1056 research outputs found
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Dataset for "Pioneering Net Zero Carbon Construction Policy in Bath & North East Somerset"
No full textThis data was collected as part of a collaborative project between the University of Bath and Bath and North East Somerset Council, which studied the implementation of new sustainable construction planning policies, the first of their kind in the UK to require that:
• All new residential and major non-residential building developments must achieve net zero operational energy, conforming to ambitious energy consumption targets, matching this with on-site renewables, and only offsetting the difference in exceptional circumstances.
• Major residential and non-residential developments must demonstrate an embodied carbon lower than a threshold value, including the substructure, superstructure and finishes, with no offsetting permitted.
The data includes analysis of incoming planning application, relating to the characteristics of proposed buildings and key parameters submitted to comply with the net zero energy requirements.
A questionnaire was also sent out to applicants, with both written and numerical responses included in this dataset
Dataset for "How Harassment is Depriving Universities of Talent: A national survey of STEM academics in the UK"
Get PDFWe recruited 835 faculty members from 40 universities in the United Kingdom (UK) via our networks within UK STEM departments. Participants were drawn from various STEM departments, including biological science (18%), computer science (7%), engineering (28%) mathematical science (16%), and physics (13%). Respondents completed an online survey in which details about their employment were collected at the beginning and additional demographic information was collected at the end. The middle section of the survey contained measures of: identity and career perceptions; staying in academia; collaborative working style, received opportunities; workplace diversity and inclusion and affective workplace climate; experience of harassment; and assessment of a workshop intervention.An cross sectional online survey was conducted via Qualtrics. The survey was distributed via networks to STEM Departments across the UK. The full data set is contained in the SPSS file. Coding of the data and creation of new variables in contained in the R-script. Also contained in the R-script is analysis associated with a paper testing a model predicting intentions to leave academia.R Script included to assist in interpretation of coding and analysis
Dataset for “Long-range lightning interferometry using coherency”
No full textThe dataset contains data used to investigate the long-range interferometric method. It comprises an illustration of the quantitative study of coherency, the simulation of the interferometric method with lightning raw data and filtered data.Please see the associated paper for the accompanying methodology
Dataset for "Conductive polymer-coated 3D printed microneedles: biocompatible platforms for minimally invasive biosensing interfaces"
No full textThis dataset includes all the data presented and analyzed in the aforementioned paper, "Conductive polymer-coated 3D printed microneedles: biocompatible platforms for minimally invasive biosensing interfaces". These include: CAD designs, SEM and AFM micrographs, FTIR, Raman, and EDS spectra, water sessile drop images, DMA compression tests, ex vivo skin penetration bright-field microscopy images, cyclic voltammograms, four-point probe resistivity measurements, battery-LED system photographs, and cytotoxicity assay measurements.The methodology can be found in the associated paper.Equipment:
1. Formlabs form 3, FormWash and FormCure (FormLabs, USA);
2. Zepto Model 2 Diener Plasma Reactor (Diener Electronics, Germany);
3. iD7 attenuated total reflectance (ATR)-mode of a Nicolet™ iS5 FTIR spectrometer (Thermo Fisher Scientific, USA);
4. inVia™ confocal Raman microscope (Renishaw, UK);
5. Contact angle measurement system, OCA 25 (Data Physics, UK);
6. SU3900 scanning electron microscopy (SEM) instrument (Hitachi, Japan);
7. Jupiter XR (Oxford Instruments) atomic force microscope (AFM) was used in blueDrive™ Tapping Mode with AC160TS-R3 tips;
8. 10X-200X USB digital microscope with a 0.3-megapixel resolution (United Scope, Netherlands);
9. Jandel RM3000 (Jandel Engineering, UK);
10. µAutolab type II potentiostat/galvanostat (Metrohm, Switzerland);
11. Mettler Toledo DMA1 (Mettler Toledo, USA) dynamic mechanical analyser (DMA);
12. BMG FLUOstar Omega (BMG Labtech, UK) plate reader.
Software:
1. Excel and PowerPoint (v. 2016, Microsoft, USA);
2. Origin (v. 2022b, Electronic Arts);
3. Fiji-ImageJ, contact angle add-on, v.1.52 (National Institutes of Health, USA)
4. Spectragryph (v. 1.2, effemm2.de);
5. Nova (v. 2.1, Metrohm, Switzerland);
6. AZtec software package (Oxford Instruments, UK);
7. AutoCAD (Autodesk, USA);
8. Prism (v.9, GraphPad Software, USA)
Experimental data for one-day-ahead predictions of power balance in PV-based grid-tied micogrids
No full textThis dataset consists of measurements that indicate the state and performance of a grid-tied microgrid for supplying energy resources to Internet of Things (IoT) devices. This microgrid includes as input 3.2 kWp solar photovoltaic (PV) generation, and uses a lead-acid battery energy storage system (BESS) to supply energy to devices representing both critical and non-critical controllable loads. The measurements include PV input voltage and power; battery voltage, power, capacity, charging and discharge current; and output voltage, power (active and apparent) and frequency. The dataset is useful for demonstrating the resilience of the microgrid to various scenarios.Full details of the microgrid from which this dataset derives may be found in the paper "SIEMS: A Secure Intelligent Energy Management System for Industrial IoT applications". In summary, the microgrid includes a PV system consisting of 18 Suntech STP175S-24/Ac modules which have been individually wired to reconfigure the PV field from the laboratory. The BESS consists of two Hewlett–Packard Power Trust II A1357 A battery packs, each with 12 units of 12 V, 8 Ah batteries. A supervisory control and data acquisition (SCADA) unit detects and diagnoses faults, and mitigates their effects through structural redundancies. The server is an Ammonit Meteo-40 data logger with 12-bit ADC and 22 channels, and an HTTPS Web configuration interface, ethernet output through RS485, data encryption, and compatibility with SCADA systems. The experiment was performed on a Windows 10 64-bit OS server with Python 3.6.4, an eight-core Intel Core i7 4 GHz CPU and 16 GB RAM.All measured quantities use the standard SI units. FP refers to false positives in the detection of faults
Dataset for a Multivariate Genome-Wide Association Study of Psycho-Cardiometabolic Multimorbidity
No full textThis dataset contains summary statistics from a multivariate genome-wide association study of major depression, coronary artery disease, and type 2 diabetes (i.e., psycho-cardiometabolic multimorbidity).
There are two sets of summary statistics: version with UK Biobank (effective sample size = 562,507) and version without UK Biobank (effective sample size = 156,717). Summary statistics for non-heterogeneous variants are also provided (obtained by removing variants with Q SNP P < 5e-8 and directionally discordant univariate effect estimates).
For a given single nucleotide polymorphism (SNP), the summary statistics include the chromosome; position; minor allele frequency; effect allele; non-effect allele; effect of the SNP on the common factor; standard error; ratio of effect/SE; p-value of Z_Estimate; chi-square statistic, providing the heterogeneity estimate for that SNP (equivalent to the Q SNP index); chi-square degrees of freedom; chi-square p-value; whether effect estimates from three input genome-wide association studies were directionally concordant.This dataset was created using genomic structural equation modeling (Genomic SEM) package in R. First, a common factor model was specified to capture the shared variance between major depression, coronary artery disease, and type 2 diabetes. The shared variance reflected the latent psycho-cardiometabolic multimorbidity factor. Subsequently, summary statistics for psycho-cardiometabolic multimorbidity were generated by regressing the latent factor on each SNP, resulting in 6,820,149 SNPs. Full details of the methodology may be found in the Methods section of the associated paper.When using multimorbidity-associated variants as genetic instruments in Mendelian randomization analysis we recommend to only use non-heterogeneous SNPs. At the minimum, we recommend removing variants with QSNP P < 5e-8 and directionally discordant univariate effect estimates (option 1 below), as done in the non-heterogenous summary statistics versions (PCM_multimorbidity_summary_stats_noUKBB_non-het.txt.gz and
PCM_multimorbidity_summary_stats_withUKBB_non-het.txt.gz). To ensure your analysis captures multimorbidity between coronary artery disease, type 2 diabetes and major depression, you may also apply a more stringent threshold for removing heterogeneous variants (e.g., QSNP P < 5e-6; option 2 below). A sample code for implementing this in R is included below:
```
# load tidyverse package
library(tidyverse)
# read in summary statistics
sumstats <- read.delim("PCM_multimorbidity_summary_stats_withUKBB_non-het.txt.gz")
# Option 1: remove discordant variants with QSNP P < 5e-8
data_not_concordant % filter(concordant == "FALSE")
mm_snp_remove % filter(SNP %in% data_not_concordantSNP)
# save file
write.table(sumstas_nonhet, file = “newfile.txt", quote =F, sep = "\t", row.names = F)
# Option 2: remove variants with QSNP P < 5e-6
sumstas_nonhet % filter(chisq_pval < 0.000005)
# save file
write.table(sumstas_nonhet, file = “newfile.txt", quote =F, sep = "\t", row.names = F)
```For a detailed description of both sets of statistics, please see the associated publication. Summary statistics include:
- SNP = single nucleotide polymorphism (SNP);
- CHR = chromosome;
- BP = position;
- MAF = minor allele frequency;
- A1 = effect allele;
- A2 = non-effect allele;
- BETA = effect of the SNP on the common factor;
- SE = standard error;
- Z_Estimate = ratio of effect/SE;
- P = p-value of Z_Estimate;
- chisq = chi-square statistic, providing the heterogeneity estimate for that SNP (equivalent to the Q SNP index);
- chisq_df = chi-square degrees of freedom;
- chisq_pval = chi-square p-value;
- concordant = indicates whether effect estimates from three input genome-wide association studies were directionally concordant (TRUE = all three estimates were concordant; FALSE = at least one estimate was not concordant
Dataset for "Height Determination of a Blue Discharge Observed by ASIM/MMIA on the International Space Station"
No full textThe dataset contains data and code used to determine the height of a blue discharge observed by the Modular Multispectral Imaging Array (MMIA) of the Atmosphere–Space Interactions Monitor (ASIM) on the International Space Station. It comprises an illustration of the analysis of the meteorology and storm structure, the pace-based optical measurements, the height determination from ground-based electric field measurements, and the estimation of the source altitude from the optical pulse.For details of the methodology used, please see the Measurements section of the associated paper.Figure 1, Figure 2, and Figure 3 can be plotted using the files provided. Figures 4-7, Figure 9 and supporting information Figure S1 are in .fig format. Figure 8 can be plotted using Ncview
Data set for "Case study of developing an affordable undergraduate observatory" and image of M51 galaxy
No full textThis data set contains the raw fits files needed to produce an astrophotograph of Galaxy M51 as shown in the associated paper. It includes bias frames, fark-frames, flat-frames, and exposures of M51 taken in LRGB filters. See individual file descriptions for LRGB wavelength ranges and fits file headers for exposure times etc.The headers of the fits files have all the required information to allow processing in the usual fashion. See fits headers for details of exposure times, dates collected, UTC time, pixels size etc. See file meta-data for details of filter wavelength ranges.Files were combined using the free software DeepSkyStacker (DSS): http://deepskystacker.free.fr/english/index.html See DSS tutorial and help pages for how to combine the light-files with the calibrations files. The resulting LRGB master images were then combined using GIMP and various colour stretches to priduce a pleasing image
Dataset for "Antimicrobial Releasing Hydrogel Forming Microneedles"
No full textThis dataset pertains to a study involving the use of 3D printed microneedles (MNs) to form hydrogel-forming MNs (HFMs) for the delivery of antibiotics (amoxicillin and vancomycin) transdermally. It includes:
- the HFMs ability to swell in fluids over time rapidly and take up drugs in solution as a result (including characterisation of the HFMs and drug loaded-HFMs);
- the mechanical properties and skin penetration of both HFMs and Drug-loaded HFMs;
- the drug release profiles of the drug-loaded HFMs and how to control the release;
- the antimicrobial properties of the antibiotic HFMs against susceptible bacteria.
The data provides a suggestion towards the use of HFMs for the effective transdermal delivery of antibiotics, towards reducing the rate of antimicrobial resistance increase.Antimicrobial Properties: Disk Diffusion Assays using E.coli and S.aureus. Samples incuated for 24 hours. Optically monitored the area of inhibition surrounding the HFMs. Data provided are images, analysed using ImageJ with data saved in text file.
Drug Release: Drug loaded HFMs were submerged in PBS. PBS samples were taken over time and UV-Vis analysis was used to calculate the concentration of drug that has been release. Data is in the form of Excel document.
Mechanical properties: HFMs were tested on a DMA up to 10 newtons. Data is proved in the form of Excel document.
Skin Penetration: HFMs were placed on porcine skin and a known force was applied and the resultant skin pores were optically imaged. Data is in the form of images.
Swelling: HFMs were submerged in PBS. The mass of the HFMs was taken at various time points. The data provided is in the form of an Excel file.
Raw data and data analysis can been found in the respective files.The data is provided in MS Excel format
A multi-disciplinary team-based classroom exercise for small molecule drug discovery
Get PDFIndustrial drug discovery teams encompass scientists from multiple specialities and require participants to communicate effectively across disciplinary boundaries. In this resource we present an undergraduate or graduate classroom simulation of this environment. Over a series of five workshops, student teams of mixed scientific backgrounds perform five iterations of the chemistry cycle of small molecule drug discovery. Students analyze physicochemical, structural and (fictional) assay data, and use these to design new compounds for testing. Simulated assay results are returned to students who use the information in the design of subsequent compounds. After workshop 5, each team submits a single lead compound, supported by a potential synthetic route, a portfolio of assay data and logical scientific decision-making. Our exercise provides students with opportunities for hands-on student-responsive data handing, team-building, and technical knowledge acquisition – all within an industrially relevant scientific scenario
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