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DiSSCo, the Distributed System of Scientific Collections, is a pan-European Research Infrastructure (RI) mobilising, unifying bio- and geo-diversity information connected to the specimens held in natural science collections and delivering it to scientific communities and beyond. Bringing together 120 institutions across 21 countries and combining earlier investments in data interoperability practices with technological advancements in digitisation, cloud services and semantic linking, DiSSCo makes the data from natural science collections available as one virtual data cloud, connected with data emerging from new techniques and not already linked to specimens. These new data include DNA barcodes, whole genome sequences, proteomics and metabolomics data, chemical data, trait data, and imaging data (Computer-assisted Tomography (CT), Synchrotron, etc.), to name but a few; and will lead to a wide range of end-user services that begins with finding, accessing, using and improving data. DiSSCo will deliver the diagnostic information required for novel approaches and new services that will transform the landscape of what is possible in ways that are hard to imagine today. With approximately 1.5 billion objects to be digitised, bringing natural science collections to the information age is expected to result in many tens of petabytes of new data over the next decades, used on average by 5,000 – 15,000 unique users every day. This requires new skills, clear policies and robust procedures and new technologies to create, work with and manage large digital datasets over their entire research data lifecycle, including their long-term storage and preservation and open access. Such processes and procedures must match and be derived from the latest thinking in open science and data management, realising the core principles of 'findable, accessible, interoperable and reusable' (FAIR). Synthesised from results of the ICEDIG project ("Innovation and Consolidation for Large Scale Digitisation of Natural Heritage", EU Horizon 2020 grant agreement No. 777483) the DiSSCo Conceptual Design Blueprint covers the organisational arrangements, processes and practices, the architecture, tools and technologies, culture, skills and capacity building and governance and business model proposals for constructing the digitisation infrastructure of DiSSCo. In this context, the digitisation infrastructure of DiSSCo must be interpreted as that infrastructure (machinery, processing, procedures, personnel, organisation) offering Europe-wide capabilities for mass digitisation and digitisation-on-demand, and for the subsequent management (i.e., curation, publication, processing) and use of the resulting data. The blueprint constitutes the essential background needed to continue work to raise the overall maturity of the DiSSCo Programme across multiple dimensions (organisational, technical, scientific, data, financial) to achieve readiness to begin construction. Today, collection digitisation efforts have reached most collection-holding institutions across Europe. Much of the leadership and many of the people involved in digitisation and working with digital collections wish to take steps forward and expand the efforts to benefit further from the already noticeable positive effects. The collective results of examining technical, financial, policy and governance aspects show the way forward to operating a large distributed initiative i.e., the Distributed System of Scientific Collections (DiSSCo) for natural science collections across Europe. Ample examples, opportunities and need for innovation and consolidation for large scale digitisation of natural heritage have been described. The blueprint makes one hundred and four (104) recommendations to be considered by other elements of the DiSSCo Programme of linked projects (i.e., SYNTHESYS+, COST MOBILISE, DiSSCo Prepare, and others to follow) and the DiSSCo Programme leadership as the journey towards organisational, technical, scientific, data and financial readiness continues. Nevertheless, significant obstacles must be overcome as a matter of priority if DiSSCo is to move beyond its Design and Preparatory Phases during 2024. Specifically, these include: Organisational: Strengthen common purpose by adopting a common framework for policy harmonisation and capacity enhancement across broad areas, especially in respect of digitisation strategy and prioritisation, digitisation processes and techniques, data and digital media publication and open access, protection of and access to sensitive data, and administration of access and benefit sharing. Pursue the joint ventures and other relationships necessary to the successful delivery of the DiSSCo mission, especially ventures with GBIF and other international and regional digitisation and data aggregation organisations, in the context of infrastructure policy frameworks, such as EOSC. Proceed with the explicit aim of avoiding divergences of approach in global natural science collections data management and research. Strengthen common purpose by adopting a common framework for policy harmonisation and capacity enhancement across broad areas, especially in respect of digitisation strategy and prioritisation, digitisation processes and techniques, data and digital media publication and open access, protection of and access to sensitive data, and administration of access and benefit sharing. Pursue the joint ventures and other relationships necessary to the successful delivery of the DiSSCo mission, especially ventures with GBIF and other international and regional digitisation and data aggregation organisations, in the context of infrastructure policy frameworks, such as EOSC. Proceed with the explicit aim of avoiding divergences of approach in global natural science collections data management and research. Technical: Adopt and enhance the DiSSCo Digital Specimen Architecture and, specifically as a matter of urgency, establish the persistent identifier scheme to be used by DiSSCo and (ideally) other comparable regional initiatives. Establish (software) engineering development and (infrastructure) operations team and direction essential to the delivery of services and functionalities expected from DiSSCo such that earnest engineering can lead to an early start of DiSSCo operations. Adopt and enhance the DiSSCo Digital Specimen Architecture and, specifically as a matter of urgency, establish the persistent identifier scheme to be used by DiSSCo and (ideally) other comparable regional initiatives. Establish (software) engineering development and (infrastructure) operations team and direction essential to the delivery of services and functionalities expected from DiSSCo such that earnest engineering can lead to an early start of DiSSCo operations. Scientific: Establish a common digital research agenda leveraging Digital (extended) Specimens as anchoring points for all specimen-associated and -derived information, demonstrating to research institutions and policy/decision-makers the new possibilities, opportunities and value of participating in the DiSSCo research infrastructure. Establish a common digital research agenda leveraging Digital (extended) Specimens as anchoring points for all specimen-associated and -derived information, demonstrating to research institutions and policy/decision-makers the new possibilities, opportunities and value of participating in the DiSSCo research infrastructure. Data: Adopt the FAIR Digital Object Framework and the International Image Interoperability Framework as the low entropy means to achieving uniform access to rich data (image and non-image) that is findable, accessible, interoperable and reusable (FAIR). Develop and promote best practice approaches towards achieving the best digitisation results in terms of quality (best, according to agreed minimum information and other specifications), time (highest throughput, fast), and cost (lowest, minimal per specimen). Adopt the FAIR Digital Object Framework and the International Image Interoperability Framework as the low entropy means to achieving uniform access to rich data (image and non-image) that is findable, accessible, interoperable and reusable (FAIR). Develop and promote best practice approaches towards achieving the best digitisation results in terms of quality (best, according to agreed minimum information and other specifications), time (highest throughput, fast), and cost (lowest, minimal per specimen). Financial Broaden attractiveness (i.e., improve bankability) of DiSSCo as an infrastructure to invest in. Plan for finding ways to bridge the funding gap to avoid disruptions in the critical funding path that risks interrupting core operations; especially when the gap opens between the end of preparations and beginning of implementation due to unsolved political difficulties. Broaden attractiveness (i.e., improve bankability) of DiSSCo as an infrastructure to invest in. Plan for finding ways to bridge the funding gap to avoid disruptions in the critical funding path that risks interrupting core operations; especially when the gap opens between the end of preparations and beginning of implementation due to unsolved political difficulties. Strategically, it is vital to balance the multiple factors addressed by the blueprint against one another to achieve the desired goals of the DiSSCo programme. Decisions cannot be taken on one aspect alone without considering other aspects, and here the various governance structures of DiSSCo (General Assembly, advisory boards, and stakeholder forums) play a critical role over the coming years.

New innovations are needed to speed up digitisation of insect collections. More than one half of all specimens in scientific collections are pinned insects. In Europe this means 500-1,000 million such specimens. Today's fastest mass-digitisation (i.e., imaging) systems for pinned insects can achieve circa 70 specimens/hour and 500/day by one operator (Tegelberg et al. 2014, Tegelberg et al. 2017). This is in contrast of the 5,000/day rate of the state-of-the-art mass-digitisation systems for herbarium sheets (Oever and Gofferje 2012). The slowness of imaging pinned insects follows from the fact that they are essentially 3D objects. Although butterflies, moths, dragonflies and similar large-winged insects can be prepared (spread) as 2D objects, the fact that the labels are pinned under the insect specimen makes even these samples 3D. In imaging, the labels are often removed manually, which slows down the imaging process. If the need for manual handling of the labels can be skipped, the imaging speed can easily multiplied. ENTODIG-3D (Fig. 1) is an automated camera system, which takes pictures of insect collection boxes (units and drawers) and digitizes them, minimizing time-consuming manual handling of specimens. "Units" are small boxes or trays contained in drawers of collection cabinets, and are being used in most major insect collections. A camera is mounted on motorized rails, which moves in two dimensions over a unit or a drawer. Camera movement is guided by a machine learning object detection program. QR-codes are printed and placed underneath the unit or drawer. QR-codes may contain additional information about each specimen, for example the place it originated from in the collection. Also, the object detection program detects the specimen, and stores its coordinates. The camera mount rotates and tilts, which ensures that the camera may take photographs from all angles and positions. Pictures are transferred into the computer, which calculates a 3D-model with photogrammetry, from which the label text beneath the specimen may be read. This approach requires heavy computation in the segmentation of the top images, and in the creation of a 3D model of the unit, and in extraction of label images of many specimens. Firstly, a sparse point cloud is calculated. Secondly, a dense point cloud is calculated. Finally, a textured mesh is calculated. With machine learning object detection, the top layer, which consists of the insect, may be removed. This leaves the bottom layer with labels visible for later processing by OCR (optical character recognition). This is a new approach to digitise pinned insects in collections. The physical setup is not expensive. Therefore, many systems could be installed in parallel to work overnight to produce the images of tens of drawers. The setup is not physically demanding for the specimens, as they can be left untouched in the unit or drawer. A digital object is created, consisting of label text, unit or drawer QR-code, specimen coordinates in a drawer with unique identifier, and a top-view photo of the specimen. The drawback of this approach is the heavy computing that is needed to create the 3D-models. ENTODIG-3D can currently digitise one sample in five minutes, almost without manual work. Theoretically, potentially sustainable rate is approximately one hundred thousand samples per year. The rate is similar as the current insect digitisation system in Helsinki (Tegelberg & al. 2017), but without the need for manual handling of individual specimens. By adding more computing power, the rate may be increased in linear fashion.

Additional file 1. ‘How-to’ guidelines for the biodiversity virtual e-laboratory.

Additional file 3: Table S2. Workflows for biodiversity science.

Additional file 4. Scientific studies using the BioVeL platform.

Additional file 2: Table S1. Service groups and capabilities for processing and analysis in biodiversity science.

Project acronym: EU BON Project name: EU BON: Building the European Biodiversity Observation Network Call: ENV.2012.6.2-2 Grant agreement: FP7-308454 Project duration: 01/12/2012 – 31/05/2017 (54 months) Co-ordinator: MfN, Museum für Naturkunde - Leibniz Institute for Research on Evolution and Biodiversity, Germany uploaded by Plazi

This report reviews the state-of-the-art and research trends for each the three science areas selected for the BioVeL project. The three science areas are: a) biospheric carbon sequestration; b) ecosystem functioning and valuation; and c) invasive species management. For each science area the report addresses: i) the science-policy context; ii) the main scientific questions being addressed in the area; iii) the most relevant research groups, networks and projects; iv) the existing models and methods; v) data source requirements; vi) the typical and most challenging workflows; and vii) web service requirements. The report concludes that the needs of biodiversity and ecology sciences for new leading-edge integrative IT tools are growing rapidly, while existing, reliable and open web services and workflow management services to fulfill these needs are still scarce. The planned and on-going developments of the BioVeL project, demonstrating the success of the virtual laboratory approach will go a long way towards meeting this need, and will open new perspectives for biodiversity analysis. However several gaps will remain unbridged and there are some constraints as well.