READ ME File For 'Dataset to support the journal article '3D positional metrology of a virus-like nanoparticle with topologically structured light''

Dataset DOI: 10.5258/SOTON/D3080

Date that the file was created: May, 2024

-------------------
GENERAL INFORMATION
-------------------

ReadMe Author: Yu Wang, University of Southampton [https://orcid.org/0000-0002-0636-5102]

Date of data collection: February, 2024

Information about the geographic location of data collection: University of Southampton, Building 53, 2029

Related projects:

This work is supported by the UK Engineering and Physical Science Research Council (grants EP/T02643X/1), the Singapore National Research Foundation (grant No. NRF-CRP23-2019-0006), and the Singapore Ministry of Education (grant MOE2016-T3-1-006).


--------------------------
SHARING/ACCESS INFORMATION
-------------------------- 

Licenses/restrictions placed on the data, or limitations of reuse: CC BY


This dataset supports the publication:

AUTHORS:Yu Wang, Eng Aik Chan, Carolina Rendón-Barraza, Yijie Shen, Eric Plum, Kevin F. MacDonald, Jun-Yu Ou, and Nikolay I. Zheludev
TITLE:3D positional metrology of a virus-like nanoparticle with topologically structured light
JOURNAL:Applied Physics Letters


--------------------
DATA & FILE OVERVIEW
--------------------

This dataset contains: 3 Excel files including all initial data for plotting Figures 2-4.
Fig.2 data.xlsx contains 2 sheets, where sheet1 is the data for plotting Fig.2(a) and sheet2 is the data for plotting Fig.2(b).
Fig.3 data.xlsx contains data for plotting Fig.3.
Fig.4 data.xlsx contains 2 sheets, where sheet1 is the data for plotting Fig.4(a) and sheet2 is the data for plotting Fig.4(b).



--------------------------
METHODOLOGICAL INFORMATION
--------------------------

Fig.2 data was generated from the neural network prediction minus the truth, using Formula (1).
Fig.3 data was generated from the neural network learning different diffraction images with different Field of Views and the plotting values are calculated by using Formula (2).
Fig.4 data was generated from the neural network prediction minus the truth, using Formula (1).