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Species lists are widely used in legislation and regulation to manage and conserve biodiversity. In this paper, we explore the issues caused by the lack of an adequately governed and universally accepted list of the world’s species. These include lack of quality control, duplicated effort, conflicts of interest, lack of currency, and confusion in the scientific use of taxonomic information. If species lists are to fulfill their role efficiently, then the governance systems underlying their creation must keep pace. Fortunately, modernization of species list governance is now possible as a result of advances in biodiversity informatics and two decades of experience working to create the backbone of a global species list.

PurposeThis paper aims to investigate the use of crowdsourcing in the enhancement of an ontology of taxonomic knowledge. The paper proposes a conceptual architecture for the incorporation of crowdsourcing into the creation of ontologies.Design/methodology/approachThe research adopted the design science research approach characterised by cycles of “build” and “evaluate” until a refined artefact was established.FindingsData from a case of a fruit fly platform demonstrates that online crowds can contribute to ontology enhancement if engaged in a structured manner that feeds into a defined ontology model.Research limitations/implicationsThe research contributes an architecture to the crowdsourcing body knowledge. The research also makes a methodological contribution for the development of ontologies using crowdsourcing.Practical implicationsCreating ontologies is a demanding task and most ontologies are not exhaustive on the targeted domain knowledge. The proposed architecture provides a guiding structure for the engagement of online crowds in the creation and enhancement of domain ontologies. The research uses a case of taxonomic knowledge ontology.Originality/valueCrowdsourcing for creation and enhancement of ontologies by non-experts is novel and presents opportunity to build and refine ontologies for different domains by engaging online crowds. The process of ontology creation is also prone to errors and engaging crowds presents opportunity for corrections and enhancements.

Biodiversity informatics is a new and evolving field, requiring efforts to develop capacity and a curriculum for this field of science. The main objective was to summarise the level of activity and the efforts towards developing biodiversity informatics curricula, for work-based training and/or academic teaching at universities, taking place within the Global Biodiversity Information Facility (GBIF) countries and its associated network. A survey approach was used to identify existing capacities and resources within the network. Most of GBIF Nodes survey respondents (80%) are engaged in onsite training activities, with a focus on work-based professionals, mostly researchers, policy-makers and students. Training topics include data mobilisation, digitisation, management, publishing, analysis and use, to enable the accessibility of analogue and digital biological data that currently reside as scattered datasets. An initial assessment of academic teaching activities highlighted that countries in most regions, to varying degrees, were already engaged in the conceptualisation, development and/or implementation of formal academic programmes in biodiversity informatics, including programmes in Benin, Colombia, Costa Rica, Finland, France, India, Norway, South Africa, Sweden, Taiwan and Togo. Digital e-learning platforms were an important tool to help build capacity in many countries. In terms of the potential in the Nodes network, 60% expressed willingness to be recruited or commissioned for capacity enhancement purposes. Contributions and activities of various country nodes across the network have been highlighted and a working curriculum framework has been defined.

The bdverse is a collection of packages that form a general framework for facilitating biodiversity science in R (programming language). Exploratory and diagnostic visualization can unveil hidden patterns and anomalies in data and allow quick and efficient exploration of massive datasets. The development of an interactive yet flexible dashboard that can be easily deployed locally or remotely is a highly valuable biodiversity informatics tool. To this end, we have developed 'bddashboard', which serves as an agile framework for biodiversity dashboard development. This project is built in R, using the Shiny package (RStudio, Inc 2021) that helps build interactive web apps in R. The following key components were developed: Core Interactive Components The basic building blocks of every dashboard are interactive plots, maps, and tables. We have explored all major visualization libraries in R and have concluded that 'plotly' (Sievert 2020) is the most mature and showcases the best value for effort. Additionally, we have concluded that 'leaflet' (Graul 2016) shows the most diverse and high-quality mapping features, and DT (DataTables library) (Xie et al. 2021) is best for rendering tabular data. Each component was modularized to better adjust it for biodiversity data and to enhance its flexibility. Field Selector The field selector is a unique module that makes each interactive component much more versatile. Users have different data and needs; thus, every combination or selection of fields can tell a different story. The field selector allows users to change the X and Y axis on plots, to choose the columns that are visible on a table, and to easily control map settings. All that in real-time, without reloading the page or disturbing the reactivity. The field selector automatically detects how many columns a plot needs and what type of columns can be passed to the X-axis or Y-axis. The field selector also displays the completeness of each field. Plot Navigation We developed the plot navigation module to prevent unwanted extreme cases. Technically, drawing 1,000 bars on a single bar plot is possible, but this visualization is not human-friendly. Navigation allows users to decide how many values they want to see on a single plot. This technique allows for fast drawing of extensive datasets without affecting page reactivity, dramatically improving performance and functioning as a fail-safe mechanism. Reactivity Reactivity creates the connection between different components. The changes in input values automatically flow to the plots, text, maps, and tables that use the input, and cause them to update. Reactivity facilitates drilling down functionality, which enhances the user’s ability to explore and investigate the data. We developed a novel and robust reactivity technique that allows us to add a new component and effectively connect it with all existing components within a dashboard tab, using only one line of code. Generic Biodiversity Tabs We developed five useful dashboard tabs (Fig. 1): (i) the Data Summary tab to give a quick overview of a dataset; (ii) the Data Completeness tab helps users get valuable information about missing records and missing Darwin Core fields; (iii) the Spatial tab is dedicated to spatial visualizations; (iv) the Taxonomic tab is designed to visualize taxonomy; and (v) the Temporal tab is designed to visualize time-related aspects. Performance and Agility To make a dashboard work smoothly and react quickly, hundreds of small and large modules, functions, and techniques must work together. Our goal was to minimize dashboard latency and maximize its data capacity. We used asynchronous modules to write non-blocking code, clusters in map components, and preprocessing and filtering data before passing it to plots to reduce the load. The 'bddashboard' package modularized architecture allows us to develop completely different interactive and reactive dashboards within mere minutes.

In an effort to characterise the various dimensions of activity within the biodiversity informatics landscape, we developed a framework to survey these dimensions for ten major organisations*1 relative to both their current activities and long-term strategic ambitions. This survey assessed the contact between these infrastructure organisations by capturing the breadth of activities for each infrastructure across five categories (data, standards, software, hardware and policy), for nine types of data (specimens, collection descriptions, opportunistic observations, systematic observations, taxonomies, traits, geological data, molecular data, and literature), and for seven phases of activity (creation, aggregation, access, annotation, interlinkage, analysis, and synthesis). This generated a dataset of 6,300 verified observations, which have been scored and validated by leading members of each infrastructure organisation. In this analysis of the resulting data, we address a set of high-level questions about the overall biodiversity informatics landscape, looking at the greatest gaps, overlap and possible rate-limiting steps. Across the infrastructure organisations, we also explore how far each is in relation to achieving its ambitions and the extent of its niche relative to other organisations. Our results show that when viewed by scope, most infrastructures occupy a relatively narrow niche in the overall landscape of activity, with the notable exception of the Global Biodiversity Information Facility (GBIF) and possibly LifeWatch. Niches associated with molecular data and biological taxonomy are very well filled, suggesting there is still considerable room for growth in other areas, with the Distributed System of Scientific Collections (DiSSCo) and the Integrated European Long-Term Ecosystem Research Infrastructure (eLTER RI) showing the highest levels of difference between their current activities and stated ambitions, potentially reflecting the relative youth of these organisations. iNaturalist, the Biodiversity Heritage Library and Catalogue of Life all occupy narrow and tightly circumscribed niches. These organisations are also amongst the closest to achieving their stated ambitions within their respective areas of activity. The largest gaps in infrastructure activity relate to the development of hardware and standards, with many gaps set to be addressed if the stated ambitions of those surveyed come to fruition. Nevertheless, some gaps persist, outlining a potential role for this survey as a planning tool to help coordinate and align investment in future biodiversity informatics activities. GBIF and LifeWatch are the two infrastructures where there is the most similarity in ambition with DiSSCo, with the greatest overlap concentrated on activities related to data/content, specimen data and their shared ambition to interlink information. While overlap appears intense, the analysis is limited by the resolution of the survey framework and ignores existing collaborations between infrastructures. In addition to presenting the results of this survey, we outline our plans to publish this work and a proposal to develop the methodology as an interactive web-based tool. This would allow other projects and infrastructures to self-score their activities and visualise their niche within the current landscape, encouraging better global alignment of activities. For example, our results should make it easier for initiatives to strengthen collaboration and differentiate work when their activities overlap. Likewise, this approach would be useful for funding agencies when targeting gaps in the informatics landscape or increasing the technical maturity of certain critical activities, e.g., to improve immature data standards. While no framework is perfect, we hope to encourage a dialogue on the potential for taking an algorithmic approach to community alignment and see this as a means of strengthening community cooperation when addressing problems that require global cooperation.

Biodiversity informatics have emerged as a key asset in wildlife and ecological conservation around the world. This is especially true in Antarctica, where climate change continues to threaten marine and terrestrial species. It is well documented that the polar regions experience the most drastic rate of climate change compared to the rest of the world (IPCC 2021). Research approaches within the scope of polar biodiversity informatics consist of computational architectures and systems, analysis and modelling methods, and human-computer interfaces, ranging from more traditional statistical techniques to more recent machine learning and artificial intelligence-based imaging techniques. Ongoing discussions include making datasets findable, accessible, interoperable and reusable (FAIR) (Wilkinson et al. 2016). The deployment of biodiversity informatics systems and coordination of standards around their utilization in the Antarctic are important areas of consideration. To bring together scientists and practitioners working at the nexus of informatics and Antarctic biodiversity, the Expert Group on Antarctic Biodiversity Informatics (EG-ABI) was formed under the Scientific Committee on Antarctic Research (SCAR). EG-ABI was created during the SCAR Life Sciences Standing Scientific Group meeting at the SCAR Open Science Conference in Portland Oregon, in July 2012, to advance work at this intersection by coordinating and participating in a range of projects across the SCAR biodiversity science portfolio. SCAR, meanwhile, is a thematic organisation of the International Science Council (ISC), which is the primary entity tasked with coordinating high-quality scientific research on all aspects of Antarctic sciences and humanities, including the Southern Ocean and the interplay between Antarctica and the other six continents. The expert group is led by an international steering committee of roughly ten members, who take an active role in leading related initiatives. Currently, researchers from Australia, Belgium, the United Kingdom, Chile, Germany, France, and the United States are represented on the committee. The current steering committee is comprised of a diverse range of scientists, including early-career researchers and scientists that have primary focuses in both the computational and ecological aspects of Antarctic biodiversity informatics. Current projects that are being coordinated or co-coordinated by EG-ABI include the SCAR/rOpenSci initiative, which is a collaboration with the rOpenSci community to improve resources for users of the R software package in Antarctic and Southern Ocean science. Additionally, EG-ABI has contributed to the POLA3R project (Polar Omics Linkages Antarctic Arctic and Alpine Regions), which is an information system dedicated to aid in the access and discovery of molecular microbial diversity data generated by Antarctic scientists. Furthermore, EG-ABI has trained and helped collate additional species trait information such as feeding and diet information, development, mobility and their importance to society, documented through Vulnerable Marine Ecosystem (VME) indicator taxa, in The Register of Antarctic Species (http://ras.biodiversity.aq/), and the comprehensive inventory of Antarctic and Southern Ocean organisms, which is also a component of the World Register of Marine Species (https://marinespecies.org/). The efforts highlighted are only some of the projects that the expert groups have contributed to. In our presentation, we discuss the previous accomplishments of the EG-ABI from the perspective of a currently serving steering committee member and outline its state in the status quo including collaborations and coordinated activities. We also highlight opportunities for engagement and the benefits for various stakeholders in terms of interacting with EG-ABI on multiple levels, within the SCAR ecosystem and elsewhere. Developing consistent and practical standards for data use in Antarctic ecology, in addition to fostering interdisciplinary and cross-sectoral collaborations for the successful deployment of conservation mechanisms, are key to a sustainable and biodiverse Antarctica, and EG-ABI is one of the premier organizations working towards these aims.

Taxonomy is foundational to all biological sciences. Names allow us to organize and communicate information about biological groups. This process is critical for understanding and preserving the biodiversity of our planet. There are an estimated 8.7 million extant eukaryotes (Mora et al. 2011) and possibly as many as 1 trillion microbial species (Locey and Lennon 2016), with untold numbers of extinct taxa yet to be discovered in the fossil record. Accounting for all these taxa and maintaining their nomenclatural resources is one of the great challenges in biology. A few major hurdles in overcoming this challenge are the inability to find, share, and update taxonomic resources efficiently in real time. Efforts to standardize and continually update taxonomic names in a sustainable way have been limited. The problem is complex, and solutions must deal with the large backlog of names, a constant stream of new names, the confusing merging and splitting of taxonomic synonyms, the subjective nature of taxonomic concepts, and the fundamental limitations on available expertise and curators' time to prepare and maintain such resources. Hyperdiverse groups such as arthropods are especially challenging as there are relatively few experts on any given lineage and changes in taxonomy can be rapid as new species are continually being discovered and described. After struggling to wrangle taxonomic resources in support of specimen digitization efforts, I began development of TaxoTracker as a proof-of-concept, web-based platform for facilitating expert curation and dissemination of biological taxonomies. TaxoTracker is still in development, but its current and planned functionalities will be shown through a combination of demonstration and discussion. TaxoTracker identifies and implements features that attempt to simplify the production and maintenance of expert-curated resources, while also limiting the responsibilities that are placed on individual experts who are often already overburdened and underfunded. These features include: Centralized, searchable, and easily obtained resources in useful formats Community-driven, citation-based curatorial suggestions Expert-reviewed curatorial recommendations Consensus-driven curatorial decisions Effort tracking and credit for suggestions and reviews Centralized, searchable, and easily obtained resources in useful formats Community-driven, citation-based curatorial suggestions Expert-reviewed curatorial recommendations Consensus-driven curatorial decisions Effort tracking and credit for suggestions and reviews

Biodiversity informatics has advanced rapidly with the maturation of major biodiversity data infrastructures (BDDIs), such as the Global Biodiversity Information Facility sharing unprecedented data volumes. Nevertheless, taxonomic, temporal and spatial data coverage remains unsatisfactory. With an increasing data need, the global BDDIs require continuous inflow from local data mobilisation, and national BDDIs are being developed around the world. The global BDDIs are specialised in certain data types or data life cycle stages which, despite possible merits, renders the BDDI landscape fragmented and complex. That this often is repeated at the national level creates counterproductive redundancy, complicates user services, and frustrates funders. Here, we present the Finnish Biodiversity Information Facility (FinBIF) as a model of an all-inclusive BDDI. It integrates relevant data types and phases of the data life cycle, manages them under one IT architecture, and distributes the data through one service portal under one brand. FinBIF has experienced diverse funder engagement and rapid user uptake. Therefore, we suggest the integrated and inclusive approach be adopted in national BDDI development.

The Atlas of Living Australia (ALA) is Australia’s national biodiversity database, delivering data and related services to more than 80,000 Australian and international users annually. Established under the Australian Government’s National Collaborative Research Infrastructure Strategy to provide trusted biodiversity data to support the research sector, its utility now extends to government, higher education, non-government organisations and community groups. These partners provide data to the ALA and leverage its data and related services. The ALA has also played an important leadership role internationally in the biodiversity informatics and infrastructure space, both through its partnership with the Global Biodiversity Information Facility and through support for the international Living Atlases programmes which has now delivered 24 instances of ALA software to deliver sovereign biodiversity data capability around the world. This paper begins with a historical overview of the genesis of the ALA from the collections, museums and herbaria community in Australia. It details the biodiversity and related data and services delivered to users with a primary focus on species occurrence records which represent the ALA's primary data type. Finally, the paper explores the ALA's future directions by referencing results from a recently completed national consultation process.

If scientific research can be described as "arm's length'' cooperative work, then the sciences engaged in the creation and maintenance of cooperatively built classification systems might be said to require extremely long arms; workers must reach across great distances of time, space and awareness to independently contribute to the creation and maintenance of a conceptual infrastructure. This is particularly the case for the field of biological taxonomy, in which researchers across the globe have, for more than three hundred years, cooperatively developed a massive knowledge organization system describing all life on earth. As taxonomists move from paper-based workflows to born-digital, data-first modes of publishing, new tools and approaches are needed to support their work. In this paper, we present research conducted as part of the "Transforming Taxonomic Interfaces" project aimed at improving interfaces for taxonomic software. We describe biological taxonomy in the context of CSCW, and identify key strategies taxonomists deploy to facilitate loosely coupled cooperative work. We also contribute a discussion of semantic refactoring, a unique kind of "articulation work'' entailed in developing, maintaining, and migrating classification systems. This work has implications for the design and study of knowledge organization systems and infrastructure, classification research, and for the development of general semantic web tools and software as well as those specifically for biological taxonomy.

The field of biodiversity informatics is in a massive, “grow-out” phase of creating and enabling large-scale biodiversity data resources. Because perhaps 90% of existing biodiversity data nonetheless remains unavailable for science and policy applications, the question arises as to how these existing and available data records can be mobilized most efficiently and effectively. This situation led to our analysis of several large-scale biodiversity datasets regarding birds and plants, detecting information gaps and documenting data “leakage” or attrition, in terms of data on taxon, time, and place, in each data record. We documented significant data leakage in each data dimension in each dataset. That is, significant numbers of data records are lacking crucial information in terms of taxon, time, and/or place; information on place was consistently the least complete, such that geographic referencing presently represents the most significant factor in degradation of usability of information from biodiversity information resources. Although the full process of digital capture, quality control, and enrichment is important to developing a complete digital record of existing biodiversity information, payoffs in terms of immediate data usability will be greatest with attention paid to the georeferencing challenge.

Biodiversity data (e.g., for aquatic organisms, marine creatures and terrestrial animals) and environmental data (e.g., air pollution statistics, water supply and sanitation information, soil contamination data) are examples of big data. Embedded in these big data are implicit, previously unknown and potentially useful information and knowledge that could help improve the ecosystem. As such, data science solutions for big data analytics and mining are in demand. In this paper, we present a data science solution for biodiversity informatics, environmental analytics and sustainability analysis. Specifically, our solution analyzes and mines both biodiversity data and environmental data to examine the impacts of pollution to moving objects. The convex-hull-based method in our solution estimates the pollution exposure to these objects. For evaluation, we conducted case studies on analyzing, mining and visualizing both marine biodiversity data and plastic exposure data to examine the impacts of the plastic exposure to marine creatures. Knowledge discovered by our solution help decision and policy makers to take appropriate actions in building and maintaining a sustainable environment.

Automated plant identification has recently improved significantly due to advances in deep learning and the availability of large amounts of field photos. As an illustration, the classification accuracy of 10K species measured in the LifeCLEF challenge (Goëau et al. 2018) reached 90%, very close to that of human experts. However, the profusion of field images only concerns a few tens of thousands of species, mainly located in North America and Western Europe. Conversely, the richest regions in terms of biodiversity, such as tropical countries, suffer from a shortage of training data (Pitman 2021). Consequently, the identification performance of the most advanced models on the flora of these regions is much lower (Goëau et al. 2019). Nevertheless, for several centuries, botanists have systematically collected, catalogued, and stored plant specimens in herbaria. Considerable recent efforts by the biodiversity informatics community, such as DiSSCo (Addink et al. 2018) and iDigBio (Matsunaga et al. 2013), have made millions of digitized specimens from these collections available online. A key question is therefore whether these digitized specimens could be used to improve the identification performance of species for which we have very few (if any) photos. However, this is a very difficult problem from a machine learning point of view. The visual appearance of a herbarium specimen is actually very different from a field photograph because the specimens are dried and crushed on a herbarium sheet before being digitized (Fig. 1). To advance research on this topic, we built a large dataset that we shared as one of the challenges of the LifeCLEF 2020 (Goëau et al. 2020) and 2021 evaluation campaigns (Goëau et al. 2021). It includes more than 320K herbarium specimens collected mostly from the Guiana Shield and the Northern Amazon Rainforest, focusing on about 1K plant species of the French Guiana flora. A valuable asset of this collection is that some of the specimens are accompanied by a few photos of the same specimen, allowing for more precise machine learning. In addition to this training data, we also built a test set for model evaluation, composed of 3,186 field photos collected by two of the best experts on Guyanese flora. Based on this dataset, about ten research teams have developed deep learning methods to address the challenge (including the authors of this abstract as the organizing team). A detailed description of these methods can be found in the technical notes written by the participating teams (Goëau et al. 2020, Goëau et al. 2021). The methods can be divided into two categories: those based on classical convolutional neural networks (CNN) trained simply by mixing digitized specimens and photos and those based on advanced domain adaptation techniques with the objective of learning a joint representation space between field and herbarium representations. those based on classical convolutional neural networks (CNN) trained simply by mixing digitized specimens and photos and those based on advanced domain adaptation techniques with the objective of learning a joint representation space between field and herbarium representations. The domain adaptation methods themselves were of two types, those based on adversarial regularization (Motiian et al. 2017) to force herbarium specimens and photos to have the same representations, metric learning to maximize inter-species distances and minimize intra-species distances in the representation space adversarial regularization (Motiian et al. 2017) to force herbarium specimens and photos to have the same representations, metric learning to maximize inter-species distances and minimize intra-species distances in the representation space In Table 1, we report the results achieved by the different methods evaluated during the 2020 edition of the challenge. The evaluation metric used is the mean reciprocal rank (MRR), i.e., the average of the inverse of the rank of the correct species in the list of the predicted species. In addition to this main score, a second MRR score is computed on a subset of the test set composed of the most difficult species, i.e., the ones that are the least frequently photographed in the field. The main outcomes we can derive from these results are the following: Classical deep learning models fail to identify plant photos from digitized herbarium specimens. The best classical CNN trained on the provided data resulted in a very low MRR score (0.011). Even with the of use additional training data (e.g. photos and digitized herbarium from GBIF) the MRR score remains very low (0.039). Domain adaptation methods provide significant improvement but the task remains challenging. The best MRR score (0.180) was achieved by using adversarial regularization (FSDA Motiian et al. 2017). This is much better than the classical CNN models but there is still a lot of progress to be made to reach the performance of a truly functional identification system (the MRR score on classical plant identification tasks can be up to 0.9). No method fits all. As shown in Table 1, the metric learning method has a significantly better MRR score on the most difficult species (0.107). However, the performance of this method on the species with more photos is much lower than the adversarial technique. In 2021, the challenge was run again but with additional information provided to train the models, i.e., species traits (plant life form, woodiness and plant growth form). The use of the species traits allowed slight performance improvement of the best adversarial adaptation method (with a MRR equal to 0.198). In conclusion, the results of the experiments conducted are promising and demonstrate the potential interest of digitized herbarium data for automated plant identification. However, progress is still needed before integrating this type of approach into production applications.

Biodiversity is a concept of great scientific interest and social value studied in different subjects of the secondary education curriculum. Citizen&ndash, science programs may contribute to increasing the engagement of students when studying biodiversity. This work aimed to explore the use of the citizen&ndash, science platform iNaturalist as a complement of the elaboration of herbaria in an outdoor activity for 4th course 16-year-old students in the Basaula Reserve. The platform iNaturalist was chosen for its suitability to develop collaborative projects in an educational context. The Basaula project was created and 122 students were trained to record plant species in an outdoor activity. A total of 32 species were recorded, among them the most abundant were beech (Fagus sylvatica) and holm oak (Quercus ilex). The students positively evaluated their experience, highlighting its adequacy to record biodiversity data and make a virtual herbarium. Students valued the innovative character of iNaturalist and its usefulness for research but also the opportunity to integrate mobile devices in school education. We concluded that iNaturalist is a valuable tool to carry out collaborative projects dealing with biodiversity in secondary education.

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

Gene bank collections are at a pivotal point in their history. We review their development and explain how their role is changing with new technologies in computation and analytics and the widespread application of whole genome sequencing for characterizing genetic diversity. Development of genomics technologies is likely to significantly alter the way we search for, use, and valorize plant genetic resources (PGR) in the future. The prospect of searching for alleles of agronomic importance is not far off. We review genomics/ sequencing approaches used to catalog genomic variation and their analytical methods useful in describing diversity in PGR. Importantly, we review how population genomics approaches can be used to characterize diverse collection accessions and the way in which genomic data can help provide information on identity, integrity, and population history for samples maintained in gene banks. We explore sampling approaches used to capture variation within and among accessions. We review case studies of diverse crop systems that provide important examples of how genomic data can be used to both provide critical information for agronomic variation for breeders and provide characterization information integral to managing this diversity. Lastly, we describe how gene banks can be positioned more centrally in the ecosystem of breeding and gene discovery projects acting both as a resource of genetic diversity and a consumer data describing of functional variation. These trends are likely to transform gene banks from merely germplasm service providers to interactive biodiversity informatics research centers.

Many natural history museums share a common problem: a multitude of legacy collection management systems (CMS) and the difficulty of finding a new system to replace them. Kotka is a CMS developed starting in 2011 at the Finnish Museum of Natural History (Luomus) and Finnish Biodiversity Information Facility (FinBIF) (Heikkinen et al. 2019, Schulman et al. 2019) to solve this problem. It has grown into a national system used by all natural history museums in Finland, and currently contains over two million specimens from several domains (zoological, botanical, paleontological, microbial, tissue sample and botanic garden collections). Kotka is a web application where data can be entered, edited, searched and exported through a browser-based user interface. It supports designing and printing specimen labels, handling collection metadata and specimen transactions, and helps support Nagoya protocol compliance. Creating a shared system for multiple institutions and collection types is difficult due to differences in their current processes, data formats, future needs and opinions. The more independent actors there are involved, the more complicated the development becomes. Successful development requires some trade-offs. Kotka has chosen features and development principles that emphasize fast development into a multitude of different purposes. Kotka was developed using agile methods with a single person (a product owner) making development decisions, based on e.g., strategic objectives, customer value and user feedback. Technical design emphasizes efficient development and usage over completeness and formal structure of the data. It applies simple and pragmatic approaches and improves collection management by providing practical tools for the users. In these regards, Kotka differs in many ways from a traditional CMS. Kotka stores data in a mostly denormalized free text format and uses a simple hierarchical data model. This allows greater flexibility and makes it easy to add new data fields and structures based on user feedback. Data harmonization and quality assurance is a continuous process, instead of doing it before entering data into the system. For example, specimen data with a taxon name can be entered into Kotka before the taxon name has been entered into the accompanying FinBIF taxonomy database. Example: simplified data about two specimens in Kotka, which have not been fully harmonized yet. Taxon: Corvus corone cornix Country: FI Collector: Doe, John Coordinates: 668, 338 Coordinate system: Finnish uniform coordinate system Taxon: Corvus corone cornix Country: FI Collector: Doe, John Coordinates: 668, 338 Coordinate system: Finnish uniform coordinate system Taxon: Corvus cornix Country: Finland Collector: Doe, J. Coordinates: 60.2442, 25,7201 Coordinate system: WGS84 Taxon: Corvus cornix Country: Finland Collector: Doe, J. Coordinates: 60.2442, 25,7201 Coordinate system: WGS84 Kotka’s data model does not follow standards, but has grown organically to reflect practical needs from the users. This is true particularly of data collected in research projects, which are often unique and complicated (e.g. complex relationships between species), requiring new data fields and/or storing data as free text. The majority of the data can be converted into simplified standard formats (e.g. Darwin Core) for sharing. The main challenge with this has been vague definitions of many data sharing formats (e.g. Darwin Core, CETAF Specimen Preview Profile (CETAF 2020), allowing different interpretations. Kotka trusts its users: it places very few limitations on what users can do, and has very simple user role management. Kotka stores the full history of all data, which allows fixing any possible errors and prevents data loss. Kotka is open source software, but is tightly coupled with the infrastructure of the Finnish Biodiversity Information Facility (FinBIF). Currently, it is only offered as an online service (Software as a Service) hosted by FinBIF. However, it could be developed into a more modular system that could, for example, utilize multiple different database backends and taxonomy data sources.

Specimen digitization software and tooling is moving well past its third decade of development, yet in many ways new tools have yet to leapfrog or overcome the initial innovation realized years ago. Here I argue that a biodiversity informatics bubble has emerged, creating demands of digitization tools that are not always in line with the requirements of physical specimen curators (or others doing actual science). Pressuring tools to keep up with concepts that have emerged from this bubble, for example Life Science Identifiers (LSIDs), and its parallels in the tech industry, for example microservices, has detracted from advancements that could be made with respect to day-to-day workflows and practices of the curators themselves. These advances in turn might provide a more enjoyable, intuitive, and ultimately sustainable foundation perhaps more immune to inevitable bubble bursts, hype-based derailments, and changes in scientific goals. How then should development proceed? We can observe that existing digitization software largely fits into two sides of the spectrum: commercial monoliths like EMu and "home-grown" efforts, e.g. Specify, Arctos, and Symbiota. I argue the latter are much more in-tune with user needs, because they were first built by the users themselves. Our approach, therefore, should be to go back to the well, the curator, the digitizer, the student hourly, and the person who has to fulfill requests of those using the physical collection itself, and seek their needs, and understand their experiences. With this understanding in place, i.e., a solid user-interface/experience foundation, we can build out tooling (and standards) that developers will want to utilize in their own software. These arguments and ideas are contextualized against TaxonWorks (http://taxonworks.org) and the experiences of the five collections now using it to digitize collections to illustrate their shortcomings and potentialities.

We present a visual and interactive taxonomic Artificial Intelligence (AI) tool, the Automated Taxonomic Concept Reasoner (ATCR), whose graphical web interface is under development and will also become available via an Application Programming Interface (API). The tool employs automated reasoning (Beeson 2014) to align multiple taxonomies visually, in a web browser, using user or expert-provided taxonomic articulations, i.e. "Region Connection Calculus (RCC-5) relationships between taxonomic concepts, provided in a specific logical language (Fig. 1). It does this by representing the problem of taxonomic alignment under these constraints in terms of logical inference, while performing these inferences computationally and leveraging the powerful Microsoft Z3 Satisfiability Modulo Theory (SMT) solver (de Moura and Bjørner 2008). This tool represents further development of utilities for the taxonomic concept approach, which fundamentally addresses the challenge of robust biodiversity data aggregation in light of multiple conflicting sources (and source classifications) from which primary biodiversity data almost invariably originate. The approach has proven superior to aggregation, based just on the syntax and semantics provided by the Darwin Core standard Franz and Sterner 2018). Fig. 1 provides an artificial example of such an alignment. Two taxonomies, A and B, are shown. There are five taxonomic concepts, A.One, A.Two, A.Three, B.One and B.Two. A.Two and A.Three are sub-concepts (children) of A.One, and B.Two is a sub-concept (child) of B.One. These are represented by the direction of the grey arrows. The undirected mustard-coloured lines represent relationships, i.e., the articulations referred to in the previous paragraph. These may be of five kinds: congruent (==), includes (), overlap (> "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. ATCR is related to Euler/X (Franz et al. 2015), an existing tool for the same kinds of taxonomic alignment problems, which was used, for example, to obtain an alignment of two influential primate classifications (Franz et al. 2016). It differs from Euler/X in that it employs a different logical encoding that enables more efficient and more informative computational reasoning, and also in that it provides a graphical web interface, which Euler/X does not.

Biospytial is a modular open source knowledge engine designed to import, organise, analyse and visualise big spatial ecological datasets using the power of graph theory. Specifically, it handles species occurrences and their taxonomic classification for performing ecological analysis on biodiversity and species distributions. The engine uses a hybrid graph-relational approach to store and access information. The data are linked with relationships that are stored in a graph database, while tabular and geospatial (vector and raster) data are stored in a relational database management system (RDBMS). The graph data structure provides a scalable design that eases the problem of merging datasets from different sources. The linkage relationships use semantic structures (objects and predicates) to answer scientific questions represented as complex data structures stored in the graph database. In this sense, we used species occurrences, taxonomic classification, and climatic datasets to build a knowledge graph of the Tree of Life embedded in an environmental and geographical grid. Biospytial comprises three interconnected components: i) a Geospatial Processing unit (GPU) supported by a RDBMS with geoprocessing capabilities, ii) a Graph Storage and Querying Unit, and iii) a graph-relational package, called: The Biospytial Computing Engine (BCE) that integrates all the system’s components. It also includes tools like: interactive notebooks (Jupyter), graph analytic libraries (NetworkX) and statistical frameworks (PyMC3). The Biospytial approach reduces the complexity of joining datasets using multiple primary-foreign key relations, a drawback in RDBMS. Applied to ecological data, it allows the discovery and inference of relationships using the interconnected network of taxonomic and spatial relationships. Its modular and scalable design makes it possible to run and distribute several instances simultaneously, allowing fast and efficient handling of big and complex ecological datasets. An example applied to the conservation of threatened species from the IUCN Red List using the co-occurrence of jaguars (Panthera onca) is included. This example demonstrates the engine’s capabilities in performing basic taxonomic trees manipulation, analysis and visualization of taxonomic groups co-occurring in space.

For those biologists and biodiversity data managers who are unfamiliar with information science data practices of data standardization, the use of complex software to assist in the creation of standardized datasets can be a barrier to sharing data. Since the ratification of the Darwin Core Standard (DwC) (Darwin Core Task Group 2009) by the Biodiversity Information Standards (TDWG) in 2009, many datasets have been published and shared through a variety of data portals. In the early stages of biodiversity data sharing, the protocol Distributed Generic Information Retrieval (DiGIR), progenitor of DwC, and later the protocols BioCASe and TDWG Access Protocol for Information Retrieval (TAPIR) (De Giovanni et al. 2010) were introduced for discovery, search and retrieval of distributed data, simplifying data exchange between information systems. Although these protocols are still in use, they are known to be inefficient for transferring large amounts of data (GBIF 2017). Because of that, in 2011 the Global Biodiversity Information Facility (GBIF) introduced the Darwin Core Archive (DwC-A), which allows more efficient data transfer, and has become the preferred format for publishing data in the GBIF network. DwC-A is a structured collection of text files, which makes use of the DwC terms to produce a single, self-contained dataset. Many tools for assisting data sharing using DwC-A have been introduced, such as the Integrated Publishing Toolkit (IPT) (Robertson et al. 2014), the Darwin Core Archive Assistant (GBIF 2010) and the Darwin Core Archive Validator. Despite promoting and facilitating data sharing, many users have difficulties using such tools, mainly because of the lack of training in information science in the biodiversity curriculum (Convention on Biological Diversiity 2012, Enke et al. 2012). However, most users are very familiar with spreadsheets to store and organize their data, but the adoption of the available solutions requires data transformation and training in information science and more specifically, biodiversity informatics. For an example of how spreadsheets can simplify data sharing see Stoev et al. (2016). In order to provide a more "familiar" approach to data sharing using DwC-A, we introduce a new tool as a Google Sheet Add-on. The Add-on, called Darwin Core Archive Assistant Add-on can be installed in the user's Google Account from the G Suite MarketPlace and used in conjunction with the Google Sheets application. The Add-on assists the mapping of spreadsheet columns/fields to DwC terms (Fig. 1), similar to IPT, but with the advantage that it does not require the user to export the spreadsheet and import it into another software. Additionally, the Add-on facilitates the creation of a star schema in accordance with DwC-A, by the definition of a "CORE_ID" (e.g. occurrenceID, eventID, taxonID) field between sheets of a document (Fig. 2). The Add-on also provides an Ecological Metadata Language (EML) (Jones et al. 2019) editor (Fig. 3) with minimal fields to be filled in (i.e., mandatory fields required by IPT), and helps users to generate and share DwC-Archives stored in the user's Google Drive, which can be downloaded as a DwC-A or automatically uploaded to another public storage resource like a user's Zenodo Account (Fig. 4). We expect that the Google Sheet Add-on introduced here, in conjunction with IPT, will promote biodiversity data sharing in a standardized format, as it requires minimal training and simplifies the process of data sharing from the user's perspective, mainly for those users not familiar with IPT, but that historically have worked with spreadsheets. Although the DwC-A generated by the add-on still needs to be published using IPT, it does provide a simpler interface (i.e., spreadsheet) for mapping data sets to DwC than IPT. Even though the IPT includes many more features than the Darwin Core Assistant Add-on, we expect that the Add-on can be a "starting point" for users unfamiliar with biodiversity informatics before they move on to more advanced data publishing tools. On the other hand, Zenodo integration allows users to share and cite their standardized data sets without publishing them via IPT, which can be useful for users without access to an IPT installation. Additionally, we are working on new features and future releases will include the automatic generation of Global Unique Identifiers for shared records, the possibility of adding additional data standards and DwC extensions, integration with GBIF REST API and with IPT REST API.

The relationship between Biodiversity and Business is complex and involves risks as well as opportunities. Various frameworks, policies, tools and indicators for assessing corporate impacts on biodiversity are established. IUCN advocates a “Biodiversity Net Gains” approach for businesses in their operations and at a landscape level that will contribute to the global goals for biodiversity conservation. Biodiversity informatics can help corporates in identifying their impacts, planning interventions and finally reporting Biodiversity Net Gains on a global scale. This study aimed to review Indian biodiversity informatics infrastructure and its usefulness to corporate agencies for reporting Biodiversity Net Gains.

Vast amounts of Primary Biodiversity Data exist online (~109 records, each documenting an individual species at a point in space and time). These data hold immense but unrealized promise for science and society, including use in biogeographic research addressing issues such as zoonotic diseases, invasive species, threatened species and habitats, and climate change. Ongoing and envisioned changes in biodiversity informatics involving data providers, aggregators, and users should catalyze improvements to allow efficient use of such data for diverse analyses. We discuss relevant issues from the perspective of modeling species distributions, currently the most common use of Primary Biodiversity Data. Key cross-cutting principles for progress include harnessing feedback from users and increasing incentives for improving data quality. Critical challenges include: (1) establishing individual and collective stable unique identifiers across all of biodiversity science, (2) highlighting issues regarding data quality and representativeness, and (3) improving feedback mechanisms. Such changes should lead to ever-better data and increased utility and impact, including greater data integration with various research areas within and beyond biogeography (e.g., population demography, biotic interactions, physiology, and genetics). Building on existing pilot functionalities, biodiversity informatics could see transformative changes over the coming decade via a combination of community consensus building, coordinated efforts to justify and secure funding, and technical innovations., RPA acknowledges funding from the U.S. National Science Foundation (NSF; DBI-1661510) and National Aeronautics and Space Administration (80NSSC18K0406 to Mary E. Blair), and JMS recognizes support from NSF (DBI-1458640).

Pl@ntNet is a scientific and citizen platform based on artificial intelligence techniques to help participants more easily identify plants with their smartphones. The identification of plant species is indeed an important step for many scientific, educational and land management activities (for natural or cultivated spaces). This step, which is integrated into various biology training courses, is difficult to develop on a large scale, even for professionals. This difficulty in naming plants by a very large part of the society limits the positive interactions between humans and their environment, and thus reduces awareness of the contribution of plants to the well-being of humanity. It is therefore essential to attempt to solve this problem on a large geographical, taxonomic and sociological scale in order to develop more responsible and environmentally precautionary societies. In the framework of the European Cos4Cloud project (2019 - 2023), which involves several European citizen science platforms (such as iSpot in the UK, Natusfera in Spain or Artportalen in Sweden), Pl@ntNet is developing innovative digital services aimed at: facilitating the integration of automated species identification into other citizen science portals and enabling researchers to use Pl@ntNet data and tools for their own research. facilitating the integration of automated species identification into other citizen science portals and enabling researchers to use Pl@ntNet data and tools for their own research. The services implemented will be provided on the European Open Science Cloud (EOSC), to increase the capacity and interest of European scientists to implement citizen science projects aimed at contributing to the sustainable development goals identified by the United Nations. The tools currently available on the Pl@ntNet monitoring platform, as well as the data extraction methodologies that have enabled the publication of two large datasets on the Global Biodiversity Information Facility (GBIF) portal, will be presented. In particular, we will present our latest advances allowing: the adaptation of Pl@ntNet services to nature reserves or botanical gardens, the monitoring of target species for a given geographical area, the prediction of species distribution at the national scale, based on high-resolution remote sensing imagery. the adaptation of Pl@ntNet services to nature reserves or botanical gardens, the monitoring of target species for a given geographical area, the prediction of species distribution at the national scale, based on high-resolution remote sensing imagery. The results of the LifeCLEF campaign, an annual international scientific and technological benchmark based on Pl@ntNet data, will be presented to compare the latest machine-learning techniques used to enable automated species identification and species prediction based on geolocation. The fruit of this work, involving a large multi-disciplinary team will illustrate the benefits of working at the frontier between biological, digital and citizen sciences.

The field of biodiversity informatics has developed rapidly in recent years with broad availability of large-scale information resources. However, online biodiversity information are biased (Boakes et al. 2010, Stropp et al. 2016) as a result of the relatively slow capture and digitization of existing data resources. The West African Plants (WAP) initiative approach to data capture is a prototype of a novel solution to the challenge of the traditional model, in which the institutional “owner” of the specimens is responsible for digital capture of associated data. The WAP Initiative is a consortium of West African researchers in botany, in coordination with six institutions across Europe and North America; its goal is to digitize and mobilize available, high-quality, primary biodiversity occurrence data resources for West African plant diversity (http://jrsbiodiversity.org/grants/university-of-ghana-herbaria/). Here, we developed customized workflows for data capture in formats directly and permanently useful to the “owner” herbarium, and digitized significant new biodiversity records adding to the information available for the plants of the region. Data records were captured strictly in accordance with DarwinCore standards, achieved either by (a) capturing data records from existing images (e.g., images supplied by Naturalis Bodiversity Centre), or (b) capturing data from images taken quickly and efficiently by project personnel in West African Herbaria. Digitization of images and data began in 2015 in West African partner institutions, and by middle of 2018 resulted in 190,953 records of species in 1965 genera and 331 families from 16 West African countries (Fig. 1). Our approach is cost-effective, allows development of information resources even for regions in which political situations make it impossible, and it provides a historical context against which to compare newer data as the latter become available (Peterson et al. 2016). Further measures of success of the initiative will center on whether the institutions “owning” the specimens follow through and put the new data records online. Already, several project institutions have put initial project data online as part of their GBIF data contributions, but—of course—success would consist of all project-generated data being completely available online. Note that this model is the reverse of the traditional model, in which the institutions holding the specimens create the information resources that are used by the rest of the world. This new paradigm in specimen digitization has considerable promise to accelerate and improve the process of generating biodiversity information, and can be replicated and applied in many biodiversity-rich, information-poor regions to remedy the oft-cited massive gaps in information availability.

The use of Web-based informatics tools such as eBird, iNaturalist, and NatureAtlas that allow anyone to find and share occurrences and observations of organisms in nature could readily be integrated with the time-honored specimen collection and field journaling components of taxon-based natural history courses. We find, however, that fewer than 2% of such courses have used any such tool. Consequently, the far majority of students receive no formal exposure to the 21st-century technologies and concepts that are transforming the data landscapes of the very fields the courses should be preparing them to enter. We conducted a seven-year, empirical assessment of the integration of such technology with coursework, and our results reveal why recognizing and correcting this shortcoming is critical. Our data indicate that such technology can enhance student engagement and student perception of learning, and that its broader integration with coursework could be a boon to regional and global efforts to document and conserve biodiversity. We conclude that the academic community is missing a tremendous opportunity to better engage future biologists and potential citizen-scientists in a critically important, emergent paradigm in biodiversity informatics.

The Natural History Museum, London has a number of online databases that describe interactions between species, including the HOSTS database of lepidopteran host plants (Robinson et al. 2010) and a database of Dipterocarp Seed Predators. These databases were generally bespoke software, which has increased the technical work necessary to sustain these resources. The decision was taken to migrate these to either the Scratchpads Virtual Research Environment (VRE) (Smith et al. 2011) or to the museum's Data Portal (Scott et al. 2019), depending on the complexity of the existing resource, as both are being sustained by the Informatics Group at the Natural History Museum, London. Resources that can be best represented as a single table were moved to the Data Portal, while those best represented in a relational model were transferred to Scratchpads. In addition, the Phthiraptera.info Scratchpad (Smith and Broom 2019), which already contained ecological interaction data, was migrated to the new system. This paper describes the implementation within the Scratchpads VRE of a new ecological interactions module that is capable of handling the needs of these projects, while at the same time is flexible to handle the needs of future projects with different data sources.

Natural history collections have stimulated insights into systematics and evolution, but the extensive biodiversity sampling held in museums is increasingly employed to address other critical societal concerns, especially those related to changing environmental conditions on our planet. Due to large-scale digitization efforts in the last decade, specimen information can now be collated across natural history museums. Here, we leverage the availability of digital records of specimens in the United States that span the past ∼135 years to explore the vitality of this resource. Using mammals as an example, we document a significant decline in recent specimen acquisition at a time of extreme environmental degradation and loss of mammalian populations. To stimulate rigorous assessments of the impacts of changing conditions and future-proof this basic infrastructure for mammalogy, we recommend a renewed effort to build temporally deep, geographically extensive, and site-intensive collections of holistic specimens. Targeted fieldwork should be designed to leverage historic sampling to enable retrospective environmental analyses and derive more complete perspectives of change.

Biodiversity informatics has been characterized as a rapidly growing interdisciplinary field, which aims to bring together the areas of biodiversity and informatics. A study was conducted looking at the current level of activity within the GBIF Participant countries and its associated network in relation to work-based training and/or academic teaching at universities, in the field of biodiversity informatics. It was intended to get an overview of GBIF Node Managers, (hence, member countries), already engaged in developing course curricula, or in providing training, and whether they would be willing to share resources or enter into collaborations, to further elaborate this field of science. This investigation followed a survey approach, conducted globally across the GBIF community to identify the existing capacities and resources within the network. The results indicated that the vast majority of GBIF Nodes survey respondents, are engaged in onsite training activities in biodiversity informatics areas, with a focus on professionals, mostly researchers, policy makers and students. Training includes data digitization, management, publishing, analysis and use, to enable the accessibility of analogue and digital biological data which currently resides as scattered databases/datasets. A list containing the associated URL's for training and dissemination activities in GBIF Nodes has been developed, based on survey results, and will be presented. An initial assessment of the academic teaching activities indicated that many countries across most regions were already engaged in the conceptualisation, development and/or implementation of formal academic programs in biodiversity informatics including Benin, Colombia, Costa Rica, Finland, France, India, Norway, South Africa, Sweden, Taiwan and Togo. This study also identified that digital e-learning platforms were a very important tool to help build capacity in a number of countries. To assess the level of potential in the network to support academic teaching and work-based training, sixty percent indicated that they would be willing to be recruited or commissioned to support teaching activities, demonstrating the value of the Nodes network to support the development of biodiversity informatics globally. The contributions and activities of various nodes across the network will be highlighted and a working high-level curriculum framework will be discussed.

With over 8 million specimens of cultural and natural heritage in its custody, the National Museums of Kenya (NMK) are designated as the country’s national repository. Reference collections are at the heart of the institutions' functions of preserving, studying, documenting and presenting past and present heritage for the purpose of enhancing knowledge, appreciation, respect and sustainable utilization of resources, nationally and globally. In spite of its immense resource base and many success stories as a regional hub in heritage research, NMK acknowledges the limitations imposed by hitherto largely ‘analogue’ collections. For instance, a large percentage of the collections are yet to be digitised and therefore, information about them is not easily accessible to users outside the immediate research fraternity. This by extension means that more often than not, the investment made towards accumulation and management of data hardly ever finds its way beyond the storage cabinets and into the decision/policy making process. This obviously impedes NMK’s and the country’s contribution to the important global search for solutions to challenges in climate change and adaptation, human health, as well as food security. In a number of pertinent Needs Assessments (NA) undertaken in the last decade, inadequate capacity for digitisation was identified as the single greatest impediment to heritage information transfer within NMK, Kenya and the vast East African region. This capacity relates to scarcity of both trained human resources, infrastructure such as computing/digitisation equipment, and clear and simple policy guidelines in relation to biodiversity data management and use. In a nutshell, the need to not only embrace but understand innovative technologies and communication tools has been identified as being critical to addressing issues of unrealized research potential and boosting relevance to societal needs. Nevertheless, there has been a concerted effort through various entities such as the project funded by the JRS Biodiversity Foundation on the development of Kenya’s Biodiversity Atlas; an open access platform for hosting and supporting biodiversity data, the Global Biodiversity Information Facility (GBIF) through the Biodiversity Information for Development (BID) programme with the aim of increasing the amount of biodiversity data available about a country so as to respond to national priorities and the Intergovernmental Authority on Development’s (IGAD) Biodiversity Management Programme (BMP), whose main objective was to establish biodiversity databases, build technical capacity and digitise biodiversity collections so as to generate a map of biodiversity and other key information products for the IGAD Region.

Difficulty in correctly identifying species in biological collections is an important impediment in confronting the current biodiversity crisis. The development of tools to improve taxonomic knowledge would help reverse this deficiency. Here, we propose an informatics system for the creation and use of polykeys on the web as tools for the identification of taxa (species, genera, families, etc.). The design is based on 4 actions: the ease of use of the software (usability), polythetic identification, a theoretical model of dynamic identification, and the use of relational databases. A system that applies this design is presented and exemplified using the FAMEX polykey, a tool for identifying the families of flowering plants (Magnoliophyta) of Mexico. The AbaTax system (www.abatax.abaco2.org) allows the creation of polykeys that are published and made available to all web users. The system considers the use of responsive web design, which is adapted in real time so that the interface is properly displayed to the type of device from which it is accessed, be it a desktop computer, laptop, tablet or cell phone.

Species inventories, i.e. the recording of multiple species for a specific place and time, are routinely performed and offer particular value for characterizing biodiversity and its change. However, reporting standards allowing these inventories to be re-used, compared to one another, and further integrated with other sources of biodiversity data are lacking, impeding their broadest utility. Here we provide a conceptual informatics framework for capturing, in a standardized and general way, core information about processes underpinning inventory work. We dub this set of terms Humboldt Core, and demonstrate its utility. This proposed framework is based on a community input process, followed by extensive refinement and testing using published inventories. We first develop a typology of inventories and inventory processes, distinguishing between single, elementary inventories, extended and summary inventories, representing increasing levels of sampling event aggregation. We then further describe typical reporting content for inventory processes, along with their value for inferring absence and use in occupancy modeling. Next we provide an overview of the Humboldt Core terms for capture of geospatial, temporal, taxonomic, and environmental conditions, along with methodological descriptors related to the assessment of sampling effort and inventory completeness. Finally, we introduce a pilot implementation of Humboldt Core for the ingestion and provision of inventory process metadata into Map of Life, demonstrating standardized mobilization of metadata from several hundred previously published summary inventories. Humboldt Core helps facilitate integration of different types of inventories and their use in model-supported assessment of spatial biodiversity and its change, critical for meeting global monitoring goals. Humboldt Core will benefit from further enhancements based on community testing and input, but represents a step toward significantly expanding biodiversity dataset discovery, interoperability, and modeling utility for a type of data essential to the assessment of biodiversity variation in space and time. This article is protected by copyright. All rights reserved.

Biodiversity loss is a global issue and is especially of pressing concern in mega diverse countries, such as Indonesia. To prevent any further catastrophe, the Ministry of Forestry and Environment of Republic of Indonesia has been promoting the resort based management to be implemented in Indonesia to maximize the performance of conservation activity. The lack of data standardization made it hard to organize and manage archipelagic country that consist 17,504 islands with no technology provision in most of them. In this paper we develop a framework of integration mobile-web technology for biodiversity and conservation in Indonesia. We introduced a new framework to maintain the biodiversity and conservation data in Indonesia.

Background Scientific names in biology act as universal links. They allow us to cross-reference information about organisms globally. However variations in spelling of scientific names greatly diminish their ability to interconnect data. Such variations may include abbreviations, annotations, misspellings, etc. Authorship is a part of a scientific name and may also differ significantly. To match all possible variations of a name we need to divide them into their elements and classify each element according to its role. We refer to this as ‘parsing’ the name. Parsing categorizes name’s elements into those that are stable and those that are prone to change. Names are matched first by combining them according to their stable elements. Matches are then refined by examining their varying elements. This two stage process dramatically improves the number and quality of matches. It is especially useful for the automatic data exchange within the context of “Big Data” in biology. Results We introduce Global Names Parser (gnparser). It is a Java tool written in Scala language (a language for Java Virtual Machine) to parse scientific names. It is based on a Parsing Expression Grammar. The parser can be applied to scientific names of any complexity. It assigns a semantic meaning (such as genus name, species epithet, rank, year of publication, authorship, annotations, etc.) to all elements of a name. It is able to work with nested structures as in the names of hybrids. gnparser performs with ≈99% accuracy and processes 30 million name-strings/hour per CPU thread. The gnparser library is compatible with Scala, Java, R, Jython, and JRuby. The parser can be used as a command line application, as a socket server, a web-app or as a RESTful HTTP-service. It is released under an Open source MIT license. Conclusions Global Names Parser (gnparser) is a fast, high precision tool for biodiversity informaticians and biologists working with large numbers of scientific names. It can replace expensive and error-prone manual parsing and standardization of scientific names in many situations, and can quickly enhance the interoperability of distributed biological information. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1663-3) contains supplementary material, which is available to authorized users.

This article proposes a conceptual architecture for citizen-expert knowledge exchange in biodiversity management. Expert services, such as taxonomic identification, are required in many biodiversity management activities, yet these services remain inaccessible to poor communities, such as small-scale farmers. The aim of this research was to combine ontology and crowdsourcing technologies to provide taxonomic services to such communities. The study used a design science research (DSR) approach to develop the conceptual architecture. The DSR approach generates knowledge through building and evaluation of novel artefacts. The research instantiated the architecture through the development of a platform for experts and farmers to share knowledge on fruit flies. The platform is intended to support rural fruit farmers in Kenya with control and management of fruit flies. Expert knowledge about fruit flies is captured in an ontology that is integrated into the platform. The non-expert citizen participation includes harnessing crowdsourcing technologies to assist with organism identification. An evaluation of the architecture was done through an experiment of fruit fly identification using the platform. The results showed that the crowds, supported by an ontology of expert knowledge, could identify most samples to species level and in some cases to sub-family level. The conceptual architecture may guide and enable creation of citizen-expert knowledge exchange applications, which may alleviate the taxonomic impediment, as well as allow poor citizens access to expert knowledge. Such a conceptual architecture may also enable the implementation of systems that allow non-experts to participate in sharing of knowledge, thus providing opportunity for the evolution of comprehensive biodiversity knowledge systems.

Species distribution modelling, or ecological niche modelling, is a collection of techniques for the construction of correlative models based on the combination of species occurrences and GIS data. Using such models, a variety of research questions in biodiversity science can be investigated, among which are the assessment of habitat suitability around the globe (e.g. in the case of invasive species), the response of species to alternative climatic regimes (e.g. by forecasting climate change scenarios, or by hindcasting into palaeoclimates), and the overlap of species in niche space. The algorithms used for the construction of such models include maximum entropy, neural networks, and random forests. Recent advances both in computing power and in algorithm development raise the possibility that deep learning techniques will provide valuable additions to these existing approaches. Here, we present our recent findings in the development of workflows to apply deep learning to species distribution modelling, and discuss the prospects for the large-scale application of deep learning in web service infrastructures to analyze the growing corpus of species occurrence data in biodiversity information facilities.

The Finnish Biodiversity Information Facility FinBIF (LINK: species.fi), operational since late 2016, is one of the more recent examples of comprehensive, all-inclusive national biodiversity research infrastructures. FinBIF integrates a wide array of biodiversity information approaches under the same umbrella. These include species information Fig. 1 (e.g. descriptions, photos and administrative attributes); citizen science platforms enabling recording, managing and sharing of observation data; an e-learning environment for species identification; management and sharing of restricted data among authorities; building a national DNA barcode reference library and linking it to species occurrence data; community-driven species identification support; large-scale and multi-technology digitisation of natural history collections; and IUCN Red Listing to conduct a periodic national assesment of the status of the threatened species. To improve the taxonomic coverage and the content of species information, FinBIF is starting a process to collaborate with the species information community at large, in order to collate already existing but not yet openly distributed information. This also means digitisation of information from analogue sources. In addition, the attempt is to join forces with Scandinavian counterparts, namely Artdatabanken (LINK: https://www.artdatabanken.se/) and Artsdatabanken (LINK: https://www.artsdatabanken.no/), for more efficient knowledge exchange within the countries sharing the same biogeographical region and thus similar species composition. The aim is also to reach politically high level agreement for deeper and wider commitment to collaborate in compiling, digitising and sharing relevant biodiversity information over the national borders.

Our knowledge of global biodiversity remains incomplete and beset by knowledge shortfalls affecting both the census of species (i.e. the Linnean shortfall) and our understanding of their distributions (i.e. the Wallacean shortfall; Hortal et al. 2015). While alarming rates of species extinction have been reported in most groups of organisms, our capacity to assess extinction threats is limited by these shortfalls and it has become imperative to optimize our use of existing information for the analyses of biodiversity data. There are two major challenges when integrating biodiversity data from heterogeneous sources to ultimately analyzing them: The frequent disparity in taxon names used to refer to the same organisms; Geographical inconsistencies on specimen information. The frequent disparity in taxon names used to refer to the same organisms; Geographical inconsistencies on specimen information. The first refers to disagreements about the taxon concepts attached to names alongside the different interpretations and applications (e.g. gender agreement in taxonomic names) of the existing nomenclatural rules that ensure universality and stability of scientific names. The development of new methods for species delineation, and in particular with the growing integration of genetic data in the practice of taxonomy (e.g. DNA barcoding; Ratnasingham and Hebert 2013), has increased our ability to discriminate closely related species. This enhances the resolution level at which biodiversity is documented, described and analyzed(Goldstein and DeSalle 2011). One frequent outcome is the redefinition of species boundaries; either through merging (synonymy) or splitting of previously recognized species. In understudied groups such as insects, the resulting inflation of names, sometimes provisional, further defies the reconciliation of names used by different sources. The second challenge refers to the completeness and accuracy of geographical information. Specimen records in biodiversity databases often lack GPS coordinates. Consequently we need to accurately infer the latitude and longitude from place names. Other frequent inaccuracies include erroneous georeferencing, imprecision and/or error in the location of a record (Soberón and Peterson 2004). Integration of data on the basis of taxon names and their geographic information is a major challenge that either results in excluding a significant number of records, or in merging incompatible records, leading to erroneous outcomes. Therefore, we have developed WF.ACTIAS, a computational workflow that gathers data from several sources and provides the user with tools to make objective and reproducible decisions to assign records to a consensus species name, while detecting and correcting geographical inconsistencies. Its main objective is to automate a process that can integrate information about nomenclature, taxon concepts and geographical information to reconcile taxon names from different sources. Here, we present the WF.ACTIAS workflow in the context of the analysis of diversity in two sister families of moths – the Saturniidae and Sphingidae. Species boundaries in these insects have been thoroughly and comprehensively revisited through the integration of DNA barcodes and we are tackling the reconciliation of taxon names in ca. 282 000 records of which more than 77 000 have DNA barcodes. The outcome of this data integration is essential to study patterns of biodiversity and distributions and sets the ground to extend this process to other groups of organisms.

Beginning in 2012, the JRS Biodiversity Foundation funded the University of Kansas to carry out a series of courses covering the breadth of the field of biodiversity informatics in cities across Africa. The Biodiversity Informatics Training Curriculum (BITC) was created from these events that used in-person courses taught by world experts in biodiversity informatics fields. The courses reached 120+ students and young professionals from 23 countries across Africa, and the digital videos of the courses reached thousands of viewers and users worldwide. BITC1 concluded in 2015 and a biodiversity informatics "curriculum" was published in the form of a compendium of BITC course materials (Peterson and Ingenloff 2016)—this curriculum has now been used as the basis for multiple programs of study around the world. BITC2 is now funded, again thanks to the JRS Biodiversity Foundation—this second version involves longer, more thematically oriented courses to be held in three east and southern African countries. The first will be held in Rwanda, in September 2019, and will focus on "Measuring Essential Biodiversity Variables and Ecosystem Services." BITC2 courses will begin with an open symposium, and then will proceed with intensive, hands-on, goal-oriented workshops with a small number of participants. The overall objective of the BITC is a community-oriented and community-run set of training events that can provide avenues to advanced study, international collaboration, and region-wide integration of efforts in biodiversity informatics. BITC2 has four main goals: implement new training dimensions in both an open symposium forum and an intensive, goal-and-product-focused approach that we tested in the later courses of BITC1; expand access to learning resources via subtitling and translation; build collaborative BITC communities via social media platforms; and build within-Africa capacity in biodiversity informatics via high-level involvement of a number of African scientists and educators. implement new training dimensions in both an open symposium forum and an intensive, goal-and-product-focused approach that we tested in the later courses of BITC1; expand access to learning resources via subtitling and translation; build collaborative BITC communities via social media platforms; and build within-Africa capacity in biodiversity informatics via high-level involvement of a number of African scientists and educators. These goals will be achieved via three, once-yearly symposium/course combination events which will be organized and implemented by combined leadership teams from the UK, USA, and African countries, and are intended to provide a combined total of 30-33 days of training to 36-42 trainees. Here we present the format of the BITC1 and BITC2 courses, and the positive and negative outcomes that have resulted, with an eye to optimal design of future such initiatives.

The collection and classification of data into meaningful categories is a key step in the process of knowledge making. In the life sciences, the design of data discovery and integration tools has relied on the premise that a formal classificatory system for expressing a body of data should be grounded in consensus definitions for classifications. On this approach, exemplified by the realist program of the Open Biomedical Ontologies Foundry, progress is maximized by grounding the representation and aggregation of data on settled knowledge. We argue that historical practices in systematic biology provide an important and overlooked alternative approach to classifying and disseminating data, based on a principle of coordinative rather than definitional consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or “settled” science.

There has been major progress over the last two decades in digitising historical knowledge of biodiversity and in making biodiversity data freely and openly accessible. Interlocking efforts bring together international partnerships and networks, national, regional and institutional projects and investments and countless individual contributors, spanning diverse biological and environmental research domains, government agencies and non-governmental organisations, citizen science and commercial enterprise. However, current efforts remain inefficient and inadequate to address the global need for accurate data on the world's species and on changing patterns and trends in biodiversity. Significant challenges include imbalances in regional engagement in biodiversity informatics activity, uneven progress in data mobilisation and sharing, the lack of stable persistent identifiers for data records, redundant and incompatible processes for cleaning and interpreting data and the absence of functional mechanisms for knowledgeable experts to curate and improve data. Recognising the need for greater alignment between efforts at all scales, the Global Biodiversity Information Facility (GBIF) convened the second Global Biodiversity Informatics Conference (GBIC2) in July 2018 to propose a coordination mechanism for developing shared roadmaps for biodiversity informatics. GBIC2 attendees reached consensus on the need for a global alliance for biodiversity knowledge, learning from examples such as the Global Alliance for Genomics and Health (GA4GH) and the open software communities under the Apache Software Foundation. These initiatives provide models for multiple stakeholders with decentralised funding and independent governance to combine resources and develop sustainable solutions that address common needs. This paper summarises the GBIC2 discussions and presents a set of 23 complementary ambitions to be addressed by the global community in the context of the proposed alliance. The authors call on all who are responsible for describing and monitoring natural systems, all who depend on biodiversity data for research, policy or sustainable environmental management and all who are involved in developing biodiversity informatics solutions to register interest at https://biodiversityinformatics.org/ and to participate in the next steps to establishing a collaborative alliance. The supplementary materials include brochures in a number of languages (English, Arabic, Spanish, Basque, French, Japanese, Dutch, Portuguese, Russian, Traditional Chinese and Simplified Chinese). These summarise the need for an alliance for biodiversity knowledge and call for collaboration in its establishment.

Africa is a continent grappling with many challenges, but it is also alive with possibility and booming with optimism. To some, Africa is the birthplace of humankind, a place with exceptional beauty, brimming with amazing wildlife and human ingenuity. To others, its narrative has evolved from yesterday’s story, a story of everything gone wrong, a history of extractive resource utilisation by foreign interests, a 'dark continent', to an Africa Rising. A place "not so dark', to a continent with significant foreign direct investment that has increased over time. Africa is one of the most megadiverse continents in the world. It boasts a substantial share of the world’s biodiversity, including one-fifth of all mammal species and one-quarter of all bird species. Despite this, African species are dramatically underrepresented in the world’s freely-accessible biodiversity information resources. It was reported that only 4% of the >1 billion records available through the Global Biodiversity Information Facility (GBIF) concern African biodiversity, the majority of which were published by non-African institutions. Even within Africa, distributional biodiversity databases exhibit strong spatial bias due to uneven efforts in sampling, storing and sharing data, which may, in turn, reflect high regional variation in capacity, funding and political will. The South African National Biodiversity Institute (SANBI) is a knowledge-based organisation, where biodiversity information is the key resource that drives research and innovation, informs planning and policy development processes, informs decisions and is the basis for evaluating progress and impact. It is therefore important that biodiversity and biodiversity information are managed as strategic assets that will leverage shared value, supporting sustainable decisions towards the broader developmental objectives. In light of this, SANBI-GBIF has developed a regional engagement strategy and has been identified to lead the regional African Coordinating Mechanism (ACM) for biodiversity informatics on the African continent. The ACM is the ultimate vision for a formal regional structure for biodiversity information management and has been identified as far back as 2010, by the GBIF Africa Nodes, as a requirement to foster the implementation of the GBIF strategic plan in Africa. Six strategic priority areas to advance the biodiversity informatics efforts (driven by the GBIF-Africa Nodes and strategically identified partners) have been identified, across the value chain to support the generation, management, publication and use of biodiversity information for conservation, decision-making and sustainable development in Africa. These strategic areas include: 1. data mobilisation, 2. the data-science-policy interface 3. capacity building in biodiversity information management, 4. leveraging science technology and innovation, 5. regional engagement 6. growing the community of practice. Capacity development has also been identified as critical to ensure advancement of the biodiversity informatics agenda.

Deep learning is one machine learning method based on the layers used in artificial neural networks. The breakthrough of deep learning in classification led to its rapid application in speech recognition, natural language understanding, and image processing and recognition. In the field of biodiversity informatics, deep learning efforts are being applied in rapid species identification and counts of individuals identified based on image, audio, video, and other data types. However, deep learning methods hold great potential for application in all aspects of biodiversity informatics. We present a case study illustrating how to increase data collection quality and efficiency using well-established technology such as optical character recognition (OCR) and some image classification. Our goal is to image data from the scanned documents of various butterfly atlases, add species, specimens, collections, photographs and other relevant information, and build a database of butterfly images. Information collection involves image annotation and text-based descritpion input. Although the work of image annotation is simple, this process can be accelerated by deep learning-based target segmentation to make the selection process easier, such as changing box select to a double click. The process of information collection is complicated, involving input of species names, specimen collection, specimen description, and other information. Generally, there are many images in atlases, the text layout is rather messy, and overall OCR success is poor. Therefore, the measures we take are as follows: Step A: select the screenshot of the text and then call the OCR interface to generate the text material; Step B: proceed with NLP- (natural language processing) related processing; Step C: perform manual operations on the results, and introduce the NLP function again to this process; Step D: submit the result. The deep learning applications we integrated in our client tool include: target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. Some simple machine learning methods such as k-nearest neighbor can be used to automatically determine gender, pose, and so on. While complex information such as collection place, time, and collector can be analyzed by deep learning-based NLP methods in the future. In our infomation collection process, ten fields are required to submit one single record. Of those, about 4-5 input fields can be dealt with the AI-assistant. It can thus be seen from the above process that deep learning has reduced the workload of manual information annotation by at least 30%. With improvements in accuracy, the era of using automatic information extraction robots to replace manual information annotation and collection is just around the corner.

Scholarly publications in taxonomy are used as the sole carrier of the communication channel to publicize the description of new species, more generally any kind of taxon, their augmentations in form of re-descriptions to small notes such as additional observation records, or deprecations when the name of a taxon is changing. This is communicated in a highly standardized way. For nomenclatural issues, the Codes (e.g. International Code of Zoological Nomenclature) require certain elements, and for comparative reasons, highly formalized language, document structure, illustrations and citation systems are used. This estimated corpus of 500M printed pages includes all we know about the biodiversity in the sciences as well as what has been processed in terms of specimens in the natural history collections. This includes tens of millions of scientific illustrations, taxonomic treatments, i.e. parts of articles that are clearly delimited and headed by a label including a taxonomic name, materials citation, citations of articles and treatments, specimens by their specimen codes, collections by their collection codes, and collectors by their name. Implicitly, the entire catalogue of life is waiting to be liberated. Only recently, with the advent of text and data mining and of publishing, a change is taking place to make all this data after publishing immediately widely accessible and digitally accessible knowledge in terms of findability, accessibility, interoperability and reuse (FAIR). The Biodiversity Literature Repository (BLR) is a community in Zenodo, the open science repository at CERN and part of the European project OpenAIRE. Its goal is to provide a long-term, stable, open access repository for the long tail, preferably FAIR research data. Together with Plazi (http://plazi.org)and Pensoft, BLR has been created to allow deposition of taxonomic articles enhanced with links to data extracted from therein and deposited in BLR, such as illustrations or taxonomic treatments. The metadata for these deposits is enriched with extensive links to cited sources, or the source article. For each of the deposits, a DataCite DOI is minted, unless one exists already. The deposits can be found using either the search functionality of Zenodo, or the BLR website, which makes use of the millions of facts extracted from the publications via the Plazi workflow. A prime user of the BLR data is GBIF which is using the taxonomic treatments and the figures in their respective view of the taxonomic articles. The input of new taxa allows updating the GBIF taxonomic backbone almost instantaneously and keeping it up-to-date with publishing of new taxa or synonymies. Currently, 35,600 articles, and 190,000 images are available, with an expected 300,000 taxonomic treatments by Fall 2019 and almost 30% of the annually new described species are accessible online, both in human and machine readable format. With the support of the Arcadia Fund, the goal is to provide access to over 50% of the annually described new species, from the necessary number of taxonomic journals, over 600,000 taxonomic treatments and 350,000 illustrations by 2021, hopefully with an increasing community adding to this new resource as author, publisher, or data liberator.

Scratchpads launched in 2007 and became an extremely popular resource adopted for a variety of communities. Primarily, Scratchpads are used to manage and publish biodiversity data, but many sites were organised around projects, societies and regions. Demand for Scratchpads peeked at requests for more than 80 new sites over a 3 month period in 2014. Today we have over 1000 Scratchpad sites. This has not been a pain-free journey. In 2015 the grants funding Scratchpad support and development came to an end, and whilst the Natural History Museum, London, provided some institutional support, this was alongside several competing initiatives. For a period of nearly two years, the Scratchpads had no dedicated developers. The Scratchpads suffered from this neglect, bugs remained unfixed and the platform became increasingly unstable. This situation has now been rectified, in 2017 the Informatics Group expanded, enabling us to provide a dedicated resource to the Scratchpads again. This has been a challenging but valuable learning experience - and one that many Virtual Research Environments (VREs) in our community have, or will, experience. Current funding models encourage a boom and bust development cycle, described by Hine (2008) as the "dance of the initiatives," as projects constantly need to re-invent themselves in order to receive new external funding. We need to move beyond this, to start working collectively to develop a common roadmap for these systems, so we can begin to mutually benefit from each others development activities. Building on lessons learnt from the Scratchpads, we highlight a draft set of principles that may provide a framework for such a collaboration. While it is unrealistic to expect existing projects at different stages of maturity, and supporting very different use cases, to re-write their codebase in order to facilitate collaboration, we propose a microservices framework that would allow these related systems to converge on the delivery of a common set of services, provided by many current VRE's. This convergence, coupled with the development of a common and mutually agreed roadmap for these systems, has the potential to build a more sustainable future for VRE user and developer communities, as these systems evolve to support new use cases and improve existing functionality.