Code and Dataset for Thesis "Bayesian Analysis of Spatial Log-Gaussian Cox Processes"

These files contain the relevant code and data to produce the results presented in the thesis titled "Bayesian Analysis of Spatial Log-Gaussian Cox Processes" by Nadeen Khaleel. These files contain the input data and the output results for the implementation of the models and exploratory analysis as well as the implementation of the Grid Mesh Optimisation method and the INLA within MCMC algorithms. Some of the input data corresponds to the processed crime data in US cities, in particular incidences of homicide and motor vehicle theft in Los Angeles, New York and Portland, aggregated to census-tract level or discretisation grids. The raw third party data is not included; however, a document detailing how to access the relevant data is provided and all of the code used to clean and extract the necessary data from the raw data is included. These files additionally contain the relevant code for the data tidying, manipulation and simulation as well as the code to implement the Grid Mesh Optimisation method and the INLA within MCMC algorithms

Dataset for "Machine learning outperforms clinical experts in classification of hip fractures"

Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%. This data set contains the source data for figures 2 and 4, which are the main Results figures. Data are given in both csv and MAT file formats. The MATLAB scripts for generating the figures are also provided

Dataset for article entitled "An empirical evaluation of methodologies used for emotion recognition via EEG signals"

The data is split into two parts according to the two experiments described within the article. The dataset includes movies and python codes for classifying emotions from experiment 1, and EEG and ERP measurements from experiment 2 along with associated code for analyzing those data. Experiment 1 tests the validity of the SEED dataset collated by Zheng, Dong, & Lu (2014) and, subsequently, our own stimuli. The objective was to test whether previous literature using such datasets as the aformentioned dataset by Zheng et al. is purportedly classifying between emotions based on emotion-related signals of interest and/or non-emotional ‘noise’. Experiment 2 used stimuli that have been well-validated within the psychological literature as reliably evoking specific embodiments of emotions within the viewer, namely the NimStim face and ADFES-BIV datasets with the objective of classifying a person's emotional status using EEG. All data was processed and analyses run in MATLAB or Python. All datasets used are included within the folders accompanied by the MATLAB or Python scripts for collating separable matrices and running the action

Dataset for "Reformulating Reactivity Design for Data-Efficient Machine Learning"

This dataset contains the Gaussian 16 output files for the dataset of aza-Michael addition reactions used in the publication "Fast Identification of Reactions with Desired Barriers by Reformulating Machine Learning Activation Energies". The structures of the methylamine nucleophile, the 1000 Michael acceptor electrophiles and their 1000 transition states were all optimised at the wB97X-D/def2-TZVP level of theory with the IEFPCM(water) implicit solvent model. Before optimisation all Michael acceptors and transition states were conformationally searched using the MMFF force field in Schrödinger's MacroModel software and the lowest energy conformer was selected for DFT calculation. This dataset also contains the Gaussian 16 output files for the SVWN/def2-SVP single-point energy calculations on the dihydrogen activation catalyst and transition state structures

Full Body Kinematics and Ground Reaction Forces of Fifty Heterogeneous Runners Completing Treadmill Running at Various Speeds and Gradients

This dataset includes 3-dimensional ground reaction force data (1000 Hz) collected from a gradient adjustable split belt Bertec instrumented treadmill (ITC-21-20) during running at a range of speeds and gradients. Alongside the ground reaction forces are marker based motion capture data. A full body markerset was tracked (250 Hz) using 12 Qualisys Miqus cameras and Qualisys Track Manager 2022, with additional anatomical markers tracked only during the static trial. Data was also collected from six inertial measurement unit sensors (Delsys Trigno) at 519 Hz, the sensors were secured to the following locations using either tape or Velcro strapping: medial left tibia, lateral left thigh, sacrum, T10 vertebrae, lateral left upperarm, and left wrist. All of this data was collected synchronously and saved to the typical motion capture format of c3d files. Fifty runners with mixed levels of experience and fitness levels are included in this dataset (25 males, 25 females)

Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"

This dataset includes the input features and target labels needed to train and test FootNet. The input features include the distal tibia anteroposterior velocity, ankle plantar/dorsi flexion angle and foot centre of mass anteroposterior and vertical velocities. Additionally, ground reaction force data and trial names are also included

Dataset for "Comparison of the within-reader and inter-vendor agreement of left ventricular circumferential strains and volume indices derived from cardiovascular magnetic resonance imaging"

This dataset contains left ventricle endocardial contour data (and derived quantities) generated as part of a study to test the inter-user and inter-software repeatability of strain and volume measures of cardiac function. Seven users used two different software packages (ScanIP and OsiriX) to segment 30 short-axis MR images of a porcine heart, and repeated this twice. The same segmentation was also performed automatically using a third software, CVI42. Each instance of the data therefore contains the points that define the endocardial border for each attempt, as well as derived quantities such as circumferential strains and ;eft ventricular volume fractions Finally, the specific data used to generate the plots, such as Bland-Altman plot, that appear in the paper are also presented