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A research object (RO) is defined as a semantically rich aggregation of (potentially distributed) resources that provide a layer of structure on top of information delivered as linked data (Bechhofer et al. 2013, Soiland-Reyes et al. 2022). A RO provides a container for the aggregation of resources, produced, and consumed by common services and shareable within and across organisational boundaries. This work sees research digital objects as composites which may consist of objects hosted in different repositories.In catalysis research, the characterisation of a sample may require analysing experimental data obtained from an instrument, data from a computer model, and/or comparing to data from a specialized database. Additionally, data may need to be reduced and cleaned before analysis, resulting in intermediate data. In this scenario the composite research object is integrated by all these data objects and their corresponding metadata. UK Catalysis Hub (UKCH) researchers perform these tasks as part of their day-to-day work. However, most of the time they need to manually collect, catalogue, and preserve all these data assets.The UKCH aims to support researchers with tools and services for the management and processing of data, through the development of the Catalysis Data Infrastructure (CDI Nieva de la Hidalga et al. 2022a) and the Catalysis Research Workbench (CRW Nieva de la Hidalga et al. 2022b). This work is integrated in the context of the Physical Sciences Data Infrastructure (PSDI Coles and Knight 2022). The PSDI aims to provide a layer that enables transparent access to existing resources whilst ensuring that they remain dedicated to its specific application. The intention is to explore the concept of the composite research digital object and the services required to facilitate both human and programmatic interactions with those objects to browse, review, retrieve, and use digital objects in the context of the research produced by UKCH scientists. The CDI will act as a thematic portal presenting data managed through the PSDI and serve as an example for the development of similar portals targeting specific research domains.In this case, the CDI is in the process of being redesigned with a sematic metadata model. The basic ontologies being considered for this model are: DCAT (Albertoni et al. 2022) will encode the metadata of digital objects; PROV-O (Belhajjame et al. 2013) will track the generation of digital objects. SPAR (Peroni and Shotton 2018) to encode publications data; SCHOLIX to encode the links between publications and data objects (Burton et al. 2017); FOAF (Brickley and Miller 2014) to encode researcher information; the Organization Ontology (ORG Reynolds 2014) to encode institution information; EXPO (Soldatova et al. 2006) to encode experiment information; and various domain specific ontologies for adding metadata about experiments, for instance CHEBI (Hastings et al. 2011), CHEMINF (Hastings et al. 2015), and FIX (Chebi-Administrators 2005).The implementation of the CDI using these ontologies will provide a roadmap for the integration of FAIR data object repositories with a service infrastructure which supports reproducibility, reuse of data, reuse of processing tools and implementation of advanced processing tools.The integration of the CDI and CRW with existing and new infrastructures will further support the work of catalysis scientists. In this context, a researcher can access the CDI to look for publications, see if there are data objects linked to them, and then look for processing tools which can be used to reproduce the results. An experiment for an early use case demonstrated the feasibility of reproducing published results using data and metadata linked to existing publications (Nieva de la Hidalga et al. 2022b). In the experiment, papers citing processable data were used to retrieve, process, and reproduce published results with no need for contacting the authors. Fig. 1 presents a view of the experiment performed.The current practices of publishing catalysis research data can be seen as aligned to the FAIR data principles, for instance Fig. 1 above can be also seen as Fig. 2Reproducing results required several human-centered activities, partly due to the encoding of the metadata as text documents. The challenge is to accelerate and automate these processes. It is important to highlight the role of cataloguing interfaces, such as the CDI, containing DO crates with only metadata and links to the different data assests that constitute the composite digital objects. The users of these interfaces will in turn rely on transparent services which do not require them to manually track the location and formats of the data assets they want to retrieve and use.

The UK Catalysis Hub (UKCH) is designing and implementing an infrastructure to facilitate the management of research data produced by researchers, the Catalysis Data Infrastructure (CDI). The CDI is proposed to encompass the presentation of research outputs (publications and data) in a digital repository that brings together an array of heterogeneous data types. The CDI is designed to hold references to research outputs, maintains links between them and promotes publishing and sharing of data. The proposal is to create persistent relationships between the different types of data and publications complying with FAIR data principles (findability, accessibility, interoperability, and reuse). In this paper, we will discuss how the elicited requirements for data management are being incorporated in the design of the CDI. The prototype has been used in discussion with researchers and in presentations to the UKCH community, generating increased interest and providing ideas for further development. Additionally, the CDI prototype and its code are publicly available for further analysis.

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.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.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.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).FinancialBroaden 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.

Digitisation of natural history collections has evolved from creating databases for the recording of specimens’ catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows. However, digital imaging of large numbers of fragile specimens is an expensive and time-consuming process that should be performed only once. To achieve this, the digital images produced need to be useful for the largest set of applications possible and have a potentially unlimited shelf life. The constraints on digitisation speed need to be balanced against the applicability and longevity of the images, which, in turn, depend directly on the quality of those images. As a result, the quality criteria that specimen images need to fulfil influence the design, implementation and execution of digitisation workflows. Different standards and guidelines for producing quality research images from specimens have been proposed; however, their actual adaptation to suit the needs of different types of specimens requires further analysis. This paper presents the digitisation workflow implemented by Meise Botanic Garden (MBG). This workflow is relevant because of its modular design, its strong focus on image quality assessment, its flexibility that allows combining in-house and outsourced digitisation, processing, preservation and publishing facilities and its capacity to evolve for integrating alternative components from different sources. The design and operation of the digitisation workflow is provided to showcase how it was derived, with particular attention to the built-in audit trail within the workflow, which ensures the scalable production of high-quality specimen images and how this audit trail ensures that new modules do not affect either the speed of imaging or the quality of the images produced.