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Purpose: The purpose of this paper is to discuss how, despite increasing data availability from a wide range of sources unlocks unprecedented opportunities for disaster risk reduction, data interoperability remains a challenge due to a number of barriers. As a first step to enhancing data interoperability for disaster risk reduction is to identify major barriers, this paper presents a case study on data interoperability in disaster risk reduction in Europe, linking current barriers to the regional initiative of the European Science and Technology Advisory Group. Design/methodology/approach: In support of Priority 2 ("Strengthening disaster risk governance to manage disaster risk") of the Sendai Framework and SDG17 ("Partnerships for the goals"), this paper presents a case study on barriers to data interoperability in Europe based on a series of reviews, surveys and interviews with National Sendai Focal Points and stakeholders in science and research, governmental agencies, non-governmental organizations and industry. Findings: For a number of European countries, there remains a clear imbalance between long-term disaster risk reduction and short-term preparation and the dominant role of emergency relief, response and recovery, pointing to the potential of investments in ex ante measures with better inclusion and exploitation of data. Originality/value: Modern society is facing a digital revolution. As highlighted by the International Council of Science and the Committee on Data for Science and Technology, digital technology offers profound opportunities for science to discover unsuspected patterns and relationships in nature and society, on scales from the molecular to the cosmic, from local health systems to global sustainability. It has created the potential for disciplines of science to synergize into a holistic understanding of the complex challenges currently confronting humanity; the Sustainable Development Goals are a direct reflectance of this. Interdisciplinary is obtained with integration of data across relevant disciplines. However, a barrier to realization and exploitation of this potential arises from the incompatible data standards and nomenclatures used in different disciplines. Although the problem has been addressed by several initiatives, the following challenge still remains: to make online data integration a routine. [ABSTRACT FROM AUTHOR]

Background: Medical data from family doctors are of great importance to health care researchers but seem to be locked in German practices and, thus, are underused in research. The RADAR project (Routine Anonymized Data for Advanced Health Services Research) aims at designing, implementing and piloting a generic research architecture, technical software solutions as well as procedures and workflows to unlock data from family doctor's practices. A long-term medical data repository for research taking legal requirements into account is established. Thereby, RADAR helps closing the gap between the European countries and to contribute data from primary care in Germany.Methods: The RADAR project comprises three phases: (1) analysis phase, (2) design phase, and (3) pilot. First, interdisciplinary workshops were held to list prerequisites and requirements. Second, an architecture diagram with building blocks and functions, and an ordered list of process steps (workflow) for data capture and storage were designed. Third, technical components and workflows were piloted. The pilot was extended by a data integration workflow using patient-reported outcomes (paper-based questionnaires).Results: The analysis phase resulted in listing 17 essential prerequisites and guiding requirements for data management compliant with the General Data Protection Regulation (GDPR). Based on this list existing approaches to fulfil the RADAR tasks were evaluated-for example, re-using BDT interface for data exchange and Trusted Third Party-approach for consent management and record linkage. Consented data sets of 100 patients were successfully exported, separated into person-identifying and medical data, pseudonymised and saved. Record linkage and data integration workflows for patient-reported outcomes in the RADAR research database were successfully piloted for 63 responders.Conclusion: The RADAR project successfully developed a generic architecture together with a technical framework of tools, interfaces, and workflows for a complete infrastructure for practicable and secure processing of patient data from family doctors. All technical components and workflows can be reused for further research projects. Additionally, a Trusted Third Party-approach can be used as core element to implement data privacy protection in such heterogeneous family doctor's settings. Optimisations identified comprise a fully-electronic consent recording using tablet computers, which is part of the project's extension phase. [ABSTRACT FROM AUTHOR]

Hydrography and hydrogeology data contain spatial references and as such are part of spatial data infrastructure. On the European level, these data are part of European spatial data infrastructure, well known as INSPIRE. The objective of INSPIRE is to make public spatial data available and accessible for a broad range of users in a simple, interoperable, and efficient way. Spatial data play an important role in facilitating data integration, enabling data-driven decision making on where and why things happen and easing communication through intuitive visualizations. Within this paper, we take the opportunity to reflect on the development and implementation of INSPIRE, with the main focus on the availability and accessibility of hydrography and hydrogeology data. By availability, we aim for the existence of metadata describing spatial data, while by accessibility, we aim for the existence of related services for spatial data viewing and downloading. The overall findings, based on the analysis in the INSPIRE Geoportal, shows that the data are still not fully available, although the deadline for INSPIRE implementation has already passed. Data accessibility is also an issue. Data that are even available in the infrastructure are sometimes not accessible. However, technological developments and recent policy initiatives could be drivers for future improvement. [ABSTRACT FROM AUTHOR]

Summary: Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems. [ABSTRACT FROM AUTHOR]

• The Shared Environmental Information System (SEIS) aim to expand the access to environmental data. • SEIS has had a significant and positive impact on the production and harmonisation of environmental indicators. • Data harmonization problems (e.g., data collection, definitions, and legislation) remain throughout the pan-European region. • Non-evidence-based policymaking highlight a disconnect between data production and data use. • Need to improve our understanding of the political and behavioural determinants of data use. Access to high-quality, timely and comparable data is a prerequisite for any effective decision-making process and having such data available for the environment is absolutely fundamental to efficient and evidence-based policymaking. This article reviews the establishment of a Shared Environmental Information System (SEIS) in Europe and Central Asia to improve our understanding of how environmental data value chains are being employed to produce, share and use reliable data on the environment and whether this data is used in policymaking. Three sources of data are utilised to analyse whether relevant environmental data and information are being drawn upon by policymakers, namely, the 2019 SEIS mid-term review, SEIS Factsheets and SEIS Gap Analysis Reports. The results reveal that the pan-European region still faces significant data harmonisation problems, owing in part to differences in types and methods of data collection, data definitions and legislation. Whilst problems in some individual country's participation have persisted since the launch of the SEIS initiative in the pan-European region, the development and successful piloting of the SEIS self-assessment framework is considered as evidence of positive progress. However, it remains difficult to adequately assess to what extent the data flows covered by this study impact on policymaking, nevertheless, the analysis serves to highlight the inter-linkages between environmental data flows, policymakers and environmental governance. In practical terms, the paper demonstrate a disconnect between data production and data use in policymaking and emphasises the need to both improve our understanding of the political determinants of data use and to further investigate how the uptake of environmental data and information can be facilitated in policymaking. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes transparency and reflexivity as two principles to be incorporated in initiatives wherein data from multiple national contexts are integrated. The necessity of these principles is derived from an ongoing study tasked with identifying and describing national bibliographic databases for research output within the social sciences and humanities (SSH) in Europe. The study is carried out within the context of the COST Action "European Network for Research Evaluation in Social Sciences and Humanities" (ENRESSH). Within ENRESSH, it is emphasised that national bibliographic databases can be instrumental in enhancing the visibility of research within SSH. Hence, one of the aims of ENRESSH is to identify and describe currently existing databases and eventually design a roadmap for a European database that would include data on research output within SSH from different European countries. The study shows that there are considerable challenges in merely acquiring a basic description of the content of databases embedded in different national contexts. To make sense of the content it is necessary to acknowledge the role of context in information systems. Emphasising context, as will be shown, it is possible to elucidate the nature of the encountered challenges as well as to highlight aspects to be incorporated in designs of research information systems. [ABSTRACT FROM AUTHOR]

In Europe, 20 to 40% of water is being wasted due to poor infrastructure, consumer negligence and lack of proper resource management. Effective and efficient management of water resources requires a holistic approach considering all the stages of water usage, which includes the application of ICT technologies that are capable of transmitting and analysing water data to deliver actionable insights to end-users. A primary requirement for ICT-based water innovations is the ability to collect and process water data from disparate locations. In this paper, we present a cloud-based cross-site data collection system, which is being developed as part of an EU funded research project named Waternomics. [ABSTRACT FROM AUTHOR]

Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)-based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems.Key Points• Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration.• The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems.• While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns. [ABSTRACT FROM AUTHOR]

The European Ocean Biogeographic Information System-EurOBIS-is an integrated data system developed by the Flanders Marine Institute (VLIZ) for the EU Network of Excellence 'Marine Biodiversity and Ecosystem Functioning' (MarBEF) in 2004. Its principle aims are to centralise the largely scattered biogeographical data on marine species collected by European institutions and to make these quality-controlled data freely available and easily accessible. It is in essence a distributed system in which individual datasets go through a series of quality control procedures before they are integrated into one large consolidated database. EurOBIS is freely available online at , where marine biogeographical data-with a focus on taxonomy, temporal and spatial distribution-can be consulted and downloaded for analyses. Over the last 6 years, EurOBIS has collected 228 datasets contributed by more than 75 institutes, representing over 13.6 million distribution records of which almost 12.5 million records are species level identifications. It is now the largest online searchable public source of European marine biological data, holding biogeographical information on 26,801 species and 9,221 genera. EurOBIS acts as the European node of OBIS, the Ocean Biogeographic Information System of the Census of Marine Life (CoML). EurOBIS shares its data with OBIS, which in its turn shares its content with the Global Biodiversity Information Facility (GBIF). This article describes the status of the European Ocean Biogeographic Information System, identifies data gaps, possible applications, uses and limitations. It also formulates a strategy for the growth and improvement of the system and wants to appeal for more contributions. [ABSTRACT FROM AUTHOR]

Dynamic vegetation models (DVMs) are important tools to understand and predict the functioning and dynamics of terrestrial ecosystems under changing environmental conditions. In these models, uncertainty in the description of demographic processes, in particular tree mortality, is a persistent problem. Current mortality formulations lack realism and are insufficiently constrained by empirical evidence. It has been suggested that empirically estimated mortality submodels would enhance DVM performance, but due to the many processes and interactions within a DVM, the claim has rarely been tested. Here, we compare the performance of three alternative growth‐dependent tree mortality submodels in the DVM ForClim: (1) a mortality function with theoretical foundation (ForClim v3.3); (2) a mortality function with parameters directly estimated based on forest inventory data; and (3) the same function, but with parameters estimated using an inverse approach through Bayesian calibration (BC). Time series of inventory data from 30 ecologically distinct Swiss natural forest reserves collected over 35+ yr, including the main tree species of Central Europe, were used for the calibration and subsequent validation of the mortality functions and the DVM. The recalibration resulted in mortality parameters that differed from the direct empirical estimates, particularly for the relationship between tree size and mortality. The calibrated parameters outperformed the direct estimates, and to a lesser extent the original mortality function, for predicting decadal‐scale forest dynamics at both calibration and validation sites. The same pattern was observed regarding the plausibility of their long‐term projections under contrasting environmental conditions. Our results demonstrate that inverse calibration may be useful even when direct empirical estimates of DVM parameters are available, as structural model deficiencies or data problems can result in discrepancies between direct and inverse estimates. Thus, we interpret the good performance of the inversely calibrated model for long‐term projections (which were not a calibration target) as evidence that the calibration did not compensate for model errors. Rather, we surmise that the discrepancy was mainly caused by a lack of representativeness of the mortality data. Our results underline the potential for learning more about elusive processes, such as tree mortality or recruitment, through data integration in DVMs. [ABSTRACT FROM AUTHOR]