Dataset for "Greater tolerance of uncertainty facilitates thriving in doctors entering postgraduate training"

Questionnaire data from 66 doctors entering UK foundation training after graduation from medical school. The questionnaire gathered data using validated measures for perceived stress, wellbeing, career success, tolerance of uncertainty, and adverse childhood experiences. Additional items enquired about lifetime stress, age, sex, and disability.Cross-sectional online survey using validated measures. Participants recruited by email from medical school graduates entering a postgraduate foundation training programme in a training region (postgraduate deanery) of the United Kingdom.Data are anonymised

Dataset for "Understanding the role of aligned porosity on the intrinsic and extrinsic contributions to the dielectric permittivity of freeze-cast ferroelectrics"

This dataset is a part of the research article 'Understanding the role of aligned porosity on the intrinsic and extrinsic contributions to the dielectric permittivity of freeze-cast ferroelectric'. It contains comprehensive characterisation data for ferroelectric lead zirconate titanate PZT NCE51 ceramic, fabricated using both freeze-casting and conventional solid state route. This dataset contains hysteresis polarisation-electric field loops, impedance spectroscopy data and X-ray diffraction (XRD) patterns, which provide insights into how the microstructure of freeze-cast samples affect the functional properties of the porous freeze-cast ceramics. In addition, the dataset also contains the results of finite element model, demonstrating how the local field distribution differ between the structures produced via freeze-casting and the burnt-out polymer spheres (BURPS) technique, despite having the same relative density. This difference demonstrate how the permittivity measured differ between these microstructures. The dataset supports further analysis of the processing-microstructure-property relationships in porous ferroelectric ceramics. This may be of interest to researchers working on design and characterisation of advanced ferroelectric composites.Full details of the methodology can be found in Section 2 of the associate research article

Dataset for "Physical activity substitution: An overlooked constraint on energy expenditure during exercise and physical activity interventions"

This dataset contains physical activity data used for analysis in “Physical activity substitution: An overlooked constraint on energy expenditure during exercise and physical activity interventions”. The data include physical activity measured using a wearable device over a 7 day period in 242 men and women recruited from primary care in the South West of England - with these data also being modelled for the impact of a LOW and HIGH theoretical prescribed exercise intervention.Full details of the data collection methodology are described in the study protocol (see the citation below).PMID: 26314577; PMCID: PMC4552151. See the associated "Readme" file

Dataset for "Redefining Accessibility: Uncovering Cultural, Social, and Economic Barriers to Urban Green Space Accessibility"

The files are data collected as part of a study on accessibility to green spaces in Bristol. The first file is over 240 participant responses to a survey on urban green space (UGS) use in Bristol. It includes information on the nature of UGS use, the frequency of use along with other details, in addition to non-identifying demographic data. The second is focus group data, the focus groups were aimed at marginalised groups, especially ethnic minorities. The participants of four focus groups were non-regular users of green space. One of the focus groups was for regular users. The third file is results of a participatory design workshop, where participants were asked to come up with design and planning solutions to make Netham park in Bristol more accessible for the diverse community it serves.JISC survey system was used for the online survey. The link was distributed through social media and community groups. For the focus groups, researcher notes were taken to document the discussion which was guided by a preset of questions and topics, then analysed using thematic analysis with NVivo. For the participatory workshop, solutions were documented during the event, and later transcribed into a file that was analysed using NVivo.NVivo was used for thematic coding, MS Excel was used for statistic analysis of the survey results

Dataset for "Low-Cost, Multi-Sensor Non-Destructive Banana Ripeness Estimation Using Machine Learning"

Processed datasets containing all numerical sensor data used for training and testing the ML algorithms discussed in the associated publication. Data from temperature, pressure, humidity, VOC and spectral sensors is included. The data is split into four datasets (as defined in Table V of the associated publication), each containing a different combination of sensor data and each subdivided into data ("x") and labels ("y") for both testing and training data. 30% of the cleaned data is randomly taken to form the testing data, while the remaining 70% forms the training data. Each data subset is balanced, as discussed in section 3.E.3 in the associated publication.The data collection methodology can be found in the associated publication.The data preparation & processing methodology can be found in the associated publication.The datasets were created with Python 3.10.13, with libraries Numpy 1.26.0 and Pandas 2.1.2. The data is saved in CSV format and does not require specialist software to read.Data organisation and encoding is described in the associated ReadMe files

Dataset for "Magnetically-controlled Vortex Dynamics in a Ferromagnetic Superconductor"

Datasets underpinning the five Figures and six Supplementary Figures for "Magnetically-controlled Vortex Dynamics in a Ferromagnetic Superconductor" in Communications Materials. The primary data are a mixture of low temperature magnetisation data, taken using a SQUID magnetometer taken at the University of Bristol, and low temperature magnetic force microscopy (MFM) scan data taken at the University of Basel. Additionally, there are data derived from analysis of the magnetisation data, e.g. critical current, coercive field, as well as the results of magnetic relaxation measurements, i.e. Ueff. In addition to the MFM scan data are line profiles taken through regions of these scans. The supplementary data include further magnetisation data, as well as examples of magnetic relaxation data. Additionally, there is a further example of a line profile taken from MFM scan data, along with corresponding fitted data.The dataset contains magnetisation and magnetic force microscopy data, with measurements performed on single crystals of the ferromagnetic iron-based superconductor EuFe2(As1-xPx)2, with x~0.21, Tc~24 K and T_FM~19 K. Magnetisation data was collected using a Quantum Design SQUID magnetometer at the University of Bristol, at temperatures ranging from 5.0 to 25.0 K and magnetic fields ranging in magnitude up to 10,000 Oe (1 Tesla). The measurements were primarily in the form of magnetic hysteresis loops, with some further measurements of magnetic relaxation. From this data, quantities such as magnetic susceptibility, coercive field, critical current density and effective pinning potential are derived. Magnetic force microscopy (MFM) data were acquired using a home-built oscillating magnetic nanowire microscope at the University of Basel. Scan data were acquired in temperatures ranging from approximately 22 K down to 4.3 K, and in magnetic fields ranging in magnitude up to 10,000 Oe (1 Tesla). Line profiles were derived from these 2D scan data.All data are in the form of .csv (comma separated values) with headers and units indicated. No specialist software is required to view the data

Data collected for "Providing an Eyewitness Testimony as an Individual who Stammers: Examining Accuracy/Completeness and Subjective Experiences"

Stammering may impede an individual's eyewitness testimony and reduce jurors' perceptions of their credibility through a complex interplay of bio-psycho-social factors. However, no research to date has explored this. Three co-produced, mixed-methods studies are reported, investigating the evidential quality, lived experiences and perceived credibility of people who stammer (PWS) as witnesses. In pre-registered Study 1, PWS recalled as much correct information as non-stammering witnesses overall. However, during the free – but not cued – recall interview phase, PWS provided fewer correct details. A reflexive thematic analysis of participants' post-testimony reflections captured how PWS experienced a cyclical relationship between communicative pressure, anxiety over listener misperceptions and stammer severity, which they navigated either by employing avoidance strategies at the expense of testimony or by speaking through their stammer. In pre-registered Study 2, mock jurors rated PWS as less confident yet more likeable and trustworthy than non-stammering witnesses. In Study 3, providing jurors with information about stammering further improved their likeability and trustworthiness but had no impact on perceived confidence. Findings provide new insight into communication disorders in legal contexts – and the unique challenges faced by PWS in particular – demonstrating the need for systemic accommodations and targeted training for legal professionals. This dataset contains * A SAV file with participant demographics, cognitive ability scores, and the data on completeness, errors, and accuracy of participants' testimony accounts in overall, free, and cued recall phases. * Another SAV file containing the number of correct details and errors, and accuracy (%) of 12 testimony accounts (6 accounts from each group) coded by two independent raters for inter-rater reliability. * Transcripts (docx) of the semi-structured interviews conducted in Study 1b. * The post-testimony survey responses (pdf) provided by participants who stammer.The primary aim of the first part of this study is to examine the accuracy and completeness of accounts in eyewitness testimony setting between people who stammer and those who do not. Therefore, there will be one between-subject factor (people who stammer vs. Do not stammer). Participants will engage in the same research procedure for the first part of the study: first they will watch a video of a mock crime before being interviewed for their memory of it using standard eyewitness interviewing procedure: first prompting their free recall of the event, before being asked questions based on what they freely recalled. The main planned analysis will examine group effects on recall of details across the interview as a whole (i.e., free and cued recall combined). Further exploratory analyses will examine whether there are group differences in the recall of correct details, errors, and accuracy in eyewitness testimony accounts within free and cued recall (respectively). The second part of this study will use a mixed methods approach using an online survey, consisting of both Likert scale questions and open-ended text-based questions. This approach will primarily involve an exploratory qualitative approach to the analysis of participants’ written responses. Quantitative (Likert scale) data will also be reported

Dataset for “Why AGG is associated with high transgene output: passenger effects and their implications for transgene design”

In bacteria, high A and low G content of the 5′ end of the coding sequence (CDS) promotes low RNA stability, facilitating ribosomal initiation and subsequently a high protein to transcript ratio. Additionally, 5′ NGG codons are suppressive owing to peptidyl-tRNA drop off. It was, therefore, surprising that the first large-scale transgene experiment to interrogate the 5′ effect by codon randomization found the NGG, G-rich codon AGG to be the most associated with high transgene output. In this study we show that this is not replicated in other large transgene datasets, where AGG and NGG are associated with low efficiency. More generally, there is limited agreement between the first experiment and others. This we find to be a consequence of non-random construct design. The results of this research have implications for both transgene and experimental design.Please see the associated paper

Dataset for "Distortion/Interaction Analysis via Machine Learning"

Machine learning (ML) has previously been applied to predict reaction barriers for a variety of different chemical reactions. This is seen as the end point for this type of study however, post-reaction barrier analysis/energy decomposition approaches can provide insight into chemical reactivity. One such approach that has previously been used to provide information on chemical reactivity, for cycloaddition reactions in particular, is distortion/interaction-activation strain analysis (DIAS). We demonstrate that ML can be coupled with cheap and rapid semi-empirical quantum mechanical methods (SQM) to predict distortion and interaction energies at a fraction of the computational cost associated with running density functional theory (DFT) calculations. This dataset includes all the structural data in the form of Gaussian16 (Revision A.03 and C.01) output files for the four datasets used in this work and, the literature dataset reactions.Ground state reactant and transition state geometries for dimethyl malonate Michael addition reactions were built using Schrödinger’s R-Group Enumeration. R-groups were placed on various different positions of the Michael acceptor. Once generated, structures were conformationally searched using Schrödinger’s MacroModel (version 12.7) with OPLS3e. The lowest energy conformation for every structure was subsequently optimised using Gaussian16 (Revisions A.03 and C.01) using AM1 (IEFPCM=Water)//AM1 and wB97X-D/def2-TZVP (IEFPCM=Water)//wB97X-D/def2-TZVP. For distortion/interaction-activation strain calculations, python code (available on the associated GitHub page: https://github.com/the-grayson-group/distortion-interaction_ML) was used to separate the distorted reactant structures before single point energies were calculated using Gaussian16 (Revision C.01) using AM1 and the DFT level of theory used in the original transition structure calculation and in solvent.Data has been re-uploaded to correct an error with ts_100_dft.log in the malonate data set

Dataset for "Highly multi-mode hollow core fibres"

This repository contains all the raw data and raw images used in the paper titled 'Highly multi-mode hollow core fibres'. It is grouped into two folders of raw data and raw images. In the raw data there are a number of .dat files which contain alternating columns of wavelength and signal for the different measurements of transmission, cutback and bend loss for the different fibres. In the raw images, simple .tif files of the different fibres are given and different near field and far field images used in Figure 2.Transmission : A length of 3/4 m of fibre was coupled to an incoherent light source (either EQ99X LDLS or tungsten halogen bulb) and its light transmission was optimised through fibre allignment and re-cleaving of fibre cleaves. Then a scan of signal versus wavelength was performed to collect the transmission data. Cutback : A long length of ~ 30 - 40 m of fibre was coupled to an incoherent light source (either EQ99X LDLS or tungsten halogen bulb) and its light transmission was optimised through fibre allignment and re-cleaving of fibre cleaves. Then a scan of signal versus wavelength was performed to collect the transmission data followed by a reduction in length to ~ 10 m and the previous process repeated. Bend loss: A length of ~ 4 m of fibre was laid in a roughly straight line and its transmission was measured. Various bends were then introduced and their transmission spectra also recorded. Near-field and far-field images: A ~ 3 m length of fibre was coupled to the white light source, filtered by a 12 nm FWHM bandpass at 1300 nm. At the fibre output a InGaAs camera imaged the near-field or far-field, using a lens for a the near-field. For the far-field the distance to the camera was calibrated by taking a number of pictures at different distances and using the intercept to determine the absolute position to the camera. For the near-field the scale was determined by the position of the capillaries using saturated images.No requirement, plotting of data requires reading a simple txt file or image file.Files include .dat text files, alternating between wavelength and signal, from which scans are easily determined by plotting all and comparing to the published paper. Images are provided along with backgrounds and can compared to the published example