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Semantic segmentation has been proposed as a tool to accelerate the processing of natural history collection images. However, developing a flexible and resilient segmentation network requires an approach for adaptation which allows processing different datasets with minimal training and validation. This paper presents a cross-validation approach designed to determine whether a semantic segmentation network possesses the flexibility required for application across different collections and institutions. Consequently, the specific objectives of cross-validating the semantic segmentation network are to (a) evaluate the effectiveness of the network for segmenting image sets derived from collections different from the one in which the network was initially trained on; and (b) test the adaptability of the segmentation network for use in other types of collections. The resilience to data variations from different institutions and the portability of the network across different types of collections are required to confirm its general applicability. The proposed validation method is tested on the Natural History Museum semantic segmentation network, designed to process entomological microscope slides. The proposed semantic segmentation network is evaluated through a series of cross-validation experiments designed to test using data from two types of collections: microscope slides (from three institutions) and herbarium sheets (from seven institutions). The main contribution of this work is the method, software and ground truth sets created for this cross-validation as they can be reused in testing similar segmentation proposals in the context of digitization of natural history collections. The cross-validation of segmentation methods should be a required step in the integration of such methods into image processing workflows for natural history collections.

The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices, yet the incentives for researchers to change their practices are presently weak. In addition, data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices. To overcome these challenges, the Canonical Workflow Frameworks for Research (CWFR) initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof. This standardised approach, with FAIR Digital Objects as anchors, will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it. This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so, highlights several projects that hold promise for the CWFR approaches, including Galaxy, Jupyter Notebook, and RO Crate, and concludes with an assessment of the state of the field and the challenges ahead.

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.

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

Approved formally as a TDWG Task Group (TG) in September 2020, TG MIDS is working to harmonise a framework for "Minimum Information about a Digital Specimen (MIDS)". MIDS clarifies what is meant by different levels of digitization (MIDS levels) and specifies the minimum information to be captured at each level. Capturing and presenting data in future digitization in standard formats is essential so that data can be more easily understood, compared, analysed and communicated via the Internet. Adopting MIDS and working to achieve specific MIDS levels in digitization ensures that enough data are captured, curated and published such that they are useful for the widest possible range of future research, teaching and learning purposes. Adopting MIDS makes it easier to consistently measure the extent of digitization achieved over time and to set priorities for the remaining work. In the year since MIDS was first introduced at TDWG 2020, the TG has focussed on the details of MIDS level 1, representing the basic minimum level of information to be expected and captured in basic digitization activities such as creating a catalogue record and (optionally) making photographic or other digital images of specimens. To help the community adopt and embed MIDS conformance as a core part of digitization and data publishing/management pipelines, the MIDS specification consists of definitions of the expected information elements, a template for mapping terms/fields in institutional collection management systems and other data management schemas to those information elements, a conformance proforma allowing declaration of how a digitization or data publishing event conforms to MIDs, and instructions for use.

International collaboration between collections, aggregators, and researchers within the biodiversity community and beyond is becoming increasingly important in our efforts to support biodiversity, conservation and the life of the planet. The social, technical, logistical and financial aspects of an equitable biodiversity data landscape – from workforce training and mobilization of linked specimen data, to data integration, use and publication – must be considered globally and within the context of a growing biodiversity crisis. In recent years, several initiatives have outlined paths forward that describe how digital versions of natural history specimens can be extended and linked with associated data. In the United States, Webster (2017) presented the “extended specimen”, which was expanded upon by Lendemer et al. (2019) through the work of the Biodiversity Collections Network (BCoN). At the same time, a “digital specimen” concept was developed by DiSSCo in Europe (Hardisty 2020). Both the extended and digital specimen concepts depict a digital proxy of an analog natural history specimen, whose digital nature provides greater capabilities such as being machine-processable, linkages with associated data, globally accessible information-rich biodiversity data, improved tracking, attribution and annotation, additional opportunities for data use and cross-disciplinary collaborations forming the basis for FAIR (Findable, Accessible, Interoperable, Reproducible) and equitable sharing of benefits worldwide, and innumerable other advantages, with slight variation in how an extended or digital specimen model would be executed. Recognizing the need to align the two closely-related concepts, and to provide a place for open discussion around various topics of the Digital Extended Specimen (DES; the current working name for the joined concepts), we initiated a virtual consultation on the discourse platform hosted by the Alliance for Biodiversity Knowledge through GBIF. This platform provided a forum for threaded discussions around topics related and relevant to the DES. The goals of the consultation align with the goals of the Alliance for Biodiversity Knowledge: expand participation in the process, build support for further collaboration, identify use cases, identify significant challenges and obstacles, and develop a comprehensive roadmap towards achieving the vision for a global specification for data integration. In early 2021, Phase 1 launched with five topics: Making FAIR data for specimens accessible; Extending, enriching and integrating data; Annotating specimens and other data; Data attribution; and Analyzing/mining specimen data for novel applications. This round of full discussion was productive and engaged dozens of contributors, with hundreds of posts and thousands of views. During Phase 1, several deeper, more technical, or additional topics of relevance were identified and formed the foundation for Phase 2 which began in May 2021 with the following topics: Robust access points and data infrastructure alignment; Persistent identifier (PID) scheme(s); Meeting legal/regulatory, ethical and sensitive data obligations; Workforce capacity development and inclusivity; Transactional mechanisms and provenance; and Partnerships to collaborate more effectively. In Phase 2 fruitful progress was made towards solutions to some of these complex functional and technical long-term goals. Simultaneously, our commitment to open participation was reinforced, through increased efforts to involve new voices from allied and complementary fields. Among a wealth of ideas expressed, the community highlighted the need for unambiguous persistent identifiers and a dedicated agent to assign them, support for a fully linked system that includes robust publishing mechanisms, strong support for social structures that build trustworthiness of the system, appropriate attribution of legacy and new work, a system that is inclusive, removed from colonial practices, and supportive of creative use of biodiversity data, building a truly global data infrastructure, balancing open access with legal obligations and ethical responsibilities, and the partnerships necessary for success. These two consultation periods, and the myriad activities surrounding the online discussion, produced a wide variety of perspectives, strategies, and approaches to converging the digital and extended specimen concepts, and progressing plans for the DES -- steps necessary to improve access to research-ready data to advance our understanding of the diversity and distribution of life. Discussions continue and we hope to include your contributions to the DES in future implementation plans.

Transdisciplinary and cross-cultural cooperation and collaboration are needed to build extended, densely interconnected information resources. These are the prerequisites for the successful implementation and execution of, for example, an ambitious monitoring framework accompanying the post-2020 Global Biodiversity Framework (GBF) of the Convention on Biological Diversity (CBD; SCBD 2021). Data infrastructures that meet the requirements and preferences of concerned communities can focus and attract community involvement, thereby promoting participatory decision making and the sharing of benefits. Community acceptance, in turn, drives the development of the data resources and data use. Earlier this year, the alliance for biodiversity knowledge (2021a) conducted forum-based consultations seeking community input on designing the next generation of digital specimen representations and consequently enhanced infrastructures. The multitudes of connections that arise from extending the digital specimen representations through linkages in all “directions” will form a powerful network of information for research and application. Yet, with the power of an extended, accessible data network comes the responsibility to protect sensitive information (e.g., the locations of threatened populations, culturally context-sensitive traditional knowledge, or businesses’ fundamental data and infrastructure assets). In addition, existing legislation regulates access and the fair and equitable sharing of benefits. Current negotiations on ‘Digital Sequence Information’ under the CBD suggest such obligations might increase and become more complex in the context of extensible information networks. For example, in the case of data and resources funded by taxpayers in the EU, such access should follow the general principle of being “as open as possible; as closed as is legally necessary” (cp. EC 2016). At the same time, the international regulations of the CBD Nagoya Protocol (SCBD 2011) need to be taken into account. Summarizing main outcomes from the consultation discussions in the forum thread “Meeting legal/regulatory, ethical and sensitive data obligations” (alliance for biodiversity knowledge 2021b), we propose a framework of ten guidelines and functionalities to achieve community building and drive application: Substantially contribute to the conservation and protection of biodiversity (cp. EC 2020). Use language that is CBD conformant. Show the importance of the digital and extensible specimen infrastructure for the continuing design and implementation of the post-2020 GBF, as well as the mobilisation and aggregation of data for its monitoring elements and indicators. Strive to openly publish as much data and metadata as possible online. Establish a powerful and well-thought-out layer of user and data access management, ensuring security of ‘sensitive data’. Encrypt data and metadata where necessary at the level of an individual specimen or digital object; provide access via digital cryptographic keys. Link obligations, rights and cultural information regarding use to the digital key (e.g. CARE principles (Carroll et al. 2020), Local Context-labels (Local Contexts 2021), licenses, permits, use and loan agreements, etc.). Implement a transactional system that records every transaction. Amplify workforce capacity across the digital realm, its work areas and workflows. Do no harm (EC 2020): Reduce the social and ecological footprint of the implementation, aiming for a long-term sustainable infrastructure across its life-cycle, including development, implementation and management stages. Substantially contribute to the conservation and protection of biodiversity (cp. EC 2020). Use language that is CBD conformant. Show the importance of the digital and extensible specimen infrastructure for the continuing design and implementation of the post-2020 GBF, as well as the mobilisation and aggregation of data for its monitoring elements and indicators. Strive to openly publish as much data and metadata as possible online. Establish a powerful and well-thought-out layer of user and data access management, ensuring security of ‘sensitive data’. Encrypt data and metadata where necessary at the level of an individual specimen or digital object; provide access via digital cryptographic keys. Link obligations, rights and cultural information regarding use to the digital key (e.g. CARE principles (Carroll et al. 2020), Local Context-labels (Local Contexts 2021), licenses, permits, use and loan agreements, etc.). Implement a transactional system that records every transaction. Amplify workforce capacity across the digital realm, its work areas and workflows. Do no harm (EC 2020): Reduce the social and ecological footprint of the implementation, aiming for a long-term sustainable infrastructure across its life-cycle, including development, implementation and management stages. Balancing the needs for open access, as well as protection, accountability and sustainability, the framework is designed to function as a robust interface between the (research) infrastructure implementing the extensible network of digital specimen representations, and the myriad of applications and operations in the real world. With the legal, ethical and data protection layers of the framework in place, the infrastructure will provide legal clarity and security for data providers and users, specifically in the context of access and benefit sharing under the CBD and its Nagoya Protocol. Forming layers of protection, the characteristics and functionalities of the framework are envisioned to be flexible and finely-grained, adjustable to fulfill the needs and preferences of a wide range of stakeholders and communities, while remaining focused on the protection and rights of the natural world. Respecting different value systems and national policies, the framework is expected to allow a divergence of views to coexist and balance differing interests. Thus, the infrastructure of the digital extensible specimen network is fair and equitable to many providers and users. This foundation has the capacity and potential to bring together the diverse global communities using, managing and protecting biodiversity.

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.

To support future research based on natural sciences collection data, DiSSCo (Distributed System of Scientific Collections) – the European Research Infrastructure for Natural Science Collections – adopts Digital Object Architecture as the basis for its planned data infrastructure. Using the outputs of one Research Data Alliance (RDA) interest group (IG) and five working groups (WGs) we show how RDA recommendations and supporting documents have been applied to the various stages of the DiSSCo data lifecycle.

There has been little work to compare and understand the operating costs of digitisation using a standardised approach. This paper discusses a first attempt at gathering digitisation cost information from multiple institutions and analysing the data. This paper has been written: for other digitisation managers who want to breakdown and compare project costs; as a potential baseline for future digitisation projects; as a starting point for prioritising research and development to reduce digitisation costs.

We examine the intersection of the FAIR principles (Findable, Accessible, Interoperable and Reusable), the challenges and opportunities presented by the aggregation of widely distributed and heterogeneous data about biological and geological specimens, and the use of the Digital Object Architecture (DOA) data model and components as an approach to solving those challenges that offers adherence to the FAIR principles as an integral characteristic. This approach will be prototyped in the Distributed System of Scientific Collections (DiSSCo) project, the pan-European Research Infrastructure which aims to unify over 110 natural science collections across 21 countries. We take each of the FAIR principles, discuss them as requirements in the creation of a seamless virtual collection of bio/geo specimen data, and map those requirements to Digital Object components and facilities such as persistent identification, extended data typing, and the use of an additional level of abstraction to normalize existing heterogeneous data structures. The FAIR principles inform and motivate the work and the DO Architecture provides the technical vision to create the seamless virtual collection vitally needed to address scientific questions of societal importance.

Contribution from the DiSSCo technical team with recommendations in response to the questions in the idea paper https://docs.gbif.org/collections-idea-paperfor a global consultation onAdvancing the Catalogue of the World’s Natural History Collections.

With projected lifespans of many decades, infrastructure initiatives such as Europe’s Distributed System of Scientific Collections (DiSSCo), USA’s Integrated Digitized Biocollections (iDigBio), National Specimen Information Infrastructure (NSII) of China and Australia’s digitisation of national research collections (NRCA Digital) aim at transforming today’s slow, inefficient and limited practices of working with natural science collections. The need to borrow specimens (plants, animals, fossils or rocks) or physically visit collections, and absence of linkages to other relevant information represent significant impediments to answering today’s scientific and societal questions. A logical extension of the Internet, Digital Object Architecture (Kahn and Wilensky 2006) offers a way of grouping, managing and processing fragments of information relating to a natural science specimen. A ‘digital specimen’ acts as a surrogate in cyberspace for a specific physical specimen, identifying its actual location and authoritatively saying something about its collection event (who, when, where) and taxonomy, as well as providing links to high-resolution images. A digital specimen exposes supplementary information about related literature, traits, tissue samples and DNA sequences, chemical analyses, environmental information, etc. stored elsewhere than in the natural science collection itself. By presenting digital specimens as a new layer between data infrastructure of natural science collections and user applications for processing and interacting with information about specimens and collections, it’s possible to seamlessly organise global access spanning multiple collection-holding institutions and sources. Virtual collections of digital specimens with unique identifiers offer possibilities for wider, more flexible, and ‘FAIR’ (Findable, Accessible, Interoperable, Reusable) access for varied research and policy uses: recognising curatorial work, annotating with latest taxonomic treatments, understanding variations, working with DNA sequences or chemical analyses, supporting regulatory processes for health, food, security, sustainability and environmental change, inventions/products critical to the bio-economy, and educational uses. Adopting a digital specimen approach is expected to lead to faster insights for lower cost on many fronts. We propose that realising this vision requires a new TDWG standard. OpenDS is a specification of digital specimen and other object types essential to mass digitisation of natural science collections and their digital use. For five principal digital object types corresponding to major categories of collections and specimens’ information, OpenDS defines structure and content, and behaviours that can act upon them: Digital specimen: Representing a digitised physical specimen, contains information about a single specimen with links to related supplementary information; Storage container: Representing groups of specimens stored within a single container, such as insect tray, drawer or sample jar; Collection: Information about characteristics of a collection; Organisation: Information about the legal-entity owning the specimen and collection to which it belongs; and, Interpretation: Assertion(s) made on or about the specimen such as determination of species and comments. Digital specimen: Representing a digitised physical specimen, contains information about a single specimen with links to related supplementary information; Storage container: Representing groups of specimens stored within a single container, such as insect tray, drawer or sample jar; Collection: Information about characteristics of a collection; Organisation: Information about the legal-entity owning the specimen and collection to which it belongs; and, Interpretation: Assertion(s) made on or about the specimen such as determination of species and comments. Secondary classes gather presentation/preservation characteristics (e.g., herbarium sheets, pinned insects, specimens in glass jars, etc.), the general classification of a specimen (i.e., plant, animal, fossil, rock, etc.) and history of actions on the object (provenance). Equivalencing concepts in ABCD 3.0 and EFG extension for geo-sciences, OpenDS is also an ontology extending OBO Foundry’s Biological Collection Ontology (BCO) (Walls et al. 2014) from bco:MaterialSample, which has preferred label dwc:specimen from Darwin Core, thus linking it also with that standard. OpenDS object content can be serialized to specific formats/representations (e.g. JSON) for different exchange and processing purposes.

DiSSCo – the Distributed System of Scientific Collections – will mobilise, unify and deliver bio- and geo-diversity information at the scale, form and precision required by scientific communities, and thereby transform a fragmented landscape into a coherent and responsive research infrastructure. At present DiSSCo has 115 partners from 21 countries across Europe. The DiSSCo research infrastructure will enable critical new insights from integrated digital data to address some of the world's greatest challenges, such as biodiversity loss, food security and impacts of climate change. A requirement analysis for DiSSCo was conducted to ensure that all of its envisioned future uses are accommodated through a large survey using epic user stories. An epic user story has the following format: As [e.g. scientist] I want to [e.g. map the distribution of a species through time] so that I [e.g. analyse the impact of climate change] for this I need [e.g. all georeferenced specimens records through time] Several consultation rounds within the ICEDIG community resulted in 78 unique user stories that were assigned to one, or more, out of seven recognized stakeholder categories: Research, Collection management, Technical support, Policy, Education, Industry, and External. Research, Collection management, Technical support, Policy, Education, Industry, and External. Each user story was assessed for the level of collection detail it required; four levels of detail were recognised: Collection, Taxonomic, Storage unit, and Specimen level. Furthermore, it was assessed whether the future envisioned use of digitised natural history collections were possible without the DiSSCo research infrastructure. Subsequently 1243 identified stakeholders were invited to review the DiSSCo user stories through a Survey Monkey questionnaire. Additionally, an invitation for review was posted in several Facebook groups and announced on Twitter. A total of 379 stakeholders responded to the invitation, which led to 85 additional user stories for the envisioned use of the DiSSCo research infrastructure. In order to assess which component of the DiSSCo data flow diagram should facilitate the described user story, all user stories were mapped to the five phases of the DiSSCo Data Management Cycle (DMC), including data: acquisition, curation, publishing, processing, and use. acquisition, curation, publishing, processing, and use. At present, the user stories are being analysed and the results will be presented in this symposium.

Preserved specimens in natural science collections have lifespans of many decades and often, several hundreds of years. Specimens must be unambiguously identifiable and traceable in the face of changes in physical location, changes in organisation of the collection to which they belong, and changes in classification. When digitizing museum collections, a clear link must be maintained between the physical specimen itself and the information digitally representing that specimen in cyberspace. The idea of a Natural Science Identifier (NSId) as a neutral, unique, universal and stable long-term persistent identifier (PID) of a ‘Digital Specimen’ is central to museums’ ambitions for widening access. An NSId allows easy identification and referencing of specific Digital Specimens, regardless of type, location, owner or user. It provides a digital doorway to physical specimens through which services for arranging loans and visits can be accessed, as well as opening the door to innovative services for manipulating specimens’ information directly; for work reliant upon discovery of related third-party information; and for demanding 3D modelling and visualization of specimens. Because the work takes place within e-Infrastructures/Cyberspace, new possibilities for analysing hundreds of thousands of specimens simultaneously are opened by exploiting large-scale cloud computing capacity and deep mining/machine learning, for example. There are several established identifier mechanisms that could be used as a basis for NSId, but some variant of Handles is most appropriate over the very long-term because of their neutrality, resistance to change and sustainability. Adopted uses of the Handle system include identification of journal articles and datasets in education and research (using Digital Object Identifiers); film and television programme assets in the entertainment sector; financial derivatives; and for international shipping and construction. Aside from being stable and sustained over time, an essential requirement of a global PID mechanism is independence from the museums/institutions assigning identifiers. NSIds are opaque insofar as no information can or should be inferred solely by inspecting the identifier. Stakeholders change, collections move, and organisations evolve, merge or disappear. Even designations and descriptions of specimens and collections can change. Information should only be revealed when the identifier is resolved via a neutral index. One can debate the most appropriate instantiation of the Handle system but this is not useful. Relevance, ease of use and added-value of the supporting ‘NSId Registry’ (NSIdR) – the index of the different kinds of natural science object and their relations – are the decisive factors. This can be seen from the example of the Entertainment Identifier Registry (EIDR) founded by the major motion picture studios to create a reliable way to identify and track film and TV content distribution. Focus on the object model, promotional branding and value perception in the target user segment are the critical factors for success. Providing such a registry, seamlessly coupled to work practices and language of the professionals addresses the last mile challenge (Koureas et al. 2016). From specimens, class characteristics, storage containers and collections, to specific identifications, images, naming, literature references and more, the NSIdR’s triple-hierarchy object model, rooted in OBO Foundry’s Biological Collections Ontology, is the key to persistently identifying, relating and indexing the entire range of collection objects of interest to scientists and others working in the bio and geo realms. The NSIdR ‘knowledge graph’, interoperable with other identifier schemes, supports novel first- and third-party value-add services such as arranging loans and visits, curation and annotation, and machine-learning for relationship discovery and pattern exploration.

Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon. © 2018 The Author(s).

This paper investigates the value of persona in relation to a conceptual product: the ENVRI reference model designed for environmental research infrastructures. Three personas have been created to understand the use of the model and the challenges faced when applying it. Personas helped identify the level of support required by different users, prioritise the audience to address first, and revealed what aspects of the model are important to different audiences. We have made significant progress in understanding how to improve communication about the model to each persona.

DiSSCo(The Distributed System of Scientific Collections) is a Research Infrastructure (RI) aiming at providing unified physical (transnational), remote (loans) and virtual (digital) access to the approximately 1.5 billion biological and geological specimens in collections across Europe. DiSSCo represents the largest ever formal agreement between natural science museums (114 organisations across 21 European countries). With political and financial support across 14 European governments and a robust governance model DiSSCo will deliver, by 2025, a series of innovative end-user discovery, access, interpretation and analysis services for natural science collections data. As part of DiSSCo's developing data model, we evaluate the application of Digital Objects (DOs), which can act as the centrepiece of its architecture. DOs have bit-sequences representing some content, are identified by globally unique persistent identifiers (PIDs) and are associated with different types of metadata. The PIDs can be used to refer to different types of information such as locations, checksums, types and other metadata to enable immediate operations. In the world of natural science collections, currently fragmented data classes (inter alia genes, traits, occurrences) that have derived from the study of physical specimens, can be re-united as parts in a virtual container (i.e., as components of a Digital Object). These typed DOs, when combined with software agents that scan the data offered by repositories, can act as complete digital surrogates of the physical specimens. In this paper we: investigate the architectural and technological applicability of DOs for large scale data RIs for bio- and geo-diversity, identify benefits and challenges of a DO approach for the DiSSCo RI and describe key specifications (incl. metadata profiles) for a specimen-based new DO type.

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals., published version, peerReviewed

Essential biodiversity variables (EBVs) have been proposed by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to identify a minimum set of essential measurements that are required for studying, monitoring and reporting biodiversity and ecosystem change. Despite the initial conceptualisation, however, the practical implementation of EBVs remains challenging. There is much discussion about the concept and implementation of EBVs: which variables are meaningful; which data are needed and available; at which spatial, temporal and topical scales can EBVs be calculated; and how sensitive are EBVs to variations in underlying data? To advance scientific progress in implementing EBVs we propose that both scientists and research infrastructure operators need to cooperate globally to serve and process the essential large datasets for calculating EBVs. We introduce GLOBIS-B (GLOBal Infrastructures for Supporting Biodiversity research), a global cooperation funded by the Horizon 2020 research and innovation framework programme of the European Commission. The main aim of GLOBIS-B is to bring together biodiversity scientists, global research infrastructure operators and legal interoperability experts to identify the research needs and infrastructure services underpinning the concept of EBVs. The project will facilitate the multi-lateral cooperation of biodiversity research infrastructures worldwide and identify the required primary data, analysis tools, methodologies and legal and technical bottlenecks to develop an agenda for research and infrastructure development to compute EBVs. This requires development of standards, protocols and workflows that are ‘self-documenting’ and openly shared to allow the discovery and analysis of data across large spatial extents and different temporal resolutions. The interoperability of existing biodiversity research infrastructures will be crucial for integrating the necessary biodiversity data to calculate EBVs, and to advance our ability to assess progress towards the Aichi targets for 2020 of the Convention on Biological Diversity (CBD).

Environmental science research is increasingly dependent on the collection and analysis of large volumes of data gathered via wide-scale deployments of sensors and other observation sources. Meanwhile, researchers are being called upon to address global societal challenges that are inextricably tied to the stability of our native ecosystems. These challenges are intrinsically interdisciplinary in nature, forcing scientists to collaborate across traditional disciplinary boundaries. The role of research infrastructure in this context is to support researchers in their interactions with a host of different data sources and analytical tools, as well as with each other, but no single environmental research infrastructure can hope to fully encompass the entire research ecosystem that has arisen to support the study of environmental science. The challenge therefore is for new environmental research infrastructures to exhibit sufficient technical interoperability between the different services they offer so as to permit researchers to freely and effectively interact with the full range of research assets potentially available to them, allowing them to collaborate and conduct innovative interdisciplinary research regardless of the particular research community to which they belong. Realising this ideal however requires a broad understanding of the fundamental commonalities of environmental science research infrastructure services as well as the development and wide adoption of common foundational services. It also requires a pragmatic bridging between the different standards and controlled vocabularies currently in use or preparation by different scientific communities, a process that can be expedited by the use of a standard reference model and the use of a formal framework for semantically linking similar concepts in different contexts.

Editorial article explaining the background to and introducing a collection of papers describing the construction and operation phase of the Greek LifeWatch distributed centre: LifeWatchGreece.

This paper presents SCRAM---CK, a method to elicit requirements by means of strong user involvement supported by prototyping activities. The method integrates two existing approaches, SCRAM and CK theory. SCRAM provides the framework for requirements management, while CK theory provides a framework for reasoning about design and its evolution. The method is demonstrated with the definition and refining of requirements for the BioVeL web toolkit. The objective of BioVeL is to allow scientists to understand, run, modify and construct workflows for data analysis with minimal training using a web-based interface. The proposed method is supported by prototyping activities for gathering user feedback, and refining requirements and design proposals. Using this method, the prototypes evolved from simple workflow execution enablers to include more complex functionalities for reviewing, modifying and building workflows in later versions. This paper presents a contribution to the application of techniques for requirements engineering. SCRAM---CK is an amalgamated method that combines a user-centred continuous refinement approach with support for design evolution through prototyping. The paper also shows the influence of the requirements engineering process in the evolution of design proposals.

Ecological niche modelling (ENM) Components are a set of reusable workflow components specialized for performing ENM tasks within the Taverna workflow management system. Each component encapsulates specific functionality and can be combined with other components to facilitate the creation of larger and more complex workflows. One key distinguishing feature of ENM Components is that most tasks are performed remotely by calling web services, simplifying software setup and maintenance on the client side and allowing more powerful computing resources to be exploited. This paper presents the current set of ENM Components in the context of the Taverna family of tools for creating, publishing and sharing workflows. An example is included showing how the components can be used in a preliminary investigation of the effects of mixing different spatial resolutions in ENM experiments.

In order to preserve the variety of life on Earth, we must understand it better. Biodiversity research is at a pivotal point with research projects generating data at an ever increasing rate. Structuring, aggregating, linking and processing these data in a meaningful way is a major challenge. The systematic application of information management and engineering technologies in the study of biodiversity (biodiversity informatics) help transform data to knowledge. However, concerted action is required to be taken by existing e-infrastructures to develop and adopt common standards, provisions for interoperability and avoid overlapping in functionality. This would result in the unification of the currently fragmented landscape that restricts European biodiversity research from reaching its full potential. The overarching goal of this COST Action is to coordinate existing research and capacity building efforts, through a bottom-up trans-disciplinary approach, by unifying biodiversity informatics communities across Europe in order to support the long-term vision of modelling biodiversity on earth. BioUnify will: 1. specify technical requirements, evaluate and improve models for efficient data and workflow storage, sharing and re-use, within and between different biodiversity communities; 2. mobilise taxonomic, ecological, genomic and biomonitoring data generated and curated by natural history collections, research networks and remote sensing sources in Europe; 3. leverage results of ongoing biodiversity informatics projects by identifying and developing functional synergies on individual, group and project level; 4. raise technical awareness and transfer skills between biodiversity researchers and information technologists; 5. formulate a viable roadmap for achieving the long-term goals for European biodiversity informatics, which ensures alignment with global activities and translates into efficient biodiversity policy.

Europe is building its Open Science Cloud; a set of robust and interoperable einfrastructures with the capacity to provide data and computational solutions through cloudbased\ud services. The development and sustainable operation of such e-infrastructures are at the forefront of European funding priorities. The research community, however, is still reluctant to engage at the scale required to signal a Europe-wide change in the mode of operation of scientific practices. The striking differences in uptake rates between\ud researchers from different scientific domains indicate that communities do not equally share the benefits of the above European investments. We highlight the need to support\ud research communities in organically engaging with the European Open Science Cloud through the development of trustworthy and interoperable Virtual Research Environments.\ud These domain-specific solutions can support communities in gradually bridging technical and socio-cultural gaps between traditional and open digital science practice, better diffusing the benefits of European e-infrastructures.

Objectives To examine the evidence base for telemonitoring designed for patients who have chronic obstructive pulmonary disease and heart failure, and to assess whether telemonitoring fulfils the principles of monitoring and is ready for implementation into routine settings. Design Qualitative data collection using interviews and participation in a multi-path mapping process. Participants Twenty-six purposively selected informants completed semi-structured interviews and 24 individuals with expertise in the relevant clinical and informatics domains from academia, industry, policy and provider organizations and participated in a multi-path mapping workshop. Results The evidence base for the effectiveness of telemonitoring is weak and inconsistent, with insufficient cost-effectiveness studies. When considered against an accepted definition of monitoring, telemonitoring is found wanting. Telemonitoring has not been able so far to ensure that the technologies fit into the life world of the patient and into the clinical and organizational milieu of health service delivery systems. Conclusions To develop effective telemonitoring for patients with chronic disease, more attention needs to be given to agreeing the central aim of early detection and, to ensure potential implementation, engaging a wide range of stakeholders in the design process, especially patients and clinicians.

Item does not contain fulltext OBJECTIVE: To propose a research agenda that addresses technological and other knowledge gaps in developing telemonitoring solutions for patients with chronic diseases, with particular focus on detecting deterioration early enough to intervene effectively. DESIGN: A mixed methods approach incorporating literature review, key informant, and focus group interviews to gain an in-depth, multidisciplinary understanding of current approaches, and a roadmapping process to synthesise a research agenda. RESULTS: Counter to intuition, the research agenda for early detection of deterioration in patients with chronic diseases is not only primarily about advances in sensor technology but also much more about the problems of clinical specification, translation, and interfacing. The ultimate aim of telemonitoring is not fully agreed between the actors (patients, clinicians, technologists, and service providers). This leads to unresolved issues such as: (1) How are sensors used by patients as part of daily routines? (2) What are the indicators of early deterioration and how might they be used to trigger alerts? (3) How should alerts lead to appropriate levels of responses across different agencies and sectors? CONCLUSION: Attempts to use telemonitoring to improve the care of patients with chronic diseases over the last two decades have so far failed to lead to systems that are embedded in routine clinical practice. Attempts at implementation have paid insufficient attention to understanding patient and clinical needs and the complex dynamics and accountabilities that arise at the level of service models. A suggested way ahead is to co-design technology and services collaboratively with all stakeholders.

The research aim underpinning the Healthcare@Home (HH) information system described here was to enable 'near real time' risk analysis for disease early detection and prevention. To this end, we are implementing a family of prototype web services to 'push' or 'pull' individual's health-related data via an system of clinical hubs, mobile communication devices and/or dedicated home-based network computers. We are examining more efficient methods for ethical use of such data in timeline-based (i.e. 'longitudinal') data analysis systems. A consistent data collation infrastructure is being created for use along the 'patient path'--accessible wherever patients happen to be. This 'patient-centred' infrastructure can be applied in the evaluation of disease progression risk (in the light of clinical understanding of disease processes). In this paper we describe the requirements for making multi-data trend management 'scale-up', together with some requirements of an 'end-to-end' functioning data collection system. A Service-Oriented Architecture (SOA) approach is used to maximise benefits from (1) clinical evidence and (2) computational models of disease progression that can be made available elsewhere on the SOA. We discuss the implications of this so-called 'closed loop' approach for improving healthcare intervention outcomes, patient safety, decision support, objective measurement of service quality and in providing inputs for quantitative healthcare (predictive) modelling.

Environmental research infrastructures (RIs) support their respective research communities by integrating large-scale sensor/observation networks with data curation services, analytical tools and common operational policies. These RIs are developed as pillars of intra- and interdisciplinary research, however comprehension of the complex, pathologically interconnected aspects of the Earth's ecosystem increasingly requires that researchers conduct their experiments across infrastructure boundaries. Consequently, almost all data-related activities within these infrastructures, from data capture to data usage, needs to be designed to be broadly interoperable in order to enable real interdisciplinary innovation. The Data for Science theme in the EU Horizon 2020 project ENVRIPLUS intends to address this interoperability challenge as it relates to the design, implementation and operation of environmental science RIs; the theme focuses on key issues of data identification and citation, curation, cataloguing, processing, optimization, and provenance, supported by a generic cross-infrastructure reference model.

Environmental research infrastructures (RIs) support data-intensive research by integrating large-scale sensor/observer networks with dedicated data curation services and analytical tools. However the diversity of scientific disciplines coupled with the lack of an accepted methodology for constructing new RIs inevitably leads to incompatibilities between the data models, metadata standards and service descriptions used by different RIs, inhibiting their usefulness for interdisciplinary research. In the absence of a common global ontology of science and infrastructure, these inconsistencies may best be counteracted by selectively bridging the semantics of the various vocabularies, standards and models used by the RIs at present. Open Information Linking for Environmental RIs (OIL-E) was developed within the FP7 project ENVRI to provide a framework for semantic linking of knowledge resources used by different environmental RIs. Built around a multi-viewpoint reference model ENVRI-RM, OIL-E is intended to act as a central exchange for linking information fragments and identifying gaps in the conceptual models of RIs.

Marine biological invasions have increased with the development of global trading, causing the homogenization of communities and the decline of biodiversity. A main vector is ballast water exchange from shipping. This study evaluates the use of ecological niche modelling (ENM) to predict the spread of 18 non-indigenous species (NIS) along shipping routes and their potential habitat suitability (hot/cold spots) in the Baltic Sea and Northeast Atlantic. Results show that, contrary to current risk assessment methods, temperature and sea ice concentration determine habitat suitability for 61% of species, rather than salinity (11%). We show high habitat suitability for NIS in the Skagerrak and Kattegat, a transitional area for NIS entering or leaving the Baltic Sea. As many cases of NIS introduction in the marine environment are associated with shipping pathways, we explore how ENM can be used to provide valuable information on the potential spread of NIS for ballast water risk assessment.

Ecosystem functioning, climate change, and multiple interactions among biogeochemical cycles, climate system, site conditions and land use options are leading-edge topics in recent environmental modelling. Terrestrial ecosystem models are widely used to support carbon sequestration and ecosystem studies under various ecological circumstances. Our team uses the Biome-BGC model (Numerical Terradynamic Simulation Group, University of Montana), and develops an improved model version of it, called Biome-BGC MuSo. Both the original and the improved model estimate the ecosystem scale storage and fluxes of energy, carbon, nitrogen and water, controlled by various physical and biological processes on a daily time-scale. Web services were also developed and integrated with parallel processing desktop grid technology. Taverna workflow management system was used to build up and carry out elaborated workflows like seamless data flow to model simulation, Monte Carlo experiment, model sensitivity analysis, model-data fusion, estimation of ecosystem service indicators or extensive spatial modelling. Straightforward management of complex data analysis tasks, organized into appropriately documented, shared and reusable scientific workflows enables researchers to carry out detailed and scientifically challenging ‘in silico’ experiments and applications that could open new directions in ecosystem research and in a broader sense it supports progress in environmental modelling. The workflow approach built upon these web services allows even the most complicated computations to be initiated without the need of programming skills and deep understanding of model structure and initialization. The developments enable a wider array of scientists to perform ecosystem scale simulations, and to perform analyses not previously possible due to high complexity and computational demand.

The Taverna workflow tool suite (http://www.taverna.org.uk) is designed to combine distributed Web Services and/or local tools into complex analysis pipelines. These pipelines can be executed on local desktop machines or through larger infrastructure (such as supercomputers, Grids, or cloud environments), using the Taverna Server. In bioinformatics, Taverna workflows are typically used in the areas of high-throughput omics analyses (for example proteomics, or transcriptomics), or for evidence gathering methods involving text mining or data mining. Through Taverna, scientists have access to several thousand different tools and resources that are freely available from a large range of Life Science institutions. Once constructed, the workflows are reusable, executable bioinformatics protocols that can be shared, reused and repurposed. A repository of public workflows is available at http://www.myexperiment.org. This article provides an update to the Taverna tool suite, highlighting new features and developments since those reported in a previous Web Server issue of this journal.

Wireless Sensor Networks (WSNs) produce large quantities of raw data from multiple sources, such as cameras, temperature or humidity sensors. We propose a network that uses the knowledge of domain experts that use the network, as well as previously sensed data, to classify incoming sensed data in near real-time. Using a field centre in Malaysia as a testbed, we are currently implementing a prototype network to classify images of animals that trigger wildlife cameras. Initial experiments to test the individual components of the network have yielded positive results and a prototype is planned for deployment in July of 2012.

Patients with chronic disease may suffer frequent acute deteriorations and associated increased risk of hospitalisation. Earlier detection of these could enable successful intervention, improving patients’ well-being and reducing costs; however, current telemonitoring systems do not achieve this effectively. We conducted a qualitative study using stakeholder interviews to define current standards of care and user requirements for improved early detection telemonitoring. We determined that early detection is not a concept that has informed technology or service design and that telemonitoring is driven by the available technology rather than by users’ needs. We have described a set of requirements questions to inform the design and implementation of telemonitoring systems and suggested the research needed to develop successful early detection telemonitoring. User-centred design and genuine interdisciplinary approaches are needed to create solutions that are fit for purpose, sustainable and address the real needs of patients, clinicians and healthcare organisations.

There are many promising earth and biodiversity-monitoring projects underway across the globe, but they often operate in information islands, unable easily to share data with others. This is not convenient: It is a barrier to scientists collaborating on complex, cross-disciplinary projects which is an essential nature of biodiversity research.LifeWatch (www.lifewatch.eu) is an ESFRI (European Strategy Forum on Research Infrastructures) initiative which has just entered its construction phase. It is aiming at new ways of collaboration, in an open-access research environment to solve complex societal and scientific questions on biodiversity and ecosystems. It installs a range of new services and tools to help the researchers communicate, share data, create models, analyze results, manage projects and organize the community. The power of LifeWatch comes from linking all kinds of biodiversity related databases (e.g. collections, long-term monitoring data) to tools for analysis and modeling, opening entirely new avenues for research with the potential for new targeted data generation. At this level the interface with national data repositories becomes most important, as this opens the opportunity for users to gain advantage from data availability on the European level. LifeWatch will provide common methods to discover, access, and develop available and new data, analytical capabilities, and to catalog everything, to track citation and re-use of data, to annotate, and to keep the system secure. This includes computing tool-kits for researchers: for instance, an interoperable computing environment for statistical analysis, cutting-edge software to manage the workflow in scientific projects, and access to new or existing computing resources. The result: ‘e-laboratories’ or virtual labs, through which researchers distributed across countries, time zones and disciplines can collaborate. With emphasis on the open sharing of data and workflows (and associated provenance information) the infrastructure allows scientists to create e-laboratories across multiple organizations, controlling access where necessary.