being an open science institute in Flanders, Belgium. In 2021, a question arose from the council of ministers on the possibility and availability of a public portal for biodiversity data. The goal of this portal should be to ensure findability, availability, and optimal usability of biodiversity data, initially for policy makers, but also for the wider public. With the Living Atlas project already high on our radar, an analysis project, funded by the Flemish government, started in December 2021. All the entities in the department of 'Environment' contributed to a requirements and feasibility study, a proof of concept (POC) Living Atlas for Flanders was set up and the required budget was calculated.\u0000 During the requirements and feasibility study we questioned the agency for nature and forest (ANB), the Flanders Environment Agency (VMM), Flemish land agency (VLM) and the Department of Environment with the help of a professional inquiry agency IPSOS on the possible relevance for policy of a Flemish biodiversity portal, the need of high resolution data (geographical and temporal scale) and the availability of biodiversity data in Flanders, focussed on key species, protected species and other Flemish priority species.\u0000 During the technical proof of concept, we tested the Living Atlases (LA) software suite as the most mature candidate for a Flemish Living Atlas. We checked how we could set up a LA installation in our own Amazon Web Services (AWS) environment, evaluated all the used technologies, estimated the maintenance and infrastructure cost, the needed profiles and the number of full-time equivalent personnel we would need to run a performant Atlas of Living Flanders.\u0000 The goal of this talk is to inform the audience on the steps we took, the hurdles we encountered and how we are trying to convince our policy makers of the benefits of an Atlas of Living Flanders.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88258987","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":" Invasions, Plagues, and Epidemics: The Atlas of Living Australia’s deep dive into biosecurity","authors":"Andrew Turley, Erin Roger","doi":"10.3897/biss.7.112127","DOIUrl":"https://doi.org/10.3897/biss.7.112127","url":null,"abstract":"Early detection of new incursions of species of biosecurity concern is crucial to protecting Australia’s environment, agriculture, and cultural heritage. As Australia’s largest biodiversity data repository, the Atlas of Living Australia (ALA) is often the first platform where new species incursions are recorded. The ALA holds records of more than 2,380 exotic species and over 1.9 million occurrences of pests, weeds, and diseases—many of which are reported though citizen science. However, until recently there has been no systematic mechanism for notifying relevant biosecurity authorities of potential biosecurity threats. To address this, the ALA partnered with the (Australian) Commonwealth Department of Agriculture, Fisheries and Forestry to develop the Biosecurity Alerts System. Two years on, the project has demonstrated the benefits of biosecurity alerts, but significant barriers exist as we now work to expand this system to State and Territory biosecurity agencies, and seek new sources of biosecurity data. In our presentation, we discuss a brief history of invasive alien species in Australia, the Biosecurity Alerts System, and how we are approaching issues with taxonomy, data standards, and cultural sensitivities in aggregating biosecurity data. We conclude by detailing our progress in expanding the alerts system and tackling systemic issues to help elevate Australia’s biosecurity system.","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"222 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76987901","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":"DiversityIndia Meets: Pioneering citizen science through collaborative data mobilization","authors":"Vijay Barve, Nandita Barman, Arjan Basu Roy, Amol Patwardhan, Purab Chowdhury","doi":"10.3897/biss.7.112163","DOIUrl":"https://doi.org/10.3897/biss.7.112163","url":null,"abstract":"DiversityIndia, founded in 2001, is an online community dedicated to promoting meaningful discussions and facilitating the exchange of diverse perspectives on lesser-known taxonomic groups, including butterflies, moths, dragonflies, spiders, and more. The core idea behind DiversityIndia is to establish a network of like-minded individuals who possess a deep passion for these subjects and actively participate in various aspects of biodiversity observation and research.\u0000 Initially, the taxonomic focus of DiversityIndia centered around butterflies, which led to the creation of the ButterflyIndia Yahoo email group. The group quickly gained recognition for its significant contributions in sharing valuable insights about butterflies, including information about their habitats and lesser-known species. ButterflyIndia also played a vital role in facilitating connections among scientists and researchers who were dedicated to studying Lepidoptera. As a result of its collaborative efforts, the group actively contributed to major book projects and web portals, further enhancing the knowledge and resources available to the butterfly research community. As time progressed, the group expanded its presence to include various social media platforms like Orkut, Facebook, Flickr and more, thereby expanding its influence and reach.\u0000 The realization of a significant need for empirical research on butterflies, requiring the involvement of both specialists and enthusiasts across diverse habitats, led to the first ButterflyIndia Meet in 2004 at Shendurney, Kerala. This pioneering concept garnered immense success, attracting participants from diverse regions of the country and backgrounds. Since then, several ButterflyIndia Meets have been organized, resulting in the documentation of numerous butterfly species. Building upon this success, DragonflyIndia and SpiderIndia were established with similar objectives and have successfully coordinated multiple gatherings (Fig. 1).\u0000 One of the most notable DiversityIndia Meets occurred in April 2022, held in Sundarbans, West Bengal. This particular meet marked a significant milestone as the documented dataset, comprising information on all taxonomic groups observed during the event, was published through the Global Biodiversity Information Facility (GBIF) (Roy et al. 2022). This publication allowed for wider accessibility and utilization of the valuable biodiversity data collected during the meet.\u0000 In addition to the Sundarbans meet, ongoing efforts are currently underway to gather occurrence data from all the previous meetings conducted by DiversityIndia (Table 1). The aim is to compile and mobilize this data on GBIF as datasets, involving active participation from the members who attended these meetings. This endeavor seeks to maximize the availability and usefulness of the biodiversity information gathered through the various DiversityIndia Meets over time.\u0000 According to the published records so far (Global Biodiversity Informa","PeriodicalId":9011,"journal":{"name":"Biodiversity Information Science and Standards","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80036704","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}