Mechanical metamaterials for sports helmets: structural mechanics, design optimisation, and performance

Sports concussions are a public health concern. Improving helmet performance to reduce concussion risk is a key part of the research and development community response. Head impacts with compliant surfaces that cause long duration moderate or high linear and rotational accelerations are associated with a high rate of clinical diagnoses of concussion. As engineered structures with unusual combinations of properties, mechanical metamaterials are being applied to sports helmets, with the goal of improving impact performance and reducing brain injury risk. Replacing established helmet material (i.e., foam) selection with a metamaterials design approach (structuring material to obtain desired properties) allows development of near optimal properties. Objective functions based on up to date understanding of concussion could be applied to topology optimisation regimes, when designing mechanical metamaterials for helmets. Such regimes balance computational efficiency with predictive accuracy, both of which could be improved under high strains and strain rates to allow helmet modifications as knowledge of concussion develops. Researchers could also share mechanical metamaterial data, topologies and computational models in open, homogenised repositories, to improve the efficiency of their development

Developing an ontological framework for effective data quality assessment and knowledge modelling

Big data has become a major challenge in the 21st century, with research being carried out to classify, mine and extract knowledge from data obtained from disparate sources. Abundant data sources with non-standard structures complicate even more the arduous process of data integration. Currently, the major requirement is to understand the data available and detect data quality issues, with research being conducted to establish data quality assessment methods. Further, the focus is to improve data quality and maturity so that early onset of problems can be predicted and handled effectively. However, the literature highlights that comprehensive analysis, and research of data quality standards and assessment methods are still lacking. To handle these challenges, this paper presents a structured framework to standardise the process of assessing the quality of data and modelling the knowledge obtained from such an assessment by implementing an ontology. The main steps of the framework are: (i) identify user’s requirements; (ii) measure the quality of data considering data quality issues, dimensions and their metrics, and visualise this information into a data quality assessment (DQA) report; and (iii) capture the knowledge from the DQA report using an ontology that models the DQA insights in a standard reusable way. Following the proposed framework, an Excel-based tool to measure the quality of data and identify emerging issues is developed. An ontology, created in Protégé, provides a standard structure to model the data quality insights obtained from the assessment, while it is frequently updated to enrich captured knowledge, reducing time and costs for future projects. An industrial case study in the context of Through life Engineering Services, using operational data of high value engineering assets, is employed to validate the proposed ontological framework and tool; the results show a well-structured guide that can effectively assess data quality and model knowledge

Embedding open and reproducible science into teaching: A bank of lesson plans and resources

Recently, there has been a growing emphasis on embedding open and reproducible approaches into research. One essential step in accomplishing this larger goal is to embed such practices into undergraduate and postgraduate research training. However, this often requires substantial time and resources to implement. Also, while many pedagogical resources are regularly developed for this purpose, they are not often openly and actively shared with the wider community. The creation and public sharing of open educational resources is useful for educators who wish to embed open scholarship and reproducibility into their teaching and learning. In this article, we describe and openly share a bank of teaching resources and lesson plans on the broad topics of open scholarship, open science, replication, and reproducibility that can be integrated into taught courses, to support educators and instructors. These resources were created as part of the Society for the Improvement of Psychological Science (SIPS) hackathon at the 2021 Annual Conference, and we detail this collaborative process in the article. By sharing these open pedagogical resources, we aim to reduce the labour required to develop and implement open scholarship content to further the open scholarship and open educational materials movement

The replication crisis has led to positive structural, procedural, and community changes

The emergence of large-scale replication projects yielding successful rates substantially lower than expected caused the behavioural, cognitive, and social sciences to experience a so-called ‘replication crisis’. In this Perspective, we reframe this ‘crisis’ through the lens of a credibility revolution, focusing on positive structural, procedural and community-driven changes. Second, we outline a path to expand ongoing advances and improvements. The credibility revolution has been an impetus to several substantive changes which will have a positive, long-term impact on our research environment

The replication crisis has led to positive structural, procedural, and community changes

In response to large-scale replication projects yielding successful replication rates substantially lower than expected, the behavioural, cognitive and social sciences have found themselves amidst a ‘replication crisis’. In this narrative review, we reframe this ’crisis’ through the lens of a credibility revolution, focusing on positive structural, procedural and community-driven changes. Second, we outline a path to expand ongoing advances and improvements. The credibility revolution has been an impetus to several substantive changes which will have a positive, long-term impact upon our research environment

Teaching open and reproducible scholarship: A critical review of the evidence base for current pedagogical methods and their outcomes

In recent years, the scientific community has called for improvements in the credibility, robustness and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence student outcomes is needed. In this paper, we provide the first critical review of literature surrounding the integration of open and reproducible scholarship into teaching and learning and its associated outcomes in students. Our review highlighted how embedding open and reproducible scholarship appears to be associated with (i) students' scientific literacies (i.e. students’ understanding of open research, consumption of science and the development of transferable skills); (ii) student engagement (i.e. motivation and engagement with learning, collaboration and engagement in open research) and (iii) students' attitudes towards science (i.e. trust in science and confidence in research findings). However, our review also identified a need for more robust and rigorous methods within pedagogical research, including more interventional and experimental evaluations of teaching practice. We discuss implications for teaching and learning scholarship

Teaching open and reproducible scholarship: a critical review of the evidence base for current pedagogical methods and their outcomes

In recent years, the scientific community has called for improvements in the credibility, robustness, and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence student outcomes is needed. In this paper, we provide the first critical review of literature surrounding the integration of open and reproducible scholarship into teaching and learning and its associated outcomes in students. Our review highlighted how embedding open and reproducible scholarship appears to be associated with (1) students’ scientific literacies (i.e., students’ understanding of open research, consumption of science, and the development of transferable skills); (2) student engagement (i.e., motivation and engagement with learning, collaboration, and engagement in open research), and (3) students’ attitudes towards science (i.e., trust in science and confidence in research findings). However, our review also identified a need for more robust and rigorous methods within pedagogical research, including more interventional and experimental evaluations of teaching practice. We discuss implications for teaching and learning scholarship

Characterisation of thermoplastic polyurethane (TPU) for additive manufacturing

Despite increasing use, many 3d printing material properties are not openly available, which causes barriers to simulation and application. Four additively manufactured thermoplastic polyurethanes were characterised at low (0.006 1/s) to high (16 1/s) compressive and tensile strain rates, and during stress relaxation. Quasi-static Young’s moduli were determined to be 24 – 247 MPa, increasing by up to 183% at 16 1/s.Trotz der zunehmenden Verwendung sind viele Materialeigenschaften für den 3D-Druck nicht offen zugänglich, was die Simulation und Anwendung erschwert. Vier additiv hergestellte thermoplastische Polyurethane wurden bei niedrigen (0,006 1/s) bis hohen (16 1/s) Druck- und Zugdehnungsraten sowie während der Spannungsrelaxation charakterisiert. Die quasistatischen Elastizitätsmodule wurden mit 24 - 247 MPa bestimmt, wobei sie bei 16 1/s um bis zu 183 % anstiegen

Characterisation of thermoplastic polyurethane (TPU) for additive manufacturing

Despite increasing use, many 3d printing material properties are not openly available, which causes barriers to simulation and application. Four additively manufactured thermoplastic polyurethanes were characterised at low (0.006 1/s) to high (16 1/s) compressive and tensile strain rates, and during stress relaxation. Quasi-static Young’s moduli were determined to be 24 – 247 MPa, increasing by up to 183% at 16 1/s.Trotz der zunehmenden Verwendung sind viele Materialeigenschaften für den 3D-Druck nicht offen zugänglich, was die Simulation und Anwendung erschwert. Vier additiv hergestellte thermoplastische Polyurethane wurden bei niedrigen (0,006 1/s) bis hohen (16 1/s) Druck- und Zugdehnungsraten sowie während der Spannungsrelaxation charakterisiert. Die quasistatischen Elastizitätsmodule wurden mit 24 - 247 MPa bestimmt, wobei sie bei 16 1/s um bis zu 183 % anstiegen