1056 research outputs found
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ChemEngML/MP_FO_ML: MP_FO Scripts
No full textAI-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
Dataset for "The influence of occupant behaviour on indoor air quality and COVID-19 risk in refugee shelters and temporary houses"
No full textThe 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
Dataset for "Highly multi-mode hollow core fibres"
No full textThis 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
Carbon capability survey 2024
No full textThis survey dataset analyses the ‘carbon capability’ of a representative sample of the UK population (n = 2001). The focus is on attitudes and behaviours, as well as norms and structural factors which enable and constrain individuals’ ability to lead low-carbon lifestyles. The sample is representative of age, gender, region, education and ethnicity. Participants responded to questions spanning the main components of household-level carbon footprints: energy use, transportation, food and diet, and shopping habits. The survey was designed to enable the calculation of individual-level carbon footprints. Questions on the role of influence and citizenship with respect to climate change are also included
Dataset for "Physical activity substitution: An overlooked constraint on energy expenditure during exercise and physical activity interventions"
Full text linkThis 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 "Assessing climate ambition through policy outputs: a comparative measure of 35 major emitters"
No full textClimate ambition flexibly engages with broad policy frameworks of countries, typically referring to the holistic policy efforts in the climate field. However, levels of ambition vary greatly across countries. Understanding of these cross-national variations is limited, owing to conceptual complexity, limited data, and methodological challenges. I propose a comparative measure of ‘climate policy ambition’ based on the combination of depth and breadth of policy outputs. Drawing on this comparative framework, I introduce a new dataset to explore the climate policy ambitions of 35 major emitters (each contributing at least 0.5% of global emissions) from 1990 to 2020. The added value of this measurement model lies in its ease of construction, data transparency, and adaptability for extension across various timeframes and localities
Dataset for "Correlation Between the Delay and Rise Time of VLF/LF Amplitudes During 20 Solar X-ray Flares Observed in February 2014 at Mid-Latitude"
No full textThe dataset contains figures that illustrate the received disturbed amplitudes and the data process procedure during 20-28 February 2014 that related to the impact of X-ray flares. The figures include amplitude of received signals and the solar X-ray flux recorded by GOES geostationary satellite, and further details on data selection and integration.The full details of the methodology used are contained in the paper associated with this dataset.The data file are in .fig format and can be viewed and analysed using MATLAB
Dataset for Integrating Online Stop Smoking Support with Online Talking Therapies
No full textThe data comprises of baseline characteristics of participants recruited to a Feasibility Randomised Control Trial (RCT) such as age, gender, education, ethnicity and smoking behaviour prior to starting the study. The trial study tested if an online smoking cessation intervention could be delivered within an existing online mental health service platform, for people who suffer from common mental health disorders such as anxiety and depression. The follow up data was collected at 7 time points over the duration of the feasibility which was 6 months. Responses recorded were to questions about any attempts to stop smoking, any periods of abstinence and any use of smoking medication. Measures were also taken at multiple time points using PHQ_9 and GAD_7 to assess levels of anxiety and depression. A set of questions at three months and six months asked about levels of satisfaction with the trial procedures, and the stop smoking intervention itself.The trial was a parallel two arm, online randomised controlled feasibility and pilot trial. Participants were recruited within an existing online mental health services platform called Silver Cloud when they accessed their Silver Cloud account for the first time. An invitation to take part in the feasibility study appeared in the online platform, and after consent was taken, they were randomly allocated to the control or intervention condition. An algorithm in Qualtrics contains the ‘Mersenne Twister’, a standard general-purpose pseudorandom number generator to calculate a randomisation sequence. Qualtrics surveys sitting within the Qualtrics platform were linked to the online Silver cloud Platform, to collect baseline information initially. The smoking cessation programme was a set of modules for intervention participants only which they could access along with the mental health support modules provided for their usual care. The control participants received usual care only. All participants had immediate access to the modules after randomisation. On a monthly basis, exports were taken from the Silver Cloud platform containing activity data such as; number of viewings of the modules pages, and time spent within SilverCloud. Exports also included data on the number of clients eligible for the study and mental health assessment data (PHQ_9 and GAD_7). These exports were extracted for all 17 services sites in excel spreadsheets every month and were cumulative and sent to the Bath Research Team. The Trust sites also provided a monthly excel spreadsheet with clinical information and attendance at review meetings. This data was requested by the Bath Team from the service sites on a monthly basis. Follow up data was collected by sending emails to participants at the set time points and 7 Qualtrics questionnaires were designed for this purpose (5 short surveys sent out every 2 weeks for 10 weeks, and a follow up survey at 3 and 6 months. All Qualtrics survey data was exported in csv files and cleaned using R, before being merged into one spreadsheet using email address as key variable, using the join functions in R. The exports from SilverCloud were also in excel files. All site data for each measure (e.g. PHQ_9) was merged into one datafile and then converted from long to wide format. Each merged Silver cloud spreadsheet was then joined to the Qualtrics data using the SilverCloud ID number as the key joining variable. Finally the excel spreadsheets for each of the 17 sites containing additional clinical information were cleaned and merged into one datafile. The merged clinical data file was then joined to the rest of dataset using R joining function. A set of descriptive statistics was calculated using R. Finally, completed case and also imputed data were analysed using logistic regression and linear regression methods in STATA.Silver Cloud datasets only contained SilverCloud ID numbers and so anonymised already. The data in the excel spreadsheets were in long format and using R, converted to wide format so one row per participant. All Qualtrics surveys were joined using email addresses and then merged with the Silver Cloud data using the Silver Cloud id numbers. All personally identifiable information was taken out. Finally, the data collected from Trust sites were merged into one data set for all 17 sites, PID was removed, and the service site data was joined in R using the SilverCloud ID as the joining variable.All analyses were conducted in R (v4.2.0) or STATA (v18.0).The variables are labelled with an extension to indicate the time points at which the data were collected. e.g. b for baseline, t1, t2, t3,t4,t5 and 3 month or 6 month. The code book presents the names and labels and coding of variables in the order in which they appear in the dataset
Dataset for "Do participant testimony videos make people more interested in taking part in a clinical trial? A randomised Study Within a Trial (SWAT)"
No full textThis is a dataset from a Study Within a Trial (SWAT) that examined whether watching participant testimony videos might improve recruitment. The dataset includes 7 variables (including anonymous ID number) for 480 participants in the SWAT. The Variables are:-
Participant ID
Allocated SWAT condition - 2 way comparison
Allocated SWAT condition - 3 way comparison
Did participant express interest in host trial?
Was participant screened for eligibility for host trial?
Was participant assessed as eligible to take part in host trial?
Was participant randomised into host trial?The host trial was a randomised controlled trial of a digital intervention for paranoia (STOP; Successful Treatment of Paranoia). People who were referred to the host trial and opted to receive an online version of the Participant Information Sheet were eligible for enrolment into the SWAT. Before accessing the online Participant Information Sheet, people were randomly allocated to watch either a brief online video with patient testimonies about taking part in physical health research (general), mental health research (tailored to topic of host trial), or a no video control. We collected data on who expressed interest in taking part, and who went on to be randomised into the trial in each condition
Dataset for "Exploratory Proof-of-Concept: Predicting the Outcome of Tennis Serves Using Motion Capture and Deep Learning"
No full textDataset used for training a Machine Learning model to classify tennis serves into “In”, “Out” and “Net” as well as predict the outcome coordinates of the serve. A marker-based motion capture system provided and operated by The University of Bath’s Applied BioMechanics Suite was used to collect spatio-temporal data on participants completing tennis serves, whilst a high-speed video camera recorded the outcome. Dataset has two parts: 1) The spatio-temporal data used for training and validation 2) The serve outcome and coordinates for labelling of the data.The methodology can be found in the associated publication.Further information on the data collection and pre-processing can be found within the associated publication. This includes a detailed experimental set-up, trajectory-filling algorithms for occluded markers, filtering processes for trajectories, and extensive data formatting of spatio-temporal data for compatibility with Machine Learning models
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