58622 research outputs found
Sort by
Dataset for "Co-creation of an airflow and COVID-19 transmission risk model for shelter design"
No full textThis dataset underpins a journal article titled "Co-creation of an Airflow and COVID-19 Transmission Risk Model for Shelter Design." The paper introduces the first collaboratively developed tool designed to guide shelter design by ensuring adequate natural ventilation, optimal indoor air quality, and minimized airborne transmission risks. This study explores the development and application of this tool to promote healthier shelters and enhance the shelter design process. Data was collected using the JISC online tool across two phases: the first before the tool's creation and the second after its implementation by participants. The dataset includes responses from online surveys conducted with participants from various global locations. It encompasses information on shelter designers' experience in shelter construction, their background knowledge of natural airflow and indoor air quality, and feedback on the usability of the co-created tool
Dataset for Threshold screw insertion torque for carbon fibre-reinforced polyetheretherketone and titanium (Ti-6Al-4V) locking plate constructs
No full textDescription of the data file This dataset contains the raw and processed mechanical testing data generated for the study investigating the influence of insertion torque on the performance of locking screw constructs in carbon-fibre reinforced polyetheretherketone (CFR-PEEK) and titanium alloy (Ti-6Al-4V) plates. The data include results from single-screw and two-screw constructs tested across predefined insertion torque levels (0.5–3.0 Nm). Mechanical outcomes were assessed using axial push-out testing and cantilever bending testing. For single-screw constructs, additional video-based measurements of screw rotation during insertion are provided to quantify the relationship between applied torque and angular displacement. Each entry records plate material, construct configuration (single- or two-screw), insertion torque, test modality (push-out or cantilever bending), and the corresponding mechanical performance metrics. Statistical groupings used in the analyses reported in the manuscript are identifiable within the dataset. The dataset is provided in tabulated format and is sufficient to reproduce all analyses and figures presented in the associated manuscript, as well as to enable secondary analysis of torque–performance relationships in locking plate constructs
ConcreteShellFEA: A surrogate modelling dataset for the buckling and stress behaviour of concrete thin-shells
No full textConcreteShellFEA is a dataset designed for the training of deep learning models to predict buckling loads and stress fields in concrete thin-shell structures. It contains 3 smaller datasets, which can be used for different use cases: 1. PerfectShell_LinearFEA: A dataset of 20,000 thin-shells (with various span, height, thickness, and Young's modulus), for which buckling factors and stress fields under design loads were determined using linear Finite Element analysis. The data is presented in three formats (tabular, image, graph) to enable different types of deep learning models (Multilayer Perceptrons, Convolutional Neural Networks, and Graph Neural Networks) to be trained. 2. ImperfectShell_LinearFEA: A dataset of 20,000 imperfect thin-shells (with various span, height, thickness, Young's modulus, and geometric imperfections), for which buckling factors and stress fields under design loads were determined using linear Finite Element analysis. The data is presented in two formats (tabular, image) to enable different types of deep learning models (Multilayer Perceptrons, Convolutional Neural Networks) to be trained. 3. PerfectShell_NonlinearFEA: A dataset of 25,000 thin-shells (with various span, height, thickness, and Young's modulus, and geometric imperfections), for which buckling factors under design loads were determined using Finite Element analysis. The buckling factors were determined using linear Finite Element analysis for 20,000 thin-shells, and using nonlinear Finite Element analysis for 5,000 thin-shells, to enable mixed-fidelity applications. The data is presented in a single format (tabular)
The Narration of Status in Far-Right Populist Foreign Policy:The United States of Trump 2.0
Full text linkThis paper focuses on how, during his second mandate, far-right leader Donald Trump tells a story of his nation as having been disrespected in the recent past by national elites and global ones, while the leader and their close circle have the mission to repair that status as part of United States foreign policy (i.e. respect for the status of the US). When narrating a better future, Trump travels to a remote national past to show the possibility of reinstating US stature in the international. While constructing that better future, Trump also starts to unfold a foreign policy story of success to cement the brighter future in a retrospective way given this future has purportedly been previously lived in a more remote national past. Relied on here is symbolic interactionist role theory, strategic narrative analysis and the notion of ‘heartland’ from populism scholarship; this paper also contributes to the study of narratives of roles and populism in the field of foreign policy analysis by engaging with the IR notion of ‘status’. Taking an interpretative analysis approach, this case study shows how far-right leaders like Trump can conceive and play the status or master role of their states in foreign policy via strategic narratives
From Intention to Action:Understanding Youth Electoral Participation Across Countries through Civic Education
Full text linkDeclining youth electoral participation threatens the long-term legitimacy of representative democracy. However, timely cross-national indicators of early disengagement remain scarce. This study draws on data from the IEA International Civic and Citizenship Education Study (ICCS) to examine (a) whether eighth-grade students’ stated voting intentions [are associated with] their cohort’s eventual electoral participation, and (b) which individual-level factors best explain those intentions after controlling for country-level characteristics. First, we align students’ voting intentions from IEA ICCS 2009 and 2016 with [corresponding] official age-specific turnout rates in each cohort’s first national election. The analysis reveals a moderate, statistically significant association, indicating that higher proportions of “likely future voters” in grade eight are associated with higher turnout once these cohorts reach voting age. Second, to identify the drivers of voting intentions, we pool microdata from all three IEA ICCS cycles (2009, 2016, 2022; N ≈ 316 000 students) and estimate fixed-effects models to account for time-invariant national confounders. Results show that students’ political interest emerges as the strongest predictor, followed by civic knowledge, self-efficacy, trust in the political system and its institutions, and parental political interest. Student background characteristics (e.g., gender or language at home) lose statistical significance once these factors are accounted for. The findings validate adolescent voting intentions as an early-warning indicator and highlight malleable psychological levers (i.e., interest, knowledge, efficacy) that civic-education policy can target
Open Strategy as Institutional Work
Full text linkPurposeThis article positions institutional work as a central construct in open strategy research. While open strategy is widely celebrated for fostering transparency and inclusion, its potential as a mechanism for field-level institutional change remains underexplored. The study examines how managed openness enabled UK universities to perform institutional work that reshaped research culture and institutional logics in response to evolving field expectations around equality, diversity, and transparency.Design/methodology/approachDrawing on 17 in-depth interviews with senior leaders and 25 documentary sources from the N8 group of UK research-intensive universities, the study applies the Gioia methodology and critical discourse analysis to trace how open strategy practices were mobilized to enact institutional work. The analysis identifies three interrelated processes – motivating, signalling, and enacting – through which openness was purposefully managed to facilitate cultural and institutional transformation.FindingsOpen strategy practices operated as discursive, symbolic, and material mechanisms of institutional work. By framing change imperatives, demonstrating commitment, and empowering participation, leaders used managed openness to align organizational practices with emergent field-level logics. These processes culminated in research culture action plans that institutionalized new norms of transparency and inclusion across the N8 universities.Originality/valueThe article advances open strategy theory by establishing institutional work as a powerful lens for understanding how openness extends beyond organizational boundaries to orchestrate field-level change. It also introduces institutional critique as a precursor to institutional work, highlighting the role of elite actors and discursive legitimation in shaping openness as a strategic and institutional practice.<br/
Dataset for "Fast structural analysis of concrete thin-shells using deep learning"
No full textThis dataset contains scripts and data supporting the following research article: Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. Fast structural analysis of concrete thin-shells using deep learning. Computers & Structures, 320, 108042. Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. Their shape is typically determined iteratively and evaluated through Finite Element Analysis (FEA). This research proposes the use of surrogate models as faster alternatives to FEA, thus enabling wider design space exploration. This dataset contains deep learning models – Multilayer Perceptrons, Convolutional Neural Networks, and Graph Neural Networks – that have been trained to predict the buckling factor and stress fields of concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related research article
Dataset for "Assessing the susceptibility to mould growth of mycelium-based composite insulation"
No full textThis dataset contains the raw experimental results generated in the characterisation of mycelium-based composite (MBC) insulation materials. It includes primary measurement data for laboratory-produced specimens (MBC A) and two commercially sourced materials (MBC B and MBC C), covering thermal conductivity measurements, liquid water absorption by immersion, surface wettability (contact angle) measurements, and mould susceptibility assessments. The mould dataset includes individual specimen ratings after 28 days of incubation across five temperature and relative humidity conditions, as well as ratings after subsequent liquid-water exposure. All files report unprocessed specimen-level results used to generate the figures and statistical summaries in the associated publication
Marine soundscapes of the Arctic and human impacts:going beyond the “shipping bands”
Full text linkIn the Arctic, amplified climate change enables increased human activity, adding to sounds in the ocean. Future guidelines need to know local baselines and how best to measure anthropogenic impacts. The EU Marine Strategy Framework Directive uses “shipping bands”, third-octave bands centred on 63 Hz and 125 Hz. Addressing the lack of measurements, acoustic models often use satellite recordings of ship tracks, We investigate sound levels in Cambridge Bay (Nunavut, Canada) between 2015 and 2024, comparing May (full ice cover, no shipping) and August (little to no ice, shipping activity). We show “shipping bands” should include frequencies up to several kHz and sounds include snowmobiles, aircraft and small vessels untracked by satellites. This will need addressing in future guidelines. This is particularly important because of the development of Arctic shipping routes, increasing resource exploration and tourism, amplified by current plans for the expansion of mining, drilling and other geostrategic pressures
An intelligent data-driven flow regime recognition method for horizontal air-water two-phase flow
Full text linkFlow regime recognition is very important in the two-phase flow measurement. However, considering that two-phase flow is much more complicated than single-phase flow, flow regime cannot be accurately identified by mechanism model. In this work, a novel intelligent data-driven method based on image encoding and transformer is proposed to recognize typical flow regimes encountered in the horizontal air-water flow. Dynamic experiment is carried out using a ring-shaped conductance sensor to collect voltage signals of bubble flow, bubble-slug flow, slug flow, slug-stratified flow and stratified flow. To highlight the characteristic differences between different flow regimes, the measured signals are encoded into two-dimensional images. To classify the encoded images of the five flow regimes, transformer models are then established. With the encoded images as the input of the model, flow regime identification is implemented by training of the model. The results demonstrate that the characteristics of different flow regimes can be better reflected in the encoded image with Gramian angular field. Meanwhile, the recognition accuracy of Swin Transformer is advantageous to that of Vision Transformer in the classification of the encoded images of the five flow regimes. Comparing with other identification methods, the method which combines Gramian angular field with Swin Transformer shows the best performance in the recognition of the flow regimes. The total accuracy reaches as high as 99.1 % This study offers an alternative for accurate flow regime recognition in two-phase flow measurement
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇