‘openDS’ – A New Standard for Digital Specimens and Other Natural Science Digital Object Types

International mass digitization efforts through infrastructures like the European Distributed System of Scientific Collections (DiSSCo), the US resource for Digitization of Biodiversity Collections (iDigBio), the National Specimen Information Infrastructure (NSII) of China, and Australia’s digitization of National Research Collections (NRCA Digital) make geo- and biodiversity specimen data freely, fully and directly accessible. Complementary, overarching infrastructure initiatives like the European Open Science Cloud (EOSC) were established to enable mutual integration, interoperability and reusability of multidisciplinary data streams including biodiversity, Earth system and life sciences (De Smedt et al. 2020). Natural Science Collections (NSC) are of particular importance for such multidisciplinary and internationally linked infrastructures, since they provide hard scientific evidence by allowing direct traceability of derived data (e.g., images, sequences, measurements) to physical specimens and material samples in NSC. To open up the large amounts of trait and habitat data and to link these data to digital resources like sequence databases (e.g., ENA), taxonomic infrastructures (e.g., GBIF) or environmental repositories (e.g., PANGAEA), proper annotation of specimen data with rich (meta)data early in the digitization process is required, next to bridging technologies to facilitate the reuse of these data. This was addressed in recent studies (Younis et al. 2018, Younis et al. 2020), where we employed computational image processing and artificial intelligence technologies (Deep Learning) for the classification and extraction of features like organs and morphological traits from digitized collection data (with a focus on herbarium sheets). However, such applications of artificial intelligence are rarely—this applies both for (sub-symbolic) machine learning and (symbolic) ontology-based annotations—integrated in the workflows of NSC’s management systems, which are the essential repositories for the aforementioned integration of data streams. This was the motivation for the development of a Deep Learning-based trait extraction and coherent Digital Specimen (DS) annotation service providing “Machine learning as a Service” (MLaaS) with a special focus on interoperability with the core services of DiSSCo, notably the DS Repository (nsidr.org) and the Specimen Data Refinery (Walton et al. 2020), as well as reusability within the data fabric of EOSC. Taking up the use case to detect and classify regions of interest (ROI) on herbarium scans, we demonstrate a MLaaS prototype for DiSSCo involving the digital object framework, Cordra, for the management of DS as well as instant annotation of digital objects with extracted trait features (and ROIs) based on the DS specification openDS (Islam et al. 2020). Source code available at: https://github.com/jgrieb/plant-detection-service

The Extended Specimen was first described by Webster (2017). He defined a “constellation of specimen preparations and data types,” centered around an occurrence of an organism, which captures the breadth of empirical facts about an organism’s phenotype, genotype, and ecology in space and time. The Extended Specimen Network was embraced by the collections community in the Biodiversity Collections Network Extended Specimen Report (Lendemer et al. 2020) and the National Academies of Science, Engineering, and Medicine Future of Collections report (Lendemer et al. 2020, National Academies of Science, Engineering, and Medicine 2020). Several global discussions are underway to build a common definition of the Digital Extended Specimen (DES) and elucidate next steps in building the infrastructure to support Digital Extended Specimens and their network of associated data (including efforts among Distributed System of Scientific Collections (DiSSCo), Biodiversity Collections Network (BCoN), GBIF’s Alliance for Biodiversity Knowledge, TDWG's Task Group on Minimum Information about a Digital Specimen (MIDS), and others.) At the foundation of the DES is the occurrence of an organism in time and space, which is represented by physical specimens or observations serving as tokens of reality. Tokens are translated to digital records, which can be extended through a network of linkages between them and with derived and associated data, e.g. project methodologies, environmental conditions, habitat characteristics, and associated taxa. For digital records to be integrated with the larger network of Digital Extended Specimens, they must become FAIR digital objects that are Findable, Accessible, Interoperable, and Reusable (Wilkinson et al. 2016). By translating the Digital Extended Specimen concept to the local project scale, we provide opportunities to move beyond a theoretical understanding of the DES and towards a practical framework for its implementation. Here we present and discuss the power, limits, and questions in the implementation of the Digital Extended Specimen framework by applying it to the case study of an invasive aquatic plant in the Laurentian Great Lakes region. European frog-bit (Hydrocharis morsus-ranae L.; EFB) is native to western and northern Eurasia and invasive in North America and India. Dense mats of EFB may hinder commercial and recreational use of waterways and decrease light, dissolved oxygen, and native species diversity. We describe a multi-taxonomic study that examined EFB along with associated plant species, animal species, and environmental characteristics (Monfils et al. 2021). The integration of such diverse types of empirical data is a necessary prerequisite for determining the factors associated with EFB establishment, the impacts of EFB on native coastal wetland ecosystems, and the development of suitable management regimes for the conservation of native biodiversity. Data gathered from this study are housed in a local database. In our database, we consider both physical specimens and recorded observations as tokens of concrete occurrences of EFB, which define the base units. These tokens are linked to their collection events, which provide environmental and sampling context, as well as co-occurrences of other taxa including plants, invertebrates, fish, anurans, reptiles, and birds. Digitally linked, these extensions of each digital representation of a collected token provide not only empirical evidence of an EFB occurrence, but also directly connect it with all additionally sampled, derived, and associated information. Through this network of extensions we gain a more holistic understanding of EFB’s species associations, habitats, and ecosystem impacts at the level of populations and communities. The application of the Digital Extended Specimen framework at the project level illustrates how the DES can be used in a real-world context and highlights challenges in translating the concept from a theoretical to a practical perspective.

European Natural Science Collections (NSC) are part of the global natural and cultural capital and represent 80% of the world bio-and geo-diversity. Data derived from these collections underpin thousands of scholarly publications and official reports (used to support legislative and regulatory processes relating to health, food, security, sustainability and environmental change) and let to inventions and products that today play an important role in our bio-economy. In the last decades, the research practice in natural sciences changed dramatically. Advances in digital, genomic and information technologies enable natural science collections to provide new insights but also ask for changing the current operational and business models of individual collections held at local natural history museums and universities. A new business model that provides unified access to collection objects and all scientific data derived from them. Although aggregating infrastructures like the Global Biodiversity Information Facility, GenBank and Catalogue of Life now successfully aggregate data on specific data classes, the landscape remains fragmented with limited capacity to bring together this information in a systematic and robust manner and with scattered access to the physical objects. The Distributed System of Scientific Collections (DiSSCo) represents a pan-European initiative, and the largest ever agreement of natural science museums, to jointly address the fragmentation of European collections. DiSSCo is unifying European natural science collections into a coherent new research infrastructure, able to provide bio- and geo-diversity data at the scale, form and precision required by a multi-disciplinary user base in science. DiSSCo is harmonising digitisation, curation and publication processes and workflows across the scientific collections in Europe and enables linking of occurrence, genomic, chemical and morphological data classes as well as publications and experts to the physical object. In this paper we will present the socio-cultural and governance aspects of this research infrastructure. DiSSCo is receiving political support from 11 countries in Europe and will gradually change its funding model from institutional to national funding, with temporary funding from the EC to support the preparation and development. Solutions to achieve large scale digitisation are currently designed in the EC funded ICEDIG project to underpin the future large scale digitisation carried out by the countries. Unified virtual (digitisation on demand) and transnational physical access to the collections is over the next four years being developed in the EC funded SYNTHESYS+ project. The governance of DiSSCo is designed to gradually change from a steering committee composed of a few large natural history museums contributing in cash to initiate the development into a legal entity in which national consortia are represented, with a central coordination office for daily management. Each country individually decides how its entities (scientific collection facilities, research councils, governmental bodies) are organised in their national consortium. A stakeholder and user forum, Scientific Advisory Board and International Advisory Board will ensure that DiSSCo will be functional in enabling science across disciplines and within the international landscape of infrastructures. Training and short scientific missions are being developed in the MOBILISE COST Action to build capacity in FAIR data production, publication and usage of scientific collection-derived data in Europe and to initiate the socio-cultural changes needed in the collection-holding institutes. A Helpdesk is being constructed in the SYNTHESYS+ and DiSSCo Prepare projects to further facilitate the use and scientific use cases have been collected in ICEDIG to develop and facilitate e-services tailored to scientific needs.

Specimens have long been viewed as critical to research in the natural sciences because each specimen captures the phenotype (and often the genotype) of a particular individual at a particular point in space and time. In recent years there has been considerable focus on digitizing the many physical specimens currently in the world’s natural history research collections. As a result, a growing number of specimens are each now represented by their own “digital specimen”, that is, a findable, accessible, interoperable and re-usable (FAIR) digital representation of the physical specimen, which contains data about it. At the same time, there has been growing recognition that each digital specimen can be extended, and made more valuable for research, by linking it to data/samples derived from the curated physical specimen itself (e.g., computed tomography (CT) scan imagery, DNA sequences or tissue samples), directly related specimens or data about the organism's life (e.g., specimens of parasites collected from it, photos or recordings of the organism in life, immediate surrounding ecological community), and the wide range of associated specimen-independent data sets and model-based contextualisations (e.g., taxonomic information, conservation status, bioclimatological region, remote sensing images, environmental-climatological data, traditional knowledge, genome annotations). The resulting connected network of extended digital specimens will enable new research on a number of fronts, and indeed this has already begun. The new types of research enabled fall into four distinct but overlapping categories. First, because the digital specimen is a surrogate—acting on the Internet for a physical specimen in a natural science collection—it is amenable to analytical approaches that are simply not possible with physical specimens. For example, digital specimens can serve as training, validation and test sets for predictive process-based or machine learning algorithms, which are opening new doors of discovery and forecasting. Such sophisticated and powerful analytical approaches depend on FAIR, and on extended digital specimen data being as open as possible. These analytical approaches are derived from biodiversity monitoring outputs that are critically needed by the biodiversity community because they are central to conservation efforts at all levels of analysis, from genetics to species to ecosystem diversity. Second, linking specimens to closely associated specimens (potentially across multiple disparate collections) allows for the coordinated co-analysis of those specimens. For example, linking specimens of parasites/pathogens to specimens of the hosts from which they were collected, allows for a powerful new understanding of coevolution, including pathogen range expansion and shifts to new hosts. Similarly, linking specimens of pollinators, their food plants, and their predators can help untangle complex food webs and multi-trophic interactions. Third, linking derived data to their associated voucher specimens increases information richness, density, and robustness, thereby allowing for novel types of analyses, strengthening validation through linked independent data and thus, improving confidence levels and risk assessment. For example, digital representations of specimens, which incorporate e.g., images, CT scans, or vocalizations, may capture important information that otherwise is lost during preservation, such as coloration or behavior. In addition, permanently linking genetic and genomic data to the specimen of the individual from which they were derived—something that is currently done inconsistently—allows for detailed studies of the connections between genotype and phenotype. Furthermore, persistent links to physical specimens, of additional information and associated transactions, are the building blocks of documentation and preservation of chains of custody. The links will also facilitate data cleaning, updating, as well as maintenance of digital specimens and their derived and associated datasets, with ever-expanding research questions and applied uses materializing over time. The resulting high-quality data resources are needed for fact-based decision-making and forecasting based on monitoring, forensics and prediction workflows in conservation, sustainable management and policy-making. Finally, linking specimens to diverse but associated datasets allows for detailed, often transdisciplinary, studies of topics ranging from local adaptation, through the forces driving range expansion and contraction (critically important to our understanding of the consequences of climate change), and social vectors in disease transmission. A network of extended digital specimens will enable new and critically important research and applications in all of these categories, as well as science and uses that we cannot yet envision.

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

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.

In the landscape of general-purpose repositories, Zenodo was built at the European Laboratory for Particle Physics' (CERN) data center to facilitate the sharing and preservation of the long tail of research across all disciplines and scientific domains. Given Zenodo’s long tradition of making research artifacts FAIR (Findable, Accessible, Interoperable, and Reusable), there are still challenges in applying these principles effectively when serving the needs of specific research domains. Plazi’s biodiversity taxonomic literature processing pipeline liberates data from publications, making it FAIR via extensive metadata, the minting of a DataCite Digital Object Identifier (DOI), a licence and both human- and machine-readable output provided by Zenodo, and accessible via the Biodiversity Literature Repository community at Zenodo. The deposits (e.g., taxonomic treatments, figures) are an example of how local networks of information can be formally linked to explicit resources in a broader context of other platforms like GBIF (Global Biodiversity Information Facility). In the context of biodiversity taxonomic literature data workflows, a general-purpose repository’s traditional submission approach is not enough to preserve rich metadata and to capture highly interlinked objects, such as taxonomic treatments and digital specimens. As a prerequisite to serve these use cases and ensure that the artifacts remain FAIR, Zenodo introduced the concept of custom metadata, which allows enhancing submissions such as figures or taxonomic treatments (see as an example the treatment of Eurygyrus peloponnesius) with custom keywords, based on terms from common biodiversity vocabularies like Darwin Core and Audubon Core and with an explicit link to the respective vocabulary term. The aforementioned pipelines and features are designed to be served first and foremost using public Representational State Transfer Application Programming Interfaces (REST APIs) and open web technologies like webhooks. This approach allows researchers and platforms to integrate existing and new automated workflows into Zenodo and thus empowers research communities to create self-sustained cross-platform ecosystems. The BiCIKL project (Biodiversity Community Integrated Knowledge Library) exemplifies how repositories and tools can become building blocks for broader adoption of the FAIR principles. Starting with the above literature processing pipeline, the concepts of and resulting FAIR data, with a focus on the custom metadata used to enhance the deposits, will be explained.

Figure 1 shows Digital Object Architecture enabling networked data and information interoperability and management across heterogeneous systems. Figure 2 shows DiSSCo Digital specimen concept (top) and relations to Digital Object Architecture concepts (light blue) and components (dark blue). PID = persistent identifier. NSId = Natural Science Identifier.

(097–100) Proposals to clarify Articles 8.2 and 8.3 and their footnotes with regard to specimens, gatherings and duplicates

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.

Setiap tahun terdapat 39 juta bayi baru lahir beresiko memiliki kapasitas intelektual rendah akibat defisiensi yodium. Terdapat 5% ibu hamil yang kadar TSH dan TPO-Abnya meningkat. Konsekuensi defisiensi yodium dapat dihindari jika pada saat hamil ibu sejak awal mengkonsumsi yodium secara adekuat. Sumber yodium yang tersedia dan cukup murah adalah garam yang difortifikasi yodium. Namun, waktu penyimpanan, kondisi penyimpanan, dan jenis garam berpengaruh terhadap penurunan kandungan yodium dalam garam beryodium. Tujuan penelitian untuk mengetahui kandungan iodium pada garam beryodium dengan berbagai wadah penyimpanan selama penyimpanan 6 hari. Jenis penelitian ini merupakan penelitian deskriptif. Sampel garam diambil secara acak di toko kelontong dengan merk dagang yang paling banyak digunakan konsumen di kota Palu dan sekitarnya. Wadah yang digunakan adalah wadah plastik dan wadah kaca. Setiap wadah dibagi dua, ada wadah transparan dan ada wadah yang tidak transparan (dibungkus). Penyimpanan dilakukan selama 6 hari dengan analisa kandungan yodium garam dilakukan pada hari ke 0, hari ke-2, hari ke-4 dan hari ke-6. Hasil penelitian menunjukan bahwa baik wadah plastik maupun wadah kaca, terdapat penurunan kandungan yodium. Rata-rata persentase penurunan kandungan yodium terbesar terjadi pada penyimpanan dalam wadah plastik dan kaca yang transparan (9,49% dan 4,75%). Sedangkan pada wadah yang tidak transparan rata-rata penurunan kandungan yodiumnya sebesar 4,99% pada wadah plastik dan 3,18 % pada wadah kaca. Persentase penurunan kandungan yodium terbesar setiap dua hari penyimpanan terdapat pada garam yang disimpan dalam wadah plastik transparan yakni rata-rata sebesar 4,77%. Kesimpulan; Penyimpanan garam beryodium yang terbaik adalah dalam wadah kaca yang tidak transparan

To study the influence of temperature and containers on seed storability of Albizia lebbeck, seeds were stored at three different temperatures viz. -4±1oC, 4±1oC and room temperature (19-22oC) and three different storage containers viz. Canvas bag, plastic bags and glass jars for 16 months. The storage study revealed that the seed stored at 4±1oC remained 52 per cent germination even after 16 months of storage, while it was 46 per cent at -4±1oC. Among the seed storage containers, plastic containers showed their superiority over other containers. The interaction study revealed that maximum germination percent (54%) was obtained when the seeds were stored in plastic jars at 4±1oC temperature after 16 months of storage.

Detection of Mutant BRAF Alleles in the Plasma of Patients with Metastatic Melanoma

We determined the nucleotide sequence of the first 287 base pairs in the mitochondrial control region from 74 narwhals, Monodon monoceros, collected in the North-west Atlantic. We detected four polymorphic sites that defined five haplotypes, two of which were found in single specimens. The same DNA sequence was characterized in an additional 353 specimens by digestion with two restriction endonucleases. In this manner each specimen could be assigned to one of the three most common haplotypes. The nucleotide diversity for the total sample (as well as the sequenced subset) was estimated as 0.0017 and pairwise genetic distances between haplotypes ranged from 0.0035-0.0070. The low nucleotide diversity and the low average pairwise genetic distance between haplotypes suggest a recent expansion in abundance from a small founding population. Despite the low degree of variation, frequencies of the common haplotypes differed markedly between areas. The results indicate isolation, even between geographically close areas, as well as fidelity to specific summer and autumn feeding grounds. Heterogeneity within a presumed single breeding ground suggests mixing of pods with different haplotypic composition.

Uncompetitive N-methyl-d-aspartate (NMDA) receptor antagonists with fast off-rate (UFO) may represent promising drug candidates for various neurodegenerative disorders. In this study, we report that bis(propyl)-cognitin, a novel dimeric acetylcholinesterase inhibitor and gamma-aminobutyric acid subtype A receptor antagonist, is such an antagonist of NMDA receptors. In cultured rat hippocampal neurons, we demonstrated that bis(propyl)-cognitin voltage-dependently, selectively, and moderately inhibited NMDA-activated currents. The inhibitory effects of bis(propyl)-cognitin increased with the rise in NMDA and glycine concentrations. Kinetics analysis showed that the inhibition was of fast onset and offset with an off-rate time constant of 1.9 s. Molecular docking simulations showed moderate hydrophobic interaction between bis(propyl)-cognitin and the MK-801 binding region in the ion channel pore of the NMDA receptor. Bis(propyl)-cognitin was further found to compete with [(3)H]MK-801 with a K(i) value of 0.27 mum, and the mutation of NR1(N616R) significantly reduced its inhibitory potency. Under glutamate-mediated pathological conditions, bis(propyl)-cognitin, in contrast to bis(heptyl)-cognitin, prevented excitotoxicity with increasing effectiveness against escalating levels of glutamate and much more effectively protected against middle cerebral artery occlusion-induced brain damage than did memantine. More interestingly, under NMDA receptor-mediated physiological conditions, bis(propyl)-cognitin enhanced long-term potentiation in hippocampal slices, whereas MK-801 reduced and memantine did not alter this process. These results suggest that bis(propyl)-cognitin is a UFO antagonist of NMDA receptors with moderate affinity, which may provide a pathologically activated therapy for various neurodegenerative disorders associated with NMDA receptor dysregulation.