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Dataset for "Fouling Resistant 2D Boron Nitride Nanosheet – PES Nanofiltration Membranes"
No full textA novel fouling-resistant nanofiltration mixed-matrix membrane was obtained by the incorporation of 2D boron nitride nanosheets (BNNS) in polyethersulfone (PES). The addition of just 0.05 wt% of BNNS into the PES matrix led to a 4-fold increase in pure water permeance with a 10% decrease in the rejection of the dye Rose Bengal; up to 95% rejection of humic acid and nearly 100% flux recovery over two cycles in cross-flow fouling tests without the need for chemical cleansing. This performance is attributed to the uniform distribution of the BNNS in the PES matrix, observed via Raman mapping, and the surface chemistry and structure of the BNNS, which hydrophilised the polymer matrix and reduced its surface roughness. The low amount of BNNS filler needed to render the mixed-matrix membrane fouling-resistant opens the way to its use in waste-water treatment applications where organic fouling remains a major challenge.
A summary of the data analysis, plotted figures and microscopy images can be found in this dataset.Full details of the data collection methodology and subsequent calculations may be found in the "Materials and Methods" section of the associated paper
Design Dependencies Visualisation Tool - initial alpha release
No full textHeuristics play a great role in design creativity and learning. There is generally no systematic method designers can be provided with in order to derive optimal design solutions. In other words, it is neither possible to list all solutions to a given problem nor to prove the absolute superiority of a given solution. However, guidance can be provided to designers in the form of heuristics, i.e. set of solutions validated by practice, which have proven to lead towards satisfactory solutions, but which are not guaranteed to be optimal or perfect. Heuristics express a supposed causal relation between two design parameters. For example, in the context of automobile development, “overall weight” has a direct relation with “energy consumption in use.”
This tool allows displaying the network built by all causal relations between design parameters. It provides an environment to explore and navigate in this network in order to support creativity in design
DFT Dataset: Mg Intercalation into (OH,F)-Substituted Anatase TiO2
No full textDFT calculation inputs and output, and analysis code to support the computational results in "Controlled Fluorination Reaction of Anatase to Promote Mg2+ Mobility in Rechargeable Magnesium Batteries" Ma et al. Chem. Comm. 2018.All DFT calculations were performed using VASP 5.4.4 (vasp.5.4.4.18Apr17-6-g9f103f2a35).The VASP output data have been parsed and collated using the `vasp_summary` script contained in the `vasppy` Python package.
This dataset is then used to calculate a set of Mg intercalation energies within a Jupyter notebook.
The data parsing and analysis steps are described as a Snakemake workflow.The .zip archive can be extracted by most zip applications.
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.
The data analysis workflow has the following Python package requirements:
```
numpy
pandas
vasppy >= 0.4.0.4
jupyter
version-information
matplotlib
snakemake
pyyaml
nbconvert >= 4.2.0
```
From the top level directory, the analysis workflow can be run from a *nix command prompt with
```
pip install -r requirements
snakemake clean
snakemake
```
Full details are given in the top-level `README.md` fil
Python scripts for investigating Open Source Hardware GitHub repositories
No full textThis dataset contains Python scripts applying repository mining and social network analysis (SNA) techniques for investigating the transparency and workload distribution of open source hardware (OSH) product development projects hosted on GitHub. Starting from a list of projects and the reference of their corresponding repositories, the scripts extract file versioning metadata from the GitHub API and compute GraphML graphs depicting the full history of commit information for each project. Three types of graphs are computed: commit graphs, file co-edition graphs and file change graphs. They then apply SNA indicators (size, centrality and clustering index) to characterize the topology of file co-edition graphs. Finally, they apply a k-means clustering to these indicators in order to identify different types of projects based on the topology of their co-edition graphs. These scripts have been developed and applied to 105 OSH product development projects in the frame of a study published in the following article (in open access): Bonvoisin, J., Tom Buchert, Maurice Preidel, Rainer Stark. 2018. “How participative is open source hardware? Insights from online repository mining”. Design Science, 4, E19. doi:10.1017/dsj.2018.15Instructions for use are given in the header of each of the six scripts.
Script "goMine.py":
- extracts metadata from GitHub API;
- takes a list of project references as input (a CSV where each line is a list of GitHub repository references / affiliated to a project);
- produces for each project a JSON file with a reference all branches;
- produces for each project a JSON file with all commits of all branches.
Script "goCreateGraphs.py":
- takes as input a list of JSON files containing all commit information related to a project produced by goMine.py;
- creates for each project the following graphs in GraphML:
* a commit graph (as seen in Insights/Network in GitHub),
* contributor graphs (where each node is a contributor and each edge is the edition of the same file by two contributors), filtered per filetype,
* graphs of all committed file changes (one subgraph per file), filtered per filetype.
Script "analysisActivityVolume.py":
- computes indicators related to activity volume (number of file changes over time and per project);
- takes as input the graphs of file changes produced by goCreateGraphs.py.
Script "analysisActivityDistribution.py":
- computes indicators related to activity distribution;
- takes as input the contributor graphs produced by goCreateGraphs.py;
- produces a CSV with computed indicators for all considered projects.
Script "clustering.py":
- applies a k-means clustering to the topological indicators computed on the contributor graphs;
- takes as input the computed list of topological indicators produced by analysisActivityDistribution.py.
Script "timeStop.py" is just a utility to add timestamps in traces.The scripts require Python 3.7
DFT dataset: Impact of Anionic Vacancies on the Local and Electronic Structures of Iron-based Oxyfluoride Electrodes
No full textThis dataset contain the supporting density functional theory (DFT) data and analysis for the paper "Impact of Anionic Vacancies on the Local and Electronic Structures of Iron-based Oxyfluoride Electrodes".
It contains inputs and outputs for a series of DFT calculations on anion-substituted HTB-structured FeF3. All calculations were performed using VASP (Vienna Ab-initio Simulation Package).
The dataset includes a snakemake workflow for automated processing and plotting of projected densities-of-states and absorption coefficient data.All DFT calculations were performed using VASP 5.4.4 (vasp.5.4.4.18Apr17-6-g9f103f2a35). Input files for each calculation are contained within this dataset.The VASP output data have been parsed and collated using the `vasp_summary` script contained in the `vasppy` Python package.
This collated data is then used to plot the quantitative data included in the manuscript (absorption coefficients and projected densities of states).
The data parsing and analysis steps are described as a Snakemake workflow.
From the top level directory, the analysis workflow can be run from a *nix command prompt with
```
pip install -r requirements
snakemake clean
snakemake
```
Full details are given in the top-level `README.md` fileThe dataset is downloadable as a gzipped tar file (.tgz). To extract the files run
```
tar -zxvf FeF3_DFT_data.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.
The data analysis workflow has the following Python package requirements:
- vasppy;
- snakemake;
- pymatgen
Dataset for Optical characterization of magnesium incorporation in p-GaN layers for core–shell nanorod light-emitting diodes
No full textThis dataset contains the results of scanning electron microscopy (SEM), micro-photoluminescence (PL), cathodoluminescence (CL), Raman, and Electron Beam Induced Current (EBIC) measurements carried out on GaN-based core-shell nanostructures. The samples are highly regular arrays of GaN etched cores onto which various p-doped layers were grown using metal organic vapour phase epitaxy (MOVPE). The level of p-doping was varied between different samples.Secondary electron images were captured using a Hitachi S-4300 scanning electron microscope (SEM).
Micro-PL characterization was performed with a He–Cd 325 nm laser excitation source at 0.135 mW power output and a 2400 lines mm−1 diffraction grating in order to obtain a good signal to noise ratio. A 40× magnification, 0.50 numerical aperture (NA) objective produced a spot size of ~0.8 µm, small enough to probe single NRs on a 2 µm pitch.
CL maps were obtained on a subset of samples, using an electron beam with a low energy of 2 keV.
For the Raman measurements, a high-resolution confocal Raman spectrometer (Renishaw inVia) with a 532 nm laser source, 3000 lines mm−1 grating and 150×, 0.95 NA objective was used. The laser power was 10 mW, the lateral spatial resolution was 0.3 µm and the measurement resolution of the spectral shift was 0.01cm−1.
Electrically tuneable polyaniline membranes: Influence of polymerization temperature on in-filtration electrical tuneability of flux and MWCO
No full textThis dataset underpins the results published in the paper, "Stimuli responsive conductive polyaniline membrane: In-filtration electrical tuneability of flux and MWCO". Included are the results of Fourier transform infrared spectroscopy (FTIR), UV–visible spectroscopy (UV–vis),
dynamic contact angle goniometry, and measurements of four-point probe conductivity, membrane permeance, electrical tuneability under applied potential, and molecular weight cut-off (MWCO), as applied to a conductive polyaniline (PANI) membrane.Full details of the methodology can be found in the 'Experimental methods' section of the associated manuscript
Data for "Juvenile bovine bone is an appropriate surrogate for normal and reduced density human bone in biomechanical testing"
Get PDFJuvenile bovine bone has the potential to be a model of normal human bone given its dimensions and comparatively reduced ethical restrictions. Previous studies have shown that demineralisation techniques can reduce bone density and alter bone properties. This project aims to establish and validate juvenile bone as a usable model for biomechanical testing including using demineralisation techniques to model reduced density bone. This data set provides the raw data for the models created. The methodology is described in detail in the related manuscript: Fletcher, J., Williams, S., Whitehouse, M., Gill, H. and Preatoni, E. "Juvenile bovine bone is an appropriate surrogate for normal and reduced density human bone in biomechanical testing: a validation study." Scientific Reports
Dataset for "Wound Fibre Reinforced Polymer shear reinforcement for non-prismatic concrete beams"
No full textThe dataset includes:
1. experimental data measuring the strain of W-FRP (Wound Fibre Reinforced Polymer) reinforced tapered beams under different load conditions;
2. MATLAB files of the prediction-making following the revised ACI 440, CSA S806 and MCFT model;
3. a spreadsheet to summarise the predictions.The experimental data were collected during the tests of the tapered beams.
They include the load, displacement, strain of longitudinal bars and strain of shear reinforcement.
Respectively, they were collected by the loading cell, transducers, strain gauges.
The predictions were calculated by the models built based on MATLAB
Dataset for "TEMPO-oxidized cellulose nanofibrils; probing the mechanisms of gelation via Small Angle X-Ray Scattering"
No full textThese data are associated with the study of oxidized cellulose nanofibril (OCNF) suspensions upon concentration and salt. The set includes TEM (transmission electron microscopy) and cryo-TEM micrographs, rheology files, SAXS (small-angle X-ray scattering) data and SAXS fits.Details concerning the methodology used for data collection may be found in the associated manuscript.Regarding the rheology data, the .tri files may be opened with TA Instruments TRIOS software (http://www.tainstruments.com/support/software-downloads-support/downloads/).
Regarding the SAXS data, the .dat, .fit, and .rds files are all plain text and may be viewed in a text editor.The data are arranged into folders according to the data collection method. Each contains an explanations.txt file describing how files have been named and how the data have been structured
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