Dataset for "Cavity-based optical switching via phase modulation in rubidium warm vapor"

This dataset contains the data that describes an optical switch using a ring cavity filled with rubidium vapour. The all-optical control is provided by a controlled phase shift interaction by a strong control field on a weak signal field detuned from the near-degenerate two-photon absorption ladder in warm rubidium vapor. The signal field is routed through a doubly resonant ring cavity in which the phase shift selects either the transmitted or reflected output port.The signal field was generated by a 780 nm external cavity diode laser (ECDL, MOGLabs CEL), which was blue detuned from the $5S_{1/2}(F'=2)$ feature. The control field was generated by a 776 nm ECDL (MOGLabs CEL) and was intensity modulated by an electro-optic modulator (Exail NIR-MPX800), and then amplified (MOGLabs MOAL) to produce a square control pulse of approximately 25 mW peak power and down to 42 ns pulse duration. Each laser was frequency stabilized using a scanning transfer cavity. The relative position of the signal and control field resonances are measured and then an error signal was generated from a RedPitaya locking system. The cavity consists of two beamsplitters and two mirrors, with an atomic vapor cell placed inside. The atomic medium is isotropically enriched rubidium-87 vapor, and is placed inside the ring cavity. The vapor is contained within a 5 cm glass cell with anti-reflection coatings, heated to approximately 60$^\circ$C to increase the optical depth whilst minimizing atomic absorption. A lens inside the cavity focuses the signal and control to the centre of the atomic vapor cell. The signal and control light is coupled into the fundamental transverse cavity mode, such that sharp resonances are seen on an oscilloscope. When the control field is applied, the signal field undergoes a phase shift, and is switched from the transmitted to reflected port. The reflected signal travels back on the control arm of the experiment and is detected after a circulator, and the transmitted light exits the cavity on the second port and is detected in free-space

Dataset for Nonlinear viscoelastic models improve characterisation of 6 DOF intervertebral disc load response at low strain rates

The data presented in this file comprises the 6 DOF position control data from tests performed on 6 porcine lumbar isolated spinal disc specimens using a triangular displacement waveform at a frequency of 0.1Hz. Data is presented relative to the centre of the intervertebral disc. The final three cycles are presented here. For each of the six specimens and for each of the six axes, data is provided for the applied displacement and each of the 6 resulting loads (forces and moments). The principal elements (highlighted in the tables) occur when the direction of applied displacement and measured load are the same - for example, axial torsion displacement (RZ) and torsional load (MZ). The data has been filtered to remove unwanted noise but no other pre-processing steps have been performed on this data - for example, cycle averaging and offsetting the central point to the origin.The methodology can be found in the associated paper

Dataset for a framework for assessing the impact of geometric imperfections in concrete shell structures using deep learning

This dataset contains scripts and data supporting the following following thesis: Pollet, M. (2025). Rapid structural analysis of prefabricated thin concrete shells using deep learning (Thesis). University of Bath. Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. However, geometric imperfections, which may occur during production can negatively impact their structural behaviour. While this impact can be assessed through Finite Element Analysis (FEA), a faster analysis method, such as surrogate modelling, could benefit concrete shell manufacturers. This dataset contains deep learning models – Multilayer Perceptrons, and Convolutional Neural Networks – that have been trained to predict the buckling factor and stress fields of geometrically imperfect concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related thesis.The methods used to generate this data can be found in the related thesis.The data in the models and results folders was generated using the Python code in scripts folder. These scripts rely on the dependencies listed in requirements.txt.The original folder structure is given in README.md. To reproduce it, create a new folder and extract the "models.zip" and "results.zip" folders inside. Additionally, create a "scripts" folder and store all Python scripts inside. The path to the ConcreteShellFEA dataset needs to be specified in each script, under the DATASET_ROOT variable

Dataset for "High freeze-casting cooling rates enhance the piezoelectric responses and reproducibility of porous lead zirconate titanate for sensing and energy harvesting"

This dataset is a part of the research article 'High freeze-casting cooling rates enhance the piezoelectric responses and reproducibility of porous lead zirconate titanate for sensing and energy harvesting'. It contains comprehensive characterization data for ferroelectric lead zirconate titanate PZT NCE51 ceramic, fabricated using a range of freeze-casting cooling rates ranging from 1 to 4 °C/min. This dataset contains hysteresis polarization-electric field loops, impedance spectroscopy data and scanning electron micrographs, which provide insights into the hierarchical relationships between processing, microstructure, and properties in freeze-cast ferroelectrics. The dataset also contains the results from finite element modeling, demonstrating the effects of wall thickness (or mechanical clamping), pore channel defects (i.e., ceramic grains within pore channels) and wall defects (i.e., pores within ceramic walls) on bulk electromechanical properties. A model representing the residual stress state after poling, which demonstrates how residual stresses influence thermal stability in terms of piezoelectric properties of lead zirconate titanate near the Curie temperature. This may be of interest to researchers focused on the design and characterization of advanced ferroelectric composites.Full details of the methodology can be found in Section 2 of the associated research article

Data sets for "Bond counting strategies in an oxygen centric perspective on the structure of oxide glasses"

Data sets used to prepare Figures 2-10 in the Journal of the Ceramic Society of Japan article entitled "Bond counting strategies in an oxygen centric perspective on the structure of oxide glasses." The data sets describe the structures of several oxide glasses and provide the results obtained from bond counting strategies for examining the connectivity and nature of the network forming motifs. It is noted that the oxygen packing fraction acts as a marker for structural change in network-forming oxides under high-pressure conditions.The data sets were collected 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 a 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 "Influence of Block Microstructure on the Interaction of Styrene-Maleic Acid Copolymer Aggregates and Lipid Nanodiscs"

Copolymers between styrene and maleic acid are able to extract membrane proteins directly from cells, reconstituting lipid membranes into nanodiscs. RAFT copolymerisation was used to generate copolymers of equivalent molecular mass but inverted block sequences and end group termini. This dataset contains characterisation data for the copolymers (GPC, NMR, FTIR, UV-vis), included deuterated variants for neutron scattering experiments, as well as the structures formed in solution. Aggregates were assed by a combination of DLS and surface tension measurements, and nanodisc formation kinetics through UV-vis using both model DMPC vesicle and E.coli membrane suspensions. It was found that mismatched hydrophilic and hydrophobic end groups on the respective styrene block and alternating block, impeded membrane solubilisation. This highlights not only how the amphiphilic balance of these blocks is important for efficient nanodisc formation, but also how end groups influence these and may be optimised towards the extraction of more challenging MPs.Data collection methods are described in full in the publication "Influence of Block Microstructure on the Interaction of Styrene-Maleic Acid Copolymer Aggregates and Lipid Nanodiscs". Briefly, various copolymers between styrene and maleic anhydride were prepared by RAFT polymerisation, which, when using DDMAT, results in a relatively-large and hydrophobic SC12 end group (SMAnh-SC12). This block sequence was then inverted by first synthesising a poly(sty) macro-RAFT agent, from which a Sty:MA alternating block may be polymerised. A commercial variant, SMA2000, synthesised by free-radical polymerisation was also used for comparison. All copolymers were then hydrolysed to the acid form (SMA) before workup and purification.1H and 13C NMR: Spectra were analysed using Mestrelab MNova 11.0 software where spectra were baseline corrected and line broadening used to allow accurate integration of peak area. GPC: Chromatograms were analysed in Agilent GPC/SEC software to extract Mn and PDI values. UV-vis: The presence of the SC12 end group can be monitored by the peak at 310 nm in UV-vis spectra. Resultant spectra were normalised by the styrenic absorbance peak at 262 nm.FTIR: FTIR measurements were conducted on a Perkin Elmer ATR desktop spectrometer with solid-state polymer samples at room temperature. 1H & 13C NMR: 1H and 13C NMR spectra were recorded on an Agilent 500 MHz spectrometer at room temperature using d6-acetone (for anhydride species) or D2O (for acid species) as the solvent. GPC: GPC was conducted using an Agilent GPC 1260 Infinity chromatograph using two PLgel 5μM MIXED-D 30 cm x 7.5 mm columns with a guard column PLgel 5 μm MIXED Guard 50 x 7.5 mm. The column oven was maintained at 35 °C, with GPC-grade THF as the eluent at a flow rate of 1.00 mL/min and refractive index detection and polymer concentrations between 1.0 – 2.0 mg/mL. The system was calibrated against 12 narrow molecular weight polystyrene standards with a range of Mw from 1050 Da to 2650 kDa. DLS: DLS was conducted using a Malvern Zetasizer Nanoseries at theta = 173 degrees (backscattering) and wavelength = 633 nm. Pendant Drop Tensiometry: Tensiometry was conducted on a FTA 1000 contact angle/surface tension analyser and processed using FTA 32 surface tension image analysis software. Syringe needles were prepared by extensive washing before SMA polymers in PBS at variant concentrations were passed through these to produce a small hanging droplet which was imaged at a typical rate of 10 images per second for 10 seconds. SANS: SANS was performed at the ISIS Neutron and Muon Source (Rutherford Appleton Laboratory, Didcot, UK), on the SANS2D instrument (doi:10.5286/ISIS.E.RB2010215), using 1 mm quartz Hellma cells at 25 °C. Prior to experiments, samples were mounted in a temperature controlled multi-position sample changer. Data were subsequently reduced using Mantid software and the varying solution contrasts simultaneously fit using the NIST SANS analysis package within IgorPro

Dataset for "Fast structural analysis of concrete thin-shells using deep learning"

This dataset contains scripts and data supporting the following research article: Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. Fast structural analysis of concrete thin-shells using deep learning. Computers & Structures, 320, 108042. Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. Their shape is typically determined iteratively and evaluated through Finite Element Analysis (FEA). This research proposes the use of surrogate models as faster alternatives to FEA, thus enabling wider design space exploration. This dataset contains deep learning models – Multilayer Perceptrons, Convolutional Neural Networks, and Graph Neural Networks – that have been trained to predict the buckling factor and stress fields of concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related research article.Full details of the methodology used may be found in the associated article.The data in the models and results folders was generated using the Python code in scripts folder. These scripts rely on the dependencies listed in requirements.txt.The original folder structure is given in README.md. To reproduce it, create a folder "FastStructuralAnalysisOfConcreteThinShellsUsingDeepLearning" and extract the "models.zip" and "results.zip" folders inside. Additionally, create a "scripts" folder and store all Python scripts inside. The path to the ConcreteShellFEA dataset needs to be specified in each script, under the DATASET_ROOT variable

Dataset supporting: How can a paediatric neurorehabilitation service best meet the needs of parents and carers?

The dataset includes a copy of the questionnaire developed for this study and the responses of participants, presented in a Microsoft Excel spreadsheet. Both qualitative and quantitative responses to open and closed questions were collected to address the question of how a paediatric neurorehabilitation service could best meet the needs of parents and carers.A questionnaire was created by the researcher which was presented via Qualtrics, an online survey platform. Participants completed the questionnaire through Qualtrics, which recorded the responses. Responses were exported from Qualtrics into a Microsoft Excel spreadsheet, ready for analysis

SheltAir. Co-created airflow and COVID-19 transmission risk model for shelter design.

SheltAir is the first tool co-created with aid workers to model natural ventilation in shelters for displaced populations. Developed by Anna Conzatti during her PhD research at the University of Bath, SheltAir addresses the critical issue of poor indoor air quality (IAQ) and the spread of airborne diseases in shelters. SheltAir is an Excel-based tool that employs simplified airflow equations to model natural ventilation and IAQ. The tool also implements a COVID-19 transmission model. SheltAir requires only 20 inputs and provides results in less than 30 minutes, making it accessible even to non-experts. Key features include: • Simplified Airflow Models: Utilizes simplified equations8 to calculate ventilation rates based on CO2 levels. • Occupant Behavior: Incorporates behavioural data to enhance accuracy in predicting IAQ. • COVID-19 Transmission Risk: Includes a model9 to assess the risk of airborne disease transmission in shelters. SheltAir can simulate five ventilation strategies using different schedules for single-room shelters in 3000 locations worldwide, making it versatile for various scenarios. It offers outputs into indoor CO2 levels for different seasons and evaluates COVID-19 transmission risks, helping designers make informed decisions to improve living conditions in shelters. SheltAir addresses poor IAQ in shelters, reducing health risks associated with inadequate ventilation and indoor activities. It aims to improve the living conditions for displaced populations, especially vulnerable groups like children and the elderly. Its user-friendly design empowers human- itarian workers and shelter designers to implement effective ventilation strategies without specialised expertise, leading to global improvements in shelter conditions and significantly benefiting displaced communities.SheltAir was developed through a co-creative methodology. This method included a detailed examination of mathematical models for natural ventilation, a monitoring campaign in Japan, and extensive collaboration with shelter designers, aid workers, and NGOs. This co-creative approach ensures that SheltAir is based on sound scientific principles and practical insights. This tool has been developed using Python coding and Excel.For best results, the tool should be opened in MS Excel 2016+ on Windows.For information on how to use the tool, please consult the README sheet it contains

Dataset for "Pioneering Net Zero Carbon Construction Policy in Bath & North East Somerset: Evaluating the effectiveness of novel planning policies over time"

This data was collected as part of a continuing collaboration between the University of Bath and Bath and North East Somerset Council, exploring the impacts of (and reception to) pioneering sustainable planning policies for new buildings which were first introduced in January 2023. This project evaluates the success of the policies two years on, establishing long-term trends, opportunities for refinement, and the national policy implications of this unique policy case study. The deposited data relates to two parts of the methodology. The first is an analysis of incoming planning application, relating to the characteristics of proposed buildings and key parameters submitted to comply with the net zero energy requirements. The second is the results of a questionnaire sent out to applicants.This dataset was generated through a mixed‑methods approach designed to capture both quantitative performance data and qualitative stakeholder perspectives relating to the B&NES sustainable construction policies. First, all eligible planning applications submitted between May 2024 and June 2025 were systematically reviewed. Energy summary data, SAP/PHPP modelling outputs and associated documentation were extracted to assess compliance with operational energy and embodied carbon requirements. These submissions were analysed at the individual‑plot level, enabling comparison of design parameters such as space heating demand, total energy use, U‑values and air permeability. To complement this, a structured questionnaire was distributed to planning agents and consultants to gather insights into applicant experience, perceived challenges and evolving practice.Data was anonymised with any personal identifying information redacted