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Research data supporting: 'Effect of Water upon Deep Eutectic Solvent Nanostructure: An Unusual Transition from Ionic Mixture to Aqueous Solution'
No full textThis dataset contains the full set of reduced, background-corrected wide Q-range neutron diffraction data (.mint01 format) that were used for EPSR modelling of the hydrated deep eutectic solvent systems in the publications: 10.1002/anie.201702486R1 (international edition) and 10.1002/ange.201702486R1 (German edition).Neutron diffraction data were collected using the NIMROD and SANDALS instruments at the STFC ISIS Pulsed Neutron and Muon Source, UK. Samples of the Deep Eutectic Solvent reline (1 choline chloride:2 urea) and aqueous mixtures thereof were measured in different isotopic substitutions (choline:urea:water H/D compositions of H:H:H, H:D:D, D:H:D, D:D:H, and D:D:D) in DES:water molar ratios of 1:1, 1:2, 1:5, 1:10, 1:15, 1:20, and 1:30.These data have been processed using the freely-available software GudrunN [1], provided by STFC ISIS Neutron and Muon Source. Corrections were made for the sample environment background, the data are normalised, and the inelastic scattering of hydrogen is subtracted. [1] Soper, A.K., 2011. GudrunN and GudrunX: Programs for correcting raw neutron and X-ray diffraction data to differential scattering cross section. Science and Technology Facilities Council.Data were created using GudrunN software [1]. They are simple text files that can be plotted and read with any typical graphing software. They are designed to be used as input files for the freely-available EPSR software suite, provided by STFC ISIS Neutron and Muon Source. [1] Soper, A.K., 2011. GudrunN and GudrunX: Programs for correcting raw neutron and X-ray diffraction data to differential scattering cross section. Science and Technology Facilities Council
Computational Dataset for "Reversible Magnesium and Aluminium-ions Insertion in Cation-Deficient Anatase TiO2"
No full textThis dataset contains the computational data and analysis for the paper "Reversible Magnesium and Aluminium-Ions Insertion in Cation-Deficient Anatase TiO2" (https://doi.org/10.1038/nmat4976).
The repository contains:
1. Input and output files for the DFT calculations, performed using VASP. This is detailed below in the Data section.
2. A `vasp_summary` script, that collects the relevant VASP data into a file `F-TiO2_intercalation_data.yaml`.
3. A Jupyter notebook, `F-TiO2 intercalation energies.ipynb`, containing the data analysis, and code for plotting intercalation energies.
2 and 3 both depend on the vasppy Python module (https://github.com/bjmorgan/vasppy, https://doi.org/10.5281/zenodo.801663), available under the MIT licence.All calculations were performed using VASP 5.3.5. Input files for each calculation are contained within the dataset.
For further details, please see the associated paper, available at
http://opus.bath.ac.uk/57334/.Relevant data (energies of optimised structures) were extracted using the included `vasp_summary`. Intercalation energies and voltages were calculated with the included Jupyter notebook. Both steps use the `vasppy` Python module, available at https://github.com/bjmorgan/vasppy
Dataset for "Understanding the AC conductivity and permittivity of trapdoor chabazites for future development of next-generation gas sensors"
Get PDFSynthetic K+ chabazite (KCHA), Cs+ chabazite (CsCHA) and Zn2+ chabazite (ZnCHA) were synthesized and compared in order to relate the differences in their crystalline structures to their thermal stability (TGA data), moisture content (TGA data) and frequency dependent alternating current (AC) conductivity (AC conductivity heating and cooling data), permittivity (permittivity heating and cooling data) and phase angle (phase angle heating and cooling data) at a range of temperatures. Cation migration activation energies for KCHA (0.66 ± 0.10) eV, CsCHA (0.88 ± 0.01) eV and ZnCHA (0.90 ± 0.01) eV were determined (activation energy data). Good thermal stability of the materials was observed up to 710 °C (TGA data) and below 200 °C the electrical properties were strongly influenced by hydration level (conductivity, permittivity and phase angle data). Overall, it was determined that when either hydrated or dehydrated, KCHA had the highest conductivity and lowest cation migration activation energy of the three studied chabazites (activation energy data)
Repository-Residuum model shape and volume assessment
Get PDFThe dataset includes data that have been presented and discussed in the paper entitled 'Validity and reliability of a novel 3D scanner for assessment of the shape and volume of amputees’ residual limb models', published in PLOS ONE Journal. Objective assessment methods to monitor residual limb volume following lower-limb amputation are required to enhance practitioner-led prosthetic fitting and the data collected in this database include the results and the statistical analysis performed to assess the validity and reliability of the 3D Artec Eva scanner (practical measurement) against a high precision laser 3D scanner (criterion measurement) for the determination of residual limb model shape and volume. Data include results of volumes, cross sectional areas, perimeters, body centre of mass position and sizes of ten different residual limb models (both transtibial and transfemoral).In this study, ten residual limb models were scanned by three independent observers, each on three separate occasions, using two different scanners (i.e. 180 scans), over a 4 months’ period (May – August 2016). The models were selected from anonymous transtibial (n=5) and transfemoral (n=5) amputees to evaluate a large range of representative shapes and volumes. The models were distributed via prosthetics centres. They were manufactured using a standard carver for milling out foam models and a standard negative plaster-bandage wrap cast as a mould in which liquid plaster could set for plaster models.Three independent observers were trained to use both the Artec Eva and Romer scanners. Prior to data collection, they completed two 2-hour familiarisation sessions, using both scanners. Ten different sessions were organised to measure each of the ten selected residual limb models. During each session, each model was measured three times by each observer with the two different scanners. This resulted in a total of 18 measurements (2 scanners × 3 observers × 3 repetitions) per model/session. The observers performed the measurements in randomised order (both observer sequence and scan sequence), with a 10-min break between each scan. Time per scan was between 1 and 3 minutes for the Artec Eva and Romer scanners, respectively. Prior to scanning, three 4 mm diameter hemispherical adhesive markers made of soft rubber were placed on the 3D surface of each residual limb model to approximately identify three anatomical landmarks for transfemoral (greater trochanter, Scarpa’s triangle and ischial tuberosity) and transtibial (tibial crest, fibula head and popliteal fossa) models. The distal borders of these markers were used to determine a plane used as the proximal end of each scan. Each model was placed on a metrology table for the Romer scans and on a normal table for the Artec Eva scans, with the distal end of the residual limb pointing upward.
Artec Eva and Romer data files were processed using the same software used for data collection: Artec Studio 9.2 (Artec Group, Luxembourg, Luxembourg) and Geomagic Studio 2014 (Geomagic - 3D Systems, USA). Both the Artec Eva and Romer mesh models were exported and aligned manually in the same reference system x, y and z, using a graphical user interface according to the positions of the anatomical markers on the model. The volume of each residual limb model was calculated using the distal end to the proximal end of the residual limb as indicated by the plane and defined by the three anatomical markers. Parallel to this first plane 19 other planes were defined across the residual limb volume, obtaining a set of 20 parallel different sections at intervals of 5% across the residual limb length, with the first section (i.e. 0%) indicating the first proximal section of the residual limb model. For each section created by the 20 planes the relative Cross Sectional Area (CSA) and the perimeter (PE) were calculated. To assess residual limb model geometrical differences between the two scanners, the Root Mean Square Error (RMSE) between each pair of aligned scans was calculated. In addition, assuming the residual limb to be confined in a bounding box, residual limb sizes (width, depth and length) along three axes (x, y, and z, respectively) were calculated. Body Centre of Mass (BCOM) coordinates were calculated assuming the material of the models to be homogenous. For these calculations, each volume was processed using the Compute geometric measures filter in Meshlab software.The use of high precision and resolution laser scanner has been suggested for evaluation of new scanning systems. For this reason, we used the Romer high precision and resolution scanner (Romer scanner, CMS108, Hexagon, UK) as the criterion measure to validate the ‘trueness’ of the Artec Eva scanner (practical measure). The Romer scanner is a powerful tool integrated with a Romer coordinate measuring arm that comprises different rotation axes to allow freedom of movement. It uses a laser line to reconstruct the 3D model with an accuracy of about 0.04 mm. In contrast, the Artec Eva (practical measure) is relatively small and uses regular flash bulb technology, illuminating the object with patterns of stripes by normal visible light to reconstruct 3D data from the surface with a reported accuracy of 0.5 mm.STATISTICAL ANALYSIS: Accuracy of the Artec Eva scanner was assessed in terms of validity (trueness) and reliability (precision) using the statistical approach suggested by Hopkins, which was used to assess the validity and reliability of DEXA imaging methods. The following scanning variables were considered: residual limb volume, residual limb sizes (width, depth and length) along the three different axes (x, y, and z, respectively), BCOM coordinates, CSA and PE for each of the 20 levels of the residual limb length, where level 0 was defined as the plane passing through the distal border of the three anatomical/reference points. To ensure normality of the sampling distribution, each measurement was log transformed before analysis and back transformed after analysis. Log transformation was necessary to ensure uniformity of error, particularly where larger values of the original variable have greater absolute but a similar relative (%) error. Log transformation was not applied to the BCOM variables, since they were expressed in the relative Romer reference system
Dataset for "Using Finite Element Analysis to Influence the Infill Design of Fused Deposition Modelled Parts"
No full textThis data archive contains the underlying data for the conference publication entitled "Using Finite Element Analysis to Influence the Infill Design of Fused Deposition Modelled Parts". The paper has been accepted for publication in the journal: Progress in Additive Manfacturing.
The paper describes a process that uses results attained from Finite Element Analysis (FEA) to influence the design of the internal structure (i.e. infill) of 3D printed parts by locally varying the composition of the infill based upon the associated stress values.The zip file contains G-code (.x3g, .gcode) written for MakerBot 3D printers
Dataset for "Thermal comfort in desert refugee camps: An interdisciplinary approach"
Get PDFSocial and thermal comfort survey were conducted in Azraq and Zaatari refugee camps in Jordan in summer 2016 and winter 2017, The results are published in "Albadra et al; Thermal comfort in desert refugee camps: an interdisciplinary approach. Building and environment, 2017 (124) pp. 460-477". Two sets of data (thermal comfort survey data and social survey data) are included in .xlsx format, in addition to the thermal comfort survey questionnaire used in the survey as .docx.The families were selected randomly. Given the range of backgrounds, intra-household dynamics, education and literacy levels, all surveys were administered through interview. The questions were explained in detail in order to guarantee common understanding amongst occupants. The summer survey consisted of 75 families (38 families in Azraq and 37 families in Zaatari). Fifty-six of the 75 families were visited again for the winter survey, and an additional 24 families were interviewed in winter to compensate for those who were not available. The respondents were interviewed in their residence (shelters). First, the respondents as a family unit were asked to answer the social survey questionnaire; all family members present discussed the questions and one response per family per season was recorded as the main interest of the social survey was to find out, what aspects of the shelter design worked (or didn’t) for them as a family. This took about twenty minutes allowing them to physically acclimatise in case they were doing other activities prior to the survey. Then they were asked individually about their thermal sensation and thermal preference while spot measurements of indoor environmental variables were recorded using hand-held devices at 1m high. Respondents’ height, weight, age, clothing level, and activity level, were noted. The thermal comfort survey: In order to address ambiguities in translating the ASHRAE Thermal sensation scales; the respondents were first asked whether they felt absolutely neutral (hiyadi) or felt a sensation of heat or cold. If they answered neutral, (hiyadi), their thermal sensation was registered as such. If they said they felt a discomfort or sensation of heat or cold, then they were asked to say on a scale 1 to 3 how hot or cold they felt with 1 being a little bit, and 3 being too much. A similar numerical approach was used for the thermal preference scale
Dataset for 'Modelling and fabrication of porous sandwich layer barium titanate with improved piezoelectric energy harvesting figures of merit'
No full textExperimental and averaged finite element modelling data for barium titanate with a porous sandwich layer surrounded by dense outerlayers. The experimental data includes measurements of piezoelectric strain coefficients, permittivity and energy harvesting figure of merit for sandwich layer porosity of 50 and 60 vol.% and varying porous layer thickness.
The data from the finite element modelling includes calculated effective piezoelectric and dielectric properties for barium titanate with sandwich layer porosity of 0 to 60 vol.% and porous layer relative thickness of zero (i.e. no porous layer) to one (i.e. no dense outerlayers), as well as detailing the fraction of material poled due to the distribution of porosity throughout the complex structure.Experimental data was obtained from measuring material properties of barium titanate with a high porosity layer sandwiched between dense outerlayers.
Modelled data was obtained from finite element analysis of porous sandwich layer barium titanate with varying layer porosity and porous layer relative thickness.
Details of sample preparation, experimental methods and equipment and modelling methods are given in the article 'Modelling and fabrication of porous sandwich layer barium titanate with improved piezoelectric energy harvesting figures of merit,' Acta Materialia 128 (2017) 207-217. The accepted author manuscript is available from https://researchportal.bath.ac.uk/en/publications/846c5e90-b502-4024-b670-74fed7738708
Justification in Judgment
No full textThis dataset contains the data from two experiments investigating to what degree process accountability motivates decision makers to shift from retrieval of past exemplars to rule-based integration processes. The first experiment concerns retrieval-based configural judgement tasks, while the second concerns elemental judgement tasks requiring weighing and integrating information. The data includes results from both training trials and test trials, the images used for pictorial stimuli, the code needed to run the experiments in PsychoPy, and the code used to analyse the results in R.For details of the data collection methodology, see the associated publication.Experiments were run in PsycoPy (https://www.psychopy.org/) and analyses were conducted in R
Energy behaviour change model validation
Get PDFThis dataset includes psychological data (environmental values, success expectancy, perceived barriers for pro-environmental behaviour, general energy literacy) and electricity consumption data for 20 households in Exeter, UK. This dataset was created within the ENLITEN project funded by EPSRC (grant number EP/K002724/1)The conceptual model described in the paper, “A Cognitive Agent-Based Model for Energy Behaviour Change Interventions” (Mogles, N., Padget, J., Gabe-Thomas, E., Walker, I., Lee, J.) was implemented in the MATLAB environment.
The file containing the code for running model simulations is called energy_beh_change_model.m and it was run using MATLAB version MATLAB_R2014b
Data set for "Acceleration of Convergence to Equilibrium in Markov Chains by Breaking Detailed Balance"
No full textSimulation result and Matlab code used in the paper "Acceleration of Convergence to Equilibrium in Markov Chains by Breaking Detailed Balance
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