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Dataset for "Current and Future Test Reference Years at a 5km Resolution"
Get PDFThe weather data (.EPW format) was created for use in building simulations. The weather data is available at a 5km by 5km spatial resolution across the UK. Thus, building practitioners are able to assess thermal performance of buildings with localised weather for more convincing results. In addition, it is possible to investigate spatial variability of thermal performance of buildings for the whole UK.Current and future weather data for use in building simulations were created based on the synthetic weather data provided by Newcastle University. The synthetic weather data have been generated from the Spatial Urban Weather Generator (SUWG) for each 5km by 5km grid over the whole UK landscape. There are eight types of weather files available at a 5km grid resolution for the UK which comes to 11326 locations in total. The methodologies used for the creation of type A, B, C, D, E and F weather files can be found in published articles. The methods for type G and H can be found in the README file.
Type A: 10th, 50th, and 90th percentile TRYs and DSYs for the control year (i.e. 1961 - 1990).
Reference: Eames, M., Kershaw, T. and Coley, D., 2011. On the creation of future probabilistic design weather years from UKCP09. Building Services Engineering Research and Technology, 32(2), pp.127-142. https://doi.org/10.1177/0143624410379934
Type B: 10th, 50th, and 90th percentile TRYs and DSYs for the 2080s (i.e. 2070 - 2099).
Reference: Eames, M., Kershaw, T. and Coley, D., 2011. On the creation of future probabilistic design weather years from UKCP09. Building Services Engineering Research and Technology, 32(2), pp.127-142. https://doi.org/10.1177/0143624410379934
Type C: 10th, 50th, and 90th percentile TRY-max and TRY-min for the control year (i.e. 1961 - 1990).
Reference: Liu, C., Chung, W., Cecinati, F., Natarajan, S., & Coley, D. (2019). Current and future test reference years at a 5 km resolution. Building Services Engineering Research and Technology. https://doi.org/10.1177/0143624419880629
Type D: 10th, 50th, and 90th percentile TRY-max and TRY-min for the 2080s (i.e. 2070 - 2099).
Reference: Liu, C., Chung, W., Cecinati, F., Natarajan, S., & Coley, D. (2019). Current and future test reference years at a 5 km resolution. Building Services Engineering Research and Technology. https://doi.org/10.1177/0143624419880629
Type E: 10th, 50th, and 90th percentile HSYs for the control year (i.e. 1961 - 1990).
Reference: Liu, C., Kershaw, T., Eames, M.E. and Coley, D.A., 2016. Future probabilistic hot summer years for overheating risk assessments. Building and Environment, 105, pp.56-68. https://doi.org/10.1016/j.buildenv.2016.05.028
Type F: 10th, 50th, and 90th percentile HSYs for the 2080s (i.e. 2070 - 2099).
Reference: Liu, C., Kershaw, T., Eames, M.E. and Coley, D.A., 2016. Future probabilistic hot summer years for overheating risk assessments. Building and Environment, 105, pp.56-68. https://doi.org/10.1016/j.buildenv.2016.05.028
Type G: SSHW for the control year (i.e. 1961 - 1990).
Type H: SSHW for the 2080s (i.e. 2070 - 2099).
Type I: All the above weather types: pTRY, pDSY, pTRY-max, pTRY-min, pHSY and SSHW for the 2020s (2010 - 2039)The Spatial Urban Weather Generator was used to generate current and future weather data for 11326 grid locations across the UK. MATLAB R2016b and the Balena HPC provided by the University of Bath have been used to process the synthetic weather data of which size is approximately 30 TB. The .epw format is a plain text format that may be processed with EnergyPlus open source software (https://energyplus.net/); the format is documented in the Auxiliary Programs documentation accompanying the software
Dataset for "‘Double’ Displacement Talbot Lithography: fast, wafer-scale, direct-writing of complex periodic nanopatterns"
Get PDFThis study developed a new low-cost nanolithographic tool for creating periodic arrays of complex, nano-motifs, across large areas within minutes. Displacement Talbot Lithography is combined with lateral nanopositioning to enable large-area patterning with the flexibility of a direct-write system. This enables the creation of different periodic patterns in short timescales using a single mask with no mask degradation. The dataset includes images of Matlab models (in .csv format) and SEM experimental pictures of the different experiments realised (discrete lateral illumination, continuous displacements during one illumination).Secondary electron images were captured using a Hitachi S-4300 scanning electron microscope (SEM). An accelerating voltage of 5 kV was used to collect the images.
The modelling has been performed by a code in MATLAB
Dataset for "Intrinsic flexibility of the EMT zeolite framework under pressure"
No full textA collection of the data used, and reported in the article "Intrinsic flexibility of the EMT zeolite framework under pressure". This includes high pressure powder X-ray diffraction data collected on the ID15B and ID27 beamlines at the European Synchrotron Radiation Facility (ESRF). It also includes the data from first-principles comparative DFT and lattice-dynamics calculations to calculate the lattice energy and vibrational entropy of the EMT and FAU frameworks. The purpose of this research was to gain a more coherent understanding as to the role of the organic additive 18-crown-6 ether to differentiate between synthesis of EMT and FAU-type zeolites. From the results, it is demonstrated that the 18-crown-6 ether molecule plays a crucial role in the free-energy of crystallisation, driving the framework assembly process towards the EMT topology.The high pressure X-ray diffraction data within this archive was collected on the ID15B and ID27 beamlines at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. The data collection process was as follows:
The samples were first loaded into diamond-anvil cells (DACs), suspending in a non-penetrating pressure-transmitting medium (PTM) alongside a ruby chip. On the ID15B beamline, the PTM used was Daphne 7373 oil, the incident X-ray radiation was of wavelength 0.4113 A (Angstroms), and calibrations were performed using silicon. As for the ID27 beamline, the PTM used was silicone oil, the incident X-ray radiation of wavelength 0.3738 A, and calibrations performed with cerium dioxide (CeO2).
The DAC pressure was increased in gradual steps, with three 2D diffraction images taken at each pressure step. The pressure was recorded by exciting the ruby chip with a laser and determining the shift of the R1 emission line. The pressure was recorded before and after each pressure point, with an average calculated. The sample was compressed until pressure-induced amorphisation was seen to be imminent. Following this the samples were decompressed to ambient conditions, with several diffraction images taken during the this cycle.
Concerning the computational modelling, density-functional theory (DFT) calculations were performed on the empty FAU and EMT framework structures using the plane-wave pseudopotential code VASP. The PBEsol generalised-gradient approximation functional, including the semi-empirical DFT-D3 dispersion correction (PBEsol+D3) were used to determine the quantum-mechanical exchange and correlation. Plane-wave basis augmented-wave (PAW) pseudopotentials of the O 2s/2p and Si 3s/3p electrons in the valence shells were used to represent the electronic structures of the relevant atoms. The PAW projection was performed in real space, and the structures fully optimised. The influence of solvent molecules occupying the framework cages was calculated using the implicit-solvent VASPsol model. Bulk moduli was obtained by compressing/expanding the optimised structures by 1% volume increments, and re-optimising at fixed volumes before fitting the energy/volume curves to the Birch-Murnaghan equations of state. Lattice-dynamics calculations were performed on the optimised structures using the Phonopy package, with a finite-displacement step size of 10^-2 A. To calculate the phonon density of states (DoS) curves, the phonon frequencies were interpolated onto a regular Γ-centered grid of q-points, with 24x24x24 subdivisions. The simulated infrared spectra were calculated using the density-functional perturbation theory (DEPT) routines in VASP, within the open-source SpectroscoPy package. Calculations of the phonon frequencies, elastic constants and Born changes, the PAW projection was applied in reciprocal space.Before analysis of the high-pressure diffraction data, the 2D diffraction images were processed in the following way. The three images taken at each pressure point were averaged using the FIT2D software, producing an average image. The area of the 2D image was subsequently integrated over in the Dioptas software, to produce a 1D diffraction pattern. This produced the .xy files found in this archive. The .xy files were used in the TOPAS Academic software using Pawley refinements in order to calculate the unit cell parameters at each pressure point. First the refinement at ambient conditions was performed, with subsequent refinements performed using the Batch mode. In this mode, the refinement process is iterative, meaning the input structure for each pressure point is the output from the automated refinement from the previous pressure point. For both the filled and empty zeolite EMC-2 samples the P63/mmc space group was used in the Pawley refinements. The bulk moduli were determined using the PASCal webtool, using data within the 0-2.2 GPa range. The data was fit to the 2nd order Birch-Murnaghan equation of state, weighted using the estimated 0.1 GPa pressure error within the DACs.The relevant article and dataset include the flexibility window of the EMT framework (filled and empty) simulated using the GASP software. These geometric simulations have been performed previously, and are referenced in "Methodology link" below
Dataset for "The Impact of Physical Inactivity, Ageing, and Nutrition on Adipose Tissue Function"
No full textThese files contain the raw expression (in FPKM) data from RNA sequencing data sets generated from adipose tissue samples collected before and at the end of a long-term head-down tilt bed rest study in healthy young males (ref: AO-13-BR). These files are also RNA sequencing data sets generated from adipose tissue and skeletal muscle samples collected from healthy young and older adults in a cross-sectional study (ref: 16/SW/0003).Adipose tissue samples were collected under local anaesthesia from abdominal subcutaneous adipose tissue depots, 5cm lateral to the umbilicus by needle aspiration. Skeletal muscle samples were collected from the Vastus lateralis using the Bergstrom biopsy technique . All samples were collected following an overnight fast in the rested state.
Samples were briefly cleaned of visible signs of blood, weighed and immediately snap frozen in liquid nitrogen and stored at -80 degrees until analysis.
Tissue samples were thawed and digested in QUIzol reagent, to extract RNA fractions. RNA was DNase treated and QC'd to assess sample quantity and quality.
Samples at a set concentration were sent for next-generation sequencing at the Wellcome Trust Oxford Genomics Centre on a HiSeq4000 Illumina instrument, Deoxynucleotide triphosphate (dUTP) was incorporated into the second strand, to facilitate selective degradation of dUTP-tagged cDNA to generate a cDNA library suitable for sequencing. cDNA was end-repaired, A-tailed, and adapter-ligated. Samples also underwent uridine digestion. Prepared libraries were size-selected, multiplexed across 8 lanes, and quality controlled before paired end sequencing over 12 lanes of a flow cell.
FastQ sequencing files were uploaded to the Galaxy web platform (usegalaxy.org) for quality control analysis. Raw sequencing files were splice-aligned to the GRCh38/hg38 reference genome using Hisat2 with a mapping distance <500 kb between reads.
Genome annotation was performed using Ensembl against the reference genome.
Expression levels (fragments per kilobase of transcript per million mapped reads (FPKM]) were estimated using Stringtie.Equipment and software used:
- 14G adipose biopsy needle and 10 or 50mL syringe
- Bergstrom biopsy needle.
- Quizol reagent.
- DNase I and DNase buffer (Qiagen)
- RNase/DNase free water
- Phenol-Chloroform-Isoamyl alcohol 25:24:1
- Tris EDTA
- Sodium acetate
- nucleotide free water
- Qubit
- SpectroStar Nano
- Agilent tapestation 4200
- usegalaxy.org
- Hisat2
- Ensembl
- Stringti
Dataset for "Atomic dispensers for thermoplasmonic control of alkali vapor pressure in quantum optical applications"
No full textThis dataset contains data supporting the results presented in the paper "Atomic dispensers for thermoplasmonic control of alkali vapor pressure in quantum optical applications". It includes the data used to plot each figure, together with the raw oscilloscope data in .csv format, associated with this publication.
This study uses plasmonic nanoparticles as an alternative to the conventional means, such as bulk heating or laser desorption to convert light into localized thermal energy and to achieve optical depths in warm vapors, which was proven to produce far improved results. The response is over a thousand times faster than previously observed corresponding to a ~16 times increase in vapour pressure in less than 20 ms., with possible reload times much shorter than an hour. The results enable robust and compact light-matter devices, such as efficient quantum memories and photon-photon logic gates, in which strong optical nonlinearities are crucial.
Supplementary Information of the publication contains more details on the methodology and data preparation.Full details of the methodology may be found in the supplementary information of the associated paper.
.CSV files were recorded with an oscilloscope using the setups in Figures 2 and 6a of the paper.
Extinction spectra were recorded with a commercial Applied Photophysics Chirascan. All spectra were recorded over the range of 300 nm – 1100 nm with a resolution of 1 nm.
AFM profile was obtained with a Multimode Scanning Probe Microscope (Veeco, Plainview, NY) with a Nanoscope IIIA controller in contact mode in ambient conditions.Full details of how the data were processed may be found in the supplementary information of the associated paper
Dataset for "Temporal quadratic solitons and their interaction with dispersive waves in Lithium Niobate nano-waveguides"
No full textThis dataset includes simulated effective refractive index and dispersion data for two examples of Lithium Niobate nanowaveguides and XFROG spectrogram data from simulated propagation of different nonlinear pulses in those two example waveguides.
The refractive index data was collected by simulating the waveguides in COMSOL multiphysics (5.3a), a commercial eigenmode solver. The dispersion data was produced by processing the refractive index data (as described in the associated publication). The XFROG spectrogram data was produced by simulating the propagation of pulses in the two waveguides using the well known Split-step Fourier method. The method by which the XFROG spectrograms is briefly explained in the associated publication.
The associated publication is open access.The data collection methods are described in detail in the associated publication which is open access.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.
Data for figures 2, 3 and 4 are given.
The data for figure 1 is omitted as it is simple to reproduce from the explanation in the associated publication
Dataset for "Hydration status affects thirst and salt preference but not energy intake or postprandial ghrelin in healthy adults: A randomised control trial"
No full textThis dataset provides the data collected for a trial investigating the role of hydration status on glycaemic regulation and appetite control in healthy adults (n = 16; n = 8 male). The trial was a randomised crossover trial, with each trial arm lasting 5 days. The first 3 days were lifestyle monitoring, day 4 was a dehydration/rehydration day (including lifestyle monitoring), and day 5 was the full trial day. The trial arms were hypohydrated (HYPO), or rehydrated (RE).
The data for the project relating to glycaemia has been previously published (https://doi.org/10.15125/BATH-00547) and may be useful for further analysis of this dataset, which relates to the appetite part of the study. Key hydration biomarkers have been duplicated in this dataset for convenience.This was a randomised crossover study investigating the role of hydration status on appetite control. Participants underwent 3-days of diet and physical activity standardisation, followed by a standardised intervention (hypohydration versus rehydration) day, and then a test day in the laboratory. We tested appetite using visual analogue scales, a desire to consume computer task, an ad libitum pasta meal, and postprandial blood sampling.Data are split by HYPO (hypohydrated trial arm) and RE (rehydrated trial arm). Participant IDs are marked as "HPXX" where the "XX" has been replaced by their participant number (ranging from 01 to 17). Trial registrations can be found at: osf.io (osf.io/ptq7m) and clinicaltrials.gov (NCT02841449)
Interviewing autistic adults: Adaptations to support recall in police, employment and healthcare interviews 2017-2019
No full textThe current study tested the efficacy of different prompting techniques to support autistic adults’ recall of specific personal memories. Thirty autistic and 30 typically developing (TD) adults (IQs > 85) were asked to recall specific instances from their past, relevant to CJS, healthcare, and employment interviews. Questions comprised ‘open questions’, ‘semantic prompting’ (where semantic knowledge was used to prompt specific episodic retrieval), and ‘visual-verbal prompting’ (V-VP; a pie-diagram with prompts to recall specific details, e.g., who, what, where, etc). Half the participants received the questions in advance. Consistent with previous research, autistic participants reported memories with reduced specificity. For both groups, V-VP support improved specificity and episodic-relevance, while semantic prompting also aided recall for employment questions (but not health or CJS). Findings offer new practical insight for interviewers to facilitate communication with TD and autistic adults.Thirty autistic and thirty typically developing (TD) adults (IQs > 85) received an autobiographical memory interview whereby they were asked to recall specific instances from their past, relevant to CJS, healthcare, and employment interviews. Questions comprised ‘open questions’, ‘semantic prompting’ (where semantic knowledge was used to prompt specific episodic retrieval), and ‘visual-verbal prompting’ (V-VP; a pie-diagram with prompts to recall specific details, e.g., who, what, where, etc). Half the participants received the questions in advance ('preparation' condition); the other half did not.
Recalling specific past experiences is critical for most formal social interactions, including when being interviewed for employment, as a witness or defendant in the Criminal Justice System (CJS), or as a patient during a clinical consultation. Such interviews can be difficult for autistic adults under standard open questioning, however applied research into effective methods to facilitate autistic adults’ recall is beginning to emerge
Data for "Validating the use of multi-sensor devices to estimate physical activity energy expenditure in UK military amputees"
No full textThese data were collected to support a study that assessed the influence of a tri-axial accelerometer (Actigraph GT3X+) worn at the side of the hip on the shortest residual limb in combination with a physiological variable (heart rate) versus a research grade multi-sensor device with pre-determined algorithms (Actiheart) on the prediction of physical activity energy expenditure (PAEE) in traumatic lower-limb amputees during walking in order to develop valid population-specific prediction algorithms.
The dataset consists of indirect calorimetric data, accelerometer and heart rate data collected from participants whilst walking on a treadmill at range of velocities and performing an upper body exercise protocol on the arm crank ergometer at incremental speeds with a fixed resistance.Twenty-eight participants [unilateral (n=9), bilateral (n=10) with lower-limb amputations, and non-injured controls (n=9)] completed eight activities; rest, ambulating at 5 progressive treadmill velocities (0.48, 0.67, 0.89, 1.12, 1.34m.s-1) and 2 gradients (3 and 5%) at 0.89m.s-1. Patient information relating to age, body mass, body height, hip and waist circumference, level of amputation and length of rehabilitation was measured prior to commencing the activities. During each task, expired gases were collected using indirect calorimetry, an Actigraph GT3X+ accelerometer was worn on the hip of the shortest residual limb, an Actiheart monitor was attached to the chest using electrodes and a Polar heart rate belt attached around the chest.The Metamax 3B and the three GT3X+ activity monitors were synchronised before use. Breath-by-breath data was exported into Microsoft Excel from the Metamax 3b. PAEE was then calculated using the V̇O2 and CO2 values (l·min-1) from the Metamax in an Excel spreadsheet using the Weir equation. Resting metabolic rate (RMR; kcal·min-1) was subtracted from total energy expenditure to determine PAEE. Metabolic equivalent (METs) were then calculated using measured exercise V̇O2 divided by resting V̇O2 to derive individual METs in the last 2 minutes of each treadmill intensity. Comparisons between accelerometer outputs (PAC) from the Actigraph, PAEE from the Actiheart, heart rate (bpm) from the Polar and criterion PAEE were made between the final two-minutes of each activity (representative of steady-state).
The GT3X+ accelerometer units were downloaded using ActiLife software (ActiGraph, Pensocola, FL, USA). Data was exported to Microsoft Excel in a time and date stamped comma-separated value (CSV) file format. AHR data was ascertained via entering the measured RMR (via indirect calorimetry), age, weight, height and sleeping HR (measured the night before testing) into the ActiheartTM software (Version 4.0.23), according to the manufacturer’s instructions. Activity counts (counts·min-1) from the GT3X+, heart rate (bpm) and PAEE (kcal.min-1) from the Actiheart were then averaged over the corresponding final two minutes of each activity. This data is presented in the attached file.
Statistics:
PAEE estimation models for the GT3X+HR were developed using corresponding data from each task, using multiple linear regression analyses. The dependent variable was indirect calorimetry PAEE (kcal·min-1). The independent variables were PAC (counts·min-1) from the GT3X+ with HR (bpm). Pearson product moment correlation coefficients (r) and coefficients of determination (R2) statistics were conducted to assess the association between the criterion PAEE and predicted PAEE for GT3X+HR, HR and AHR (AHR data; using proprietary group calibration). Standard Error of the Estimate (SEE) statistics was also calculated for each relationship. Ideally the population specific equation (GT3X+HR) Ideally the population specific equation (GT3X+HR) would have been cross-validated using an independent sample. However, this is not always possible in hard to reach populations due to recruitment issues. Therefore, we adopted a leave-one-out analysis as performed previously by Nightingale et al.
Error statistics involved calculating the mean absolute error, mean absolute percentage error and mean signed error for each activity (displayed graphically using modified box and whisker plots), Bland-Altman plots with 95% limits of agreement analysis and root mean squared error (RMSE). One-way ANOVA tests by group were performed with post-hoc Bonferroni corrections applied when comparing across 8 activities (rest, five progressive treadmill speeds and 2 gradients). Statistical significance was set a priori of P<0.05. All analyses were performed using IBM SPSS Statistics 21 for Windows (IBM, Armonk, NY, USA).
Error statistics involved calculating the mean absolute error, mean absolute percentage error and mean signed error for each activity; the later displayed graphically using Bland and Altman plots and limits of agreement analysis. A two way mixed model ANOVA was performed to determine differences between criterion PAEE and predicted PAEE at each treadmill task. Where a significant interaction effect was observed, a Bonferroni correction was applied to Post Hoc tests where multiple comparisons were considered. This was to identify the specific treadmill tasks in which there was a significant difference between the criterion and predicted PAEE. Statistical significance was set a priori of P < 0.05. All analysis was performed using IBM SPSS Statistics 21 for Windows (IBM, Armonk, NY, USA).Technology used:
Accelerometer: Actigraph GT3X+
Actiheart TM
Polar Heart Rate Belt
Treadmill: Woodway Desmo
Portable metabolic system: Metamax 3B
IBM SPSS Statistics 21
Acronyms: RMR = resting metabolic rate, VO2 = volume of oxygen, VCO2 = volume of carbon dioxide, METS = metabolic equivalent, TEE = Total Energy Expenditure, PAEE = physical activity energy expenditure, RPE = rate of perceived exertion, ACE = Arm Crank Ergometer.The tabs along the bottom of the spread sheet demonstrate the indirect calorimetry, GT3X+ accelerometer, Polar heart rate and Actiheart outputs during each activity. On each sheet the data is presented in relation to their group (Unilateral, Bilateral and Control).
Indirect calorimetry outputs are highlighted in yellow. Outputs taken from the Metamax 3B software include VO2, METs, CO2, TEE and PAEE. This data is expressed as the mean and standard deviation.
Actiheart data are highlighted in green and expressed as kcal.min-1.
Polar heart rate (bpm) is highlighted in brown. Rate of perceived exertion (RPE) is highlighted in pink.
Actigraph accelerometer outputs worn at the hip of the shortest residual limb are highlighted in red. Raw signal (physical activity counts) are displayed as the mean and standard deviation (counts·min-1).
Highlighted in bold along the bottom of the unilateral, bilateral and control data is the mean and standard deviation of each group
Computational Supporting Dataset for "Exploiting Cationic Vacancies for Increased Energy Densities in Dual-Ion Batteries"
No full textThe dataset contains inputs and outputs for a series of VASP calculations on Mg/Li-intercalated F-doped anatase TiO2, and scripts for processing this DFT data to produce the related manuscript figures.The computational methods and codes used to generate this dataset are described in the associated manuscript: "Exploiting Cationic Vacancies for Increased Energy Densities in Dual-Ion Batteries
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