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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Dataset for "Nanoscale Confinement of Skyrmions in FeGe Device shapes"

Authors: A. C. Twitchett-Harrison; Loudon, J. C.; Pepper, R. A.; Birch, M. T.; Fangohr, H.; Midgley, P. A.; Balakrishnan, G.; +1 Authors

Dataset for "Nanoscale Confinement of Skyrmions in FeGe Device shapes"

Abstract

Dataset for "Confinement of Skyrmions in Nanoscale FeGe Device Structures" This dataset comprises of the data from the simulation part of the study of the paper "Confinement of Skyrmions in Nanoscale FeGe Device Structures." In this study, a series of five FeGe nanostructures were milled by Focused Ion Beam (FIB) lithography, into two 'blocks' and three 'dumbbells' in order to investigate the formation of skyrmions around a geometric constriction. The experimental samples were imaged by way of Lorentz Transmission Electron Microscopy (LTEM). Simulations were then performed in order to help understand the physics of skyrmion formation within these samples. Prerequisites In order to run the scripts contained in the subfolders, the following are needed: An installation of MuMax 3.10 (https://mumax.github.io/download.html). In order to set this up, you must first install the NVidia graphics drivers appropriate to your system, and the CUDA libraries. Python > 3.6 A number of Python dependencies which are specified in the requirements.txt file in the root directory. Once Python is installed, we recommend installing these via a virtualenv: virtualenv env source env/bin/activate python -m pip install -r requirements.txt Dataset Details This dataset consists of the following tar.xz archives: preprocessing.tar.xz - A small preprocessing library and script for converting experimental TEM images into a 'mask' that can be used for forming a simulation mesh. To run these, run the following command: tar xf preprocessing.tar.xz cd preprocessing python generate-masks.py simulation-scripts.tar.xz - This contains five folders labelled B1, B2 and D1, D2 and D3 representing the five different sample types. In each folder is the MuMax simulation script used for this study, along with the cropped final mask image which is used in the simulation script. To rerun each part of the study (please note that the run time is several weeks per script on current hardware), for e.g. for the B1 sample you can do the following: tar xf simulation-scripts.tar.xz cd simulation-scripts/B1/ mumax3 B1.mx3 B1.tar.xz, B2.tar.xz, D1.tar.xz, D2.tar.xz, D3.tar.xz - These archives contain the simulation results for each of the five samples in OVF format. They can be extracted into the simulation-scripts folder to allow the postprocessing analysis to be repeated. postprocessing.tar.xz - This archive contains a postprocessing script which calculates the projection of the magnetisation in the z-direction from the simulation output data. To run this, run the simulations, or download the results archives and place the extracted files into the simulation-scripts folder and then run: tar xf postprocessing.tar.xz cd postprocessing python postprocess.py

Keywords

skyrmion,physics,spin dynamics,magnetism

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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