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Alien insects represent one of the most species rich groups of organisms introduced to Europe, with some responsible for adverse social-economic, human-health, biodiversity and ecosystem impacts. The impacts of invasive alien species, especially on island ecosystems, have been a hot topic of research worldwide. Cyprus is a Mediterranean island at the biogeographic crossroads of Asia, Africa and Europe. This study presents the database of the alien insects of the island of Cyprus as a whole, created through an extensive review including grey literature and online sources. The Alien to Cyprus Entomofauna (ACE) triples the known number of alien insects and adds supplemental information to existing species. Data concerning a total of 349 alien insects are presented alongside an updated checklist and recommendations for informing management. The status of alien insects on the island, their origin, trophic guilds, establishment, pathways of introduction and impacts are discussed. Developing an alien species inventory for the island is challenging due to its geographic position and the increasing movement of people and goods leading to new species introductions. This publication constitutes an important first step towards providing information for effective actions to tackle invasive alien insects on Cyprus. The checklist and accompanying information can underpin understanding of the status and trends of alien species including providing information for risk assessments. ACE will continue to be maintained and updated as new records for Cyprus are made.

A special‐purpose Committee on DNA Sequences as Types was established at the XIX International Botanical Congress (IBC) in Shenzhen, China, in 2017, with a mandate to report to the XX IBC in Madrid in 2024 with recommendations on a preferred course of action with respect to potential amendments of the International Code of Nomenclature for algae, fungi, and plants to allow DNA sequences as types. This is the first in an expected series of papers from the Special‐purpose Committee on this issue. We set out the background to the establishment of the Committee, explore key issues around typification that are pertinent to the question of DNA sequences as types, enumerate pros and cons of allowing DNA sequences as types, and foreshadow options for future discussion and potential recommendations.

Harmonia axyridis (Pallas, 1773), also known as the harlequin ladybird, is an invasive non-native species intentionally introduced to many countries as a biological control agent of agricultural pests. In Greece, H. axyridis was first introduced as a biological control agent in 1994, with releases taking place between 1994 and 2000. For many years there was no evidence to indicate that H. axyridis had established self-sustaining populations. In 2008, a citizen science campaign was initiated aimed at raising awareness regarding the invasive status of H. axyridis to farmers and agronomists. The campaign did not yield results, and it was discontinued in 2011. During this study, the distribution, phenology, and presence of H. axyridis in different habitat types and protected areas in Greece are investigated, using both citizen science data and literature records. Records from iΝaturalist, the Alientoma database and social media examined herein demonstrate that H. axyridis has been established in Greece since 2010. Harmonia axyridis is currently present in 13 administrative districts of Greece, most of them at a considerable distance from the initial release sites. The harlequin ladybird is present in urban and agricultural habitats as well as seventeen NATURA 2000 sites. The adverse socioeconomic and environmental impacts of H. axyridis are briefly discussed alongside suggestions for management activities. Based on our findings, we propose the establishment of a national monitoring scheme for H. axyridis and native ladybirds that will also encourage public participation in recording ladybird observations and provide information on the distribution, spread and impact of this invasive non-native species.

Natural history collections are the foundations upon which all knowledge of natural history is constructed. Biological specimens are the best documentation of variation within each species, increasingly serve as curated sources for reference DNA, and are frequently our only evidence for historical species distribution. Collections represent an enormous multigenerational investment in research infrastructure for the biological sciences, but despite this importance most of the holdings of these institutions remain invisible on the Internet, inaccessible to taxonomists from other countries and hidden from computational biodiversity research. Although comprehensive digitisation of the complete holdings of each natural history collection is the long-term goal, this is an expensive and labor-intensive task and will not be completed in the near future for all collections. However, many benefits could quickly be achieved by publishing high-quality metadata on each collection to increase its visibility, provide the foundations for further digitisation and enable researchers to discover and communicate with collections of interest. This paper summarises the results from a consultation activity carried out in 2020 as part of the SYNTHESYS+ (Synthesys of Systematic Resources), “Developing implementation roadmaps for priority infrastructure areas as part of cooperative RI for biodiversity” project. This consultation was primed through an ideas paper, and introductory webinars and conducted as a facilitated two-week online multilingual discussion around 26 topics grouped under four broad headings (Users, Content, Technology and Governance). The results of these discussions are summarised here, along with the wider context of existing and planned initiatives.

Invasive alien species (IAS) are a key driver of global biodiversity loss. Reducing their spread and impact is a target of the Sustainable Development Goals (SDG target 15.8) and of the EU IAS Regulation 1143/2014. The use of citizen science offers various benefits to alien species’ decision-making and to society, since public participation in research and management boosts awareness, engagement and scientific literacy and can reduce conflict in IAS management. We report the results of a survey on alien species citizen science initiatives within the framework of the European Cooperation in Science and Technology (COST) Action Alien-CSI. We gathered metadata on 103 initiatives across 41 countries, excluding general biodiversity reporting portals, spanning from 2005 to 2020, offering the most comprehensive account of alien species citizen science initiatives on the continent to date. We retrieved information on project scope, policy relevance, engagement methods, data capture, data quality and data management, methods and technologies applied and performance indicators such as the number of records coming from projects, the numbers of participants and publications. The 103 initiatives were unevenly distributed geographically, with countries with a tradition of citizen science showing more active projects. The majority of projects were contributory and were run at a national scale, targeting the general public, alien plants and insects, and terrestrial ecosystems. These factors of project scope were consistent between geographic regions. Most projects focused on collecting species presence or abundance data, aiming to map presence and spread. As 75% of the initiatives specifically collected data on IAS of Union Concern, citizen science in Europe is of policy relevance. Despite this, only half of the projects indicated sustainable funding. Nearly all projects had validation in place to verify species identifications. Strikingly, only about one third of the projects shared their data with open data repositories such as the Global Biodiversity Information Facility or the European Alien Species Information Network. Moreover, many did not adhere to the principles of FAIR data management. Finally, certain factors of engagement, feedback and support, had significant impacts on project performance, with the provision of a map with sightings being especially beneficial. Based on this dataset, we offer suggestions to strengthen the network of IAS citizen science projects and to foster knowledge exchange among citizens, scientists, managers, policy-makers, local authorities, and other stakeholders.

The BiCIKL project is born from a vision that biodiversity data are most useful if they are presented as a network of data that can be integrated and viewed from different starting points. BiCIKL’s goal is to realise that vision by linking biodiversity data infrastructures, particularly for literature, molecular sequences, specimens, nomenclature and analytics. To make those links we need to better understand the existing infrastructures, their limitations, the nature of the data they hold, the services they provide and particularly how they can interoperate. In light of those aims, in the autumn of 2021, 74 people from the biodiversity data community engaged in a total of twelve hackathon topics with the aim to assess the current state of interoperability between infrastructures holding biodiversity data. These topics examined interoperability from several angles. Some were research subjects that required interoperability to get results, some examined modalities of access and the use and implementation of standards, while others tested technologies and workflows to improve linkage of different data types. These topics and the issues in regard to interoperability uncovered by the hackathon participants inspired the formulation of the following recommendations for infrastructures related to (1) the use of data brokers, (2) building communities and trust, (3) cloud computing as a collaborative tool, (4) standards and (5) multiple modalities of access: If direct linking cannot be supported between infrastructures, explore using data brokers to store links Cooperate with open linkage brokers to provide a simple way to allow two-way links between infrastructures, without having to co-organize between many different organisations Facilitate and encourage the external reporting of issues related to their infrastructure and its interoperability. Facilitate and encourage requests for new features related to their infrastructure and its interoperability. Provide development roadmaps openly Provide a mechanism for anyone to ask for help Discuss issues in an open forum Provide cloud-based environments to allow external participants to contribute and test changes to features Consider the opportunities that cloud computing brings as a means to enable shared management of the infrastructure. Promote the sharing of knowledge around big data technologies amongst partners, using cloud computing as a training environment Invest in standards compliance and work with standards organisations to develop new, and extend existing standards Report on and review standards compliance within an infrastructure with metrics that give credit for work on standard compliance and development Provide as many different modalities of access as possible Avoid requiring personal contacts to download data Provide a full description of an API and the data it serves If direct linking cannot be supported between infrastructures, explore using data brokers to store links Cooperate with open linkage brokers to provide a simple way to allow two-way links between infrastructures, without having to co-organize between many different organisations Facilitate and encourage the external reporting of issues related to their infrastructure and its interoperability. Facilitate and encourage requests for new features related to their infrastructure and its interoperability. Provide development roadmaps openly Provide a mechanism for anyone to ask for help Discuss issues in an open forum Provide cloud-based environments to allow external participants to contribute and test changes to features Consider the opportunities that cloud computing brings as a means to enable shared management of the infrastructure. Promote the sharing of knowledge around big data technologies amongst partners, using cloud computing as a training environment Invest in standards compliance and work with standards organisations to develop new, and extend existing standards Report on and review standards compliance within an infrastructure with metrics that give credit for work on standard compliance and development Provide as many different modalities of access as possible Avoid requiring personal contacts to download data Provide a full description of an API and the data it serves Finally, the hackathons were an ideal meeting opportunity to build, diversify and extend the BiCIKL community further, and to ensure the alignment of the community with a common vision on how best to link data from specimens, samples, sequences, taxonomic names and taxonomic literature.

Scientific collections have been built by people. For hundreds of years, people have collected, studied, identified, preserved, documented and curated collection specimens. Understanding who those people are is of interest to historians, but much more can be made of these data by other stakeholders once they have been linked to the people’s identities and their biographies. Knowing who people are helps us attribute work correctly, validate data and understand the scientific contribution of people and institutions. We can evaluate the work they have done, the interests they have, the places they have worked and what they have created from the specimens they have collected. The problem is that all we know about most of the people associated with collections are their names written on specimens. Disambiguating these people is the challenge that this paper addresses. Disambiguation of people often proves difficult in isolation and can result in staff or researchers independently trying to determine the identity of specific individuals over and over again. By sharing biographical data and building an open, collectively maintained dataset with shared knowledge, expertise and resources, it is possible to collectively deduce the identities of individuals, aggregate biographical information for each person, reduce duplication of effort and share the information locally and globally. The authors of this paper aspire to disambiguate all person names efficiently and fully in all their variations across the entirety of the biological sciences, starting with collections. Towards that vision, this paper has three key aims: to improve the linking, validation, enhancement and valorisation of person-related information within and between collections, databases and publications; to suggest good practice for identifying people involved in biological collections; and to promote coordination amongst all stakeholders, including individuals, natural history collections, institutions, learned societies, government agencies and data aggregators.

For centuries, naturalists from the Global North have traveled southwards to collect specimens of species from regions where there were many. The legacy of this is that the large natural history collections and the biodiverse regions of the world are often distant from one another. The unique and often disappearing species and ecosystems in these regions need conservation, yet much of the data on this biodiversity resides in collections and with the experts that work in them, rather than in the countries where it is needed. In this poster we zoom in on Grand Cayman, a small island in the Caribbean, facing severe threats to its endemic biodiversity - from habitat loss to introduced species. More specifically we focus on the endemic blue iguana (Cyclura lewisi) and the invasive green iguana (Iguana iguana). Specimens from the Cayman Islands are dispersed over American and European natural history collections and give data on the species present, their abundances and the interactions among those species (e.g. their food plants and parasites). Such data can be used in risk assessments and conservation planning, and the specimens themselves can inform research, such as population genetics. While the Cayman Islands are small they provide an excellent case study of the issues of conservation, society, climate and other environmental change in the tropics. Islands are highly sensitive to environmental change, but the lessons learned from them can be extended to other areas, including continents. On this poster, we describe the From Blue Iguanas to Blue Vervain project (Fig. 1), which aims to connect the biodiverse Caribbean UK Overseas Territories of Montserrat and the Cayman Islands with natural science collections around the world. We aim to learn ways to address issues of access and benefit-sharing, particularly how residents of these biodiverse places can benefit from the data and research linked to specimens originating in their islands.

Evolutionary understanding is central to biology. It is also an essential prerequisite to understanding and making informed decisions about societal issues such as climate change. Yet, evolution is generally poorly understood by civil society and many misconceptions exist. Citizen science, which has been increasing in popularity as a means to gather new data and promote scientific literacy, is one strategy through which people could learn about evolution. However, despite the potential for citizen science to promote evolution learning opportunities, very few projects implement them. In this paper, we make the case for incorporating evolution education into citizen science, define key learning goals, and suggest opportunities for designing and evaluating projects in order to promote scientific literacy in evolution., X.S.-P. is funded through FCT – Fundação para a Ciência e a Tecnologia, I.P., in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of 19 July. A.M. benefited from funding by the laboratory ‘Evolution et Diversité Biologique’ as part of the ‘Laboratoires d’Excellence’ LABEX TULIP (ANR -10-LABX-41) and LABEX CEBA (ANR-10-LABX-25-01).This article is based upon work from COST Actions ‘CA17127 EuroScitizen’ ‘CA17122 Alien CSI’, ‘CA15212 Citizen Science’, supported by COST (European Cooperation in Science and Technology; www.cost.eu).

People are involved with the collection and curation of all biodiversity data, whether they are researchers, members of the public, taxonomists, conservationists, collection managers or wildlife managers. Knowing who those people are and connecting their biographical information to the biodiversity data they collect helps us contextualise their scientific work. We are particularly concerned with those people and communities involved in the collection and identification of biological specimens. People from herbaria and natural science museums have been collecting and preserving specimens from all over the world for more than 200 years. The problem is that many of these people are only known by unstandardized names written on specimen labels, often with only initials and without any biographical information. The process of identifying and linking individuals to their biographies enables us to improve the quality of the data held by collections while also quantifying the contributions of the often underappreciated people who collected and identified these specimens. This process improves our understanding of the history of collecting, and addresses current and future needs for maintaining the provenance of specimens so as to comply with national and international practices and regulations. In this talk we will outline the steps that collection managers, data scientists, curators, software engineers, and collectors can take to work towards fully disambiguated collections. With examples, we can show how they can use these data to help them in their work, in the evaluation of their collections, and in measuring the impact of individuals and organisations, local to global.

At least 29% of the world’s terrestrial ecosystems have been significantly modified by human activity (Ellis 2011). Total livestock biomass is 15 times greater than that of wild mammals (Bar-On et al. 2018). Crops such as maize, soybean, rice, and wheat cover 23% of available agricultural land (Ritchie and Roser 2013). Even where land is not farmed, it is often converted to highly modified habitats such as urban ecosystems. Within the matrix of anthropogenic ecosystems spread around the world are many wild species (i.e., free-living, non-domesticated or feral). Some native species persist from the original ecosystems, while others colonize anthropogenic ecosystems including alien invasive species. Thus, wild flora and fauna in many areas are part of ecosystems that are dominated by, and often characterised by, the domestic and cultivated organisms that exist in them. Examples include large herbivores raised for milk and meat production, and omnivorous and carnivorous species such as both domestic and feral dogs and cats. Moreover, crops are typically grown in monoculture plots including tree plots in commercial forestry, and an enormous array of cultivated plants are grown in gardens, parks, and along streets in urban areas. Tellingly, in many ecosystems the domestic bee is the primary pollinator and domestic cats are the main predator. Domestic and cultivated organisms are not isolated from wild organisms and indeed they interact in many ways, including predation, parasitism, and herbivory, competition for biotic and abiotic resources, and pathogen transmission. If we are to understand modern ecosystems, clearly we cannot ignore the domesticated and cultivated components of ecosystems. So where are the observational data on these species? iNaturalist reluctantly tolerates them but does not publish them to the Global Biodiversity Information Facility (GBIF), eBird actively discourages recording them, and GBIF doesn’t encourage their submission either. Some information can be gleaned from land cover maps and remote sensing, but without knowing the specific species present in the land use category it is hard to build a holistic picture of an ecosystem. Data on domesticated and cultivated organisms have many uses, including horizon scanning and risk assessment for invasive species, understanding the impacts of invasive species, predicting the spread of disease, the spillover of disease to and from wild and domestic organisms, and many others (Groom et al. 2021). Evidently there are important components of ecosystems largely missing from our public databases. As data on biodiversity becomes more intergrated, such as implementing Digital Extended Specimens (Webster et al. 2021), it will become increasingly important to access data from all aspects of an ecosystem, included those that are domestic and cultivated. In this talk we will expand on why we should be mobilising such data. They can be published without ruining the integrity of data on wild organisms, so why don’t we embrace and use these fundamentally important, but much ignored, data?

The Country Compendium of the Global Register of Introduced and Invasive Species (GRIIS) is a collation of data across 196 individual country checklists of alien species, along with a designation of those species with evidence of impact at a country level. The Compendium provides a baseline for monitoring the distribution and invasion status of all major taxonomic groups, and can be used for the purpose of global analyses of introduced (alien, non-native, exotic) and invasive species (invasive alien species), including regional, single and multi-species taxon assessments and comparisons. It enables exploration of gaps and inferred absences of species across countries, and also provides one means for updating individual GRIIS Checklists. The Country Compendium is, for example, instrumental, along with data on first records of introduction, for assessing and reporting on invasive alien species targets, including for the Convention on Biological Diversity and Sustainable Development Goals. The GRIIS Country Compendium provides a baseline and mechanism for tracking the spread of introduced and invasive alien species across countries globally. Design Type(s) Data integration objective ● Observation design Measurement Type(s) Alien species occurrence ● Evidence of impact invasive alien species assessment objective Technology Type(s) Agent expert ● Data collation Factor Type(s) Geographic location ● Origin / provenance ● Habitat Sample Characteristics - Organism Animalia ● Bacteria ● Chromista ● Fungi ● Plantae ● Protista (Protozoa) ● Viruses Sample Characteristics - Location Global countries

Astorga, Francisca, Groom, Quentin, Shimabukuro, Paloma Helena Fernandes, Manguin, Sylvie, Noesgaard, Daniel, Orrell, Thomas, Sinka, Marianne, Hirsch, Tim, Schigel, Dmitry (2023): Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities. One Health (100484) 16: 1-9, DOI: 10.1016/j.onehlt.2023.100484, URL: http://dx.doi.org/10.1016/j.onehlt.2023.100484

BackgroundPandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a “commons.” Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions.ResultsAs a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates.ConclusionsAlthough this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

Semantic segmentation has been proposed as a tool to accelerate the processing of natural history collection images. However, developing a flexible and resilient segmentation network requires an approach for adaptation which allows processing different datasets with minimal training and validation. This paper presents a cross-validation approach designed to determine whether a semantic segmentation network possesses the flexibility required for application across different collections and institutions. Consequently, the specific objectives of cross-validating the semantic segmentation network are to (a) evaluate the effectiveness of the network for segmenting image sets derived from collections different from the one in which the network was initially trained on; and (b) test the adaptability of the segmentation network for use in other types of collections. The resilience to data variations from different institutions and the portability of the network across different types of collections are required to confirm its general applicability. The proposed validation method is tested on the Natural History Museum semantic segmentation network, designed to process entomological microscope slides. The proposed semantic segmentation network is evaluated through a series of cross-validation experiments designed to test using data from two types of collections: microscope slides (from three institutions) and herbarium sheets (from seven institutions). The main contribution of this work is the method, software and ground truth sets created for this cross-validation as they can be reused in testing similar segmentation proposals in the context of digitization of natural history collections. The cross-validation of segmentation methods should be a required step in the integration of such methods into image processing workflows for natural history collections.

Invasive alien species (IAS) are a key driver of global biodiversity loss. Reducing their spread and impact is a target of the Sustainable Development Goals (SDG target 15.8) and of the EU IAS Regulation 1143/2014. The use of citizen science offers various benefits to alien species (AS) decision making and to society, since public participation in research and management boosts awareness, engagement and scientific literacy and can reduce conflict in IAS management. We report the results of a survey on AS citizen science initiatives within the framework of European Cooperation in Science and Technology (COST) Action Alien-CSI. We gathered metadata on 103 initiatives across 41 countries, excluding general biodiversity reporting portals, spanning from 2005 to 2020, offering the most comprehensive account of AS citizen science initiatives on the continent to date. We retrieved information on project scope, policy relevance, engagement methods, data capture, data quality and data management, methods and technologies applied and performance indicators such as the number of records coming from projects, the numbers of participants and publications. The 103 initiatives were unevenly distributed geographically, with countries with a tradition of CS showing more active projects. The majority of projects were contributory and were run at a national scale, targeting the general public, alien plants and insects, and terrestrial ecosystems. Most projects focused on collecting species presence or abundance data, aiming to map presence and spread. As 75% of the initiatives specifically collected data on IAS of Union Concern, citizen science in Europe is of high policy relevance. Despite this, only half of the projects indicated sustainable funding. Nearly all projects had validation in place to verify species identifications. Strikingly, only about one third of the projects shared their data to open data repositories such as the Global Biodiversity Information facility or the European Alien Species Information Network. Moreover, many did not adhere to the principles of FAIR data management. Based on this dataset, we offer suggestions to strengthen the network of IAS citizen science projects and to foster knowledge exchange among citizens, scientists, managers, policy-makers, local authorities, and other stakeholders.

BiCIKL is an European Union Horizon 2020 project that will initiate and build a new European starting community of key research infrastructures, establishing open science practices in the domain of biodiversity through provision of access to data, associated tools and services at each separate stage of and along the entire research cycle. BiCIKL will provide new methods and workflows for an integrated access to harvesting, liberating, linking, accessing and re-using of subarticle-level data (specimens, material citations, samples, sequences, taxonomic names, taxonomic treatments, figures, tables) extracted from literature. BiCIKL will provide for the first time access and tools for seamless linking and usage tracking of data along the line: specimens > sequences > species > analytics > publications > biodiversity knowledge graph > re-use.

The Distributed System for Scientific Collections (DiSSCo) Research Infrastructure will operate a number of e-services, all of which will have policy requirements for participating institutions. These policies include those related to digital and physical access to specimens, digital image and specimen metadata, and FAIR / Open Data. Previous projects have shown that the policy landscape is complex, and Task 7.3 has developed a policy self-assessment tool that will allow DiSSCo to assess policy alignment across the consortium. This deliverable describes the development of the policy self-assessment tool and provides a walkthrough of the key features. The same technical framework was used to create a digital maturity tool, which was initially proposed by Task 3.1, and this is also described within this document. A set of recommendations are included that outline the future direction for the development of the policy tool. The Distributed System for Scientific Collections (DiSSCo) Research Infrastructure will operate a number´of e-services, all of which will have policy requirements for participating institutions. These policies include those related to digital and physical access to specimens, digital image and specimen metadata, and FAIR / Open Data. Previous projects have shown that the policy landscape is complex, and Task 7.3 has developed a policy self-assessment tool that will allow DiSSCo to assess policy alignment across the consortium. This deliverable describes the development of the policy self-assessment tool and provides a walkthrough of the key features. The same technical framework was used to create a digital maturity tool, which was initially proposed by Task 3.1, and this is also described within this document. A set of recommendations are included that outline the future direction for the development of the policy tool.

Natural history collections play a pivotal role in taxonomy, which in turn supports all of biology, but particularly conservation and biodiversity policy. However, to provide this role, it is necessary to know what specimens are stored where, and how complete the collection is. The biodiversity held within collections globally remains uncertain, with an estimated 1.2 to 2.1 billion (109) specimens (Ariño 2010), of which around 200 million are represented on the Global Biodiversity Information Facility (GBIF). Here we estimate the total biodiversity in collections worldwide by extrapolating from those specimens we know about. Data aggregators such as GBIF provide an ever-changing window into the contents of collections. We use non-parametric estimators that allow for the approximation of the number of classes in an incomplete set, such as the number of species within a collection, but also the proportion of biodiversity preserved on a national or continental level (for example within a taxonomic group, compared to the world or to a continent). Because the contents of data aggregators such as GBIF, are in constant flux, our workflow is made to be repeatable on the monthly snapshots that GBIF provides. The results of the workflow expose data gaps in GBIF, namely that collections from some large geographical regions, such as Asia, are poorly represented, but also taxonomic gaps exist, such as several Coleoptera families where many more species are accepted in the backbone than are represented on GBIF. As more data are published to GBIF the estimates for these taxon groups and geographical regions will improve. The detection of data gaps within data aggregators such as GBIF, and the subsequent mobilisation of missing information remains a priority for both aggregators and researchers (GBIF Secretariat 2022, Collen et al. 2008, Hochkirch et al. 2020). Our workflow will allow for continuous monitoring of collections and groups of collections of their coverage of global biodiversity, and the results can inform their collection development strategy.

Tens of millions of images from biological collections have become available online in the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. Whilst image analysis has become mainstream in consumer applications, it is still only used on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if the images of collection objects could be made accessible as a single corpus. In this paper, we make the case for building infrastructure that could support image analysis of collection objects. We show that such an infrastructure is entirely feasible and well worth the investment.

Biodiversity is a data-intensive science and relies on data from a large number of disciplines in order to build up a coherent picture of the extent and trajectory of life on earth (Bowker 2000). The ability to integrate such data from different disciplines, geographic regions and scales is crucial for making better decisions towards sustainable development. As the Biodiversity Information Standards (TDWG) community tackles standards development and adoption beyond its initial emphases on taxonomy and species distributions, expanding its impact and engaging a wider audience becomes increasingly important. Biological interactions data (e.g., predator-prey, host-parasite, plant-pollinator) have been a topic of interest within TDWG for many years and a Biological Interaction Data Interest Group (IG) was established in 2016 to address that issue. The IG has been working on the complexity of representing interactions data and surveying how Darwin Core (DwC, Wieczorek 2012) is being used to represent them (Salim 2022). The importance of cross-disciplinary science and data inspired the recently funded WorldFAIR project—Global cooperation on FAIR data policy and practice—coordinated by the Committee on Data of the International Science Council (CODATA), with the Research Data Alliance (RDA) as a major partner. WorldFAIR will work with a set of case studies to advance implementation of the FAIR data principles (Fig. 1). The FAIR data principles promote good practices in data management, by making data and metadata Findable, Accessible, Interoperable, and Reusable (Wilkinson 2016). Interoperability will be a particular focus to facilitate cross-disciplinary research. A set of recommendations and a framework for FAIR assessment in a set of disciplines will be developed (Molloy 2022). One of WorldFAIR's case studies is related to plant-pollinator interactions data. Its starting point is the model and schema proposed by Salim (2022) based on the DwC standard, which adheres to the diversifying GBIF data model strategy and on the Plant-Pollinator vocabulary described by Salim (2021). The case study on plant-pollinator interactions originated in the TDWG Biological Interaction Data Interest Group (IG) and within the RDA Improving Global Agricultural Data (IGAD) Community of Practice. IGAD is a forum for sharing experiences and providing visibility to research and work in food and agricultural data and has become a space for networking and blending ideas related to data management and interoperability. This topic was chosen because interoperability of plant-pollinator data is needed for better monitoring of pollination services, understanding the impacts of cultivated plants on wild pollinators and quantifying the contribution of wild pollinators to cultivated crops, understanding the impact of domesticated bees on wild ecosystems, and understanding the behaviour of these organisms and how this influences their effectiveness as pollinators. In addition to the ecological importance of these data, pollination is economically important for food production. In Brazil, the economic value of the pollination service was estimated at US$ 12 billion in 2018 (Wolowski 2019). All eleven case studies within the WorldFAIR project are working on FAIR Implementation Profiles (FIPs), which capture comprehensive sets of FAIR principle implementation choices made by communities of practice and which can accelerate convergence and facilitate cross-collaboration between disciplines (Schultes 2020). The FIPs are published through the FIP Wizard, which allows the creation of FAIR Enabling Resources. The FIPs creation will be repeated by the end of the project and capture results obtained from each case study in order to advance data interoperability. In the first FIP, resources from the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI) were catalogued by the Plant-Pollinator Case Study team, and we expect to expand the existing FAIR Enabling Resources by the end of the project and contribute to plant-pollinator data interoperability and reuse. To tackle the challenge of promoting FAIR data for plant-pollinator interactions within the broad scope of the several disciplines and subdisciplines that generate and use them, we will conduct a survey of existing initiatives handling plant-pollinator interactions data and summarise the current status of best practices in the community. Once the survey is concluded, we will choose at least five agriculture-specific plant-pollination initiatives from our partners, to serve as targets for standards adoption. For data to be interoperable and reusable, it is essential that standards and best practices are community-developed to ensure adoption by the tool builders and data scientists across the globe. TDWG plays an important role in this scenario and we expect to engage the IG and other interested parties in that discussion.

Natural history specimens constitute physical evidence for past observations of nature. They hold further value as the backbone of taxonomy and as historical samples that can be subjected to further analysis. Yet, as physical objects scattered across collections around the world, their scientific use cases are limited by an overall lack of FAIRness, i.e. not easily Findable, Accessible, Interoperable or Reusable. Digitization of these specimens through imaging and categorical metadata capture can improve this FAIRness and has been done to some extent for decades already, but only recently have technical developments in the field of imaging and information technology made it possible for the fruits of these digitization efforts to be widely distributed and utilized. Digitization can be done in many different ways and while protocols may be well formulated during a project or within the responsible digitization team, they are often not communicated beyond to users, get lost with time and are not available for analysts to assess the state of digitization or make requests concerning material for which further information may be available. Hence, as digitization is ongoing, it is a difficult exercise to estimate how much has been digitized, and to what extent, at the collection level or on a larger scale. The Minimum Information about a Digital Specimen (MIDS) standard that is currently under development by a Working Group of Biodiversity Information Standards (TDWG) aims to address this problem by defining hierarchical levels of digitization, each associated with a set of criteria for a level to be achieved by an individual specimen. MIDS has been in development since work in the ICEDIG project in 2019 and its earlier drafts have been used in surveys to try and determine digitization status, often through coarse estimates based on the experience of curators. As a result, these scores cannot be considered reproducible or particularly reliable. Ideally, MIDS scores can be calculated automatically based on a mapping made between the data model of the source and the MIDS criteria. These mappings should also take into account any data value that is known not to be reflective of digitization status. While in an ideal world there would be only one accepted mapping for any data model, different practices causing interoperability conflicts and different kinds of specimens will likely continue to require slight modifications. To make this concrete, we constructed a few JSON schemas that specify such mappings, based on the current specification of the MIDS standard and the state of biodiversity data in a few sources, including Darwin Core archives for occurrence data as produced by the Global Biodiversity Information Facility (GBIF). These schemas could be incorporated into existing data publication workflows to automatically calculate MIDS levels. We have also developed an R Shiny app with a user interface to make calculations and simple adjustments of the schemas. We welcome anyone interested to further develop the syntax and philosophy behind the schemas and their integration into other systems.

A key limiting factor in organising and using information from physical specimens curated in natural science collections is making that information computable, with institutional digitization tending to focus more on imaging the specimens themselves than on efficiently capturing computable data about them. Label data are traditionally manually transcribed today with high cost and low throughput, rendering such a task constrained for many collection-holding institutions at current funding levels. We show how computer vision, optical character recognition, handwriting recognition, named entity recognition and language translation technologies can be implemented into canonical workflow component libraries with findable, accessible, interoperable, and reusable (FAIR) characteristics. These libraries are being developed in a cloud-based workflow platform—the ‘Specimen Data Refinery’ (SDR)—founded on Galaxy workflow engine, Common Workflow Language, Research Object Crates (RO-Crate) and WorkflowHub technologies. The SDR can be applied to specimens’ labels and other artefacts, offering the prospect of greatly accelerated and more accurate data capture in computable form. Two kinds of FAIR Digital Objects (FDO) are created by packaging outputs of SDR workflows and workflow components as digital objects with metadata, a persistent identifier, and a specific type definition. The first kind of FDO are computable Digital Specimen (DS) objects that can be consumed/produced by workflows, and other applications. A single DS is the input data structure submitted to a workflow that is modified by each workflow component in turn to produce a refined DS at the end. The Specimen Data Refinery provides a library of such components that can be used individually, or in series. To cofunction, each library component describes the fields it requires from the DS and the fields it will in turn populate or enrich. The second kind of FDO, RO-Crates gather and archive the diverse set of digital and real-world resources, configurations, and actions (the provenance) contributing to a unit of research work, allowing that work to be faithfully recorded and reproduced. Here we describe the Specimen Data Refinery with its motivating requirements, focusing on what is essential in the creation of canonical workflow component libraries and its conformance with the requirements of an emerging FDO Core Specification being developed by the FDO Forum.

Workflows have become a core part of computational scientific analysis in recent years. Automated computational workflows multiply the power of researchers, potentially turning “hand-cranked” data processing by informaticians into robust factories for complex research output.However, in order for a piece of software to be usable as a workflow-ready tool, it may require alteration from its likely origin as a standalone tool. Research software is often created in response to the need to answer a research question with the minimum expenditure of time and money in resource-constrained projects. The level of quality might range from “it works on my computer” to mature and robust projects with support across multiple operating systems.Despite significant increase in uptake of workflow tools, there is little specific guidance for writing software intended to slot in as a tool within a workflow; or on converting an existing standalone research-quality software tool into a reusable, composable, well-behaved citizen within a larger workflow.In this paper we present 10 simple rules for how a software tool can be prepared for workflow use.

In support of researchers considering using the bioblitz method we collateda number of recommendationsthat may help get maximum benefit from the bioblitz.This list is a supplement toa reviewofbioblitzes.

Natural history collection data available digitally on the web have so far only made limited use of the potential of semantic links among themselves and with cross-disciplinary resources. In a pilot study, botanical collections of the Consortium of European Taxonomic Facilities (CETAF) have therefore begun to semantically annotate their collection data, starting with data on people, and to link them via a central index system. As a result, it is now possible to query data on collectors across different collections and automatically link them to a variety of external resources. The system is being continuously developed and is already in production use in an international collection portal.

The general guidance is, and has always been, that handling bats should be avoided, particularly by the general public, but capturing and handling bats is often unavoidable for bat researchers. While bat researchers are aware of the potential for zoonotic pathogen transmission to occur when handling bats, most notably Rabies virus, some do not wear any (or insufficient) personal protective equipment (PPE) to reduce risks of exposure. This lack of adherence to even minimal biosafety practices may jeopardize both the safety of the bat and the handler. Such concerns became more pressing with the COVID-19 pandemic, but also had been raised as a result of previous outbreaks of human infections linked to contact with animals. The largely unknown potential for handled bats to become infected by a bat researcher, something not previously considered by most field workers, is now widely known in the research community due to the efforts of the IUCN SSC Bat Specialist Group and the Global Union of Bat Diversity Networks (GBatNet). It is also worth noting that the negative framing of bats during the pandemic may have serious consequences for bat conservation (Lopez-Baucells et al. 2017) and a refocusing of the conversation on the deficiencies of human interactions with bats would be a preferable direction. After all, bats are an important component of global ecosystems and provide many ecosystem services to humans (Kunz et al. 2011). Several bat-specific conservation groups have developed guidelines on how to safely handle bats to prevent anthropozoonotic transmission, including the IUCN SSC Bat Specialist Group (Nunez et al. 2020), the Bat Conservation Trust (Anonymous 2020), the Latin American and Caribbean Network for the Conservation of Bats (Suarez-Alvarez and Lopez-Berrizbeitia 2020) and Worksafe Queensland (Paterson 2016). These guidelines largely focus on the increased use of PPE, such as leather or nitrile/latex gloves, respirator masks, long sleeves and long trousers and protective eyewear or face shield, depending on the situation. These build on earlier PPE protocols deployed by some research groups to mitigate the risk of exposure to potential bat pathogens. Given the recent developments, we were interested to know if the use of PPE, in particular wearing gloves, has improved and whether we could detect a change by screening pictures of bats uploaded to iNaturalist. Clearly, it is not possible to evaluate the full adherance to bat handling guidlines, but we believe that use of gloves is a good indicator that best practices are being followed. iNaturalist is a mobile phone app for recording occurrences of wildlife and a social network for amateur and professional naturalists alike. It has about 100,000 to 300,000 active users and tens of thousands of observations of wildlife are recorded each day. On iNaturalist, most observations are evidenced by a picture of the organism. This allows the species to be identified, if possible, or verified by users who have the expertise, but also provides the context of the photograph as well. That context can show what the organism is doing, what developmental stage it is, what the habitat is or how it was photographed, such as whether a person is holding the organism. In our case, we wanted to verify whether people who were handling bats were wearing gloves and to determine whether there has recently been a change in behaviour (i.e. frequency of bat handlers wearing gloves).

The BiCIKL Project is born from a vision that biodiversity data are most useful if they are viewed as a network of data that can be integrated and viewed from different starting points. BiCIKL’s goal is to realize that vision by linking biodiversity data infrastructures, particularly for literature, molecular sequences, specimens, nomenclature and analytics. BiCIKL is an Open Science project creating Open FAIR data and services for the whole research community. BiCIKL intends to inspire novel, innovative, research and build services that can produce new and valuable knowledge, necessary for the protection of biodiversity and of our environment. BiCIKL will develop methods and workflows to harvest, link and access data extracted from literature. Yet, as the project gets underway, we need to better understand the existing infrastructures, their limitations, the nature of the data they hold, the services they provide and particularly how they can interoperate. To do this we organised a week-long hackathon where small teams worked on a number of pilot projects (Table 1) that were chosen to test the existing linkages between infrastructures and to extract novel ones. We will present our experience of running a hackathon and our evaluation of how successfully it achieved its aims. We will also give examples of the projects we conducted and how successful they were. Finally we will give our preliminary evaluation of what we learned about the interoperability of infrastructures and what recommendations we can give to improve their interoperability, whether that is improvements to the data standards used, the means to access the data and analyse them, or even the physical bandwidth and computational restrictions that limit the potential for research.

There are an estimated 8.7 million eukaryotic species globally and knowledge of those organisms is organised about their scientific names and the specimens we have of those species (Sweetlove 2011, Mora et al. 2011). Likewise there are between 1.2 and 2.1 billion (109) specimens held in biodiversity collections globally (Ariño 2010). These collections constitute an infrastructure and scientific tool to understand, catalogue and study biodiversity. Yet we find it hard to answer the simple question, how many species are in a collection? This is not trivial to answer, collections are not completely inventoried, do not use the same taxonomy, and the volume of data is vast (Samy et al. 2013, Ariño 2010). We have developed a method that allows us to take a list of collections and to estimate the species richness contained within them. By doing this we will have a deeper insight into the scientific value of the world's biodiversity collections. Dealing with non-homogeneous and non-random, but incomplete, sampling of sites is a common issue that occurs in many ecological studies (Magurran and McGill 2011, Colwell et al. 2012, Gotelli and Colwell 2001). By using techniques and toolboxes, such as iNEXT (Chao et al. 2014b) and vegan (Oksanen et al. 2020) we can estimate species richness under these conditions. In the case of collections we consider not only the digitized and published proportion of preserved collections, but make extrapolations to the specimens that have not made their way to the Global Biodiversity Information Facility (GBIF) yet. Nevertheless, to calculate on such large datasets we need to employ innovative Big Data analytic tools. GBIF contains 1.8 billion observations that amount to 120 GB of data compressed. This can then be interrogated in the cloud or locally using tools such as Galaxy, which has made it possible to process large numbers of records in a single batch. We can now evaluate the biodiversity within collections, and divide the result by taxon and geographical region, and compare them to one another. Ultimately, this work will allow individual collections and consortia to evaluate their coverage of biodiversity and help them better target their collecting strategies.

Community science (also often referred to as citizen science) provides a unique opportunity to address questions beyond the scope of other research methods whilst simultaneously engaging communities in the scientific process. This leads to broad educational benefits, empowers people, and can increase public awareness of societally relevant issues such as the biodiversity crisis. As such, community science has become a favourable framework for researching alien species where data on the presence, absence, abundance, phenology, and impact of species is important in informing management decisions. However, uncertainties arising at different stages can limit the interpretation of data and lead to projects failing to achieve their intended outcomes. Focusing on alien species centered community science projects, we identified key research questions and the relevant uncertainties that arise during the process of developing the study design, for example, when collecting the data and during the statistical analyses. Additionally, we assessed uncertainties from a linguistic perspective, and how the communication stages among project coordinators, participants and other stakeholders can alter the way in which information may be interpreted. We discuss existing methods for reducing uncertainty and suggest further solutions to improve data reliability. Further, we make suggestions to reduce the uncertainties that emerge at each project step and provide guidance and recommendations that can be readily applied in practice. Reducing uncertainties is essential and necessary to strengthen the scientific and community outcomes of community science, which is of particular importance to ensure the success of projects aimed at detecting novel alien species and monitoring their dynamics across space and time.The online version contains supplementary material available at 10.1007/s10530-022-02858-8.

The utopian vision is of a future where a digital representation of each object in our collections is accessible through the internet and sustainably linked to other digital resources. This is a long term goal however, and in the meantime there is an urgent need to share data about our collections at a higher level with a range of stakeholders (Woodburn et al. 2020). To sustainably achieve this, and to aggregate this information across all natural science collections, the data need to be standardised (Johnston and Robinson 2002). To this end, the Biodiversity Information Standards (TDWG) Collection Descriptions (CD) Interest Group has developed a data standard for describing collections, which is approaching formal review for ratification as a new TDWG standard. It proposes 20 classes (Suppl. material 1) and over 100 properties that can be used to describe, categorise, quantify, link and track digital representations of natural science collections, from high-level approximations to detailed breakdowns depending on the purpose of a particular implementation. The wide range of use cases identified for representing collection description data means that a flexible approach to the standard and the underlying modelling concepts is essential. These are centered around the ‘ObjectGroup’ (Fig. 1), a class that may represent any group (of any size) of physical collection objects, which have one or more common characteristics. This generic definition of the ‘collection’ in ‘collection descriptions’ is an important factor in making the standard flexible enough to support the breadth of use cases. For any use case or implementation, only a subset of classes and properties within the standard are likely to be relevant. In some cases, this subset may have little overlap with those selected for other use cases. This additional need for flexibility means that very few classes and properties, representing the core concepts, are proposed to be mandatory. Metrics, facts and narratives are represented in a normalised structure using an extended MeasurementOrFact class, so that these can be user-defined rather than constrained to a set identified by the standard. Finally, rather than a rigid underlying data model as part of the normative standard, documentation will be developed to provide guidance on how the classes in the standard may be related and quantified according to relational, dimensional and graph-like models. So, in summary, the standard has, by design, been made flexible enough to be used in a number of different ways. The corresponding risk is that it could be used in ways that may not deliver what is needed in terms of outputs, manageability and interoperability with other resources of collection-level or object-level data. To mitigate this, it is key for any new implementer of the standard to establish how it should be used in that particular instance, and define any necessary constraints within the wider scope of the standard and model. This is the concept of the ‘collection description scheme,’ a profile that defines elements such as: which classes and properties should be included, which should be mandatory, and which should be repeatable; which controlled vocabularies and hierarchies should be used to make the data interoperable; how the collections should be broken down into individual ObjectGroups and interlinked, and how the various classes should be related to each other. which classes and properties should be included, which should be mandatory, and which should be repeatable; which controlled vocabularies and hierarchies should be used to make the data interoperable; how the collections should be broken down into individual ObjectGroups and interlinked, and how the various classes should be related to each other. Various factors might influence these decisions, including the types of information that are relevant to the use case, whether quantitative metrics need to be captured and aggregated across collection descriptions, and how many resources can be dedicated to amassing and maintaining the data. This process has particular relevance to the Distributed System of Scientific Collections (DiSSCo) consortium, the design of which incorporates use cases for storing, interlinking and reporting on the collections of its member institutions. These include helping users of the European Loans and Visits System (ELViS) (Islam 2020) to discover specimens for physical and digital loans by providing descriptions and breakdowns of the collections of holding institutions, and monitoring digitisation progress across European collections through a dynamic Collections Digitisation Dashboard. In addition, DiSSCo will be part of a global collections data ecosystem requiring interoperation with other infrastructures such as the GBIF (Global Biodiversity Information Facility) Registry of Scientific Collections, the CETAF (Consortium of European Taxonomic Facilities) Registry of Collections and Index Herbariorum. In this presentation, we will introduce the draft standard and discuss the process of defining new collection description schemes using the standard and data model, and focus on DiSSCo requirements as examples of real-world collection descriptions use cases.

The natural history specimens of the world have been documented on paper labels, often physically attached to the specimen itself. As we transcribe these data to make them digital and more useful for analysis, we make interpretations. Sometimes these interpretations are trivial, because the label is unambiguous, but often the meaning is not so clear, even if it is easily read. One key element that suffers from considerable ambiguity is people’s names. Though a person is indivisible, their name can change, is rarely unique and can be written in many ways. Yet knowing the people associated with data is incredibly useful. Data on people can be used to validate other data, simplify data capture, link together data across domains, reduce duplication-of-effort and facilitate data-gap-analysis. In addition, people data enable the discovery of individuals unique to our collections, the collective charting of the history of scientific researchers and the provision of credit to the people who deserve it (Groom et al. 2020). We foresee a future where the people associated with collections are not ambiguous, are shared globally, and data of all kinds are linked through the people who generate them. The TDWG People in Biodiversity Data Task Group is therefore working on a guide to the disambiguation of people in natural history collections. The ultimate goal is to connect the various strings of characters on specimen labels and other documentation to persistent identifiers (PIDs) that unambiguously link a name “string” to the identity of a person. In working towards this goal, 150 volunteers in the Bionomia project have linked 21 million specimens to persistent identifiers for their collectors and determiners. An additional 2 million specimens with links to identifiers for people have already emerged directly from collections that make use of the recently ratified Darwin Core terms recordedByID and identifiedByID. Furthermore, the CETAF Botany Pilot conducted among a group of European herbaria and museums has connected over 1.4 million specimens to disambiguated collectors (Güntsch et al. 2021). Still, given the estimated 2 billion (Ariño 2010) natural history specimens globally, there is much more disambiguation to be done. The process of disambiguation starts with a trigger, which is often the transcription of a specimen’s label data. Unambiguous identification of the collector may facilitate this transcription, as it offers knowledge of their biographical details and collecting habits, allowing us to infer missing information such as collecting date or locality. Another trigger might be the flagging of inconsistent data during data entry or resulting from data quality processes, revealing for instance that multiple collectors have been conflated. A disambiguation trigger is followed by the gathering of data, then the evaluation of the results and finally by the documentation of the new information. Disambiguation is not always straightforward and there are many pitfalls. It requires access to biographical data, and identifiers to be minted. In the case of living people, they have to cooperate with being disambiguated and we have to follow legal and ethical guidelines. In the case of dead people, particularly those long dead, disambiguation may require considerable research. We will present the progress made by the People in Biodiversity Data Task Group and their recommendations for disambiguation in collections. We want to encourage other institutions to engage with a global effort of linking people to persistent identifiers to collaboratively improve all collection data.

Invasive alien species are repeatedly shown to be amongst the top threats to biodiversity globally. Robust indicators for measuring the status and trends of biological invasions are lacking, but essential for monitoring biological invasions and the effectiveness of interventions. Here, we formulate and demonstrate three such indicators that capture the key dimensions of species invasions, each a significant and necessary advance to inform invasive alien species policy targets: 1) Rate of Invasive Alien Species Spread, which provides modelled rates of ongoing introductions of species based on invasion discovery and reporting. 2) Impact Risk, that estimates invasive alien species impacts on the environment in space and time and provides a basis for nationally targeted prioritization of where best to invest in management efforts. 3) Status Information on invasive alien species, that tracks improvement in the essential dimensions of information needed to guide relevant policy and data collection and in support of assessing invasive alien species spread and impact. We show how proximal, model-informed status and trend indicators on invasive alien species can provide more effective global (and national) reporting on biological invasions, and how countries can contribute to supporting these indicators.

Domestic and captive animals and cultivated plants should be recognised as integral components in contemporary ecosystems. They interact with wild organisms through such mechanisms as hybridization, predation, herbivory, competition and disease transmission and, in many cases, define ecosystem properties. Nevertheless, it is widespread practice for data on domestic, captive and cultivated organisms to be excluded from biodiversity repositories, such as natural history collections. Furthermore, there is a lack of integration of data collected about biodiversity in disciplines, such as agriculture, veterinary science, epidemiology and invasion science. Discipline-specific data are often intentionally excluded from integrative databases in order to maintain the “purity” of data on natural processes. Rather than being beneficial, we argue that this practise of data exclusivity greatly limits the utility of discipline-specific data for applications ranging from agricultural pest management to invasion biology, infectious disease prevention and community ecology. This problem can be resolved by data providers using standards to indicate whether the observed organism is of wild or domestic origin and by integrating their data with other biodiversity data (e.g. in the Global Biodiversity Information Facility). Doing so will enable efforts to integrate the full panorama of biodiversity knowledge across related disciplines to tackle pressing societal questions.

A report on the results and outcome of the Green Pioneers (NL: Groene Pioniers) project (2019-2020), a citizen science project by Meise Botanic Garden, to increase awareness on invasive species and to increase quality and quantity of invasive species data. This project was financed by the Flemish government, Department of Economy, Science and Innovation (EWI)., Green Pioneers is a citizen project of Meise Botanic Garden financed by the Flemish government, Department of Economy, Science and Innovation (EWI).

When sequencing molecules from an organism it is standard practice to create voucher specimens. This ensures that the results are repeatable and that the identification of the organism can be verified. It also means that the sequence data can be linked to a whole host of other data related to the specimen, including traits, other sequences, environmental data, and geography. It is therefore critical that explicit, preferably machine readable, links exist between voucher specimens and sequence. However, such links do not exist in the databases of the International Nucleotide Sequence Database Collaboration (INSDC). If it were possible to create permanent bidirectional links between specimens and sequence it would not only make data more findable, but would also open new avenues for research. In the Biohackathon we built a semi-automated workflow to take specimen data from the Meise Herbarium and search for references to those specimens in the European Nucleotide Archive (ENA). We achieved this by matching data elements of the specimen and sequence together and by adding a “human-in-the-loop” process whereby possible matches could be confirmed. Although we found that it was possible to discover and match sequences to their vouchers in our collection, we encountered many problems of data standardization, missing data and errors. These problems make the process unreliable and unsuitable to rediscover all the possible links that exist. Ultimately, improved standards and training would remove the need for retrospective relinking of specimens with their sequence. Therefore, we make some tentative recommendations for how this could be achieved in the future.

Natural history collections constitute an enormous wealth of information of Life on Earth. It is estimated that over 2 billion specimens are preserved at institutions worldwide, of which less than 10% are accessible via biodiversity data aggregators such as GBIF. Moreover, they are a very important resource for eco-evolutionary research, which greatly depends on knowing the precise location where the specimens were collected in order to characterize the environment in which they lived. Yet, only about 55% of the accessible records are georeferenced and only 31% have coordinate uncertainty information, which is critical for conducting rigorous studies. The awareness of this gap of knowledge which hinders the enormous potential of such data in research led to the organization of a workshop which brought together key players in georeferencing of natural history collections. The discussion and outcomes of this workshop are here presented., This work has been possible thanks to the EU Cost Action CA17106: “MOBILISE. Mobilizing Data, Experts and Policies in Scientific Collections.”

Many species have been introduced beyond their native ranges and many have become global weeds. Human mediated dispersal has removed the geographic isolation of these species, reversing millions of years of independent evolution. Examples are the Oxalis species in section Corniculatae where several species have become invasive. Here we characterize and formally describe a hybrid between O. dillenii and O. corniculata, which occurs spontaneously in Belgium and Japan. Oxalis corniculata is probably native to Japan, but both species are alien to Belgium and O. dillenii is native to North America. We formally name this hybrid as Oxalis × vanaelstii. Although this hybrid is sterile, it is nevertheless vigorous and perennial. Both parent species grow as weeds in gardens; therefore, it is likely to be more common than currently appreciated in countries where these species co-occur.

Emerging in the 1990s, bioblitzes have become flagship events for biodiversity assessments. Although the format varies, a bioblitz is generally an intensive, short-term survey in a specific area. Bioblitzes collect biodiversity data and can therefore play a role in research, discovery of new species at a site and monitoring. They may also promote public engagement, community building, and education and outreach. However, the question remains, how effective are bioblitzes at achieving these goals? To evaluate the value of bioblitzes for these multiple goals, we conducted two meta-analyses, one on sixty published bioblitzes and the other on 1860 bioblitzes conducted using iNaturalist. Furthermore, we made an in-depth analysis of the data collected during a bioblitz we organized ourselves. From these analyses we found bioblitzes are effective at gathering data—collecting on average more than 300 species records—despite limitations of bias, which many types of biodiversity surveys suffer from, such as preferences for charismatic taxa, and uneven sampling effort in time and space. However, because the survey intensity, duration and extent are more controlled, a bioblitz is more repeatable than some other forms of survey. We also found that bioblitzes were highly effective at engaging people in sustained activity after they participated in a bioblitz. A bioblitz may therefore act as a trigger for participation in biological recording, which is supported by the use of technology, particularly smartphone apps. Another important aspect is the involvement of both citizen scientists and professional biologists, creating learning opportunities in both directions. Indeed, it was clear that many bioblitzes acted as brokerage events between individuals and organizations, and between professionals who work in biodiversity research and conservation. Such community building is important for communication and building trust between organizations and citizens to the benefit of biodiversity research and conservation. From the impartial perspective of hypothesis-driven science, bioblitzes may seem like a lot of work with limited scientific gain. However, this largely overlooks how important people, communities and their organizations are in gathering data, and in conserving biodiversity.

Connecting basic data about bats and other potential hosts of SARS-CoV-2 with their ecological context is crucial to the understanding of the emergence and spread of the virus. However, when lockdowns in many countries started in March, 2020, the world's bat experts were locked out of their research laboratories, which in turn impeded access to large volumes of offline ecological and taxonomic data. Pandemic lockdowns have brought to attention the long-standing problem of so-called biological dark data: data that are published, but disconnected from digital knowledge resources and thus unavailable for high-throughput analysis. Knowledge of host-to-virus ecological interactions will be biased until this challenge is addressed. In this Viewpoint, we outline two viable solutions: first, in the short term, to interconnect published data about host organisms, viruses, and other pathogens; and second, to shift the publishing framework beyond unstructured text (the so-called PDF prison) to labelled networks of digital knowledge. As the indexing system for biodiversity data, biological taxonomy is foundational to both solutions. Building digitally connected knowledge graphs of host-pathogen interactions will establish the agility needed to quickly identify reservoir hosts of novel zoonoses, allow for more robust predictions of emergence, and thereby strengthen human and planetary health systems. ispartof: LANCET PLANETARY HEALTH vol:5 issue:10 pages:E746-E750 ispartof: location:Netherlands status: published

People are one of the best known and most stable entities in the biodiversity knowledge graph. The wealth of public information associated with people and the ability to identify them uniquely open up the possibility to make more use of these data in biodiversity science. Person data are almost always associated with entities such as specimens, molecular sequences, taxonomic names, observations, images, traits and publications. For example, the digitization and the aggregation of specimen data from museums and herbaria allow us to view a scientist’s specimen collecting in conjunction with the whole corpus of their works. However, the metadata of these entities are also useful in validating data, integrating data across collections and institutional databases and can be the basis of future research into biodiversity and science. In addition, the ability to reliably credit collectors for their work has the potential to change the incentive structure to promote improved curation and maintenance of natural history collections.

Digitisation and publication of museum specimen data is happening worldwide, but far from complete. Museums can start by sharing what they know about their holdings at a higher level, long before each object has its own record. Information about what is held in collections worldwide is needed by many stakeholders including collections managers, funders, researchers, policy-makers, industry, and educators. To aggregate this information from collections, the data need to be standardised (Johnston and Robinson 2002). So, the Biodiversity Information Standards (TDWG) Collection Descriptions (CD) Task Group is developing a data standard for describing collections, which gives the ability to provide: automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). Outputs will include a data model to underpin the new standard, and guidance and reference implementations for the practical use of the standard in institutional and collaborative data infrastructures. The Task Group employs a community-driven approach to standard development. With international participation, workshops at the Natural History Museum (London 2019) and the MOBILISE workshop (Warsaw 2020) allowed over 50 people to contribute this work. Our group organized online "barbecues" (BBQs) so that many more could contribute to standard definitions and address data model design challenges. Cloud-based tools (e.g., GitHub, Google Sheets) are used to organise and publish the group's work and make it easy to participate. A Wikibase instance is also used to test and demonstrate the model using real data. There are a range of global, regional, and national initiatives interested in the standard (see Task Group charter). Some, like GRSciColl (now at the Global Biodiversity Information Facility (GBIF)), Index Herbariorum (IH), and the iDigBio US Collections List are existing catalogues. Others, including the Consortium of European Taxonomic Facilities (CETAF) and the Distributed System of Scientific Collections (DiSSCo), include collection descriptions as a key part of their near-term development plans. As part of the EU-funded SYNTHESYS+ project, GBIF organized a virtual workshop: Advancing the Catalogue of the World's Natural History Collections to get international input for such a resource that would use this CD standard. Some major complexities present themselves in designing a standardised approach to represent collection descriptions data. It is not the first time that the natural science collections community has tried to address them (see the TDWG Natural Collections Description standard). Beyond natural sciences, the library community in particular gave thought to this (Heaney 2001, Johnston and Robinson 2002), noting significant difficulties. One hurdle is that collections may be broken down into different degrees of granularity according to different criteria, and may also overlap so that a single object can be represented in more than one collection description. Managing statistics such as numbers of objects is complex due to data gaps and variable degrees of certainty about collection contents. It also takes considerable effort from collections staff to generate structured data about their undigitised holdings. We need to support simple, high-level collection summaries as well as detailed quantitative data, and to be able to update as needed. We need a simple approach, but one that can also handle the complexities of data, scope, and social needs, for digitised and undigitised collections. The data standard itself is a defined set of classes and properties that can be used to represent groups of collection objects and their associated information. These incorporate common characteristics ('dimensions') by which we want to describe, group and break down our collections, metrics for quantifying those collections, and properties such as persistent identifiers for tracking collections and managing their digital counterparts. Existing terms from other standards (e.g. Darwin Core, ABCD) are re-used if possible. The data model (Fig. 1) underpinning the standard defines the relationships between those different classes, and ensures that the structure as well as the content are comparable across different datasets. It centres around the core concept of an 'object group', representing a set of physical objects that is defined by one or more dimensions (e.g., taxonomy and geographic origin), and linked to other entities such as the holding institution. To the object group, quantitative data about its contents are attached (e.g. counts of objects or taxa), along with more qualitative information describing the contents of the group as a whole. In this presentation, we will describe the draft standard and data model with examples of early adoption for real-world and example data. We will also discuss the vision of how the new standard may be adopted and its potential impact on collection discoverability across the collections community.

A deep irony of COVID-19 likely originating from a bat-borne coronavirus (Boni et al. 2020) is that the global lockdown to quell the pandemic also locked up physical access to much basic knowledge regarding bat biology. Digital access to data on the ecology, geography, and taxonomy of potential viral reservoirs, from Southeast Asian horseshoe bats and pangolins to North American deer mice, was suddenly critical for understanding the disease's emergence and spread. However, much of this information lay inside rare books and personal files rather than as open, linked, and queryable resources on the internet. Even the world's experts on mammal taxonomy and zoonotic disease could not retrieve their data from shuttered laboratories. We were caught unprepared. Why, in this digitally connected age, were such fundamental data describing life on Earth not already freely accessible online? Understanding why biodiversity science was unprepared—and how to fix it before the next pandemic—has been the focus of our COVID-19 Taskforce since April 2020 and is continuing (organized by CETAF and DiSSCo). We are a group of museum-based and academic scientists with the goal of opening the rich ecological data stored in natural history collections to the research public. This information is rooted in what may seem an unlikely location—taxonomic names and their historical usages, which are the keys for searching literature and extracting linked ecological data (Fig. 1). This has been the core motivation of our group, enabled by the pioneering efforts of Plazi (Agosti and Egloff 2009) to build tools for literature digitization, extraction, and parsing (e.g., Synospecies, Ocellus) without which biodiversity science would be even less prepared. Our group led efforts to build an additional pipeline from Plazi to the Biodiversity Literature Repository at Zenodo, a free and unlimited data repository (Agosti et al. 2019), and then to GloBI, an open-source database of biotic interactions (Poelen et al. 2014, GloBI 2020). We also developed a direct integration from Pensoft Journals to GloBI, leveraging that publisher’s indexing of computer-readable terms (called semantic metadata; Senderov et al. 2018) to extract mammal host and virus information. Overall, considerable progress was made. In total, 85,492 new interactions were added to GloBI from 14 April to 21 May 2020 (see entire dataset on Zenodo: Poelen et al. 2020). Of those, 28,839 interactions are present when subset to "hasHost", "hostOf", "pathogenOf", "virus", and 4,101 unique name combinations are present after considering mammal species synonymies (from Meyer et al. 2015). Of those interactions, 892 species of mammals and 1,530 unique virus names are involved, which compares to 754 mammals and 586 viruses in the most recent data synthesis (Olival et al. 2017). While these liberated data may still include redundancies, they demonstrate the value of our approach and the expanse of known but digitally unconnected data that remains locked in publications. We can liberate host-virus data from publications, but doing so is expensive and does not scale to the continued influx of new articles that are inadequately digitized. Our efforts make it clear that Pensoft-style semantic publishing should be expanded to all major journals. The pandemic has created an opportunity for re-thinking the way we do science in the digital age. Thankfully, our future is not the past, so we do not have to keep wasting resources to digitially 'rediscover' biodiversity knowledge. We collectively call for changes to the publishing paradigm, so that research findings are directly accessible, citable, discoverable, and reusable for creating complete forms of digital knowledge.

Data standards allow us to aggregate, compare, compute and communicate data from a wide variety of origins. However, for historical reasons, data are most likely to be stored in many different formats and conform to different models. Every data set might contain a huge amount of information, but it becomes tremendously difficult to compare them without a common way to represent the data. That is when standards development jumps in. Developing a standard is a formidable process, often involving many stakeholders. Typically the initial blueprint of a standard is created by a limited number of people who have a clear view of their use cases. However, as development continues, additional stakeholders participate in the process. As a result, conflicting opinions and interests will influence the development of the standard. Compromises need to be made and the standard might look very different from the initial concept. In order to address the needs of the community, a high level of engagement in the development process is encouraged. However, this does not necessarily increase the usability of the standard. To mitigate this, there is a need to test the standard during the early stages of development. In order to facilitate this, we explored the use of Wikibase to create an initial implementation of the standard. Wikibase is the underlying technology that drives Wikidata. The software is open-source and can be customized for creating collaborative knowledge bases. In addition to containing an RDF (Resource Description Framework) triple store under the hood, it provides users with an easy-to-use graphical user interface (see Fig. 1). This facilitates the use of an implementation of a standard by non-technical users. The Wikibase remains fully flexible in the way data are represented and no data model is enforced. This allows users to map their data onto the standard without any restrictions. Retrieving information from RDF data can be done through the SPARQL query language (W3C 2020). The software package has also a built-in SPARQL endpoint, allowing users to extract the relevant information: Does the standard cover all use cases envisioned? Are parts of the standard underdeveloped? Are the controlled vocabularies sufficient to describe the data? Does the standard cover all use cases envisioned? Are parts of the standard underdeveloped? Are the controlled vocabularies sufficient to describe the data? This strategy was applied during the development of the TDWG Collection Description standard. After completing a rough version of the standard, the different terms that were defined in the first version were transferred to a Wikibase instance running on WBStack (Addshore 2020). Initially, collection data were entered manually, which revealed several issues. The Wikibase allowed us to easily define controlled vocabularies and expand them as needed. The feedback reported from users then flowed back to the further development of the standard. Currently we envisage creating automated scripts that will import data en masse from collections. Using the SPARQL query interface, it will then be straightforward to ensure that data can be extracted from the Wikibase to support the envisaged use cases.

To support invasive alien species risk assessments, the Tracking Invasive Alien Species (TrIAS) project has developed an automated, open, workflow incorporating state-of-the-art species distribution modelling practices to create risk maps using the open source language R. It is based on Global Biodiversity Information Facility (GBIF) data and openly published environmental data layers characterizing climate and land cover. Our workflow requires only a species name and generates an ensemble of machine-learning algorithms (Random Forest, Boosted Regression Trees, K-Nearest Neighbors and AdaBoost) stacked together as a meta-model to produce the final risk map at 1 km2 resolution (Fig. 1). Risk maps are generated automatically for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission scenarios and are accompanied by maps illustrating the confidence of each individual prediction across space, thus enabling the intuitive visualization and understanding of how the confidence of the model varies across space and scenario (Fig. 2). The effects of sampling bias are accounted for by providing options to: use the sampling effort of the higher taxon the modelled species belongs to (e.g., vascular plants), and to thin species occurrences. use the sampling effort of the higher taxon the modelled species belongs to (e.g., vascular plants), and to thin species occurrences. The risk maps generated by our workflow are defensible and repeatable and provide forecasts of alien species distributions under further climate change scenarios. They can be used to support risk assessments and guide surveillance efforts on alien species in Europe. The detailied modeling framework and code are available on GitHub: https://github.com/trias-project.

The digital era has brought about an impressive increase in the volume of published species occurrence data. Research infrastructures such as the Global Biodiversity Information Facility (GBIF), the digitization of legacy data, and the use of mobile applications have all played a role in this transition. More data implies, unavoidably, more heterogeneity at multiple levels as a result of the different methods and standards used to collect data. Data standardization and aggregation help to reduce this heterogeneity. Furthermore, intermediate data products that can be used for activities such as mapping, modeling and monitoring improve the repeatability and reproducibility of biodiversity research (Kissling et al. 2017). Occurrences can be defined as events in a three-dimensional space where the dimensions are taxonomic (what), temporal (when) and spatial (where). They are then aggregated into what we coined occurrence cube (Fig. 1). The taxonomic dimension is categorical. Research infrastructures like GBIF use a taxonomic backbone, thus making data aggregation at species level or higher rank relatively easy. The temporal dimension is a continuum and the temporal uncertainty is usually lower than the typical aggregation span, typically a year. Regarding the spatial dimension, occurrences are typically filtered to remove those with too large an uncertainty to fit the grid scheme being used. Meaning that the spatial uncertainty is largely unused. We developed a method to take into account this spatial uncertainty while aggregating data. In particular, we state that an occurrence is spatially representable as a closed plane figure such as a circle, hexagon or square, never as the geometric centre (centroid) of it. As for GBIF occurrence data, the coordinateUncertaintyInMeters is defined as the radius describing the smallest circle containing the whole of the location (see Darwin Core standard). So, spatially speaking, we refer to occurrences as circles, even if the method described below is general. After harvesting the occurrence data and providing a data quality assessment (e.g. removing occurrences without coordinates or with suspicious coordinates) we can assign occurrences to a reference grid such as the European reference grid of the European Environment Agency (EEA) at 1 km scale. In this spatial aggregation we randomly choose a point within the occurrence circle and assign it to the grid cell in which it is contained. We can aggregate further by time (e.g. by year) and taxonomy (e.g. by species), where aggregating means counting how many occurrences are in each specific taxonomic-spatial-temporal unit. The analogy with geometry goes further: the occurrence cube can, as any cube, be projected on an orthogonal plane by aggregating along one of the three dimensions. In particular, projecting the cube on the taxonomic and temporal dimensions can be done by adding up the number of occurrences, or counting the number of occupied cells, thus estimating the area of occupancy. The occurrence cube paradigm has been developed within the Tracking Invasive Alien Species (TrIAS) project (Vanderhoeven et al. 2017) following Open Science and FAIR principles. We created and published occurrence cubes at the species level for Belgium and Italy (Oldoni et al. 2020b) and the occurrence cubes for non-native taxa in Belgium and Europe (Oldoni et al. 2020a).