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Navigating Change - Voices of the Murray Darling Basin
No full text<p dir="ltr">The Navigating Change oral history project specifically aimed to understand the diverse underlying values people attach to their environments, particularly water, and how these values are revealed during periods of change. It also sought to explore how conflicts around these values have been managed and how communities imagine their future. The research focused on three basin communities: Griffith, in the Southern Basin; Bourke, in the Northern Basin; and Loxton, in the South Australian Riverland. These communities were chosen for their different histories, geographies, and demographics. The inclusion criteria for participants were that they had resided in their community for more than 15 years and were aged over 18. Five key high-level values emerged from our analysis, (1) healthy ecosystems, (2) community centrism, (3) enacting the good farmer, (4) succession and generativity, and (5) personal health and wellbeing. This project highlighted the importance of understanding and accommodating communities’ values in future water management decisions to enhance community resilience and adaptability. Notably, it has reinforced the clear view expressed by interviewees: that genuine engagement moving forward requires Government acknowledgement of local expertise and openness to its inclusion in policy processes.</p><p dir="ltr">Access to this collection is limited to researchers only - to access please contact the library <a href="https://www.latrobe.edu.au/library/about/contact#contact_form" rel="noreferrer" target="_blank">https://www.latrobe.edu.au/library/about/contact#contact_form </a></p><p dir="ltr"><br></p><p dir="ltr"><br></p>
Syuba Rotated Palms Gesture Tokens
No full text<div>Clips of Syuba speakers using a rotated palms gesture. <br></div><div><br></div><div>Used as the basis of the analysis in the paper: Contexts of Use of a Rotated Palms Gesture among Syuba (Kagate) Speakers in Nepal. <i>Gesture</i> 17(1): 44-78.</div><div><br></div><div>Includes an Excel spreadsheet of metadata for tokens, which includes file, timecode, speech transcription and translation.<br></div><div><br></div><div>Full recordings and transcripts available from the Syuba archive: http://catalog.paradisec.org.au/collections/SUY1 </div>
Machine learning models and predictions for a nucleophilic substitution reaction in ionic liquids
No full textMachine learning models were built based on published data for a nucleophilic substitution chemical reaction in ionic liquid-acetonitrile solvents. Models were built relating the rate constant of the reaction to the chemical structure of the ionic liquids.<div><br></div><div><b>Publication title</b>: </div><div><a>Towards<br>accurate predictions of the rate constants of organic processes in mixtures<br>containing ionic liquids</a></div><div><a><br><b>Abstract: </b></a></div><div>The<br>ability to tailor the constituent ions in ionic liquids (ILs) is highly<br>advantageous as it provides access to solvents with a range of physicochemical<br>properties. However, this benefit also leads to large compositional spaces that<br>need to be explored to optimise systems, often involving time consuming<br>experimental work. The use of machine learning methods is an effective way to<br>gain insight based on existing data, to develop structure-property relationships<br>and to allow the prediction of ionic liquid properties. Here we have applied<br>machine learning models to experimentally determined rate constants of a<br>representative organic process (the reaction of pyridine with benzyl bromide)<br>in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial<br>neural networks (BRANNLP) were both able to model the data well. The MLREM<br>model was able to identify the structural features on the cations and anions<br>that had the greatest effect on the rate constant. Secondly, predictive MLREM<br>and BRANNLP models were developed from the full initial set of rate constant<br>data. From these models, a large number of predictions (>9000) of rate<br>constant were made for mixtures of different ionic liquids, at different proportions<br>of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested<br>experimentally, including through the preparation of novel ionic liquids, with<br>overall good agreement between the predicted and experimental data. This study<br>highlights the benefits of using machine learning methods on kinetic data in<br>ionic liquid mixtures to enable the development of rigorous structure-property<br>relationships across multiple variables simultaneously, and to predict properties<br>of new ILs and experimental conditions. </div>
Experimental audio dataset for squeak and rattle identification
No full textThis dataset is a collection of squeak and rattle audio files experimentally created in RMIT NVH laboratory. This is a useful database potentially be used for development of features for Machine Learning models. There are two independent datasets for squeak and rattle. The rattle audio samples were organised into 8 classes based on the beam length, impact material and the decay duration of the excitation signal. The squeak sound samples were categorised to 12 classes based on the sliding beam length, contact material type, and the oscillation frequency
In-Gauge and En-Gage Datasets
No full text<p>We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. This is the first publicly available dataset studying the daily behaviours and engagement of high school students using heterogeneous methods. The combined data could be used to analyse the relationships between indoor climates and mental states of school students.</p><br><p><strong>The detailed data descriptor has been published in Nature Scientific Data</strong>. For more details on the dataset, please check the paper.<a href="https://doi.org/10.1038/s41597-022-01347-w" target="_blank"> https://doi.org/10.1038/s41597-022-01347-w</a>.</p><br><p>Please<strong> cite </strong>the following papers if the dataset is used in a publication:</p><br><p>[1] Gao, N., Marschall, M., Burry, J. , Watkins, S., Salim, F. Understanding occupants’ behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables. Sci Data 9, 261 (2022). </p><br><p>[2] Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020). n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1-26.</p>
6.1 Supplementary Table 6.1 Results.pdf
No full text<p>Dataset terbinafine susceptibility</p>
Tidy Transit Headway analysis for the Australian National Liveability Study 2018
No full text<p>An analysis of day time weekday public transport service frequency and maximum interdeparture time, for the Australian National Liveability Study 2018, using Australian GTFS transport feed data for each state and territory.</p><br><p>The code is hosted on the Healthy Liveable Cities Lab Github site at <sub><a href="https://github.com/healthy-liveable-cities/australia_gtfs_headway" target="_blank">https://github.com/healthy-liveable-cities/australia_gtfs_headway</a></sub>.  The analysis was conducted using public transport GTFS feeds for each Australian state and territory in February 2020.</p>
Extended Unimelb Corridor
No full text<p>We used a subset of synthetic images that only contained forward-looking images from the original Unimelb corridor dataset, and removed the additional images that were generated by rotating the camera along the X and Y axes. To compensate for the low number of synthetic images, we generated 900 more images along the original trajectory by reducing the spacing between the consecutive images, which finally resulted in 1400 images for the synthetic dataset. The dataset also contains 950 real images and their corresponding groundtruth camera poses in the BIM coordinate system. We removed some of the redundant images (100) at the end of the trajectory and added another 500 new real images, which resulted in 1350 real images. The synthetic and real cameras have identical intrinsic camera parameters, with an image resolution of 640 x 480 pixels. </p><br><p><br></p><br><p>Additionally, the provided Blender files can be used to render the images. Please note that SynCar dataset should be rendered with Blender 2.78 only, whereas SynPhoReal and SynPhoRealTex images can be generated using the latest Blender 3.4. </p><br><p><br></p><br><p>[1]  Acharya, D., Khoshelham, K. and Winter, S., 2019. BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images. <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>, <em>150</em>, pp.245-258. </p><br><p><br></p><br><p>[2]  Acharya, D., Singha Roy, S., Khoshelham, K. and Winter, S., 2020. A recurrent deep network for estimating the pose of real indoor images from synthetic image sequences. <em>Sensors</em>, <em>20</em>(19), p.5492. </p><br><p><br></p><br><p>[3]  Acharya, D., Tennakoon, R., Muthu, S., Khoshelham, K., Hoseinnezhad, R. and Bab-Hadiashar, A., 2022. Single-image localisation using 3D models: Combining hierarchical edge maps and semantic segmentation for domain adaptation. <em>Automation in Construction</em>, <em>136</em>, p.104152. </p>
Data - Face masks impede basic and complex emotion recognition: Variation across the broader autism phenotype
No full text<p>csv file is available for the data.</p>