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The anticipated strong growth of the infrastructure industry over the coming decades will require more modern, digital approaches to create data-centric infrastructure that allows infrastructure to be monitored and managed throughout its lifecycle. Digital twins (DTs) are currently at an early stage in terms of their implementation on infrastructure projects across the United Kingdom (UK). The purpose of this paper is to evaluate the current uptake of DTs in delivering infrastructure sector projects and how DTs can help contribute towards strengthening the industry.

A systematic literature review approach has been conducted with the research questions derived from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) article screening tool. In addition to this, inclusion and exclusion criteria have been used to screen irrelevant information and help streamline research documents. Following a screening of relevant information, 36 pieces of literature were reviewed in order to identify the key drivers, barriers, enabling technologies and use cases.

DTs have the potential to transform asset design, production and maintenance. However, to further advance the digital innovation in the UK infrastructure sector, further study is necessary. An emerging technology must be considered on a broader scale than just its technical aspects, particularly when it comes to DTs. With enabling technologies such as the Internet of Things (IoT), sensors and artificial intelligence (AI), the uptake of DTs appears promising. While current literature indicates that DTs offer clear benefits in the infrastructure sector, the uptake is low and hindered by both technical and non-technical challenges.

This paper provides a rich insight into the understanding and awareness of the DTs in delivering infrastructure sector projects and how the infrastructure sector has evolved in order to develop new ways of designing, constructing, operating and monitoring infrastructure assets. This study contributes towards informing leaders in the sector of the current uptake of DTs within the UK's infrastructure sector as well as how DTs can contribute towards strengthening the industry.

As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.

A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.

The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.

In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

Research data publishing is today widely regarded as crucial for reproducibility, proper assessment of scientific results, and as a way for researchers to get proper credit for sharing their data. However, several challenges need to be solved to fully realize its potential, one of them being the development of a global standard for links between research data and literature. Current linking solutions are mostly based on bilateral, ad hoc agreements between publishers and data centers. These operate in silos so that content cannot be readily combined to deliver a network graph connecting research data and literature in a comprehensive and reliable way. The Research Data Alliance (RDA) Publishing Data Services Working Group (PDS-WG) aims to address this issue of fragmentation by bringing together different stakeholders to agree on a common infrastructure for sharing links between datasets and literature. The paper aims to discuss these issues.

This paper presents the synergic effort of the RDA PDS-WG and the OpenAIRE infrastructure toward enabling a common infrastructure for exchanging data-literature links by realizing and operating the Data-Literature Interlinking (DLI) Service. The DLI Service populates and provides access to a graph of data set-literature links (at the time of writing close to five million, and growing) collected from a variety of major data centers, publishers, and research organizations.

To achieve its objectives, the Service proposes an interoperable exchange data model and format, based on which it collects and publishes links, thereby offering the opportunity to validate such common approach on real-case scenarios, with real providers and consumers. Feedback of these actors will drive continuous refinement of the both data model and exchange format, supporting the further development of the Service to become an essential part of a universal, open, cross-platform, cross-discipline solution for collecting, and sharing data set-literature links.

This realization of the DLI Service is the first technical, cross-community, and collaborative effort in the direction of establishing a common infrastructure for facilitating the exchange of data set-literature links. As a result of its operation and underlying community effort, a new activity, name Scholix, has been initiated involving the technological level stakeholders such as DataCite and CrossRef.

This study develops a conceptual framework and a series of instruments for capturing researchers' data-sharing practices in the social sciences, by synergizing the theory of knowledge infrastructure and the theory of remote scientific collaboration.

This paper triangulates the results of three studies of data sharing across the social sciences, with 144 participants in total, and classifies the confusion, “frictions” and opportunities arising from such sharing into four overarching dimensions: data characteristics, technological infrastructure, research culture and individual drivers.

Based on the sample, the findings suggest that the majority of faculty and students in social science research do not share their data because many of them are unaware of the benefits and methods of doing so. Additional findings regarding social scientists' data-sharing behaviors include: (1) those who do share qualitative data in data repositories are more likely to share their research tools than their raw data; and (2) perceived technical support and extrinsic motivation are both strong predictors of qualitative data sharing (a previously underresearched subtype of social science data sharing).

The study confirms the previously hypothesized nature of “friction” in qualitative data sharing in the social sciences, arising chiefly from the time and labor intensiveness of ensuring data privacy.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2020-0079.

The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

Starting from the status quo, the paper outlines perspectives and challenges for the connection and interlinking of digitised and digital archival data. The following topics are addressed: Where are fields of action and what are the means of archives? Which functional and technical requirements are to be considered, and what is the role of portal infrastructures linking together various different institutions?

Considering needs of users and general framework conditions, the paper examines new approaches emerging in Germany. It outlines recent projects and considerations aiming to improve services and visibility of archives within the national data infrastructure in Germany.

Cross-connections are no new phenomenon, but change their appearance significantly in a digital context. In this respect, both smaller and bigger archives profit from participation in larger digital networks. Furthermore, archives need to keep in mind to reflect the quality of their digital (meta)data regularly and to offer or join systems that functionally and technically support cross-connection and interlinking of data.

The paper endeavours to show the importance of digital cross-connections and the role of portal infrastructures for visibility, online-distribution and use of digital archival metadata and data.

This chapter proposes an optimized innovative information technology as a means for achieving operational functionalities of real-time portable electronic health records, system interoperability, longitudinal health-risks research cohort and surveillance of adverse events infrastructure, and clinical, genome regions – disease and interventional prevention infrastructure. In application to the Dod-VA (Department of Defense and Veteran's Administration) health information systems, the proposed modernization can be carried out as an “add-on” expansion (estimated at $288 million in constant dollars) or as a “stand-alone” innovative information technology system (estimated at $489.7 million), and either solution will prototype an infrastructure for nation-wide health information systems interoperability, portable real-time electronic health records (EHRs), adverse events surveillance, and interventional prevention based on targeted single nucleotide polymorphisms (SNPs) discovery.

The paper explores the emergence of smart city governance with a particular focus on the cognitive value of the new technologies and the different accountabilities emerging in the digital infrastructures attempting to visualize and rationalize urban dynamics.

Drawing on ethnographic, netnographic and interview data from an empirical case study of the Smart and Wise City Turku spearhead project, the study builds on the assumption that smart cities emerge from the interaction between the characteristics of technologies, constellations of actors and contextual conditions.

The results report smart city activities as an organizational process and a reconfiguration that incorporates new technology with old infrastructure. Through the lens of the empirical examples, we are able to show how smart city actors, boundaries and infrastructures are mobilized, become valuable and are rendered visible. The smart cities infrastructure traces, values and governs actors, identities, objects, ideas and relations to animate new desires and feats of imagination.

In terms of implications to practice, the situated descriptions echo recent calls to leaders and managers to ask how much traceability is enough (Power, 2019) and limits of accountability (Messner, 2009).

The central theoretical concept of “thinking infrastructure” highlights how new accounting practices operate by disclosing (Kornberger et al., 2017) new worlds where the platforms and the users discover the nature of their responsibilities to the other. The contribution of this paper is that it examines what happens when smartness is understood as a thinking infrastructure. Different theorizations of infrastructure have implications for the study of smart cities. The lens helps us grasp possible tensions and consequences in terms of accountability that arise from new forms of participation in smart cities. It helps urban governance scholarship understand how smartness informs and shapes distributed and embodied cognition.

The purpose of this study is to explore whether immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles that hinder effective communication and collaboration. Immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles hindering effective communication and collaboration. VR technologies boost discussion participants' sense of presence and immersion; however, studying emerging VR technologies for their applicability to e-participation is challenging because of the lack of affordable and accessible infrastructures. In this paper, the authors present a novel framework for analyzing serious social VR engagements in the context of e-participation.

The authors propose a novel approach for artificial intelligence (AI)-supported, data-driven analysis of group engagements in immersive VR environments as an enabler for next-gen e-participation research. The authors propose a machine-learning-based VR interactions log analytics infrastructure to identify behavioral patterns. This paper includes features engineering to classify VR collaboration scenarios in four simulated e-participation engagements and a quantitative evaluation of the proposed approach performance.

The authors link theoretical dimensions of e-participation online interactions with specific user-behavioral patterns in VR engagements. The AI-powered immersive VR analytics infrastructure demonstrated good performance in automatically classifying behavioral scenarios in simulated e-participation engagements and the authors showed novel insights into the importance of specific features to perform this classification. The authors argue that our framework can be extended with more features and can cover additional patterns to enable future e-participation immersive VR research.

This research emphasizes technical means of supporting future e-participation research with a focus on immersive VR technologies as an enabler. This is the very first use-case for using this AI and data-driven infrastructure for real-time analytics in e-participation, and the authors plan to conduct more comprehensive studies using the same infrastructure.

The authors’ platform is ready to be used by researchers around the world. The authors have already received interest from researchers in the USA (Harvard University) and Israel and run collaborative online sessions.

The authors enable easy cloud access and simultaneous research session hosting 24/7 anywhere in the world at a very limited cost to e-participation researchers.

To the best of the authors’ knowledge, this is the very first attempt at building a dedicated AI-driven VR analytics infrastructure to study online e-participation engagements.

The purpose of this paper is twofold: first, to further develop Paul Edwards’ concept of “data friction” by examining the socio-material forces that are shaping data movements in the cases of research data and online communications data, second, to articulate a politics of data friction, identifying the interrelated infrastructural, socio-cultural and regulatory dynamics of data friction, and how these are contributing to the constitution of social relations.

The paper develops a hermeneutic review of the literature on socio-material factors influencing the movement of digital data between social actors in the cases of research data sharing and online communications data. Parallels between the two cases are identified and used to further develop understanding of the politics of “data friction” beyond the concept’s current usage within the Science Studies literature.

A number of overarching parallels are identified relating to the ways in which new data flows and the frictions that shape them bring social actors into new forms of relation with one another, the platformisation of infrastructures for data circulation, and state action to influence the dynamics of data movement. Moments and sites of “data friction” are identified as deeply political – resulting from the collective decisions of human actors who experience significantly different levels of empowerment with regard to shaping the overall outcome.

The paper further develops Paul Edwards’ concept of “data friction” beyond its current application in Science Studies. Analysis of the broader dynamics of data friction across different cases identifies a number of parallels that require further empirical examination and theorisation.

The observation that sites of data friction are deeply political has significant implications for all engaged in the practice and management of digital data production, circulation and use.

It is argued that the concept of “data friction” can help social actors identify, examine and act upon some of the complex socio-material dynamics shaping emergent data movements across a variety of domains, and inform deliberation at all levels – from everyday practice to international regulation – about how such frictions can be collectively shaped towards the creation of more equitable and just societies.

The paper makes an original contribution to the literature on friction in the dynamics of digital data movement, arguing that in many cases data friction may be something to enable and foster, rather than overcome. It also brings together literature from diverse disciplinary fields to examine these frictional dynamics within two cases that have not previously been examined in relation to one another.