1056 research outputs found
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Dataset for "Structural Evolution of Iron Forming Iron Oxide in a Deep Eutectic-Solvothermal Reaction"
No full textThis dataset contains reduced data from small angle neutron and small angle X-ray scattering experiments, and wide angle scattering S(Q) data as described in the manuscript. These data can be used to model and determine the nanostructure of choline chloride:urea deep eutectic solvents containing iron species, followed during reaction with time as they react and convert into iron oxide nanoparticles. Data are presented as .csv files, and the file names are indexed to samples in the initial file (also .csv) in each folder.Small-angle neutron scattering (SANS) data were collected using the BILBY small angle scattering instrument located at the OPAL reactor neutron source of the Australian Nuclear Science and Technology Organisation, Sydney, Australia. Samples were placed into ‘banjo’ Hellma cells of pathlength 1 mm and sealed with a PTFE plug. The quartz cells were placed into individual metal holders to ensure uniform heat distribution and measured at 90 °C for up to 10h (until the reaction was complete).
SAXS measurements were made using a Xenocs nano-inXider instrument provided by the Materials Characterisation Laboratory of the STFC ISIS Neutron and Muon source. Samples were prepared by sealing 0.5 g of ChCl:urea:iron nitrate:(water) stock solutions into small vials. Samples were placed into an air-circulating temperature-regulated oven at 90 °C and removed after hourly intervals and at 90 min. At the time of removal, samples were placed directly into a freezer until use. Prior to X-ray measurement, the samples were placed into quartz glass X-ray capillaries of 1.5 mm diameter and 10 μm wall thickness and sealed using beeswax. Capillaries were measured at room temperature for several hours.
Neutron S(Q) data were collected using the NIMROD diffraction instrument at TS2 of the STFC ISIS neutron and muon facility. Samples were measured into flat-plate TiZr cells of 1mm pathlength and measured for 2 hours. X-Ray data were collected with a Panalytical X'Pert PRO II instrument, with samples loaded into quartz glass X-Ray capillaries of 2 mm pathlength.SANS data were radially averaged and background subtracted at the point of data collection using an unreacted stock solution of iron nitrate in either pure or hydrated ChCl:urea. The provided files are corrected, background subtracted data files.
SAXS data were radially averaged and background subtracted at the point of data collection using an unreacted stock solution of iron nitrate in either pure or hydrated ChCl:urea. The provided files are corrected, background subtracted data files.
Neutron and X-ray diffraction data data were normalised, reduced and processed using standard procedures in GudrunN (neutron) and GudrunX (X-Rays). The provided files are corrected total scattering files, suitable for use in EPSR modelling of scattering data.SANS data were fitted using the open source SASView software package (www.sasview.org) using a model for particles with an oblate spheroid platelet geometry.
SAXS data were analysed using the ATSAS software package (https://www.embl-hamburg.de/biosaxs/software.html); seven cycles of a simulated annealing routine in DAMMIF were averaged and filtered, before passing the filtered system through a final refinement in DAMMIN.The neutron and X-ray diffraction data can be modelled using EPSR, more details of which can be found here: https://www.isis.stfc.ac.uk/Pages/Empirical-Potential-Structure-Refinement.aspx
The raw uncorrected neutron diffraction data can also be downloaded from DOI: 10.5286/ISIS.E.RB1620292 and 10.5286/ISIS.E.RB162047
GJMeijer/FBMcw
No full textThis package contains R code and an interactive Shiny application associated with the generic form of fibre bundle models for soil reinforcement by plant roots (FBM, FBMcw) as developed by Meijer (2021)
Dataset for "The Impact of Solution Chemistry on Growth and Structural Features of Mo-substituted Spinel Iron Oxides"
No full textThis dataset contains the computational data and analysis for the paper "The Impact of Solution Chemistry on Growth and Structural Features of Mo-substituted Spinel Iron Oxides." It includes input and output files for the DFT calculations, performed using VASP, and lightweight analysis scripts used to plot some of the figures in the associated publication.All calculations were performed using VASP. Input files for each calculation are contained within the dataset.Relevant data were extracted using the included Python scripts, these can be run on the data as-provided to regenerate the data used to plot the relevant figures in the associated publication. These scripts are contained within the directory top-level directory.A range of open-source Python packages are required to run the included scripts including pymatgen, numpy and pandas, which are available from PyPI.
An additional python package, "py-sc-fermi", is required to re-run the analysis scripts. It can be accessed on GitHub: https://github.com/bjmorgan/py-sc-fermiThe dataset is downloadable as a gzipped tar file (.tgz). To extract the files run
```
tar -xzvf mo-doped_fe2o3.tgz
```
To rerun the DFT calculations in this dataset the appropriate pseudopotentials are needed. These are not included in this dataset due to the VASP license conditions. Each calculation directory contains a corresponding `POTCAR.spec` file that specifies the pseudopotentials used. These calculations use pseudopotentials from the VASP 5.4 set
Dataset for "Imaging of strong vortex pinning in GdBaCuO high temperature superconducting tapes under external fields"
No full textThis dataset contains data from the research into the vortex pinning in GdBaCuO superconducting tapes under external fields. The bulk of the data is Scanning Hall Probe Microscopy (SHPM) image files. The remainder of the data includes; electronic transport data, EDX and SEM data files and files for B-H loops.The data was acquired by scanning Hall microscopy (SHM) in the rapid 'flying' mode that makes a rapid 2D magnetic scan of the maximum field of view. The data files consist of individual SHM images collected during these scans along with the appropriate text files for their averages.
B-H loops were measured via the SHPM in fields up to 107mT and 3.2mT.
Transport data was performed in an Oxford Instruments Cryostat cooled by liquid Nitrogen. A current of 10mA was supplied while the voltage was measured using a Keithley 182 nanovoltmeter as the temperature was swept at 0.1K/min using an OI ITC 503 temperature controller.
Field Effect Scanning Electron Microscopy (FE-SEM) at 5kV and 1kV and Energy Dispersive X-Ray Analysis (EDX) at 10kV were performed in a Joel JSM-6301F FE-SEM.The SHM image data in the archive are raw as-captured data without any post-processing.SHM image datasets are formatted as the magnetic induction in Gauss measured at each point on a 1 128× 128 array of pixel positions. Temperature was varied between 10K and 88K.
The scan area changes between temperatures with the following fitting:
Area=(-1.037e-08*Temperature^4 + 5.547e-06*Temperature^3 + -0.0009229*Temperature^2 + 0.2264*Temperature + 6.483)
EDX data must be viewed via Oxford Instruments analysis suite
Dataset for "Personalised HTO with patient specific plates has mechanical safety equivalent to generic HTO: findings from a novel case-control in silico clinical trial"
No full textTo compare the mechanical safety of a new personalised 3D printed high tibial osteotomy (HTO) device with an existing generic device, a novel case-control in silico virtual clinical trial was conducted (ClinicalTrial.gov NCT03419598). Twenty-eight knee osteoarthritis patients underwent CT scanning to create a virtual cohort; the cohort was duplicated to form two arms, Generic and Personalised, on which virtual HTO was performed. Finite element analysis was performed to calculate the stresses in the plates arising from simulated physiological activities at three healing stages, simulating 2, 4 and 12 weeks after surgery.
The dataset includes the demographic information of the subjects, the applied loads, the summarised output data from the finite element models, namely the maximum Von Mises stress (MPa) in the plate and the maximum Von Mises strain (unitless) in the tibial bone adjacent to the screws holding the plate to the tibia.Data from case-control in silico clinical trial aimed at assessing the mechanical safety of a personalised high tibial osteotomy plate. Data is generated from finite element models created and solved using ANSYS R18.2. The primary outcome was the peak Von Mises stress (MPa) in the plate, the secondary values were the peak Von Mises strain in bone where the screws holding the plates were located and the interfragmentary motion of fracture gap. All these were extracted from the ANSYS results file using APLD commands to extract the data of interest. The data was in the form of Von Mises stress and Von Mises strain from all relevant nodes.The data was processed in Matlab (v2017b) to obtain the peak values.ANSYS R18.2
Matlab v2017bSummary data described in meta-data, stored as plain text format
Matlab data file stores vtrial data structure, self-documented and further documented in meta-dat
Distribution Network Design for Syrian Refugees in Kilis Optimising cost and reach: parameters and solutions
No full textThese data sets are prepared and used within a humanitarian operations project entitled "Innovating the Turkish supply chain for services in humanitarian aid".
The initial data set includes the GIS coordination and population information of a central depot (Municipality building), 18 potential service points (2 hospitals, 1 Red Crescent office, and 15 schools) together with 187 (109 villages and 78 neighbourhoods) main locations where Syrian refugees inhabit in the Kilis province in southern Turkey. Locations are classified as Villages (Köy) and Neighbourhoods (Mahalle) based on administrative divisions of Turkey. A matrix of pair-wise distances (in Km) among the identified locations is provided accordingly. The population of each identified demand location, which is obtained from the local government in 2018, is provided as well.
Two additional data sets, given in zip files, are representing the supplementary data for an academic paper submitted to EJOR during the above-mentioned project. The first zip file provides parameters and solutions of 505 instances corresponding to different capacity configurations of E-voucher distribution network for Syrian refugees in Kilis. The second zip file, provides some relatively small dummy instances, created from TSPLIB networks, for algorithm evaluation and benchmark purpose. A ‘README’ file is given in each of them explaining the details of parameter files and layout of the data to enable OR practitioners to solve the same instances by alternative approaches for comparison and assessment purposes.This dataset identifies communities by type, district, longitude, lattitude and population size (where applicable). The nodes are numbered. In a second tab, distances between nodes are calculated using geospatial software
Dataset supporting the paper: High trait anxiety enhances optimal integration of auditory and visual threat cues
No full textThis dataset includes data on behavioural outcomes for the audiovisual emotion recognition tasks used in the publication, "High Trait Anxiety Enhances Optimal Integration of Auditory and Visual Threat Cues". In this study the authors investigated perception of happy, sad and angry emotions within unimodal (audio- and visual-only) and audiovisual displays in adults with low vs. high levels of trait anxiety. The data is organised to facilitate replication of the analyses carried out in the aforementioned study, which includes two model-based analyses to elucidate how multisensory integration of emotional information operates in high trait anxiety. This was done by comparing performance in the audiovisual condition for both high and low trait anxiety groups to performance predicted by the Maximum Likelihood Estimation (MLE) model (Ernst & Banks, 2002; Rohde et al., 2016) and Miller’s Race Model (Miller, 1982; Ulrich et al., 2007). Data included in this dataset has already been pre-processed (i.e., univariate outliers have already been identified and dealt with).This dataset contains data for behavioural outcomes from the audiovisual emotion recognition task described in the paper: "High Trait Anxiety Enhances Optimal Integration of Auditory and Visual Threat Cues". Participants made emotional judgements about dynamic face and voice stimuli, having to classify them as being either happy, angry or sad by a making a speeded key press. The task included three different stimulus modality conditions: visual-only (faces), audio-only (voices) and audiovisual (faces and voices together). The stimuli were approximately 500ms long and visual noise was added to the central face region of the videos by the addition of a Gaussian blur.
Participants were screened before they were recruited in the study such that only individuals with a trait anxiety score of 37 or below (low anxiety group) and 48 or above (high anxiety group) were included. The trait subscale of the Spielberger State-Trait Anxiety scale was used to measure trait anxiety.All the data in this dataset has been anonymised prior to sharing. Univariate outliers were identified as datapoints lying more than +/- 3 times the value of the interquartile range from the median value. Outliers have been winsorized prior to sharing (i.e. replaced with the next most extreme non-outlier value in the dataset).Analyses of violation of the race model inequality were carried out using the RMITest software which implements the algorithm described in Ulrich et al. (2007).Information on the layout of the data has been provided in the README sheet in each Excel spreadsheet file
Data sets for "Structure and dynamics of aqueous NaCl solutions at high temperatures and pressures"
No full textData sets used to prepare Figures 1-11, 13-16, 18-19, 21-23, 25-26, 28, 30-31, S1-S9 and S12 in the Journal of Chemical Physics article entitled "Structure and dynamics of aqueous NaCl solutions at high temperatures and pressures." The data sets refer to the structure and dynamics of a solution of 5 molal NaCl in heavy water (D2O).The data sets were collected using the methods described in the published paper.The data sets were analysed using the methods described in the published paper.The figures were prepared using QtGrace (https://sourceforge.net/projects/qtgrace/). The data set corresponding to a plotted curve within an QtGrace file can be identified by clicking on that curve.The files are labelled according to the corresponding figure numbers. The units for each axis are identified on the plots
Dataset for: "Solitons near avoided mode crossings in χ⁽²⁾ nanowaveguides"
No full textThis dataset contains modelled dispersion of effect refractive indices for three guided modes for a particular lithium niobate nanowaveguide structure. Two of these guided modes show a particularly interesting characteristic of an avoided mode crossing which the work in the associated manuscript is based on. Data is also included for the predicted modulation instability gain for continuous wave solutions in the modelled system. Simulation data for modulation instability are also included showing its impact on the spectrum and formation of trains of solitons from an unstable continuous wave solution. This work also contains predictions made for the solitons that exist in this system. We include data for predicted pulse energy as well as mapping these solitons from their plane of existence onto the linear dispersion of the system. XFROG spectrograms are given for an example soliton solution showing their unusual specro-temporal structure as well as line plots of many solitons temporal and spectral domain separately. Finally we include simulations of soliton propagation in this system.Effective refractive index data was modelled using COMSOL Multiphysics (5.3a) combined with self-written code in MATLAB 2020b. Production of all other data is described in the associated publication.The data is organised into folders for each figure and subfigure panel.
The data is given in ".csv" format with each data column labelled with units where appropriate
Dataset for "The Effect of Crankshaft Phasing and Port Timing Asymmetry on Opposed-Piston Engine Thermal Efficiency"
No full textOpposed-piston, two-stroke engines reveal degrees of freedom that make them excellent candidates for next generation, highly efficient internal combustion engines for hybrid electric vehicles and power systems. This dataset contains results from GT-SUITE simulations of an opposed-piston, two-stroke engine that explore the influence of key control and geometrical parameters, specifically crankshaft phasing and intake and exhaust port height-to-stroke ratios, in obtaining best thermal efficiency. The results are generally reported in the form of contours of brake specific fuel consumption (BSFC), as a function of the aforementioned geometrical parameters of interest. This dataset contains folders corresponding to each figure in the corresponding article. Each folder contains the MATLAB code and any data required to generate the figure.This dataset contains results generated by GT-SUITE simulations of an opposed piston two-stroke engine model, as described in the corresponding article (https://doi.org/10.3390/en14206696). That article describes the simulation set-up and the different cases that were simulated to generate these results.The results in the corresponding article (https://doi.org/10.3390/en14206696) are generally reported in the form of contours of brake specific fuel consumption (BSFC), as a function of certain geometrical parameters of interest. This dataset contains folders corresponding to each figure in the corresponding article. Each folder contains the MATLAB code (.m files) and any data (.mat files) required to generate the figure. The interested party can run the MATLAB .m file to generate the corresponding figure.GT-SUITE and MATLAB were used respectively to generate and plot the values contained in the dataset. But only MATLAB is required to regenerate the plots in the corresponding article (https://doi.org/10.3390/en14206696).The data is contained in MATLAB .mat files
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