Transforming natural history collections through intelligent digital twins and automated analysis
Imagine accessing a priceless scientific specimen, not by traveling to a distant museum or handling a fragile century-old sample, but by clicking a button on your computer. This is the promise of the Digital Specimen—a "digital twin" of a physical specimen that brings together a wealth of scientific data into a single, dynamic online object 1 .
The Distributed System of Scientific Collections (DiSSCo), a pan-European research infrastructure, is leading this charge to transform natural history collections 6 . With over 5 million digital specimens and images already available, the scale of this endeavor is massive 7 . But the real revolution lies in what comes next: using Machine Learning as a Service (MLaaS) to breathe intelligence into these digital objects, turning static records into a living, evolving resource for science 9 .
Revolutionizing access to scientific collections
Digital specimens and images already available
Institutions across Europe
Potential specimens in European collections
A Digital Specimen is far more than a simple digital photograph or a scanned record. It is a rich, FAIR digital object that acts as a comprehensive surrogate for a physical specimen stored in a museum, herbarium, or university collection 1 7 . "FAIR" means the data is Findable, Accessible, Interoperable, and Reusable, both for humans and for machines 1 6 .
The challenge is one of scale and knowledge. DiSSCo aims to unite collections from over 175 institutions across 23 countries, representing a potential 500 million specimens in Europe alone 6 7 . Manually curating and enriching data for millions of these digital specimens is an impossible task for human curators alone. This is where artificial intelligence steps in.
Machine Learning as a Service (MLaaS) provides a solution by integrating powerful, cloud-based AI tools directly into the DiSSCo data architecture 9 . This allows for the automated analysis of digitized specimens, such as herbarium sheets, to identify and classify key features, a process essential for making the vast amounts of data usable for research.
Easy to locate by both humans and computers
Retrievable with standard protocols
Ready to be integrated with other data
Well-described for future replication
A pivotal study demonstrated how MLaaS could be integrated into DiSSCo's workflow to automate the annotation of herbarium specimens 9 . This experiment serves as a blueprint for how AI can augment human curation.
The researchers tackled a classic problem in biodiversity informatics: extracting structured data from images. Their methodology can be broken down into several key stages:
The goal was to develop a service that could automatically detect and classify Regions of Interest (ROIs) on high-resolution scans of herbarium sheets. These ROIs could be specific plant organs like leaves, flowers, or fruits 9 .
The team employed a Deep Learning approach, specifically a Region-based Convolutional Neural Network (R-CNN). This type of AI model is exceptionally good at both locating and identifying objects within an image 9 .
The prototype was designed as a service that interoperates with core DiSSCo services. It connects with the Digital Specimen Repository and uses the openDS specification to add the extracted information directly to the digital specimen as a new, machine-readable annotation 9 .
The successful implementation of this MLaaS prototype showed that AI could reliably automate the first, most labor-intensive step of data curation—feature identification.
| Method | Time per Specimen | Scalability | Consistency | Required Expertise |
|---|---|---|---|---|
| Manual Curation | High (minutes/hours) | Low | Variable | High (Taxonomic Expert) |
| MLaaS Automation | Low (seconds) | Very High | Excellent | Medium (AI Management) |
| Hybrid (Human-AI) | Medium | High | Very High | Medium to High |
Dramatically accelerates data enrichment at scale impossible for human teams
Enables large-scale ecological and evolutionary studies
Frees experts for higher-level tasks while AI handles repetitive work
The experiment and the broader DiSSCo infrastructure rely on a suite of software and standards that form the researcher's digital toolkit.
| Tool / Standard | Category | Primary Function in DiSSCo / MLaaS |
|---|---|---|
| openDS | Data Standard | Provides the common language for representing digital specimen data, ensuring interoperability 9 . |
| Deep Learning (e.g., R-CNN) | AI Model | Enables advanced image analysis, such as detecting and classifying plant organs on herbarium sheets 9 . |
| Cordra | Digital Object Framework | Manages and stores the Digital Specimen objects, handling their persistent identifiers and metadata 9 . |
| TensorFlow / PyTorch | ML Framework | Open-source libraries used to build, train, and deploy deep learning models like the one used for trait extraction 8 . |
| DiSSCover | Service Platform | The central portal for discovering, curating, and annotating digital specimens, where both humans and AI add new knowledge 7 . |
| FAIR Digital Object | Data Model | The overarching framework that makes every digital specimen a machine-actionable, citable, and provenance-tracked resource 7 . |
The integration of MLaaS into DiSSCo is more than a technical upgrade; it's a cultural shift for natural science collections. Digital specimens are no longer static records but actionable, dynamic data that evolves with science itself 1 . Through DiSSCo's platform, DiSSCover, a global community of experts can add annotations and correct information, with every change tracked for provenance 7 . This creates a living knowledge system where each contribution, whether from a human researcher or an AI service, enhances the value of the collection for everyone.
| Service | Description | Key Benefit |
|---|---|---|
| ELViS | A unified system for requesting physical loans, visits, or virtual access to collections across Europe 6 . | Simplifies and harmonizes access for researchers. |
| Digital Specimen Repository | A data repository for Digital Specimens and other FAIR Digital Objects for experimentation and storage 6 . | Provides the foundational storage for the digital twins. |
| Collection Digitisation Dashboard | An interactive dashboard showing the digitization status and content of collections across the DiSSCo community 6 . | Offers a macro-level view of progress and collection strengths. |
The journey to fully digitize and intelligently mobilize the world's natural history collections is a monumental one. By pioneering the integration of Machine Learning as a Service directly into its Digital Specimen architecture, DiSSCo is not just keeping pace with the digital age—it is defining the future of collection-based research.
This powerful synergy of cutting-edge infrastructure and artificial intelligence is transforming museums and herbaria from static repositories of the past into dynamic, intelligent hubs of discovery. It promises to unlock the secrets held within hundreds of millions of specimens, providing critical insights to address some of the most pressing challenges of our time, from biodiversity loss to climate change.