Vector-borne diseases are responsible for more than 17% of human cases of infectious diseases. In most situations, effective control of debilitating and deadly vector-bone diseases (VBDs), such as malaria, dengue, chikungunya, yellow fever, Zika and Chagas requires up-to-date, robust and comprehensive information on the presence, diversity, ecology, bionomics and geographic spread of the organisms that carry and transmit the infectious agents. Huge gaps exist in the information related to these vectors, creating an essential need for campaigns to mobilise and share data. The publication of data papers is an effective tool for overcoming this challenge. These peer-reviewed articles provide scholarly credit for researchers whose vital work of assembling and publishing well-described, properly-formatted datasets often fails to receive appropriate recognition. To address this, GigaScience's sister journal GigaByte partnered with the Global Biodiversity Information Facility (GBIF) to publish a series of data papers, with support from the Special Programme for Research and Training in Tropical Diseases (TDR), hosted by the World Health Organisation (WHO). Here we outline the initial results of this targeted approach to sharing data and describe its importance for controlling VBDs and improving public health.

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

The Horizon 2020 project Biodiversity Community Integrated Knowledge Library (BiCIKL) (started 1st of May 2021, duration 3 years) will build a new European community of key research infrastructures, researchers, citizen scientists and other stakeholders in biodiversity and life sciences. Together, the BiCIKL 14 partners will solidify open science practices by providing access to data, tools and services at each stage of, and along the entire biodiversity research and data life cycle (specimens, sequences, taxon names, analytics, publications, biodiversity knowledge graph) (Fig. 1, see also the BiCIKL kick-off presentation through Suppl. material 1), in compliance with the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. The existing services provided by the participating infrastructures will expand through development and adoption of shared, common or interoperable domain standards, resulting in liberated and enhanced flows of data and knowledge across these domains. BiCIKL puts a special focus on the biodiversity literature. Over the span of the project, BiCIKL will develop new methods and workflows for semantic publishing and integrated access to harvesting, liberating, linking, and re-using sub-article-level data extracted from literature (i.e., specimens, material citations, sequences, taxonomic names, taxonomic treatments, figures, tables). Data linkages may be realised with different technologies (e.g., data warehousing, linking between FAIR Data Objects, Linked Open Data) and can be bi-lateral (between two data infrastructures) or multi-lateral (among multiple data infrastructures). The main challenge of BiCIKL is to design, develop and implement a FAIR Data Place (FDP), a central tool for search, discovery and management of interlinked FAIR data across different domains. The key final output of BiCIKL will the future Biodiversity Knowledge Hub (BKH), a one-stop portal, providing access to the BiCIKL services, tools and workflows, beyond the lifetime of the project.

When genetic information is used to describe or classify a taxon, most users will foresee its use in the context of molecular ecology or phylogenetic research. It is important to realize that a sequence with coordinates and a timestamp is a valuable biodiversity occurrence which is useful in a much broader context than its original purpose. To realize this potential, DNA-derived data needs to be discoverable through biodiversity data platforms. This guide will teach you the principles and approaches of exposing “sequences with dates and coordinates” in the context of broader biodiversity data. The guide covers choices of particular schemas and terms, common pitfalls and good practice, without going into platform-specific details. It will benefit anyone interested in better exposure of DNA-derived data through general biodiversity data platforms, including national biodiversity portals.

GBIF—the Global Biodiversity Information Facility—and its network of more than 1,500 institutions maintain the world's largest index of biodiversity data (https://www.gbif.org), containing nearly 1.4 billion species occurrence records. This infrastructure offers a model of best practices, both technological and cultural, that other domains may wish to adapt or emulate to ensure that its users have free, FAIR and open access to data.The availability of community-supported data and metadata standards in the biodiversity informatics community, combined with the adoption (in 2014) of open Creative Commons licensing for data shared with GBIF, established the necessary preconditions for the network's recent growth.But GBIF's development of a data citation system based on the uses of DOIs—Digital Object Identifiers—has established an approach for using unique identifiers to establish direct links between scientific research and the underlying data on which it depends. The resulting state-of-the-art system tracks uses and reuses of data in research and credits data citations back to individual datasets and publishers, helping to ensure the transparency of biodiversity-related scientific analyses.In 2015, GBIF began issuing a unique Digital Object Identifier (DOI) for every data download. This system resolves each download to a landing page containing 1) the taxonomic, geographic, temporal and other search parameters used to generate the download; 2) a quantitative map of the underlying datasets that contributed to the download; and 3) a simple citation to be included in works that rely on the data.When authors cite these download DOIs, they in effect assert direct links between scientific papers and underlying data. Crossref registers these links through Event Data, enabling GBIF to track citation counts automatically for each download, dataset and publisher. These counts expand to display a bibliography of all research reuses of the data.This system improves the incentives for institutions to share open data by providing quantifiable measures demonstrating the value and impact of sharing data for others' research.GBIF is a mature infrastructure that supports a wide pool of researchers publish two peer-reviewed journal articles that rely on this data every day. That said, the citation-tracking and -crediting system has room for improvement. At present, 21% of papers using GBIF-mediated data provide DOI citations—which represents a 30% increase over 2018. Through outreach to authors and collaboration with journals, GBIF aims to continue this trend.In addition, members of the GBIF network are seeking to extend citation credits to individuals through tools like Bloodhound Tracker (https://www.bloodhound-tracker.net) using persistent identifiers from ORCID and Wikidata IDs. This approach provides a compelling model for the scientific and scholarly benefits of treating individual data records from specimens as micro- or nanopublications—first-class research objects that advancing both FAIR data and open science.

Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit arthropod population time-series data are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Such data have been collected world-wide for over a century, but remain scattered and largely inaccessible. In particular, with the ever-present and growing threat of arthropod pests and vectors of infectious diseases, there are numerous historical and ongoing surveillance efforts, but the data are not reported in consistent formats and typically lack sufficient metadata to make reuse and re-analysis possible. Here, we present the first-ever minimum information standard for arthropod abundance, Minimum Information for Reusable Arthropod Abundance Data (MIReAD). Developed with broad stakeholder collaboration, it balances sufficiency for reuse with the practicality of preparing the data for submission. It is designed to optimize data (re)usability from the "FAIR," (Findable, Accessible, Interoperable, and Reusable) principles of public data archiving (PDA). This standard will facilitate data unification across research initiatives and communities dedicated to surveillance for detection and control of vector-borne diseases and pests.

Machine vision technology now provides real-time identification of images of tens of thousands of species across a wide range of taxonomic groups—witness iNaturalist's suggestion of species IDs to users who create observation records.

Advances in machine vision technology are rapidly enabling new and innovative uses within the field of biodiversity. Computers are now able to use images to identify tens of thousands of species across a wide range of taxonomic groups in real time, notably demonstrated by iNaturalist.org, which suggests species IDs to users (https://www.inaturalist.org/pages/computer_vision_demo) as they create observation records. Soon it will be commonplace to detect species in video feeds or use the camera in a mobile device to search for species-related content on the Internet. The Global Biodiversity Information Facility (GBIF) has an important role to play in advancing and improving this technology, whether in terms of data, collaboration across teams, or citation practice. But in the short term, the most important role may relate to initiating a cultural shift in accepted practices for the use of GBIF-mediated data for training of artificial intelligence (AI). “Training datasets” play a critical role in achieving species recognition capability in any machine vision system. These datasets compile representative images containing the explicit, verifiable identifications of the species they include. High-powered computers run algorithms on these training datasets, analysing the imagery and building complex models that characterize defining features for each species or taxonomic group. Researchers can, in turn, apply the resulting models to new images, determining what species or group they likely contain. Current research in machine vision is exploring (a) the use of location and date information to further improve model results, (b) identification methods beyond species-level into attribute, character, trait, or part-level ID, with an eye toward human interpretability, and (c) expertise modeling for improved determination of “research grade” images and metadata. The GBIF community has amassed one of the largest datasets of labelled species images available on the internet: more than 33 million species occurrence records in GBIF.org have one or more images (https://www.gbif.org/occurrence/gallery). Machine vision models, when integrated into the data collection tools in use across the GBIF network, can improve the user experience. For example, in citizen science applications like iNaturalist, automated species suggestion helps even novice users contribute occurrence records to GBIF. Perhaps most importantly, GBIF has implemented uniform (and open) data licensing, established guidelines on citation and provided consistent methods for tracking data use through the Digital Object Identifiers (DOI) citation chain. GBIF would like to build on the lessons learned in these activities while striving to assist with this technology research and increase its power and availability. We envisage an approach as follows: To assist in developing and refining machine vision models, GBIF plans to provide training datasets, taking effort to ensure license and citation practice are respected. The training datasets will be issued with a DOI, and the contributing datasets will be linked through the DOI citation graph. To assist application developers, Google and Visipedia plan to build and publish openly-licensed models and tutorials for how to adapt them for localized use. Together we will strive to ensure that data is being used responsibly and transparently, to close the gap between machine vision scientists, application developers, and users and to share taxonomic trees capturing the taxon rank to which machine vision models can identify with confidence based on an image’s visual characteristics. To assist in developing and refining machine vision models, GBIF plans to provide training datasets, taking effort to ensure license and citation practice are respected. The training datasets will be issued with a DOI, and the contributing datasets will be linked through the DOI citation graph. To assist application developers, Google and Visipedia plan to build and publish openly-licensed models and tutorials for how to adapt them for localized use. Together we will strive to ensure that data is being used responsibly and transparently, to close the gap between machine vision scientists, application developers, and users and to share taxonomic trees capturing the taxon rank to which machine vision models can identify with confidence based on an image’s visual characteristics.

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.

Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit population time-series are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Arthropod data have been collected world-wide for over a century, but remain scattered and largely inaccessible. With the ever-present and growing threat of arthropod vectors of infectious diseases and pest species, there are enormous amounts of historical and ongoing surveillance. These data are currently reported in a wide variety of formats, typically lacking sufficient metadata to make reuse and re-analysis possible. We present the first minimum information standard for arthropod abundance. Developed with broad stakeholder collaboration, it balances sufficiency for reuse with the practicality of preparing the data for submission. It is designed to optimize data (re-)usability from the “FAIR,” (Findable, Accessible, Interoperable, and Reusable) principles of public data archiving (PDA). This standard will facilitate data unification across research initiatives and communities dedicated to surveillance for detection and control of vector-borne diseases and pests.