READ ME File For 'Dataset for Optimising finite-time photon extraction from emitter-cavity systems'

Dataset DOI: 10.5258/SOTON/D3091

Date that the file was created: May, 2024

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GENERAL INFORMATION
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ReadMe Author: William James Hughes, University of Southampton, ORCID: 0000-0002-7254-3464

Date of data collection: 2024

Information about geographic location of data collection: Optoelectronics Research Centre, University of Southampton, Southampton SO17 1BJ, UK, and Iridis 5 computing cluster 

Related projects:
Quantum Computing and Simulation Hub

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SHARING/ACCESS INFORMATION
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Licenses/restrictions placed on the data, or limitations of reuse: CC-BY

Recommended citation for the data:

This dataset supports the publication:
AUTHORS: W.J. Hughes, J.F. Goodwin, P. Horak
TITLE: Optimising finite-time photon extraction from emitter-cavity systems
JOURNAL: Journal of the Optical Society of America B
PAPER DOI IF KNOWN:

Links to other publicly accessible locations of the data: None

Links/relationships to ancillary or related data sets: None 


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DATA & FILE OVERVIEW
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This dataset contains:

Data and plotting code required to produce the figures in the linked paper. This contains 4 different types of files. 
1) Code files (extension .py): These are named according to the figure they produce (Figures 3, 4, 5, 6, and S1)
2) Data folders (folders/directories): These named according to their relevant figure, except for Fig S1, which uses the data for Figure 3 and therefore does not have a corresponding dataset
3) Code readme files (extension .txt): Each code file has a corresponding readme, explaining the specifics of the data for that figure in detail
4) Environment specification (environment.yml): A specification of the Python environment used to run the code 


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METHODOLOGICAL INFORMATION
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Description of methods used for collection/generation of data: Data generated using Python code implementing the methods described in the linked publication. 

Methods for processing the data: The collected data was purely computational, so data processing was the computational steps described in the linked publication.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers:
Data is stored in a combination of .json and .npy files. These can be loaded simply with the method 'json_numpy_np_save_read_from_file' in common.py (as is done in every plotting file), but can be loaded manually using the json package (automatically include in python) and the numpy.load method
The code is Python code. This must be run in an environment that has the packages numpy and matplotlib. The environment configuration that the authors used to run the code is found in environment.yml

Environmental/experimental conditions:
Data produced either on a desktop PC (Windows 10, Viglen Genie VIG830S) or the Iridis 5 computing cluster at the University of Southampton. 

Describe any quality-assurance procedures performed on the data:

People involved with sample collection, processing, analysis and/or submission:
Data production, processing, analysis, and submission: William James Hughes
Submission of linked manuscript: William James Hughes, Joseph Francis Goodwin, Peter Horak


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DATA-SPECIFIC INFORMATION
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Given in a separate readme for each figure file