Sharif Islam

The Global Genome Biodiversity Network (GGBN) is an international consortium of biodiversity biobanks and collections that strives to standardize and exchange best practices, and to share information on molecular biodiversity resources held in repositories around the globe. GGBN’s goal is to bridge the gap between biological material stored in these biobanks and the broader research community by establishing a searchable index of the world’s genomic resources, openly available to the scientific community and all stakeholders. Here, we provide examples of how GGBN interacts synergistically with other biodiversity programs and initiatives to (1) connect researchers with physical materials linked to rich, highly structured contextual metadata and (2) strengthen the biobanking community.

The Biodiversity Genomics Europe plus (BGE+) Project builds on a pan-European collaboration to scale up the production and uptake of genomic evidence for taxonomy, monitoring, and policy. It widens participation, standardises methods, and prepares a distributed European system for biodiversity genomics that links molecules to ecosystems. The BGE+ consortium unites the International Barcode of Life in Europe (iBOL Europe, barcoding), the European Reference Genome Atlas (ERGA, reference genomes) and the Consortium of European Taxonomic Facilities (CETAF, taxonomic research and natural history collections) to align end-to-end workflows from field sampling to application. This publication is an abridged version of the successful grant proposal developed jointly by iBOL Europe, ERGA, and CETAF in response to the Horizon Europe call HORIZON-CL6-2025-01-BIODIV-03. BGE+ has four overarching objectives: (1) broaden participation and capacity; (2) make distributed genomic data production interoperable and FAIR (Findable, Accessible, Interoperable, and Reusable) by design; (3) translate evidence into practice through co-designed use-case roadmaps; and (4) define the service portfolio and assess the financial and technical feasibility of a future European research infrastructure in biodiversity genomics. Through targeted cascade grants (Financial Support to Third Parties) and hands-on training, BGE+ enlarges community capacity while converging on shared protocols and standard practices, improving data interoperability and consolidating community efforts. Building on previous work, BGE+ carries forward the communities’ long-term vision: turning fragmented efforts into a coherent, scalable system that delivers reliable genomic evidence for taxonomy and policy.

In recent years, significant progress has been made in digitizing natural history collections using increasingly industrialized workflows involving conveyor belts, digital camera setups, robotics and Artificial Intelligence (AI). Also, new technologies became available to analyse the specimens. Analysis of both biodiversity and geodiversity samples has shifted from destructive analysis to non-destructive, high-resolution, and automated techniques accelerating the creation of new information.However, the resulting data is often fragmented across systems and repositories. Efforts to reconnect these data to the original specimen or derived samples frequently fail because identifiers were missing at the time of analysis, are not globally unique, change over time, or are referenced incorrectly. These issues can be solved by maintaining a digital object on the internet that is created at the time of collecting the sample, which contains contextual information and (links to) its derived data as this becomes available. This is called a Digital Specimen and different entities(human or machine) who create an analysis can add information to the digital object. A one-to-one relationship between the physical sample preserved as a specimen can be kept by giving the physical objecta persistent identifier like an IGSN, International Generic Sample Number. The digital object also gets a persistent identifier: a Digital Specimen identifier in the form of a FAIR Digital Object compliant DOI (Digital Object Identifier).The Digital Specimen is a citable, machine-actionable proxy for physical specimens that is FAIR by design (FAIR Digital Object compliant) and has a Persistent Identifier (PID) in the form of a DOI to create a self-contained unit of knowledge. This design enables seamless linkage to derived data—such as chemical analysis, digital media, and publications. To implement this, DiSSCo (Distributed System of Scientific Collections) developed the open Digital Specimen (openDS) specification. By integrating community standards like Darwin Core with W3C PROV-O and JSON-LD, openDS provides a common semantic language for global interoperability.DiSSCo is currently in transition from its project phase into becoming an operational European Research infrastructure. It has already created the first millions of FDO-compliant Digital Specimens and has developed infrastructure to allow the annotation of these digital objects with new data or improvements, either by humans or machines. AI fueled Machine Annotation Services (MAS) developed by third parties can operate in the infrastructure for analysis of the data or knowledge extraction from specimen images. In the presentation we will show how the FDO design supports advanced capabilities like multiple redirect to different digital representations for either human or machine, versioning and provenance to allow mutable objects, tooltips in journal systems that show contextual information about a referred sample in a publication through the PID record, and machine actionable metadata that supports machines to act on the data.

The EU’s Biodiversity Strategy for 2030, a core part of the European Green Deal, addresses the complex relationship between human society and its environment by prioritizing the restoration of ecosystems and building resilience against climate change, deforestation, and biodiversity loss.These environmental stressors do more than just degrade ecosystems; they create a pressing need for policymakers, researchers, and society to actively track and mitigate ecological shifts. In order to design effective mitigation strategies, new political frameworks and massive simulation infrastructures are being developed with the aim to establish a common European Green Deal Data Space. The involved initiatives rely on the integration and standardization of diverse, large-scale datasets, ranging from long-term biodiversity records (e.g., eDNA) to real-time IoT sensor data (e.g., camera traps) and global Earth observation (EO) data combined with model-derived reanalysis datasets like ERA5.‘Biodiversity Meets Data’ (BMD) is a Horizon Europe project delivering a unified access point for AI-assisted biodiversity monitoring and cross-realm (terrestrial, marine, freshwater) analysis tools representing a key contribution to the thematic expansion of the European Green Deal Data Space ecosystem. By providing a robust technical infrastructure, BMD facilitates the quantification of diverse ecological pressures - ranging from climate change to land-use shifts - on biodiversity. The project is strategically focused on the EU Natura 2000 network, equipping stakeholders such as conservation managers and policy makers with the necessary tools to implement and evaluate EU Nature Directives such as the Birds and Habitats Directives.In this talk, we will present how BMD leverages FAIR Digital Objects (FDOs) and data space concepts around governance, licensing, and provenance tracking to synthesize computational workflows and diverse datasets into actionable knowledge units (“Workflow Run RO-Crate”, Figure 1). We will demonstrate our implementation path for such data-rich, self-contained digital containers building on web-based technologies such as RO-Crate (lightweight data packages) and FAIR Signposting (machine-interpretable layer describing resources). Those webby FDOs are designed to bridge the gap between practical needs of conservation stakeholders such as supporting data-driven decision making and technical capabilities of the Green Deal Data Space ecosystem.Integration of targeted feedback from stakeholders, notably Natura 2000 site managers, into our development process ensures that the FAIR-compliant data products and FDO service framework are not only technically robust, but also socially and politically actionable. Figure 1. Throughout its life cycle in the BMD data space, data is represented as RO-Crate. Initially (left), the data and the computational workflow are bundled as Workflow RO-Crate. Following processing, this is combined with the results and enriched with retrospective provenance and metadata to form a Workflow Run RO-Crate (right). Finally, these are presented as webby FAIR Digital Objects, incorporating a machine-interpretable layer based on FAIR Signposting (bottom).

Species occurrence data from in situ measurements constitutes the fundamental ingredient for analyses of status of biodiversity, including mapping of species distributions and estimating extinction risks, and thereby supplies core scientific evidence that informs biodiversity policy. These data are a critical component in monitoring progress towards targets to protect and conserve biodiversity, such as those included in the Kunming-Montreal Global Biodiversity Framework (GBF), the EU Nature Directives, the EU Biodiversity Strategy and the EU Nature Restoration Regulation. However, the availability of these data is still lacking for many parts of the world, and even for Europe, it is highly biased towards the North and West with poor coverage of Europe’s most diverse regions in the South and East.The Horizon Europe project ‘Biodiversity Meets Data’ - BMD - is developing a single access point providing users with: a) platforms for high-throughput biodiversity monitoring using camera traps, acoustic devices, and eDNA sampling combined with AI-based species identification, and b) biodiversity analysis tools (BATs) to analyse the captured data in the terrestrial, freshwater, and marine realms. Combining species occurrence information with spatial and temporal explicit Earth Observation (EO) and abiotic data in the BATs allows for powerful quantification of the effects of habitat and climate change on the status of biodiversity and the trends in species distributions and population sizes. BMD is focussed on the effectiveness of the EU Nature Directives and their implementation in the EU Natura 2000 network, and provides Natura 200 site managers and policymakers with monitoring and analyses tools.In this presentation we identify the gaps and biases in European biodiversity data and indicate which of the high-throughput biodiversity monitoring devices can be deployed most effectively to monitor the species for which the Natura 2000 sites were designated. EO data, and their derived habitat metrics, guide Natura 2000 site managers to localities for optimal deployment of high-throughput biodiversity monitoring devices.

Environmental DNA (eDNA) has rapidly become a powerful method for large-scale biodiversity assessment and monitoring. Yet the reliability of eDNA data depends on high-quality reference material that can verify detections, resolve taxonomic uncertainty, and correct spatial or temporal biases. This is where global natural science collections, curated in natural history museums, herbaria, and other collection-holding institutes, play an essential role. These collections preserve centuries of information on species, traits, and ecological contexts, providing the authoritative baseline needed to interpret molecular observations and understand biodiversity change over time.Harnessing this potential requires that collection data be open, reusable, and integrated with emerging molecular approaches. Advances in imaging, computational methods, and molecular analysis have made it possible to link specimen-based knowledge with large-scale datasets. To support such integrated, data-driven research and adaptive conservation strategies, robust infrastructure is needed.The Distributed System of Scientific Collections (DiSSCo) – a European Research Infrastructure collaborating with more than 300 institutions across 23 countries – addresses this challenge by representing each specimen as a FAIR (Findable, Accessible, Interoperable, and Reusable) Digital Object. Building on existing standards, each specimen and its associated data are made FAIR, with provenance and trust explicitly encoded. Users can explore connections across data types and contribute annotations, corrections, or additional information, with AI-assisted services such as automated georeferencing, OCR, and morphology detection further enhancing data quality and completeness.Such linked and verified data provide the critical bridge between historical collections and modern eDNA workflows. Digital specimens data ground eDNA detections in validated reference material, strengthening taxonomic resolution and enabling more accurate interpretation of biodiversity patterns across spatial and temporal scales. Sequencing historical specimens enriches this further by revealing long-term population and ecosystem dynamics.By establishing an infrastructure capable of linking billions of historical specimen records with emerging molecular and environmental data, DiSSCo creates the conditions for future integration that will support predictive biodiversity assessments and global eDNA efforts.

The biodiversity crisis demands computational tools to integrate and analyse complex, disparate data and models. This paper presents the concept of FAIR Digital Twins (FDTs) and, drawing on the work of the Biodiversity Digital Twin (BioDT) project (2022–2025), demonstrates how combining Digital Twins with FAIR principles (Findable, Accessible, Interoperable, and Reusable) can transform biodiversity research and decision-making. We show strategies for integrating heterogeneous data, models, and computational workflows within a FAIR framework, paving the way for operational FDTs. The BioDT project developed ten prototype digital twins addressing a critical range of challenges, including grassland and forest dynamics, bird monitoring, ecosystem services, and crop wild relative genetic resources. We discuss implementation challenges such as data fragmentation, semantic interoperability, and operational complexity. Critically, we highlight the opportunities for dynamic adaptation, modular workflows, and cross-domain collaboration, detailing how tools like Research Object Crate (RO-Crate) operationalise FAIR principles for metadata packaging and standardisation. This convergence of Digital Twins with FAIR principles offers a scalable and reusable approach to advancing biodiversity modeling and simulation, providing a robust foundation for evidence-based policy decisions.

The Distributed System for Scientific Collections (DiSSCo) is a research infrastructure to integrate European natural science collections (NSCs) digitally. The aim is to facilitate and enhance the access, management and analysis of collection assets in one unified digital collection. The Machine Annotation Services (MAS) are essential components of DiSSCo’s Digital Specimen Architecture (DSArch). These services automate the annotation of digital objects to enable labelling and categorisation of NSC's digital assets. To further advance this, a Machine Learning as a Service (MLaaS) approach was developed which provides researchers with the access to pre-trained machine-learning models for complex tasks, such as instance segmentation and morphological analysis of datasets. MLaaS enhances the DiSSCo’s scalability and flexibility and allows the integration of machine-learning tools in close alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens. Machine-learning models, such as Mask R-CNN and YOLO11, are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets. Subsequently, these models are used to reconstruct the scale in the herbarium sheet and to calculate the surface area of identified plant organs. The determination of quantitative characteristics of plant specimens, such as measuring leaf area or the timestamp of the floral transition, opens up herbarium data for reuse in the large prognosis platforms currently developed in the framework of the Common European Data Spaces. In this way, plant trait data mobilised from natural science collections can improve the predictive capability of the vegetation model components of climate-related data spaces.

Effective decision-making on biodiversity restoration would greatly benefit from baseline data on intraspecific genetic diversity, the ability to integrate it across species for each location, and efficient systems for monitoring changes of genetic diversity in response to management interventions and environmental dynamics. This requires large sets of well-curated information-rich FAIR (Findable, Accessible, Interoperable, Reusable) Digital Objects (FDOs; Schultes and Wittenburg 2019), implemented as, for instance, Digital Extended Specimens (DES), which are representing digitized field samples, their derived genomic data and associated information (Hardisty et al. 2022). These use-case-driven sets of structured (meta)data from many providers need to be merged, modified and further extended on demand. Existing workflows and work environments have to be redesigned to accelerate this process and achieve seamless integration to be able to scale to the needs of efficient and effective worldwide monitoring under the United Nations Kunming-Montreal Global Biodiversity Framework. Contributing to data and infrastructure development processes in support of global monitoring is also the global effort to generate high-quality reference genomes. The Earth BioGenome communities, including the European Reference Genome Atlas (ERGA) and Biodiversity Genomics Europe (BGE) communities, drive the development of standardization and harmonization for well-designed sampling and comprehensive FAIR and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) (meta)data (Buzan et al. 2025). Their goal is reproducible analytical pipelines that can be assembled on the fly for implementing sophisticated statistical approaches and algorithms. Such research-focused pipelines prepare the development and application of globally adopted workflow templates for the calculation of Essential Biodiversity Variables (EBVs). These templates are under development within the Group on Earth Observations Biodiversity Observation Network (GEO BON) (Lumbierres et al. 2025). Their output can be used for globally aligned and interpretable planning, monitoring, reporting and reviewing (see CBD/COP/16/L.33). Achieving global agreement on a (small) set of jointly used interoperable vocabularies and ontologies for (meta)data and machine-actionable operations is a communication, community-capacity development, and negotiation process that requires significant resources, engagement across sociocultural groups and geographies, patience and time. Ongoing processes towards these goals continue to be organized and promoted by, e.g., Biodiversity Information Standards (TDWG), the Global Biodiversity Information Facility (GBIF), GEO BON, and the Ocean Biodiversity Information System (OBIS), as well as large continental networks. We propose to bridge the gap before standardization and harmonization are in place and thereby facilitate and accelerate such efforts. Taking a pragmatic approach, our objective is to contribute a lightweight "pocket" dataspace that provides interoperability and data governance in connection with a digital platform for global genetic monitoring (Fig. 1). The pocket dataspace would allow existing platforms and tools to be connected easily through community-provided mappings between workflow element-specific formats, terms, data and operations stored in an open repository. This approach would enable users to take advantage of the core strengths of existing software products and the expertise of their associated communities, while quickly sharing data and their work between specialized solutions. At the same time, data would be FAIRified and CAREd-for, promoting attribution, transparency and responsibility. The functions of the pocket dataspace can be prerequisites for a transition to machine-actionable operations usable to agentic AI. As a general-purpose interlinking and translation component, the pocket dataspace aims to be the missing link between distributed, federated, non-standard-compliant and undocumented data, governance regimes, provenance logs and software output, and the need for transparent, well-governed and versatile conservation applications. One of these conservation applications will be the proposed platform for monitoring global genetic diversity. The platform will aggregate and visualize externally-linked population-genetic data and summary metrics that are the results of analysis pipelines enabled by, e.g., the pocket dataspace. Its aim is to provide visualization and support dataset and analysis management for local to global conservation efforts. It would store uploaded or linked genetic diversity metrics, perform selected automated analyses for continuously updated genetic diversity measures, as well as provide a starting point for user-designed analyses. The objective of our initial use case is to analyze three basic measures of population-genetic diversity based on genome-wide sequencing data as a first step towards operationalizing genetic monitoring at scale. Together, the pocket dataspace and monitoring platform for genetic diversity data have the goal to support a digital ecosystem that is foremost flexible, requiring low investments by users, and be able to quickly integrate both inter- and transdisciplinary data as well as existing powerful platforms and well-tested analytical pipelines and functionality.

The 2024 BGE Barcoding Hackathon, hosted by the Biodiversity Genomics Europe (BGE) project in Leiden, Netherlands, focused on advancing digital infrastructures and data handling practices for single specimen barcoding. This event brought together 24 participants from various consortium member institutes to enhance workflows in DNA barcoding, which complements genome sequencing efforts within BGE. The hackathon was structured around four thematic pillars: Data Generation and Processing, BOLD Release Candidate, Wider Data Integration, and Reference Data Curation. Participants worked on optimizing barcode generation pipelines, developing a new version of the Barcode of Life Data Systems (BOLD) data publishing platform, harmonizing data standards for better integration with databases like ENA and UNITE, and automating curation processes for DNA barcoding records. The outcomes include improved workflows, enhanced data interoperability, and refined curation standards, which collectively support BGE's goals of achieving a step change in molecular biodiversity monitoring and research.