ORCID Profile
0000-0002-6071-6022
Current Organisations
Curtin University
,
University of Western Australia
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Industrial and Organisational Psychology | Cognitive Science | Learning motivation and emotion | Decision making | Industrial and organisational psychology (incl. human factors) | Decision Making
ANZSRC Socio-Economic Objective (SEO)
Defence not elsewhere classified | Expanding Knowledge in Psychology and Cognitive Sciences | Air Safety |
Related Links
Publications
Evidence accumulation modelling in the wild
Publisher: Elsevier BV
Date: 02-2023
Stopping timed actions.
Publisher: Center for Open Science
Date: 09-03-2021
Abstract: The ability to inhibit ongoing responses that suddenly become inappropriate is essential for safe and effective interaction with an ever-changing and unpredictable world. Response inhibition is quantified by the unobservablestop-signal reaction time (SSRT), the completion time of an inhibitory process triggered by a signal to stop responding. SSRTs can be inferred based on a model in which inhibitory and response processes race with each other to control behavior. Inhibition is usually studied in the context of choice responses, but there has been increasing interest in what is often a key component of skilled behavior, stopping a response that is timed to coincide with an anticipated event. We show that SSRT measurement via the standard race model fails for anticipated responses because the stop signal changes the perception of the passage of time, due to the long-known “filled-interval illusion”. We propose a computational model of anticipated response inhibitionthat takes account of this distortion of time perception and show that this model produces valid estimates of not only SSRT, but also another key process that determines inhibitory ability, lapses in attention. Our new modeland accompanying Bayesian estimation procedures provide a solid basis for the burgeoning study of timed-action control.
The next generation of fatigue prediction models: evaluating current trends in biomathematical modelling
Publisher: Informa UK Limited
Date: 12-11-2022
Evidence accumulation modeling of event-based prospective memory
Publisher: Routledge
Date: 21-05-2019
Prospective Memory Performance in Simulated Air Traffic Control
Publisher: SAGE Publications
Date: 20-09-2020
Abstract: To examine the effects of interruptions and retention interval on prospective memory for deferred tasks in simulated air traffic control. In many safety-critical environments, operators need to remember to perform a deferred task, which requires prospective memory. Laboratory experiments suggest that extended prospective memory retention intervals, and interruptions in those retention intervals, could impair prospective memory performance. Participants managed a simulated air traffic control sector. Participants were sometimes instructed to perform a deferred handoff task, requiring them to deviate from a routine procedure. We manipulated whether an interruption occurred during the prospective memory retention interval or not, the length of the retention interval (37–117 s), and the temporal proximity of the interruption to deferred task encoding and execution. We also measured performance on ongoing tasks. Increasing retention intervals (37–117 s) decreased the probability of remembering to perform the deferred task. Costs to ongoing conflict detection accuracy and routine handoff speed were observed when a prospective memory intention had to be maintained. Interruptions did not affect in iduals’ speed or accuracy on the deferred task. Longer retention intervals increase risk of prospective memory error and of ongoing task performance being impaired by cognitive load however, prospective memory can be robust to effects of interruptions when the task environment provides cuing and offloading. To support operators in performing complex and dynamic tasks, prospective memory demands should be reduced, and the retention interval of deferred tasks should be kept as short as possible.
OSARI, an Open-Source Anticipated Response Inhibition task
Publisher: Center for Open Science
Date: 26-04-2021
Abstract: The stop-signal paradigm has become ubiquitous in investigations of inhibitory control. Tasks inspired by the paradigm, referred to as stop-signal tasks, require participants to make responses on go trials and to inhibit those responses when presented with a stop-signal on stop trials. Currently, the most popular version of the stop-signal task is the ‘choice-reaction’ variant, where participants make choice responses, but must inhibit those responses when presented with a stop-signal. An alternative to the choice-reaction variant of the stop-signal task is the ‘anticipated response inhibition’ task. In anticipated-response inhibition tasks, participants are required to make a planned response that coincides with a predictably timed event (such as lifting a finger from a computer key to stop a filling bar at a predefined target). Anticipated-response inhibition tasks have some advantages over the more traditional choice-reaction stop-signal tasks and are becoming increasingly popular. However, currently, there are no openly available versions of the anticipated response inhibition task, limiting potential uptake. Here, we present an open-source, free, and ready-to-use version of the anticipated-response inhibition task, which we refer to as the OSARI (the Open-Source Anticipated Response Inhibition) task.
Investigating the effects of ongoing-task bias on prospective memory
Publisher: SAGE Publications
Date: 09-04-2020
Abstract: Event-based prospective memory (PM) refers to the cognitive processes required to perform a planned action upon encountering a future event. Event-based PM studies engage participants in an ongoing task (e.g., lexical decision-making) with an instruction to make an alternative PM response to certain items (e.g., items containing “tor”). The Prospective Memory Decision Control (PMDC) model, which provides a quantitative process account of ongoing-task and PM decisions, proposes that PM and ongoing-task processes compete in a race to threshold. We use PMDC to test whether, as proposed by the Delay Theory of PM costs, PM can be improved by biasing decision-making against a specific ongoing-task choice, so that the PM process is more likely to win the race. We manipulated bias in a lexical decision task with an accompanying PM intention. In one condition, a bias was induced against deciding items were words, and in another, a bias was induced against deciding items were non-words. The bias manipulation had little effect on PM accuracy but did affect the types of ongoing-task responses made on missed PM trials. PMDC fit the observed data well and verified that the bias manipulation had the intended effect on ongoing-task processes. Furthermore, although simulations from PMDC could produce an improvement in PM accuracy due to ongoing-task bias, this required implausible parameter values. These results illustrate the importance of understanding event-based PM in terms of a comprehensive model of the processes that interact to determine all aspects of task performance.
Cognitive control and capacity for prospective memory in complex dynamic environments.
Publisher: American Psychological Association (APA)
Date: 12-2019
DOI: 10.1037/XGE0000599
Abstract: Performing deferred actions in the future relies upon Prospective Memory (PM). Often, PM demands arise in complex dynamic tasks. Not only can PM be challenging in such environments, the processes required for PM may affect the performance of other tasks. To adapt to PM demands in such environments, humans may use a range of strategies, including flexible allocation of cognitive resources and cognitive control mechanisms. We sought to understand such mechanisms by using the Prospective Memory Decision Control (Strickland, Loft, Remington, & Heathcote, 2018) model to provide a comprehensive, quantitative account of dual task performance in a complex dynamic environment (a simulated air traffic control conflict detection task). We found that PM demands encouraged proactive control over ongoing task decisions, but that this control was reduced at high time pressure to facilitate fast responding. We found reactive inhibitory control over ongoing task processes when PM targets were encountered, and that time pressure and PM demand both affect the attentional system, increasing the amount of cognitive resources available. However, as demands exceeded the capacity limit of the cognitive system, resources were reallocated (shared) between the ongoing and PM tasks. As the ongoing task used more resources to compensate for additional time pressure demands, it drained resources that would have otherwise been available for PM task processing. This study provides the first detailed quantitative understanding of how attentional resources and cognitive control mechanisms support PM and ongoing task performance in complex dynamic environments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Detecting a Single Automation Failure: The Impact of Expected (But Not Experienced) Automation Reliability
Publisher: SAGE Publications
Date: 10-08-2021
DOI: 10.1177/00187208211037188
Abstract: Examine the impact of expected automation reliability on trust, workload, task disengagement, nonautomated task performance, and the detection of a single automation failure in simulated air traffic control. Prior research has focused on the impact of experienced automation reliability. However, many operational settings feature automation that is reliable to the extent that operators will seldom experience automation failures. Despite this, operators must remain aware of when automation is at greater risk of failing. Participants performed the task with or without conflict detection/resolution automation. Automation failed to detect/resolve one conflict (i.e., an automation miss). Expected reliability was manipulated via instructions such that the expected level of reliability was (a) constant or variable, and (b) the single automation failure occurred when expected reliability was high or low. Trust in automation increased with time on task prior to the automation failure. Trust was higher when expecting high relative to low reliability. Automation failure detection was improved when the failure occurred under low compared with high expected reliability. Subjective workload decreased with automation, but there was no improvement to nonautomated task performance. Automation increased perceived task disengagement. Both automation reliability expectations and task experience played a role in determining trust. Automation failure detection was improved when the failure occurred at a time it was expected to be more likely. Participants did not effectively allocate any spared capacity to nonautomated tasks. The outcomes are applicable because operators in field settings likely form contextual expectations regarding the reliability of automation.
Accumulating evidence about what prospective memory costs actually reveal.
Publisher: American Psychological Association (APA)
Date: 10-2017
DOI: 10.1037/XLM0000400
Abstract: Event-based prospective memory (PM) tasks require participants to substitute an atypical PM response for an ongoing task response when presented with PM targets. Responses to ongoing tasks are often slower with the addition of PM demands ("PM costs"). Prominent PM theories attribute costs to capacity-sharing between the ongoing and PM tasks, which reduces the rate of processing of the ongoing task. We modeled PM costs using the Linear Ballistic Accumulator and the Diffusion Decision Model in a lexical-decision task with nonfocal PM targets defined by semantic categories. Previous decision modeling, which attributed costs to changes in caution rather than rate of processing (Heathcote et al., 2015 Horn & Bayen, 2015), could be criticized on the grounds that the PM tasks included did not sufficiently promote capacity-sharing. Our semantic PM task was potentially more dependent on lexical decision resources than previous tasks (Marsh, Hicks, & Cook, 2005), yet costs were again driven by changes in threshold and not by changes in processing speed (drift rate). Costs resulting from a single target focal PM task were also driven by threshold changes. The increased thresholds underlying nonfocal and focal costs were larger for word trials than nonword trials. As PM targets were always words, this suggests that threshold increases are used to extend the time available for retrieval on PM trials. Under nonfocal conditions, but not focal conditions, the nonword threshold also increased. Thus, it seems that only nonfocal instructions cause a global threshold increase because of greater perceived task complexity. (PsycINFO Database Record
Racing to remember: A theory of decision control in event-based prospective memory.
Publisher: American Psychological Association (APA)
Date: 11-2018
DOI: 10.1037/REV0000113
Abstract: Event-based prospective memory (PM) requires remembering to perform intended deferred actions when particular stimuli or events are encountered in the future. We propose a detailed process theory within Braver's (2012) proactive and reactive framework of the way control is maintained over the competing demands of prospective memory decisions and decisions associated with ongoing task activities. The theory is instantiated in a quantitative "Prospective Memory Decision Control" (PMDC) architecture, which uses linear ballistic evidence accumulation (Brown & Heathcote, 2008) to model both PM and ongoing decision processes. Prospective control is exerted via decision thresholds, as in Heathcote, Loft, and Remington's (2015) "Delay Theory" of the impact of PM demands on ongoing-task decisions. However, PMDC goes beyond Delay Theory by simultaneously accounting for both PM task decisions and ongoing task decisions. We use Bayesian estimation to apply PMDC to experiments manipulating PM target focality (i.e., the extent to which the ongoing task directs attention to the features of PM targets processed at encoding) and the relative importance of the PM task. As well as confirming Delay Theory's proactive control of ongoing task thresholds, the comprehensive account provided by PMDC allowed us to detect both proactive control of the PM accumulator threshold and reactive control of the relative rates of the PM and ongoing-task evidence accumulation processes. We discuss potential extensions of PMDC to account for other factors that may be prevalent in real-world PM, such as failures of memory retrieval. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Strategic attention and decision control support prospective memory in a complex dual-task environment
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1016/J.COGNITION.2019.05.011
Abstract: Human performance in complex multiple-task environments depends critically on the interplay between cognitive control and cognitive capacity. In this paper we propose a tractable computational model of how cognitive control and capacity influence the speed and accuracy of decisions made in the event-based prospective memory (PM) paradigm, and in doing so test a new quantitative formulation that measures two distinct components of cognitive capacity (gain and focus) that apply generally to choices among two or more options. Consistent with prior work, in iduals used proactive control (increased ongoing task thresholds under PM load) and reactive control (inhibited ongoing task accumulation rates to PM items) to support PM performance. In iduals used cognitive gain to increase the amount of resources allocated to the ongoing task under time pressure and PM load. However, when demands exceeded the capacity limit, resources were reallocated (shared) between ongoing task and PM processes. Extending previous work, in iduals used cognitive focus to control the quality of processing for the ongoing and PM tasks based on the particular demand and payoff structure of the environment (e.g., higher focus for higher priority tasks lower focus under high time pressure and with PM load). Our model provides the first detailed quantitative understanding of cognitive gain and focus as they apply to evidence accumulation models, which - along with cognitive control mechanisms - support decision-making in complex multiple-task environments.
Prospective memory in the red zone: Cognitive control and capacity sharing in a complex, multi-stimulus task.
Publisher: American Psychological Association (APA)
Date: 12-2019
DOI: 10.1037/XAP0000224
Abstract: Remembering to perform a planned action upon encountering a future event requires event-based Prospective Memory (PM). PM is required in many human factors settings in which operators must process a great deal of complex, uncertain information from an interface. We study event-based PM in such an environment. Our task, which previous research has found is very demanding (Palada, Neal, Tay, & Heathcote, 2018), requires monitoring ships as they cross the ocean on a display. We applied the Prospective Memory Decision Control Model (Strickland, Loft, Remington, & Heathcote, 2018) to understand the cognitive mechanisms that underlie PM performance in such a demanding environment. We found evidence of capacity sharing between monitoring for PM items and performing the ongoing surveillance task, whereas studies of PM in simpler paradigms have not (e.g., Strickland et al., 2018). We also found that participants applied proactive and reactive control (Braver, 2012) to adapt to the demanding task environment. Our findings illustrate the value of human factors simulations to study capacity sharing between competing task processes. They also illustrate the value of cognitive models to illuminate the processes underlying adaptive behavior in complex environments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019)
Publisher: Springer Science and Business Media LLC
Date: 12-08-2019
Target learning in event-based prospective memory.
Publisher: American Psychological Association (APA)
Date: 08-2022
DOI: 10.1037/XLM0000900
Abstract: Event-based prospective memory (PM) tasks require in iduals to remember to perform a previously planned action when they encounter a specific event. Often, the natural environments in which PM tasks occur are embedded are constantly changing, requiring humans to adapt by learning. We examine one such adaptation by integrating PM target learning with the prospective memory decision control (PMDC) cognitive model. We apply this augmented model to an experiment that manipulated exposure to PM targets, comparing a single-target PM condition where the target was well learned from the outset, to a multiple-target PM condition with less initial PM target exposure, allowing us to examine the effect of continued target learning opportunities. Single-target PM accuracy was near ceiling whereas multiple-target PM accuracy was initially poorer but improved throughout the course of the experiment. PM response times were longer for the multiple- compared with single-target PM task but this difference also decreased over time. The model indicated that PM trial evidence accumulation rates, and the inhibition of competing responses, were initially higher for single compared to multiple PM targets, but that this difference decreased over time due to the learning of multiple-targets over the target repetitions. These outcomes provide insight into how the processes underlying event-based PM can dynamically evolve over time, and a modeling framework to further investigate the effect of learning on event-based PM decision processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Automated Decision Aids
Publisher: American Psychological Association (APA)
Date: 06-03-2023
DOI: 10.1037/XAP0000463
FIPS: An R Package for Biomathematical Modelling of Human Fatigue Related Impairment
Publisher: Center for Open Science
Date: 07-05-2020
Abstract: In many workplace contexts, accurate predictions of a human’s fatigue state can drastically improve system safety. Biomathematical models of fatigue (BMMs) are a family of dynamic phenomenological models that predict the neurobehavioural outcomes of fatigue (e.g., sleepiness, performance impairment) based on sleep/wake history (Dawson, Darwent, & Roach, 2017). However, to-date there are no open source implementations of BMMs, and this presents a significant barrier to their broadscale adoption by researchers and industry practitioners. FIPS is an open source R package (R Core Team, 2020) to facilitate BMM research and simulation. FIPS has implementations of several published bio-mathematical models and includes functions for easily manipulating sleep history data into the required data structures. FIPS also includes default plot and summary methods to aid model interpretation. Model objects follow tidy data conventions (Wickham, 2014), enabling FIPS to be integrated into existing research workflows of R users.
Inhibitory Cognitive Control Allows Automated Advice to Improve Accuracy While Minimizing Misuse
Publisher: SAGE Publications
Date: 27-09-2021
DOI: 10.1177/09567976211012676
Abstract: Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to act on incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate requirements for minimum separation. The model closely fitted the performance of 24 participants, who each made 2,400 conflict-detection decisions (conflict vs. nonconflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict-detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response that was incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first s ling information from the task environment.
The Next Generation of Fatigue Prediction Models: Evaluating Current Trends in Biomathematical Modelling for Safety Optimization
Publisher: Center for Open Science
Date: 24-01-2022
Abstract: Biomathematical models (BMMs) are parametric models that quantitatively predict fatigue and are routinely implemented in fatigue risk management systems in increasingly erse workplaces. There have been consistent calls for an improved "next generation” of BMMs that provide more accurate and targeted predictions of human fatigue. This review examines the core characteristics of next-generation advancements in BMMs, including tailoring with field data, in idual-level parameter tuning and real-time fatigue prediction, extensions to account for additional factors that influence fatigue, and emerging nonparametric methodologies that may augment or provide alternatives to BMMs. Examination of past literature and quantitative ex les suggests there are notable challenges to advancing BMMs beyond their current applications. Adoption of multi-model frameworks, including quantitative joint modelling and machine learning, was identified as crucial to next-generation models. We close with general recommendations for researchers and model developers, including focusing research efforts on understanding the cognitive dynamics underpinning fatigue-related vigilance decrements, applying emerging dynamic modelling methods to fatigue data from field settings, and improving the adoption of open scientific practices in fatigue research.
OSARI, an Open-Source Anticipated Response Inhibition Task
Publisher: Springer Science and Business Media LLC
Date: 09-11-2021
DOI: 10.3758/S13428-021-01680-9
Abstract: The stop-signal paradigm has become ubiquitous in investigations of inhibitory control. Tasks inspired by the paradigm, referred to as stop-signal tasks, require participants to make responses on go trials and to inhibit those responses when presented with a stop-signal on stop trials. Currently, the most popular version of the stop-signal task is the ‘choice-reaction’ variant, where participants make choice responses, but must inhibit those responses when presented with a stop-signal. An alternative to the choice-reaction variant of the stop-signal task is the ‘anticipated response inhibition’ task. In anticipated response inhibition tasks, participants are required to make a planned response that coincides with a predictably timed event (such as lifting a finger from a computer key to stop a filling bar at a predefined target). Anticipated response inhibition tasks have some advantages over the more traditional choice-reaction stop-signal tasks and are becoming increasingly popular. However, currently, there are no openly available versions of the anticipated response inhibition task, limiting potential uptake. Here, we present an open-source, free, and ready-to-use version of the anticipated response inhibition task, which we refer to as the OSARI (the Open-Source Anticipated Response Inhibition) task.
Dynamic models of choice
Publisher: Springer Science and Business Media LLC
Date: 29-06-2019
DOI: 10.3758/S13428-018-1067-Y
Abstract: Parameter estimation in evidence-accumulation models of choice response times is demanding of both the data and the user. We outline how to fit evidence-accumulation models using the flexible, open-source, R-based Dynamic Models of Choice (DMC) software. DMC provides a hands-on introduction to the Bayesian implementation of two popular evidence-accumulation models: the diffusion decision model (DDM) and the linear ballistic accumulator (LBA). It enables in idual and hierarchical estimation, as well as assessment of the quality of a model's parameter estimates and descriptive accuracy. First, we introduce the basic concepts of Bayesian parameter estimation, guiding the reader through a simple DDM analysis. We then illustrate the challenges of fitting evidence-accumulation models using a set of LBA analyses. We emphasize best practices in modeling and discuss the importance of parameter- and model-recovery simulations, exploring the strengths and weaknesses of models in different experimental designs and parameter regions. We also demonstrate how DMC can be used to model complex cognitive processes, using as an ex le a race model of the stop-signal paradigm, which is used to measure inhibitory ability. We illustrate the flexibility of DMC by extending this model to account for mixtures of cognitive processes resulting from attention failures. We then guide the reader through the practical details of a Bayesian hierarchical analysis, from specifying priors to obtaining posterior distributions that encapsulate what has been learned from the data. Finally, we illustrate how the Bayesian approach leads to a quantitatively cumulative science, showing how to use posterior distributions to specify priors that can be used to inform the analysis of future experiments.
FIPS: An R Package for Biomathematical Modelling of Human Fatigue Related Impairment
Publisher: The Open Journal
Date: 15-07-2020
DOI: 10.21105/JOSS.02340
Understanding fatigue in a naval submarine: Applying biomathematical models and workload measurement in an intensive longitudinal design
Publisher: Elsevier BV
Date: 07-2021
Integrated responding improves prospective memory accuracy
Publisher: Springer Science and Business Media LLC
Date: 16-12-2022
DOI: 10.3758/S13423-021-02038-0
Abstract: Prospective memory (PM) supports the planning and execution of future activities, and is particularly important in applied settings. We investigate a new response method that aims to improve PM accuracy by integrating the responses to an occasional PM task and a routine ongoing lexical-decision task. Instead of the most common three-choice method where the PM response replaces the ongoing response, participants were obligated to make explicit PM (present vs. absent) and ongoing (word vs. non-word) classifications on every trial through a four-choice response. Although replacement and obligatory responses were initially similar in PM accuracy, an advantage emerged with practice for the new obligatory method that was not simply due to slower responding associated with making four versus three choices. The nature of the errors differed between methods, with obligatory responding being characterised by fast PM errors and replacement by slower errors, suggesting avenues for further potential improvements in PM accuracy.
Human behavioral response to fluctuating automation reliability
Publisher: Elsevier BV
Date: 11-2022
DOI: 10.1016/J.APERGO.2022.103835
Abstract: Human perception of automation reliability and automation acceptance behaviours are key to effective human-automation teaming. This study examined factors that impact perceptions of automation reliability over time and the acceptance of automated advice. Participants completed a maritime vessel classification task in which they classified vessels (contacts) with the assistance of automation. In Experiment 1 automation reliability successively switched from high to low (or vice versa). In Experiment 2 automation reliability decreased by varying magnitudes before returning to high. Participants did not initially calibrate to true reliability and experiencing low automation reliability reduced future reliability estimates when experiencing subsequent high reliability. Automation acceptance was predicted by positive differences between participant perception of automation reliability and confidence in their own manual classification reliability. Experiencing low automation reliability caused perceptions of reliability and automation acceptance rates to erge. These findings have important implications for training and adaptive human-automation teaming in complex work environments.
Prospective Memory Decision Control
Publisher: American Psychological Association (APA)
Date: 20-04-2023
DOI: 10.1037/XLM0001242
All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019)
Publisher: Center for Open Science
Date: 28-05-2019
Abstract: Lee et al. (2019) make several practical recommendations for replicable and useful cognitive modeling. They also point out that the ultimate test of the usefulness of a cognitive model is its ability to solve practical problems. Solution-oriented modeling requires engaging practitioners who understand the relevant applied domain but may lack extensive modeling expertise. In this commentary, we argue that for cognitive modeling to reach practitioners there is a pressing need to move beyond providing the bare minimum information required for reproducibility, and instead aim for an improved standard of transparency and reproducibility in cognitive modeling research. We discuss several mechanisms by which reproducible research can foster engagement with applied practitioners. Notably, reproducible materials provide a starting point for practitioners to experiment with cognitive models and evaluate whether they are suitable for their domain of expertise. This is essential because solving complex problems requires exploring a range of modeling approaches, and there may not be time to implement each possible approach from the ground up. Several specific recommendations for best practice are provided, including the application of containerization technologies. We also note the broader benefits of adopting gold standard reproducible practices within the field.
Related Organisations
Related Funding Activities
Discovery Early Career Researcher Award - Grant ID: DE230100171
Start Date: 07-2023
End Date: 07-2026
Amount: $438,560.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP200101842
Start Date: 01-2021
End Date: 01-2024
Amount: $269,588.00
Funder: Australian Research Council
View Funded Activity