A survey of early-career researchers in Australia
- Authors: Christian, Katherine , Johnstone, Carolyn , Larkins, Jo-ann , Wright, Wendy , Doran, Michael
- Date: 2021
- Type: Text , Journal article
- Relation: eLife Vol. 10, no. (2021), p. 1-19
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- Description: Early-career researchers (ECRs) make up a large portion of the academic workforce and their experiences often reflect the wider culture of the research system. Here we surveyed 658 ECRs working in Australia to better understand the needs and challenges faced by this community. Although most respondents indicated a ‘love of science’, many also expressed an intention to leave their research position. The responses highlight how job insecurity, workplace culture, mentorship and ‘questionable research practices’ are impacting the job satisfaction of ECRs and potentially compromising science in Australia. We also make recommendations for addressing some of these concerns. © Christian et al.
Capability building through workplace based learning in maintenance and reliability engineering (MRE) postgraduate programmes
- Authors: Chattopadhyay, Gopinath , Larkins, Jo-ann
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Annual Conference of the Australasian Association for Engineering Education (AAEE 2020) : Disrupting Business as Usual in Engineering Education
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Validation framework of bayesian networks in asset management decision-making
- Authors: Morey, Stephen , Chattopadhyay, Gopinath , Larkins, Jo-ann
- Date: 2022
- Type: Text , Conference paper
- Relation: International Congress and Workshop on Industrial AI, IAI 2021, Virtual online, 6-7 October 2021, published in Lecture Notes in Mechanical Engineering p. 360-369
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- Description: Capital-intensive industries are under increasing pressure from capital constraints to extend the life of long-life assets and to defer asset renewals. Assets in most of those industries have complex life-cycle management challenges in aspects of design, manufacture, maintenance and service contracts, the usage environment, and changes in support personnel over the asset life. A significant challenge is the availability and quality of relevant data for informed decision-making in assuring reliability, availability and safety. There is a need for better-informed maintenance decisions and cost-effective interventions in managing the risk and assuring performance of those assets. Bayesian networks have been considered in asset management applications in recent years for addressing these challenges, by modelling of reliability, maintenance decisions, life extension and prognostics, across a wide range of technological domains of complex assets. However, models of long-life assets are challenging to validate, particularly due to issues with data scarcity and quality. A literature review on Bayesian networks in asset management in this paper shows that there is a need for further work in this area. This paper discusses the issues and challenges of validation of Bayesian network models in asset management and draws on findings from literature research to propose a preliminary validation framework for Bayesian network models in life-cycle management applications of capital-intensive long-life assets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Matching the model to the available data to predict wheat, barley, or canola yield : a review of recently published models and data
- Authors: Clark, Robert , Dahlhaus, Peter , Robinson, Nathan , Larkins, Jo-ann , Morse-McNabb, Elizabeth
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Agricultural Systems Vol. 211, no. (2023), p.
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- Description: CONTEXT: Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. OBJECTIVE: This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. METHODS: A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. RESULTS AND CONCLUSIONS: The literature search produced 11, 908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. SIGNIFICANCE: The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. © 2023 The Authors
Why have eight researcher women in STEMM left academic research, and where did they go?
- Authors: Christian, Katherine , Johnstone, Carolyn , Larkins, Jo-ann , Wright, Wendy
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal for Academic Development Vol. 28, no. 1 (2023), p. 31-44
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- Description: The workplace for early-career researchers (ECRs) in STEMM (science, technology, engineering, mathematics and medicine) is highly competitive; ECRs urgently need to publish and attract funding to secure their next job. The literature suggests this environment is more difficult for women than for men. They start the postdoctoral period in equal numbers; however more women leave academia than men and women are under-represented at the senior levels. Interviews of eight women who had recently left academic research explored their reasons for the change, providing insight into the difficult decision-making processes and the largely beneficial outcomes of their choices of new careers. © 2021 Informa UK Limited, trading as Taylor & Francis Group.