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 "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

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

Dataset for "The influence of occupant behaviour on indoor air quality and COVID-19 risk in refugee shelters and temporary houses"

The dataset contains the monitored data in six temporary houses in Japan. The dataset contains the outdoor temperature of the location and the indoor parameters of the temporary houses: indoor concentration of carbon dioxide, indoor temperature and relative humidity. The dataset also contains monitored occupant behaviour: occupancy, window and door operation, use of kitchen.The continuous measurement of CO2, indoor and outdoor air temperature and relative humidity was performed from the 1st of December 2022 to the 8th of December 2022. The indoor parameters were measured at 2 minutely intervals in all the rooms of the temporary houses for seven consecutive days (168 hours). Measurements were performed by placing one sensor in each room of the temporary house. Sensors were placed in every room as follow, away from windows and heat sources and at a height of 1.1 m to minimise the influence of the households nearby. Outdoor CO2 has been recorded through spot measurements. The adopted CO2 sensor has a calibration function (auto/manual calibration) to compensate for sensor drift that can occur over time.Sensor used for outdoor temperature: TANDD, model TR-52i Sensor used for indoor CO2 and relative humidity: TANDD, model TR-76UiThe data have been organised in six csv files, one for each temporary house

ChemEngML/MP_FO_ML: MP_FO Scripts

AI-assisted Prediction & Optimization of Micropollutants Removal with Forward Osmosis Membranes — this repo provides the curated dataset (642 experiments across 17 commercial/lab-fabricated FO membranes and 102 micropollutants, with standardized chemical, membrane, and process descriptors in Dataset.csv) plus the Python scripts used to train, tune, and interpret the GBR and ANN models for water flux and rejection-rate prediction

Multi-Modal Dataset for "Towards Robust Surface Electromyography for Upper Limb Protheses using Machine Learning "

The dataset contains sEMG recordings from 10 anatomically intact participants. The data is separated into 13 trials, 11 of which were performed under manual intervention to vary one of the following parameters: Skin Temperature, Arm Position, Electrode Position, Impedance. Within each trial the participants perform 2 repetitions of 6 different hand grasps, held for 5 seconds. The data was recorded using custom-built sEMG sensors that also permitted the recording of skin temperature and skin-electrode impedance. Recordings of these features are provided with the data, recorded following the completion of a grasp. The data were recorded following approval granted by the University of Bath Research Ethics Approval Committee for Health, study ID: EP 23 019.The data were collected using 2 custom-built sEMG sensors from the participant's forearm, placed on the flexor carpi ulnaris and the extensor carpi radialis of each participant. The sEMG data was collected at 500 Hz, with an onboard 1st-order Bandpass filter at 4.82 and 241.1 Hz applied before digital conversion. A gain of 162.5 is applied to the data. The data were recorded in an unregulated environment. The sEMG data provided are as recorded, no further filtering has been applied. For each participant, 13 recording trials were performed, in each the participant performed 2 repetitions of 6 different hand gestures, picking up appropriate objects to perform the gestures. Image depictions of the gestures can be found alongside the data. Participants performed the gestures for approximately 5 seconds, followed by approximately 11 seconds of rest. Over the first 5 seconds of the rest period the sEMG sensors do not record, and instead a skin temperature and a skin-electrode impedance recording are made by the custom sensor units. The 12 temperature and impedance data are stored within the same .mat file as the sEMG data for each trial. Temperature data is recorded in Celsius, impedance in ohm and phase angle pairs per channel, and the sEMG data is recorded in Volts. Accompanying each set of data is additionally vectors indicating the gesture being performed (or rest where appropriate) and whether it is repetition one or two, these allow for the generation of training and testing datasets for pattern recognition applications. The 13 recording trials include: 2 control trials, 2 trials in which the participant arm temperature was varied, 2 trials in which the skin-electrode impedance was changed, 3 trials in which the participant varies the arm's position, and 4 trials in which the electrodes position is shifted relative to the base position. Each trial is stored within its own recording file in the dataset, and the details of the trial order and name are presented in the accompanying text file

Peacification. Data for the elaboration of the social realities peace and security framing constitutes in conflicts.

This data reveals the association between peace and security framing in US presidential speech on the one hand, and the US impact on fatalities in US conflicts on the other. Specifically, it compares the frequency of certain key terms and references in presidential speech from the period 1993-2014, as they were coded from the text in the “Public Papers of the Presidents of the United States”, with statistics on fatalities of organised violence during the same period, drawn from Uppsala University's UCDP Georeferenced Event Dataset.The data is based on NVivo content analysis of US Presidential Papers from 1993 until the end of 2013, for the period of humanitarian interventionism. The quantified textual data is then moved to StataB 18 package, and merged with data from Uppsala University's UCDP Georeferenced Event Dataset (GED) Global version 24.1: - Davies, Shawn, Garoun Engström, Therese Pettersson & Magnus Öberg (2024). Organized violence 1989-2023, and the prevalence of organized crime groups. Journal of Peace Research 61(4). - Sundberg, Ralph and Erik Melander (2013) Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research 50(4). Some of the word frequency calculations are from the following dataset: - Kivimäki, T., 2019. Coding of US Presidential discourse on protection. Bath: University of Bath Research Data Archive.Textual content analysis is created by using NVivo R 1.6 package while the preparation of conflict data has been created by using StataB 18 package.The data consists of coding of text as explained in the codebook, and monthly observations of conflict data as explained in the codebook and in the associated article

China's National Climate Policy Database (V1)

China's National Climate Policy Database (V1) collects all national-level policies addressing climate change issued by the Chinese government between 2016 and 2022. In addition to tracking the policies issued across different sectors, the database also maps policy instruments used and measures the intensity of each policy.Please see the methodology in the associated article; the supporting codebook can be found in the supplementary information section