Plant-pollinator Interaction Data: A case study of the WorldFAIR project

Thousands of new species are discovered each year, and new results are published to add to the knowledge of existing species. A growing number of these are immediately accessible through the Biodiversity Literature Repository (BLR) and reused by Global Biodiversity Information Facility (GBIF), bringing the number of treatments covering plant species to over 25,000 treatments. This includes the findable, accessible, interoperable, and resuable (FAIR) treatments and related figures, and in many cases the material citation of the holotype, and links to the collection, specimen and gene sequences attribured to the codes. The FAIR data is deposited in the Biodiversity Literature Repository ensuring long-term access, and includes rich, customized metadata describing its content using standard vocabularies (e.g. Darwin Core (DwC) or Open Biological and Biomedical Ontology (OBO) Foundry, as well as links to related items and data reuse (e.g. GBIF and CheckListbank). TreatmentBank is a European Research Infrastructure, which annotates taxonomic publications including quality control, as much as possible by machine, and as needed by human curation. The process is versatile, the output can be customized and immediately used by GBIF and the Swiss Institute of Bioinformatics library system (SIBiLS) to add treatments akin to articles in PubMed Central. Another focus of conversion could be floras (several series published in Muséum national d'Histoire naturelle including those that are currently only available in print). In addition to the articles provided through these collaborations, other journals were processed, totaling 5,900 articles with 54,000 treatments of which 5,600 are plants, 48,000 figures, 140,000 material citations, of which 9,000 (670 plants) are new species and 670 (16) new genera described in 2021. Arcadia Fund is funding a project to include over 100 additional journals into the daily processing, as well as annotating data from legacy publications, with the possibility of including more journals covering plant taxonomy. The workflow is based on the open source software GoldenGate Imagine, a tool used to convert articles from PDF format into an Image Markup File (IMF) export file including all the annotations in a set of CSV files and allowing checking and improve the quality of data. The quality control parameter can be defined according to specific user requirements and the curation of the annotation can be done as part of the workflow or by third parties. The IMF can be uploaded to TreatmentBank where FAIR data is generated through their deposits in the Biodiversity Literature Repository. All the data is accessible from the Plazi API in various formats such as XML, DwC-A (DarwinCore Archive) or RDF, and checked when the data has been deposited or reused by returning the link of the respective recipient’s reuse identifier. Furthermore, the treatment citations annotated in the publications provide access to the treatments related to the same taxon, including synonyms. In this presentation, we explain the data annotated and made FAIR by TreatmentBank in order to discuss what would be needed to import this rapidly increasing corpus of taxonomic treatments as a new source for WFO (World Flora Online). A side effect of the discussion could be suggestions to publishers on how they should provide data so that the taxonomic data can be automatically integrated into WFO to keep it as up-to-date as possible.

Environmental DNA (eDNA)-derived data, particularly DNA sequences and species occurrence records, are powerful resources for various applications, including biodiversity monitoring, biosecurity, and agricultural bio-surveillance. An important part of eDNA data’s value lies in its reusability, which can be enhanced through the use of metadata that adheres to FAIR (Findable, Accessible, Interoperable, Reusable) data principles (Wilkinson et al. 2016, Abarenkov et al. 2023). While FAIR practices are gaining recognition within the eDNA community, adoption remains limited. A key barrier is that existing data standards, including Darwin Core (DwC, Darwin Core Task Group 2009) and the Minimum Information about any (x) Sequence (MIxS), lack sufficient terms to fully describe eDNA workflows, from sampling and processing to bioinformatic outputs (Takahashi et al. 2025). To address this challenge, we have initiated a collaborative eDNA Task Group supported by the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) organisations. We build upon current standardisation efforts to develop a comprehensive and interoperable eDNA metadata checklist for formal integration into the MIxS and DwC frameworks. See the task group descriptions at TDWG*2 and GSC.*1 Our goal is to ensure compatibility with, and seamless adoption by, major biodiversity and sequence data infrastructures, such as Global Biodiversity Information Facility (GBIF), Ocean Biodiversity Information System (OBIS), and International Nucleotide Sequence Database Collaboration (INSDC). This talk will present progress of the TDWG/GSC joint eDNA initiative, including our collaborative efforts with related working groups and initiatives, such as eDNAqua-plan, the Darwin Core Data Package, DNA-derived data extension to DwC, Minimum Information about any Ancient Sequence (MInAS), and the FAIR eDNA (FAIRe) initiatives (Takahashi et al. 2025). We invite contributions and collaboration to help shape a shared metadata framework that supports the growth, reuse, and long-term utility of eDNA data worldwide.

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Research projects in ecology or biodiversity either start with an area of study or a target species list. Working with these species lists or taxonomic lists is not as straightforward as it seems. The taxonomic names that are considered to be “standard,” are surprisingly dynamic. Over time, the names keep changing with ongoing research and advancements in taxonomy. Additionally, they undergo all sorts of reorganization, such as one species being split into multiple species and/or subspecies, the grouping of multiple species into a single species, and the reclassification of species from one genus to another. Compiling a consistent target species list can be very time consuming and tricky. However it is the initial step in most research projects and needs to be completed in order to continue the research. Advancements in biodiversity informatics are helping simplify and automate some of these tasks. There are several web services that provide taxonomic data with either a taxonomic or a geographic focus. An increasing number of experts are opening access to their carefully curated taxonomic lists. Even with the help of these services, a lot of time needs to be spent to create a working list of names that can be linked to data such as Global Biodiversity Information Facility (GBIF) mediated occurrence data. The package “taxotools” (Barve 2021) provides basic taxonomic list processing functions within the R programming environment (R Core Team 2021). Even though it is a work in progress, the functions available so far are applicable to diverse projects. The tools available can be categorized into the following broad areas: Name manipulation: A set of helper functions to check scientific names with global name resolution services like Global Names Architecture (GNA) & GBIF Name Parser, and to construct and deconstruct scientific names to and from components like genus, species and subspecific units. Name matching: Matches names either with global name services or with user-created master taxonomy lists using fuzzy matching, testing combinations of genus level synonyms, subspecies elevation to species, trying to match with higher level taxonomic entities like genus and family, and employing a user-defined lookup table to manually resolve names. List processing: Updates list fields such as unique identifiers (id), higher taxonomy and taxonomic ranks. List matching: Compares user generated lists with each other and finds differences in the two lists, then prepares the lists for merging together to form a masterlist. Format conversion: Converts taxolist to and from formats like HTML and Darwin Core (Wieczorek et al. 2021), which is useful in data exchange or checking the lists manually. Name harvesting functions: Acquires additional names from Integrated Taxonomic Information System (ITIS) and Wikipedia (taxonomy infobox). Name manipulation: A set of helper functions to check scientific names with global name resolution services like Global Names Architecture (GNA) & GBIF Name Parser, and to construct and deconstruct scientific names to and from components like genus, species and subspecific units. Name matching: Matches names either with global name services or with user-created master taxonomy lists using fuzzy matching, testing combinations of genus level synonyms, subspecies elevation to species, trying to match with higher level taxonomic entities like genus and family, and employing a user-defined lookup table to manually resolve names. List processing: Updates list fields such as unique identifiers (id), higher taxonomy and taxonomic ranks. List matching: Compares user generated lists with each other and finds differences in the two lists, then prepares the lists for merging together to form a masterlist. Format conversion: Converts taxolist to and from formats like HTML and Darwin Core (Wieczorek et al. 2021), which is useful in data exchange or checking the lists manually. Name harvesting functions: Acquires additional names from Integrated Taxonomic Information System (ITIS) and Wikipedia (taxonomy infobox). Detailed function listings under each category are listed in Table 1. This package has been effectively used in several biodiversity studies and projects like Map of Life, ButterflyNet, Terrestrial Parasite Tracker etc. It has been successfully tested on a masterlist constructed with ~1M names from World Flora Online and performs well. The package is available on The Comprehensive R Archive Network (CRAN) [https://CRAN.R-project.org/package=taxotools] and the developmental release is on GitHub [https://github.com/vijaybarve/taxotools].

Bionomia, https://bionomia.net previously called Bloodhound Tracker, was launched in August 2018 with the aim of illustrating the breadth and depth of expertise required to collect and identify natural history specimens represented in the Global Biodiversity Information Facility (GBIF). This required that specimens and people be uniquely identified and that a granular expression of actions (e.g. "collected", "identified") be adopted. The Darwin Core standard presently combines agents and their actions into the conflated terms recordedBy and identifiedBy whose values are typically unresolved and unlinked text strings. Bionomia consists of tools, web services, and a responsive website, which are all used to efficiently guide users to resolve and unequivocally link people to specimens via first-class actions collected or identified. It also shields users from the complexity of casting together and seamlessly integrating the services of four giant initiatives: ORCID, Wikidata, GBIF, and Zenodo. All of these initiatives are financially sustainable and well-used by many stakeholders, well-outside this narrow user-case. As a result, the links between person and specimen made by users of Bionomia are given every opportunity to persist, to represent credit for effort, and to flow into collection management systems as meaningful new entries. To date, 13M links between people and specimens have been made including 2M negative associations on 12.5M specimen records. These links were either made by the collectors themselves or by 84 people who have attributed specimen records to their peers, mentors and others they revere. Integration With ORCID and Wikidata People are identified in Bionomia through synchronization with ORCID and Wikidata by reusing their unique identifiers and drawing in their metadata. ORCID identifiers are used by living researchers to link their identites to their research outputs. ORCID services include OAuth2 pass-through authentication for use by developers and web services for programmatic access to its store of public profiles. These contain elements of metadata such as full name, aliases, keywords, countries, education, employment history, affiliations, and links to publications. Bionomia seeds its search directory of people by periodically querying ORCID for specific user-assigned keywords as well as directly though account creation via OAuth2 authentication. Deceased people are uniquely identified in Bionomia through integration with Wikidata by caching unique 'Q' numbers (identifiers), full names and aliases, countries, occupations, as well as birth and death dates. Profiles are seeded from Wikidata through daily queries for properties that are likely to be assigned to collectors of natural history specimens such as "Entomologists of the World ID" (= P5370) or "Harvard Index of Botanists ID" (= P6264). Because Wikidata items may be merged, Bionomia captures these merge events, re-associates previously made links to specimen records, and mirrors Wikidata's redirect behaviour. A Wikidata property called "Bionomia ID" (= P6944), whose values are either ORCID identifiers or Wikidata 'Q' numbers, helps facilitate additional integration and reuse. Integration with GBIF Specimen data are downloaded wholesale as Darwin Core Archives from GBIF every two weeks. The purpose of this schedule is to maintain a reasonable synchrony with source data that balances computation time with the expections of users who desire the most up-to-date view of their specimen records. Collectors with ORCID accounts who have elected to receive notice, are informed via email message when the authors of newly published papers have made use of their specimen records downloaded from GBIF. Integration with Zenodo Finally, users of Bionomia may integrate their ORCID OAuth2 authentication with Zenodo, an industry-recognized archive for research data, which enjoys support from the Conseil Européen pour la Recherche Nucléaire (CERN). At the user's request, their specimen data represented as CSV (comma-separated values) and JSON-LD (JavaScript Object Notation for Linked Data) documents are pushed into Zenodo, a DataCite DOI is assigned, and a formatted citation appears on their Bionomia profile. New versions of these files are pushed to Zenodo on the user's behalf when new specimen records are linked to them. If users have configured their ORCID account to listen for new entries in DataCite, a new work entry will also be made in their ORCID profile, thus sealing a perpetual, semi-automated loop betwen GBIF and ORCID that tidily showcases their efforts at collecting and identifying natural history specimens. Technologies Used Bionomia uses Apache Spark via scripts written in Scala, a human name parser written in Ruby called dwc_agent, queues of jobs executed through Sidekiq, scores of pairwise similarities in the structure of human names stored in Neo4j, data persistence in MySQL, and a search layer in Elasticsearch. Here, I expand on lessons learned in the construction and maintenance of Bionomia, emphasize the criticality of recognizing the early efforts made by a fledgling community of enthusiasts, and describe useful tools and services that may be integrated into collection management systems to help churn strings of unresolved, unlinked collector and determiner names into actionable identifiers that are gateways to rich sources of information.

Biodiversity Information for Development, or BID, is a programme funded by the European Union and led by the Global Biodiversity Information Facility (GBIF), aiming to increase the amount of biodiversity information available for use in scientific research and policymaking. In its first phase, BID provided funding to 61 projects in the nations of sub-Saharan Africa, the Caribbean and the Pacific between 2015-2019, with a strong focus on developing capacity to mobilize, manage and use biodiversity data within the project teams and their institutions, and through the establishment and strengthening of national nodes. The capacity development approach centred on establishing a community of practice to bring in the expertise in the broader GBIF network to support the project teams in meeting their goals. This involved designing curricula for two workshops in the areas of data mobilization and data use for decision making; developing activities and materials to strengthen a base of mentors and trainers; establishing technical helpdesk support; and matchmaking to provide mentoring support to the funded projects. The community of practice, through mentoring and reuse of the workshop materials, has been expanded to support the capacity development needs in other programmes, reaching other regions, including Asia, South-East Europe and Eurasia. During this presentation, we will review the main findings of the BID impact study and guiding examples from within the BID programme to identify the key successes and lessons learned relating to capacity development. As this approach has wider application to the biodiversity community, we invite discussion how we can build on the experience through the BID programme to further develop our community of practice, narrowing knowledge gaps between various groups of biodiversity professionals.

The Darwin Core (DwC) standard (Darwin Core Maintenance Group 2023) is the most extensively adopted vocabulary maintained by the Biodiversity Information Standards (TDWG), with over 3.5 billion published records since its official ratification in 2009. TDWG strives to promote equitable participation in biodiversity informatics by participating in and facilitating broader internationalization initiatives, including making standards accessible to non-English-speaking communities. The initial comprehensive translation of the Darwin Core Terms documentation, as well as the Establishment Means Controlled Vocabulary, Degree of Establishment Controlled Vocabulary and the Pathway Controlled Vocabulary were finalized and published in the French language in May 2025. The translation was done using the Crowdin platform*1 and can be accessed on the official Darwin Core website*2. This endeavor offered a comprehensive view of the translation process, highlighting lessons learned along the way and leading to recommendations for similar efforts in the future. Some other questions are still unanswered to this day and will require further discussions, such as the treatment of deprecated terms, or finding the most effective method for integrating translations into the ongoing maintenance workflows of standards. In contrast to standards that were recently ratified such as Latimer Core (Saliba et al. (2025)), Darwin Core's extensive legacy use and maturity present specific translation challenges. During the translation process, questions emerged about adapting the language to enhance clarity and inclusivity. For French in particular, questions around linguistic purism arose, as the use of anglicisms is variable among different francophone communities. Strict translation practices might suggest avoiding borrowed terms, but privileging terminology already unanimously established in professional communities leads to pragmatic compromises to foster usability and adoption. An example is the term 'standard' itself, which should be translated as norme (e.g., une norme ISO ), but that doesn't reflect the current usage of French biodiversity information scientists who use the transparent and equally possible standard (e.g., le standard Darwin Core du TDWG ) instead. In other cases, if an official wording is used in regulatory or governmental contexts (such as espèces envahissantes , see for example Comité français de l'IUCN and Office Français de la Biodiversité (2023)), it was preferred over the alternative community-used anglicism ( espèces invasives ), as a way to reflect the normative nature of standards. French expressions are often more verbose than their English counterparts. In response, translators must balance fidelity with conciseness, occasionally rephrasing or adapting rather than rendering terms word-for-word to preserve the clarity and economy required in technical vocabularies. This is proving to be a particular challenge for the Humboldt extension, which is currently being translated. For example, the literal translation of the label 'Is Degree of Establishment Scope Fully Reported' becomes the fairly verbose label Est-ce que le Champs d’Application du Degré d’Implantation est Entièrement Répertorié . Ensuring that TDWG standards are available in French is essential to broaden participation from underrepresented communities across Africa, the Caribbean, the Pacific, and beyond. By lowering language barriers, the biodiversity informatics community can foster inclusivity, enhance data sharing, and strengthen global stewardship of biodiversity knowledge.

Agrobiodiversity, or biodiversity for food and agriculture, plays a major role in the sustainability of food production. As stated by FAO 2019, agrobiodiversity can provide food production systems and society with a variety of services as ecosystem services, crops resilience to threats, sustainable intensification, livelihoods, food security and nutrition. The official definition of the concept has been given by CBD 2000 as "a broad term that includes all components of biological diversity of relevance to food and agriculture, and all components of biological diversity that constitute the agroecosystem: the variety and variability of animals, plants and micro-organisms, at the genetic, species and ecosystem levels, which are necessary to sustain key functions of the agro-ecosystem, its structure and processes". Thus, agrobiodiversity is primarily based on species and their function in agroecosystems. Many projects for sharing agrobiodiversity data in a structured way have emerged over the years. One realizes in looking at the Bioversity International (2018) crop descriptors list that the earliest groups of descriptors for crops and some associated data emerged back in the 1970s. In the same list, there are four multi-crop descriptors and derived standards, which are broad standards for crop-related data, namely: Core descriptors for in situ conservation of crop wild relatives v.1 (Thormann et al. 2013); FAO/Bioversity multi-crop passport descriptors V.2.1 (Alercia et al. 2015); Descriptors for farmers' knowledge of plants (Aknazarov et al. 2009); Descriptors for Genetic Marker Technologies (Vicente et al. 2004). Core descriptors for in situ conservation of crop wild relatives v.1 (Thormann et al. 2013); FAO/Bioversity multi-crop passport descriptors V.2.1 (Alercia et al. 2015); Descriptors for farmers' knowledge of plants (Aknazarov et al. 2009); Descriptors for Genetic Marker Technologies (Vicente et al. 2004). These standards share some core elements in common, as taxon, location, a period of collecting, and were intended to be used in the context of data on the occurrence of species in nature. Darwin Core, a TDWG standard commonly used for sharing data of taxon occurrence in nature (Wieczorek et al. 2012), is a globally used metadata standard, representing "a large majority of the 1.4 billion of species occurrence records shared by the Global Biodiversity Information Facility (GBIF), published by more than 1561 organizations in 59 countries in January 2020" (Body et al. 2020). Darwin Core is a standardized language that applies unique Internationalized Resource Identifiers (IRIs) to each element assigned as a metadata element, plus a label and a definition. It improves the interoperability between databases in the context of the Semantic Web (Duerst and Suignard 2005). We believe it is possible to use Darwin Core to represent agrobiodiversity data if a metadata extension is developed to enroll the agrobiodiversity concepts missing in Darwin Core. Thus, a research project held at the University of São Paulo in partnership with Brazilian Agricultural Research Corporation started to map concepts and descriptors from the literature for agrobiodiversity data representation. This project is the sequence of the research initiated by Soares et al. 2019. The crop descriptors published by Bioversity International (2018) may be integrated into the metadata extension, but also other standards like Global Genome Biodiversity Network (GGBN) Data Standard v1 (Droege et al. 2016) and the Darwin Core germplasm extension (DwC-germplasm). At the moment, we are designing a mind map to organize the agrobiodiversity concepts. We expect the metadata extension will be useful for the scientific community to share agrobiodiversity data as linked data, applying Resource Description Framework (RDF) as a resource relationship model, for example.

Global Biodiversity Information Facility's (GBIF) global index of primary biodiversity data is based on contributions from more than 2,200 publishing institutions and over 100,000 datasets. Data originate from a broad variety of fields, from research to citizen science, from eDNA through specimen collections to observations and monitoring projects, across all taxonomic groups, and with a wide range of datasets and data types. Equally variable is the underlying motivation and focus for data collection and digitization. Even within commonly used domain concepts like Kingdom or OccurrenceStatus, the values provided by the original sources can vary greatly. This presents challenges for any system that aims to provide joint search access across all these different resources. The vocabulary server*1 in the GBIF Registry is a tool used to standardize selected fields during data interpretation, thereby increasing the searchability of records across datasets on GBIF.org and other GBIF-hosted websites. The server hosts controlled vocabularies for relevant terms from the Darwin Core standard*2, Global Registry of Scientific Collections (GRSciColl)*3, and specific fields used in GBIF. The vocabulary development is managed in a GitHub repository*4, and both concept development and the mapping of verbatim values to concepts are supported by community participation and expert groups. During this talk, we will present some of the vocabularies implemented on GBIF.org and the work that is going into it. We will also go through the roadmap*5 for future implementations and vocabularies. We value input from the Society for the Preservation of Natural History Collections (SPNCH) and Biodiversity Information Standards (TDWG) communities on terms relevant to controlled vocabulary development in GBIF.

Poland is characterised by a relatively high variety of living organisms attributed to terrestrial and water environments. Currently, close to 57.000 species of living organisms are described that occur in Poland (Symonides 2008), including lowland and mountain species, those attributed to oceanic and continental areas, as well as species from forested and open habitats. Poland comprehensively represents biodiversity of living organisms on a continental scale and thus, is considered to have an important role for biodiversity maintenance. The Mammal Research Institute of Polish Academy of Sciences (MRI PAS), located in Białowieża Forest, a UNESCO Heritage Site, has been collecting biodiversity data for 90 years. However, a great amount of data gathered over the years, especially old data, is gradually being forgotten and hard to access. Old catalogues and databases have never been digitalized or publicly shared, and not many Polish scientists are aware of the existence of such resources, not to mention the rest of the scientific world. Recognizing the need for an online, interoperable platform, following FAIR data principles (findable, accessible, interoperable, reusable), where biodiversity and scientific data can be shared, MRI PAS took a lead in creation of an Open Forest Data (OFD) repository. OpenForestData.pl (Fig. 1) is a newly created (2020) digital repository, designed to provide access to natural sciences data and provide scientists with an infrastructure for storing, sharing and archiving their research outcomes. Creating such a platform is a part of an ongoing development of life sciences in Poland, aiming for an open, modern science, where data are published as free-access. OFD also allows for the consolidation of natural science data, enabling the use and processing of shared data, including API (Application Programming Interface) tools. OFD is indexed by the Directory of Open Repositories (OpenDOAR) and Registry of Research Data Repositories (re3data). The OFD platform is based entirely on reliable, globally recognized open source software: DATAVERSE, an interactive database app which supports sharing, storing, exploration, citation and analysis of scientific data; GEONODE, a content management geospatial system used for storing, publicly sharing and visualising vector and raster layers, GRAFANA, a system meant for storing and analysis of metrics and large scale measurement data, as well as visualisation of historical graphs at any time range and analysis for trends; and external tools for database storage (Orthanc) and data visualisation (Orthanc plugin Osimis Web Viewer and Online 3D Viewer (https://3dviewer.net/), which were integrated with the system mechanism of Dataverse. Furthermore, according to the need for specimen description, Darwin Core (Wieczorek et al. 2012) metadata schema was decided to be the most suitable for specimen and collections description and mapped into a Dataverse additional metadata block. The use of Darwin Core is based on the same file format, the Darwin Core Archive (DwC-A) which allows for sharing data using common terminology and provides the possibility for easy evaluation and comparison of biodiversity datasets. It allows the contributors to OFD to optionally choose Darwin Core for object descriptions making it possible to share biodiversity datasets in a standardized way for users to download, analyse and compare. Currently, OFD stores more than 10.000 datasets and objects from the collections of Mammal Research Institute of Polish Academy of Sciences and Forest Science Institute of Białystok University of Technology. The objects from natural collections were digitalized, described, catalogued and made public in free-access. OFD manages seven types of collection materials: 3D and 2D scans of specimen in Herbarium, Fungarium, Insect and Mammal Collections, images from microscopes (including stereoscopic and scanning electron microscopes), morphometric measurements, computed tomography and microtomography scans in Mammal Collection, mammal telemetry data, satellite imagery, geospatial climatic and environmental data, georeferenced historical maps. 3D and 2D scans of specimen in Herbarium, Fungarium, Insect and Mammal Collections, images from microscopes (including stereoscopic and scanning electron microscopes), morphometric measurements, computed tomography and microtomography scans in Mammal Collection, mammal telemetry data, satellite imagery, geospatial climatic and environmental data, georeferenced historical maps. In the OFD repository, researchers have the possibility to share data in standardized way, which nowadays is often a requirement during the publishing process of a scientific article. Beside scientists, OFD is designed to be open and free for students and specialists in nature protection, but also for officials, foresters and nature enthusiasts. Creation of the OFD repository supports the development of citizen science in Poland, increases visibility and access to published data, improves scientific collaboration, exchange and reuse of data within and across borders.

Currently there are several databases on biodiversity in different regions or countries, held in museums, herbaria, among other places, but each one has its own collection and storage data structure, which often does not allow the integration between them. This fact makes it difficult to access, to make available and to exchange information among distributed repositories around the world. Given this context, global and regional efforts have been made in order to integrate, organize and provide biodiversity data, such as the Global Biodiversity Information Facility (GBIF), Inter-American Biodiversity Information Network (IABIN) and speciesLink. These initiatives have proposed the use of protocols, standards for data exchange and open architectures through collaborative efforts for data integration. Therefore, the aim here is to identify the efforts and tools applied to the integration and interoperability of biodiversity data. Keywords: Biodiversity Informatics, data integration, standards for data exchange, protocols.

Two key issues hindering the transition toward findable, accessible, interoperable, and reusable (FAIR) data science are the poor discoverability and inconsistent instructions for the use of data extractor tools (i.e., how we go from raw data files created by instruments, to accessible metadata and scientific insight). If the existing format conversion tools are hard to find, install, and use, their reimplementation will lead to a duplication of effort and an increase in the associated maintenance burden is inevitable. The framework presented in this article addresses these issues. First, by providing a curated registry of such extractor tools, their discoverability will increase. Second, by describing them using a standardized but lightweight schema, their installation and use are machine-actionable. Finally, we provide a reference implementation for such data extraction. The framework can be used to provide a public-facing data extraction service, or be incorporated into other research data management tools providing added value. Impact statement The ongoing digitalization of materials research necessitates investment in bespoke research data management platforms. A common pain point across such infrastructures is the ingestion and extraction of data and metadata from raw data files. Often these files require significant effort to ingest in a way consistent with the FAIR data principles such that definitions are clear and the resulting data are extracted reliably and repeatedly. As research laboratories increasingly move toward coupled automated systems, handling of files exported by different instruments can introduce significant friction to the data-to-insight pipeline. This paper presents a conceptual framework and prototype implementation of Datatractor, which aims to bring together the community of efforts on open-source software tools that can perform such extractions and disseminate them in such a way that new platforms can use the tools in an automated fashion. This can reduce the duplication of effort by broadly advertising existing and tested tools, communalize the maintenance and development burden of such tools, and lower the barrier to the creation of materials data infrastructure. By building atop community consensus formed in a Materials Research Data Alliance (MaRDA) Working Group, it is hoped that this framework can grow to propagate best practices in research data management and help to foster a rich open data ecosystem in materials science and beyond. Graphical abstract

Biodiversity research is seeing unprecedented global collaboration with initiatives such as the Earth BioGenome Project, an effort to sequence all known eukaryotic life, and Genesys, a global database for sharing crop genetic resources. However, as in other disciplines, public funding and policy for scientific research in agriculture tend to follow national borders even when science and its collaborations do not. In addition, agriculture is similar to biomedicine in having significant private investment in research and development where competition could inhibit sharing. It would seem that significant challenges lie ahead for making progress on ambitious global initiatives at least where agricultural samples, collections, and data are concerned. In this talk we will review several activities at the United States Department of Agriculture that illustrate how policy and infrastructure can overcome difficulties. For example, recent policies for openness of publicly-funded research products and adoption of FAIR data principles even for private or proprietary data hold promise and have elevated the importance of data infrastructure. The US Department of Agriculture's Agricultural Research Service (ARS) launched its Ag100Pest contribution to the Earth BioGenome Project, including the use of the i5K Workspace@NAL platform for its sequenced and annotated genomes. The GRIN Global platform supports not just USDA germplasm data management but a growing network of plant and animal researchers and collections around the world. The Ag Data Commons provides standardized metadata and machine-readable data dictionaries to the publicly accessible products of these and other USDA-funded efforts. It is teaming with the ARS high performance computing system SCINet to explore cost-effective public access to big data storage for agricultural data and models. Finally, many of these efforts extend and contribute back to widely used open source software systems. While challenges remain in coordinating and sustaining these efforts with international stakeholders, engagement with groups like AgBioData, the Research Data Alliance (RDA) Interest Group on Agricultural Data, and the Global Open Data for Agriculture and Nutrition coalition will continue to bear fruit (pun intended). We seek similar engagement with the broader biodiversity data community in order to ensure that policy and infrastructure investments result in maximum mutual benefit.

PremiseHeterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources.MethodsIn 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling (Oarisma poweshiek).ResultsOver 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEINet portals.DiscussionWe highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data.

Researchers have become accustomed to online access to data about specimens held in natural history collections. Over several decades, metadata standards have been developed to facilitate the sharing and aggregation of this data, notably Darwin Core and ABCD (Access to Biological Collections Data) developed under the auspices of TDWG but other standards developed in other communities, have also proved useful notably EML (Ecological Metadata Language) and GML (Geography Markup Language). Data aggregators have arisen to both, drive standards development and take advantage of the vast number of records made available through this community effort. Examples include Atlas of Living Australia and spin off Atlas projects, EoL (Encyclopedia of Life), iDigBio, Global Biodiversity Information Facility (GBIF), WFO (World Flora Online). There are still many “dark specimens” that are not visible to the web and efforts continue to digitise metadata on these objects and make them available. The vast majority of the data that has been liberated so far, has therefore been text based and the standards reflect this, although many institutions and projects are also producing large numbers of images and other media. There have been media extensions to some standards to accommodate the sharing of images and other multimedia formats. However, these are restricted to metadata about media objects rather than the exchange of media objects themselves. For example, two extensions to Darwin Core are Audubon Core, which is designed to “determine whether a particular resource or collection will be fit for some particular biodiversity science application before acquiring the media.” and the Simple Multimedia extension, which is a “simple extension for exchanging metadata about multimedia resources”. Therefore image exchange, in particular, has not used open standards. Projects have relied on transferring high resolution versions of images (e.g. submission of type specimen images to JSTOR) or cut down compressed versions (e.g. many herbarium specimens submitted to GBIF or Europeana). The network has not allowed access to high resolution versions of images as curated by the host institutions themselves beyond basic links to web pages. If high resolution images have been published in online catalogues, they have been made available using a hotchpotch of different technologies including the now defunct Java Applets and Adobe Flash player. The network has not supported different views of the same specimen or annotations of those views, or integration of audio and moving images. In an ideal world a researcher should be able to view and annotate images of specimens held across multiple collections in a unified way, and the host institutions should have access to those annotations and statistics on how their specimens are being used. How can we achieve this? The sharing of multimedia representations of objects online is not a problem unique to the biodiversity community. Scholars in museums and archives of all kinds are facing the same issues. In 2011 the British Library, Stanford University, the Bodleian Libraries (Oxford University), the Bibliothèque nationale de France, Nasjonalbiblioteket (National Library of Norway), Los Alamos National Laboratory Research Library, and Cornell University came together to develop an exchange standard called IIIF (International Image Interoperability Framework). This framework now consists of six APIs (Application Programming Interface), four stable and two in beta, to publish and integrate image and other multimedia resources in a uniform manner and has been adopted by many institutions and commercial partners in the digital humanities. Applications based on IIIF enable many of the features desired by biodiversity researchers. The notion of sharing and annotating specimen images is not new to the natural history community. MorphBank, founded in 1998, has grown to allow much of this desirable functionality but at the cost and fragility of being a centralised database. The question we should perhaps be asking is: how can we make the biodiversity data sharing network as a whole more like MorphBank? From 2019 to 2021, part of the EU-funded Synthesys+ programme will support the adoption of IIIF as a unified way to publish images of natural history specimens. We aim to have a set of exemplar institutions publishing IIIF manifests for some millions of specimens by the end of the project and one or more demonstration applications in place. We hope this will act as a catalyst for wider adoption in the natural history community. A key goal is to integrate image data served using IIIF with metadata available via CETAF (Consortium of European Taxonomic Facilities) specimen identifiers. If IIIF were ubiquitous in the natural history community, building tools that implemented this functionality would be feasible. A brief demonstration of a herbarium specimen browser, Herbaria Mundi, will be given. It will illustrate how specimens hosted in different institutions can be manipulated in a single interface. The architecture that supports this behaviour will be explained and its challenges, by implementing the institutions discussed.