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In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.

This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.

As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.

This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

This study aims to analyse and evaluate the methodology followed by the Times Higher Education Impact Rankings (THE-IR), as well as the coverage obtained and the data offered by this ranking, to determine if its methodology reflects the degree of sustainability of universities, and whether their results are accurate enough to be used as a data source for research and strategic decision-making.

A summative content analysis of the THE-IR methodology was conducted, paying special attention to the macro-structure (university score) and micro-structure (sustainable development goals [SDG] score) levels of the research-related metrics. Then, the data published by THE-IR in the 2019, 2020 and 2021 edition was collected via web scraping. After that, all the data was statistically analysed to find out performance rates, SDGs’ success rates and geographic distributions. Finally, a pairwise comparison of the THE-IR against the Times Higher Education World University Rankings (THE-WUR) was conducted to calculate overlap measures.

Severe inconsistencies in the THE-IR methodology have been found, offering a distorted view of sustainability in higher education institutions, allowing different strategic actions to participate in the ranking (interested, strategic, committed and outperformer universities). The observed growing number of universities from developing countries and the absence of world-class universities reflect an opportunity for less-esteemed institutions, which might have a chance to gain reputation based on their efforts towards sustainability, but from a flawed ranking which should be avoided for decision-making.

University managers can be aware of the THE-IR validity when demanding informed decisions. University ranking researchers and practitioners can access a detailed analysis of the THE-IR to determine its properties as a ranking and use raw data from THE-IR in other studies or reports. Policy makers can use the main findings of this work to avoid misinterpretations when developing public policies related to the evaluation of the contribution of universities to the SDGs. Otherwise, these results can help the ranking publisher to improve some of the inconsistencies found in this study.

Given the global audience of the THE-IR, this work contributes to minimising the distorted vision that the THE-IR projects about sustainability in higher education institutions, and alerts governments, higher education bodies and policy makers to take precautions when making decisions based on this ranking.

To the best of the authors’ knowledge, this contribution is the first providing an analysis of the THE-IR’s methodology. The faults in the methodology, the coverage at the country-level and the overlap between THE-IR and THE-WUR have unveiled the existence of specific strategies in the participation of universities, of interest both for experts in university rankings and SDGs.

Resilience concepts in integrated urban transport refer to the performance of dealing with external shock and the ability to continue to provide transportation services of all modes. A robust transportation resilience is a goal in pursuing transportation sustainability. Under this specified context, while before the perturbations, robustness refers to the degree of the system’s capability of functioning according to its design specifications on integrated modes and routes, redundancy is the degree of duplication of traffic routes and alternative modes to maintain persistency of service in case of perturbations. While after the perturbations, resourcefulness refers to the capacity to identify operational problems in the system, prioritize interventions and mobilize necessary material/ human resources to recover all the routes and modes, rapidity is the speed of complete recovery of all modes and traffic routes in the urban area. These “4R” are the most critical components of urban integrated resilience.

The trends of transportation resilience's connotation, metrics and strategies are summarized from the literature. A framework is introduced on both qualitative characteristics and quantitative metrics of transportation resilience. Using both model-based and mode-free methodologies that measure resilience in attributes, topology and system performance provides a benchmark for evaluating the mechanism of resilience changes during the perturbation. Correspondingly, different pre-perturbation and post-perturbation strategies for enhancing resilience under multi-mode scenarios are reviewed and summarized.

Cyber-physic transportation system (CPS) is a more targeted solution to resilience issues in transportation. A well-designed CPS can be applied to improve transport resilience facing different perturbations. The CPS ensures the independence and integrity of every child element within each functional zone while reacting rapidly.

This paper provides a more comprehensive understanding of transportation resilience in terms of integrated urban transport. The fundamental characteristics and strategies for resilience are summarized and elaborated. As little research has shed light on the resilience concepts in integrated urban transport, the findings from this paper point out the development trend of a resilient transportation system for digital and data-driven management.

Public and private sector organisations are widely endorsing digital transformation processes, but little is known about the level of digitalisation of an organisation as a whole. The purpose of this paper is to develop a framework to assess an organisation’s level of digital transformation as a whole, taking the field of museums as an exemplary case of application.

The framework draws upon a scoping literature review of studies examining dimensions, metrics and methods for the assessment of the digital transformation of organisations. The framework has been validated by applying it to a sample of 400 Italian museums and further interviews with museum directors.

The authors propose an assessment framework composed of five main dimensions: people, technology, process, customer and strategy and investment. These dimensions are further deployed in sub-dimensions measured through a set of questions. The weighted average of results per dimension and sub-dimension supported the development of a composite index of organisational digital readiness.

The developed framework contributes to the current debate on the measurement of an organisation’s level of digital transformation as a whole, and it can offer practitioners a managerial tool to assess the organisation’s digital readiness.

The purpose of this paper is to describe the evolution of a common approach to impact assessment across the Global Libraries (GL) portfolio of grants. It presents an overview of two systems, the Performance Metrics (PMs) and the Common Impact Measurement System (CIMS). By providing a standard set of definitions and methods for use across countries, these systems enable grantees to collect data that can be compared and aggregated for the purpose of collective learning, improvement, accountability, and advocacy.

The PMs offer a standard methodology to collect library project performance management data, whereas the CIMS is a standard survey of public library users. The paper describes how the PM and CIMS data are being visualized and used, with examples of findings and lessons learned.

The paper cites examples of the type of PM and CIMS data available, with a focus on employment, gender, and case studies from Botswana and Indonesia. These highlights illustrate how libraries’ user demographics differ from other types of public internet access venues and how libraries can contribute to strong employment and growth.

The measurement systems rely on different partners collecting data for the same metrics across different countries; while each grantee adheres to a standard methodology, small procedural, and methodological differences are inevitable. Future research could focus on conducting similar studies elsewhere, outside the cohort of countries in the GL portfolio of grants.

The paper offers insights and lessons for library agencies or institutions interested in implementing a common measurement system. Recognizing that few library projects have the resources to track a comprehensive set of indicators, a case study is presented about how smaller initiatives can adapt these systems to their needs.

The indicators described in this paper enable public libraries to shift their focus from services provided to the outcomes they help individuals and communities realize, potentially increasing the potency of their programming and advocacy.

Common measurement systems are not new, but their application in the public library field is novel, as is the Data Atlas, a platform grantees use to compare results across metrics, track progress, and conduct advocacy.

This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.

In this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.

In this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.

The study is based on actual historical data and is purely data driven.

The study demonstrates the utility of the analytical methodology but carries not implementation recommendations.

This is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.

This editorial establishes the broader context for debating how the COVID-19 pandemic is affecting the global supply chains, logistics, and transportation networks in the world. The COVID-19 pandemic is an unknown-unknown risk whereby the probability of its occurrence and the possible consequences of a risk event cannot be foreseen. Furthermore, the characteristics of COVID-19 differentiate it from previous disasters in terms of the geographic scope of the impact, and the supply- and demand-side impacts, and are making relevant research challenging. In this special issue, the interwoven relationships between the COVID-19 pandemic and the challenges facing global supply chains, logistics, and transportation are discussed, along with a literature review. Selected papers on air transportation networks, emergency logistics, and digital trade are theoretically and empirically probed.

This study aims to examine the association between board gender diversity (BGD) and workplace diversity and the relative importance of various board and firm characteristics in predicting diversity.

With a novel machine learning (ML) approach, this study models the association between three workplace diversity variables and BGD using a social media data set of approximately 250,000 employee reviews. Using the tools of explainable artificial intelligence, the authors interpret the results of the ML model.

The results show that BGD has a strong positive association with the gender equality and inclusiveness dimensions of corporate diversity culture. However, BGD is found to have a weak negative association with age diversity in a company. Furthermore, the authors find that workplace diversity is an important predictor of firm value, indicating a possible channel on how BGD affects firm performance.

The effects of BGD on workplace diversity below management levels are mainly omitted in the current corporate governance literature. Furthermore, existing research has not considered different dimensions of this diversity and has mainly focused on its gender aspects. In this study, the authors address this research problem and examine how BGD affects different dimensions of diversity at the overall company level. This study reveals important associations and identifies key variables that should be included as a part of theoretical causal models in future research.