Search

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.

This report explains the processes to prepare a herbarium for digital imaging. It is divided into four main sections. The introduction summarizes the aim of mass digitisation and lists the reasons why several institutions started mass digitisation. The second chapter focuses on the preparation phase as it is a critical part of the process. Here the similarities and differences of herbarium collections are examined. Though each collection has its own peculiarities, the main topics of preparation are the same in every digitisation project. Preparation is a complex and often lengthy process. Past experience shows that details are important: assessing the exact number of specimens, addressing the issue of barcoding specimens, getting the appropriate means for transportation, down to the choice of boxes for example, accounting for possible pest infestation and dealing with unmounted specimens. The third chapter addresses the preparation of a herbarium collection for the imaging process itself. Emphasis is on weighing the merits of 1) in-house imaging by ones own staff, 2) in-house imaging by a contractor, and 3) outsourced imaging toacontractor. This section also details the elements of the workflow that are necessary to achieve mass digitisation. The last section presents five case studies of mass digitisation by renowned institutions: Meise Botanic Garden, Meise (BE), The Royal Botanic Gardens, Kew (GB), Digitarium (FI), Naturalis (NL), and the Musée National d'Histoire Naturelle, Paris (FR).

This report explains the processes to prepare a herbarium for digital imaging. It is divided into four main sections. The introduction summarizes the aim of mass digitisation and lists the reasons why several institutions started mass digitisation. The second chapter focuses on the preparation phase as it is a critical part of the process. Here the similarities and differences of herbarium collections are examined. Though each collection has its own peculiarities, the main topics of preparation are the same in every digitisation project. Preparation is a complex and often lengthy process. Past experience shows that details are important: assessing the exact number of specimens, addressing the issue of barcoding specimens, getting the appropriate means for transportation, down to the choice of boxes for example, accounting for possible pest infestation and dealing with unmounted specimens. The third chapter addresses the preparation of a herbarium collection for the imaging process itself. Emphasis is on weighing the merits of 1) in-house imaging by ones own staff, 2) in-house imaging by a contractor, and 3) outsourced imaging toacontractor. This section also details the elements of the workflow that are necessary to achieve mass digitisation. The last section presents five case studies of mass digitisation by renowned institutions: Meise Botanic Garden, Meise (BE), The Royal Botanic Gardens, Kew (GB), Digitarium (FI), Naturalis (NL), and the Musée National d'Histoire Naturelle, Paris (FR).

This report describes the state of art and work in progress on automated methods in mass-digitisation of pinned insects. The report begins by identifying the challenges, which stem from the fact that pinned insects are basically 3D objects and their numbers in collections are huge, up to one billion objects in Europe. A ten-fold increase in the speed of their digitisation from the current state of art is being sought for. Based on recent developments, state-of-the-art in their digitisation is covered. Important new technologies which seem promising in insect digitisation are described. The report describes the experiments which the ICEDIG project has carried out in order to find new innovations in mass-digitisation of pinned insects. We identify six such possible approaches, scoping their features, applicability, possible benefits and limitations, and make recommendations. Progress on the ongoing developments in three experiments is described in detail. We conclude that achieving a breakthrough in insect digitisation probably requires a combination of existing and new technologies in novel workflows., {"references":["Ylinampa T, Saarenmaa H (2019) Photogrammetrical insect collection capturing system (ENTODIG-3D). Biodiversity_next conference, Leiden, the Netherlands 2019-10-21/25. Abstract (in print).","Nieva A, Rosin P, Sun X (2018) Specifications of tests on automated methods for mass 3D imaging.H2020-INFRADEV-2017-1 Project ICEDIG, Grant Agreement Number 777483. Milestone MS16 report,11 p. Cardiff University.","Agarwal, N., Ferrier, N., & Hereld, M. (2018, March). Towards Automated Transcription of Label Textfrom Pinned Insect Collections. In2018 IEEE Winter Conference on Applications of Computer Vision(WACV) (pp. 189-198)."]}

The paper investigates how to implement open access to data in collection institutions and in the DiSSCo research infrastructure. Large-scale digitisation projects generate lots of images, but data transcription often remains backlogged for years. The paper discusses minimum information standards (MIDS) for digital specimens, and tentatively defines 4 hierarchical MIDS levels. Even partially available data can be useful for some purposes and it is recommended that data and media be made openly accessible after minimal delay. The paper then discusses the FAIR concepts and obstacles and restrictions to making data openly accessible. Legal restrictions for making data openly available are discussed from the perspective “as open as possible, as closed as (legally) necessary”. Specific legislation applying to DiSSCo data are listed and guidelines on how to deal with each are made. The information architecture of DiSSCo is briefly described from the perspective of facilitating open access to data, and the essential features of DiSSCo and GBIF are compared against each other, noting that both have their distinctive roles but need close cooperation. Data policies and data management plans (DMP) of six ICEDIG collection institutions are reviewed. Typically, only one or the other exists, depending on how digitisation and related data management is organised. Data policies are found at institution level whereas DMPs seem to belong to project level. The paper comes to the following conclusions: 1) Digital Specimen Objects (DSO) must be findable and accessible already at the lowest (MIDS-0) level. Data should be deposited in a trusted public research data repository. 2) As far as possible, projects must enable third parties to access, mine, exploit, reproduce and dis­seminate this data by using a copyright waiver such as CC0 or an open access licence such as CC-BY. 3) Exceptions to openness policy must be stated clearly and strictly limited to reasons of national security, legal or regulatory compliance, sensitivity of collection information, and third party rights.

This report describes the feasibility and potential role of national data infrastructures in large-scale digitisation of natural history collections, especially in the case of Finland. The descriptions of services, capacities, costs, and data flows from digitisation facilities to these national level systems and further to European systems are studied in the context of Finland. Discussion of use and possible designs are also included. The report is structured in five parts: The Introduction part describes the background of this study and the general status of national data infrastructures in Europe. The case of Finland is emphasized and the available computing and data services are introduced. The Infrastructure chapter describes FinBIF (Finnish Biodiversity Information Facility), CSC (the Finnish national IT centre for science) Pouta cloud computing services, and the Finnish Fairdata services. In chapter 3, the data module used in FinBIF is presented, including the identifier, metadata, and API (Application Programming Interface). The design chapter describes the overall architectural view and data flows of the digitisation process with the use of Finnish national data infrastructures. In the last chapter, the feasibility and potential role of national data infrastructures for digitisation facilities are discussed. We concluded that national data infrastructures tailored for biodiversity data, such as FinBIF, are highly necessary for the digitisation facility of natural history collections., {"references":["Ariño AH (2010) Approaches to estimating the universe of natural history collections data. BiodiversityInformatics 7: 81-92. https://doi.org/10.17161/bi.v7i2.3991","Blagoderov V, Smith V (2012) No specimen left behind: mass digitization of natural history collections. ZooKeys 209. https://zookeys.pensoft.net/browse_journal_issue_documents.php?issue_id=361","Lahti K (2017) FinBIF – Finnish Biodiversity Information Facility. Retrieved from http://koivu.luomus.fi/gbif/02_Lahti_FinBIF-Suomen_Lajitietokeskus.pdf","Oever JP, Gofferje M (2014) From pilot to production: Large scale digitization project at Naturalis Biodiversity center. ZooKeys 209, 87-92. https://zookeys.pensoft.net/article/2921/","Tegelberg R, Mononen T, Saarenmaa H (2014) High-performance digitization of natural history collections: Automated imaging lines for herbarium and insect specimens. Taxon 63 (6) 1307-1313. http://www.ingentaconnect.com/content/iapt/tax/2014/00000063/00000006/art00010","Tegelberg R, Kahanpää J, Karppinen J, Mononen T, Wu Z, Saarenmaa H 2017. Mass digitization of individual pinned insects using conveyor-driven imaging. In: Hereld M (Editor) High throughput digitization for natural history collections. 2017 IEEE 13th International Conference on e-Science (e-Science 2017). Auckland, New Zealand, 24-27 October 2017. 5 p. https://ieeexplore.ieee.org/document/8109190/","Wu Z, Kahanpää J, Sihvonen P, Koivunen A, Saarenmaa H (2019). Automated Methods in Digitisation of Pinned Insects. Biodiversity Information Science and Standards, 3, e38260. https://doi.org/10.3897/biss.3.38260"]}

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.

The paper investigates how to implement open access to data in collection institutions and in the DiSSCo research infrastructure. Large-scale digitisation projects generate lots of images, but data transcription often remains backlogged for years. The paper discusses minimum information standards (MIDS) for digital specimens, and tentatively defines 4 hierarchical MIDS levels. Also partially available data can be useful for some purposes and it is recommended that data and media be made openly accessible after minimal delay. The paper then discusses the FAIR concepts and obstacles and restrictions to making data openly accessible. Data policies and data management plans (DMP) of six ICEDIG collection institutions are reviewed. Typically only one or the other exists, depending on how digitisation and related data management is organised. Data policies are found at institution level whereas DMPs seem to belong to project level. The paper comes to the following conclusions: 1) Digital Specimen Objects (DSO) must be findable and accessible at lowest (MIDS-0) level. Data should be deposited in a public research data repository, such as Zenodo. 2) As far as possible, projects must enable third parties to access, mine, exploit, reproduce and disseminate this data by using a copyright waiver such as CC0 or an open access licence such as CC-BY. 3) Exceptions to openness policy must be stated clearly and strictly limited to reasons of national security, legal or regulatory compliance, sensitivity of collection information, and third party rights. REFERENCES: MILESTONE MS35 Work package WP6 / Task T6.1 / Lead: University of Helsinki Deadline M13 &nbsp

Essential Biodiversity Variables (EBV) are fundamental variables that can be used for assessing biodiversity change over time, for determining adherence to biodiversity policy, for monitoring progress towards sustainable development goals, and for tracking biodiversity responses to disturbances and management interventions. Data from observations or models that provide measured or estimated EBV values, which we refer to as EBV data products, can help to capture the above processes and trends and can serve as a coherent framework for documenting trends in biodiversity. Using primary biodiversity records and other raw data as sources to produce EBV data products depends on cooperation and interoperability among multiple stakeholders, including those collecting and mobilising data for EBVs and those producing, publishing and preserving EBV data products. Here, we encapsulate ten principles for the current best practice in EBV-focused biodiversity informatics as ‘The Bari Manifesto’, serving as implementation guidelines for data and research infrastructure providers to support the emerging EBV operational framework based on trans-national and cross-infrastructure scientific workflows. The principles provide guidance on how to contribute towards the production of EBV data products that are globally oriented, while remaining appropriate to the producer's own mission, vision and goals. These ten principles cover: data management planning; data structure; metadata; services; data quality; workflows; provenance; ontologies/vocabularies; data preservation; and accessibility. For each principle, desired outcomes and goals have been formulated. Some specific actions related to fulfilling the Bari Manifesto principles are highlighted in the context of each of four groups of organizations contributing to enabling data interoperability - data standards bodies, research data infrastructures, the pertinent research communities, and funders. The Bari Manifesto provides a roadmap enabling support for routine generation of EBV data products, and increases the likelihood of success for a global EBV framework.

ICEDIG is a design study for the proposed new research infrastructure Distributed System of Scientific Collections (DiSSCo), focusing on the issues around digitisation of the collections and making their data freely and openly available following the FAIR principles (data being Findable, Accessible, Interoperable, and Re-usable). As a design study, ICEDIG does not implement anything in an operational fashion, and therefore the amount of research data which ICEDIG will deal with is limited. DiSSCo, on the other hand, is expected to deal with huge amounts of data. The data management plan (DMP) for DiSSCo will be produced as one of the design documents of ICEDIG. In other words, ICEDIG will produce two data management plans: one for ICEDIG and another for DiSSCo. In order to achieve its objectives, ICEDIG will carry out a number of tasks, including specific pilots which may produce limited amounts of research data. ICEDIG will also deploy large volumes of already existing research data in order to explore how it can best be pooled in the available European Open Science Cloud infrastructures. This data management plan follows the Horizon 2020 DMP template, which has been designed to be applicable to any Horizon 2020 project that produces, collects or processes research data. The template is a set of questions that have been answered with a level of detail appropriate to the project. It has been understood that the DMP is a living document which may be updates as the implementation of the project progresses and when significant changes occur.