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Dataset for ShelTherm
No full textThe dataset includes the ShelTherm files used to validate the thermal model with the seven prototypes in the refugee camp of Azraq (Case 01), against ISO 13792 (Case 02) and in other climates (Case 03). It also includes the Energy+ files for Case 01 and Case 03, the Admittance Method file for Case 01. The dataset of the monitored shelters at Azraq camp and outdoor temperatures are included in Case 01.The indoor conditions of each shelter were monitored using an iButton DS1923 sensor placed at the centre of the shelter. The exterior conditions are recorded with Tinytag TGP-4500 onsite sensors. The temperature accuracy of both sensors is +- 0.5°C. The relative humidity accuracy +- 0.5% for the shelter sensors, and of +- 0.3% for the external sensor
Dataset for 'Measurement and analysis of air quality in temporary shelters on three continents'
No full textThis dataset includes air quality data collected from field studies in refugee and displacement camps in ten locations within Peru, Ethiopia, Djibouti, Jordan, Turkey and Bangladesh. These include samples of Volatile Organic Compounds (VOCs) and Particulate Matter (PM), and CO2.
The data Volatile Organic Compounds (VOCs) sampled over 25 minutes using Tenax A tubes, Particulate Matter (PM) sampled over 30 minutes using TSI DustTrack monitor, CO2 spot measurements in Jordan and Ethiopia only using Extech CO2 meter (model CO250), and 24hrs CO2 monitoring in two shelters in Djibouti unsing TinyTag (model TGE-0011).
Results revealed very harmful levels of pollutants that are often linked to excess mortality - with total VOC concentrations as high as 102400μg·m^-3 and PM over 3000μg·m^-3.VOCs monitoring
The concentration of VOCs in shelters was determined by air sampling using conditioned Tenax TA Perkin Elmer style tubes in accordance with the International Organization for Standardization (ISO) standard EN-ISO-16000-6:2011 [32]. supplied by Markes International Ltd. The sampling pump was a Casella Vortex, modified to use D size batteries in order to circumvent any possible issues with recharging in areas where availability of mains electricity could be intermittent.
Either active, passive or both VOC sampling was performed. Active sampling involved drawing air through the tube using a sampling pump calibrated at 12 litres/hour for 25 minutes (which will not allow the tube to become totally saturated). N.B. ISO 16000 part 6 does not specify sampling times, but it recommends flowrates between 50 and a maximum of 200 ml/min, and sampling volumes of between 1 and 5 litres of air. While it allows a lower flow rate than 50 ml/min to enable longer sampling times, the practical sampling period would be between 5 and 100 minutes.
Passive monitoring involved leaving an opened tube in the environment to be monitored for seven to 14 days. to give an idea of mean VOC concentrations over longer periods of time. In the method used (based upon ISO 16017 part 2) assumptions are made regarding the uptake rate of VOCs (in ml/min) over the period of diffusive sampling when converting the amount of compound on the tube to a time-weighted average concentration in air.
Analysis of the adsorbed VOCs on the tubes was conducted by the Building Research Establishment (BRE) in accordance with the International Organization for Standardization (ISO) standard EN-ISO-16000-6:2011 [32]. In this context, VOCs are defined as chemical compounds with boiling points between 60-280°C trapped on the Tenax-TA tubes. Analysis of the tubes was carried out on a Perkin Elmer AutoSystem XL GC, using a 350 Automatic Tube Desorber (ATD) and a Turbomass MS. Identification of VOCs was carried out using a combination of retention time and mass spectral “fingerprint”; quantification was carried out using a flame ionisation detector FID. TVOC concentration was calculated as the sum of compounds eluting between (and including) n-hexane and n-hexadecane, quantified as toluene. Therefore, the TVOC concentration can differ from the sum of the individual VOCs reported.
Particulate Matter (PM) monitoring
PM is dust and other particulates which originate from combustion (e.g cooking, smoking), the external environment or materials which can release particles. Inhalable particulates have different sizes up to 10μm in diameter. Inhalable coarse particles with a diameter less than or equal to 4 and 10μm (PM4 and PM10), and fine particles with a diameter of less than or equal to 2.5μm or 1μm (PM2.5 and PM1) were measured by air sampling using a TSI DustTrak DRX Desktop Aerosol Monitor. The PM10 fraction includes the PM4 PM2,5 and PM1 fractions, etc. The device was placed in the middle of the shelter (approx. 0.5 to 0.8m above the floor) and air was sampled for 30 minutes, the total flow rate of the DustTrack is fixed at 3 l/min. Results are reported as the total mass of particles from 0.1μm up to the particle size specified.
Carbon dioxide monitoring
CO2 is naturally present in the atmosphere at around 400ppm and doesn’t constitute a health risk at low levels up to around 1000ppm. However, exposure to higher concentrations, which may occur in under-ventilated indoor environments can cause various health issues such as headaches and dizziness. At high concentrations above 40,000ppm exposure can lead to oxygen deprivation causing brain damage, or death. In Azraq and Zaatari camps, Jordan, 136 spot measurements of CO2 were taken in summer and winter; and similarly, in Ethiopia spot measurements of CO2 were taken in 286 shelters in Hitsats refugee camp using Extech CO2 meter (model CO250) that can measure levels between 0 and 5000ppm. In Djibouti, CO2 was monitored using TinyTag (model TGE-0011) CO2 data logger. The TGE-0011 can monitor levels between 0 and 5000ppm using a 'self-calibrating' infrared sensor. 24-hours sampling from two different shelters at one-minute intervals was conducted.All data that might identify individual shelters (such as addresses) have been removed.The spreadsheet was compiled using Microsoft Excel
UK Pension Reforms: Pensions in the late 1990s and early 2000s
No full textThis dataset contains video content created and documents collected by the Institute for Policy Research at the University of Bath for the research project ‘Pension Reforms in the UK’, funded by the National Employment Savings Trust (Nest) Insight.
The videos in this collection relate primarily to the pension issues in the period between 1997 and 2002 that led to the appointment of the Turner Pensions Commission
Datasets for “OmniPhotos: Casual 360° VR Photography”
No full textThis dataset contains the raw and processed data used to validate the results for the paper. Each subdirectory in the Preprocessed and Unprocessed folders contains a 360° video captured in a circle at different locations in the world, using an Insta360 One X 360° camera on a rotating selfie stick. These subdirectories are named after these locations. Both the proprietary (.insv) video format, as well as a stitched equirectangular (.mp4) video (used by our preprocessing pipeline), have been included. As well as these videos, each subdirectory contains the Input frames, used by our software to display the scene, a Capture directory that contains structure-from-motion data for the given scene, as well as a Config directory, which contains necessary configuration files to run our software.
In the Preprocessed directory, the subdirectories also contain a Cache directory, containing optical flow (.floss) files, a CSV file linking the floss files to the relevant images in Input, and .obj files that contain the scene-dependent proxy mesh (deformed sphere) used to render the scene.Full details of the methodology may be found in the associated paper
ShelTherm: A Simple Shelter Thermal Design Assistant
No full textThis is a tool and a physics-based model (ShelTherm) of the heat transfer and air flows through simple structures. A state of the art heat transfer (balance) method is encapsulated within the tool designed to be used by humanitarian staff. The only input is a very simple description of the shelter, and the output being the time series of internal and external temperatures over an example summer and winter day. It is important to note that, unlike other reduced models, the method is capable of dealing with high ventilation/inflation rates, thin materials (such as tarpaulin) and high U-values
Dataset for "Flat-field and colour correction for the Raspberry Pi camera module"
Get PDFThis repository contains the hardware (OpenSCAD/STL files) and build instructions, software (Python scripts and Arduino firmware), data analysis (iPython notebook), and manuscript describing how to calibrate the colour response of a Raspberry Pi camera module. It also includes the calibration images acquired during the preparation of the work.Images were acquired using a Raspberry Pi and camera module, controlled by the included Python scripts.A Python 3 environment with numpy, scipy, pillow, and other libraries as detailed in the ipython notebook. The acquisition code will only run on a Raspberry Pi computer with the forked picamera library as described in the manuscript.- `analysis` contains the data analysis code.
- `data` contains the images that we used for the graphs in the manuscript.
- `neopixel_driver` is the arduino firmware.
- `image_acquisition` includes the Python code that acquired the images and controlled the neopixel.
- `calibration_jig` contains the printable files, source OpenSCAD files, and assembly instructions for the calibration jig.
- `colour_test_sheet` contains source Inkscape SVG files and PDF renders of the test target used in the experiments.
- `manuscript` contains the source files for the manuscript
Dataset for "Core-shell spheroidal hydrogels produced via charge-driven interfacial complexation"
No full textThe dataset for "Core-Shell spheroidal Hydrogels produced via charge-driven interfacial complexation" contains data, in the form of excel and txt files, for the figures shown in the main manuscript and the electronic supplementary information (ESI).
The study focuses on the structure of the spheroidal hydrogels (SH) and their resilience in aqueous media. Furthermore, SH are used as microreactors for biocatalysis, providing a proof-of-concept for this material. The dataset contains data regarding the structural information of the spheroidal hydrogels (SH) (e.g. shell thickness, small angle X-rays scattering, Z-potential and oscillatory rheology) their stability in solvents with different osmotic pressure and ionic strength (e.g. swelling kinetic, swelling ratio) and the enzymatic activity to support a proof-of-concept of the SH.
Specification of the materials and methods employed throughout the data collection are available in the main manuscript. Each excel sheet is specific for a set of data acquired utilising the same protocol.Methodology, experimental conditions and data treatment is described in the main manuscript.In the Excel file named "Data_Core_Shell_Hydrogels“, the data are presented in separate sheets, each of which is labelled as for the figure presented in the manuscript.
The spreadsheet named:
Fig. 2b shows the data regarding the thickness of the spheroidal hydrogel (SH) shell.
Fig. 2c contains data regarding the Leakage and retention of surface active molecules
Fig. 3 contains the data of the swelling dynamic of the SH in different conditions (Di_water, 1000 mM NaCl and 0.1 wt% PAA)
Fig. 4a contains data for the Swelling Ratio (SR) as function of the osmotic pressure.
Fig. 4b-Top contains data for the Z-potential measurements of a CCNF dispersion as function of the NaCl concentration.
Fig. 4b-Bottom contains data for the oscillatory rheology measurements of a 2 wt% CCNF dispersion as function of the NaCl concentration.
Fig. 6 contains the data of the enzymatic activity measurements.
The txt files contains small angle X-rays scattering (SAXS) data presented in Fig. 5a. Each txt file is named as for sample name used in Fig. 5a
Dataset for "Rigidity, normal modes and flexible motion of a SARS-CoV-2 (COVID19) protease structure"
No full textThis file contains simulations and analysis carried out on two protein structures from the SARS-CoV-2 (COVID19) virus. These structures are PDB entries 6Y2E and 6LU7. Both structures represent the same homodimeric protease. 6Y2E represents the free protease while 6LU7 includes a bound inhibitor, N3.
The folder for each structure includes README files at each level describing how the structure has been processed and what results have been produced. This study includes rigidity analysis of the crystal structures, elastic network modelling to identify normal modes of the structures, and all-atom geometric simulations of flexible motion along normal mode directions. This study makes use of the PDB, the MolProbity structure processing webserver, FIRST rigidity analysis software, the FRODA geometric simulation engine, Elnemo elastic network modelling software, and PyMOL visualisation software. The SBFIRST code for identication of covalent and noncovalent interactions in a protein structure is included in this collection of data.Input data: PDB entries 6Y2E and 6LU7Analysis and processing workflow for 6LU7 is described here. The procedure for 6Y2E was essentially identical.
Downloaded from PDB repository.
Note that this structure contains the protease structure as chain A and an inhibitor as chain C.
Opened in PyMOL v0.99.
Symmetry applied; symmetry mate selected to form homodimeric structure; symmetry mate chain reset to B; symmetry mate of inhibitor chain reset to D; homodimer saved including inhibitors.
Homodimer processed using the MolProbity website to add hydrogens at electron-cloud positions, including flipping of ambiguous residues where suggested.
Hydrogenated homodimer opened in PyMOL v0.99; heteroatoms, mostly water residues, removed, but inhibitor retained; alternative side chain configurations removed; structure saved as "6lu7inhib.pdb".`
Covalent, hydrogen-bond and hydrophobic interactions identified based on structure geometry and saved in files cov.in, hbonds.in, hphobes.in. hphobes.in has been edited to remove a phobic tether linking ALA285 of each chain and tethers linking LEU286 of one chain to THR280 of the other, as examination of the structure suggested that these tethers were not appropriate as a permanent constraint in the system. This bond analysis was carried out using the SBFIRST utility, a copy of which is included with this data set. The .in files are suitable for use as input to the rigidity analysis software FIRST, from Arizona State University. The stacked.in file is empty (would hold stacked-ring interactions in nucleic acid structures).
Static rigidity analysis of the structure is carried out in the folder Clusters. Normal mode analysis identifying nontrivial low-frequency modes intrinsic to the structure is carried out in the folder Modes. Geometric simulations of flexible motion along the normal mode directions is carried out in the folder Runs, controlled by the bash script file loopFRODA.sh.The Elnemo elastic network structure software is available online from http://www.sciences.univ-nantes.fr/elnemo/
The FIRST rigidity analysis software, including the FRODA geometric simulation engine, should be available from flexweb.asu.edu. If there are difficulties obtaining the software from this source, please contact Dr. Stephen Wells ([email protected]) who can provide a copy of FIRST for academic users.
The SBFIRST code for identication of covalent and noncovalent interactions in a protein structure is included in this collection of data.
The Elnemo website includes comprehensive citations for the Elnemo elastic network method and software. The primary citation for the SBFIRST identification of interactions is DOI:10.1088/1478-3975/ab2b5c (MacManus, Wells, Walker 2019). The primary citation for the use of Elnemo/FIRST/FRODA in combination is DOI:10.1088/1478-3975/9/1/016008 (Jimenez-Roldan, Freedman, Roemer, Wells 2012) and a detailed discussion of the method can be found as DOI:10.1007/978-1-62703-658-0_10 (Wells 2014). The primary citation for the FRODA geometric simulation engine is DOI:10.1088/1478-3975/2/4/S07 (Wells, Menor, Hespenheide, Thorpe 2005). The primary citation for the FIRST rigidity analysis software is DOI:10.1002/prot.1081 (Jacobs, Rader, Kuhn, Thorpe 2001).A folder for each protein structure contains subfolders for Clusters (containing static rigidity analysis), Modes (elastic network modelling) and Runs (geometric simulations of flexible motion). README files in each folder and subfolder provide additional details
Dataset for "Touché: Data-Driven Interactive Sword Fighting in Virtual Reality"
No full textThis is the data repository for the paper "Touché: Data-Driven Interactive Sword Fighting in Virtual Reality" by Javier Dehesa, Andrew Vidler, Christof Lutteroth and Julian Padget, presented at CHI 2020 conference in Honolulu, HI, USA. See the publication for details.
The archives gesture_recognition_data.zip and gesture_recognition_code.zip contain respectively the data and code for the gesture recognition component. Similarly, the archives animation_data.zip and animation_code.zip contain respectively the data and code for the animation component. Instructions about how to use these are provided within them.
The archive user_studies.zip contains information about our user studies. The file questionnaire_study.jasp and interactive_study.jasp contain the data and analysis of the questionnaire and interactive studies respectively. They can be consulted with the open source tool JASP (https://jasp-stats.org/). The video questionnaire_conditions.mp4 shows the full videos used as the three conditions for the questionnaire study.Gesture recognition data was collected with VR hardware in a custom-made virtual scenario, where the subject was presented with a signal indicating a gesture to perform, which they then did while pressing a button on the hand controller.
Animation data was motion captured with specialised equipment within the facilities of Ninja Theory, Ltd.
User studies data was collected through online forms, filled after each condition of each of the studies.The user studies data was preprocessed for convenience to produce an accessible JASP file. The preprocessing simply translated the raw text of the questions into short identifiers and mapped Likert points (like "strongly agree/disagree") to numerical values. For this publication, free-text comment data was removed from the dataset for anonymisation purposes.Gesture recognition data was captured with an Oculus Rift kit.
Animimation data was captured with Vicon Bonita hardware.Gesture recognition data is stored in CSV files where each row contains the position and orientation of both hands and the gesture being performed on each frame.
Animation data is stored in CSV files where each row contains the position and orientation of each joint of the skeleton.
User studies data is stored in JASP files encoding the answers of each participant to each question in the study
Dataset for "Stripping torques in human bone can be reliably predicted prior to screw insertion with optimum tightness being found between 70% and 80% of the maximum"
Get PDFDespite being the most commonly used orthopaedic implant, screws are frequently inserted poorly. This data is for the study where screw, bone and screw hole characteristics, were used to predict the maximum torque for a screw hole prior to insertion. Using this, optimum tightness as a percentage of the maximum torque is investigated as functions of compression and pullout forces in human bone. This data set provides the raw data for these investigations. The methodology is described in detail in the related manuscript, "Stripping torques in human bone can be reliably predicted prior to screw insertion with optimum tightness being found between 70% and 80% of the maximum".This study involved biomechanics testing using human cadaveric bone. The methodology is described in detail in the related manuscript.Statistical analysis was performed using IBM SPSS Statistics for Windows, version 20 (IBM SPSS Corp., Armonk, N.Y., USA)
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