Imperial College London
Alternate

DrVasaCurcin

Faculty of Medicine, School of Public Health

Honorary Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 0716vasa.curcin Website

 
 
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Location

 

320Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Chapman:2021:10.1007/978-3-030-80960-7_22,
author = {Chapman, M and Fairweather, E and Khan, A and Curcin, V},
doi = {10.1007/978-3-030-80960-7_22},
pages = {256--262},
title = {COVID-19 Analytics in Jupyter: Intuitive Provenance Integration Using ProvIt},
url = {http://dx.doi.org/10.1007/978-3-030-80960-7_22},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Whilst the need to record and understand the evolution of data, together with the processes and users associated with those changes, is now widely appreciated, the uptake of solutions to these issues remains slow. Data provenance techniques have the potential to provide such an understanding, but their use is often considered a specialist activity, requiring detailed knowledge of standards such as W3C PROV. In this work, we introduce ProvIt, a suite of tools designed to lower the barriers to entry for the use of provenance technology. We demonstrate the utility of ProvIt by using it to add provenance capabilities to the Jupyter IDE, in order to provide insight into the tools used by a group of researchers analysing a COVID-19 dataset.
AU  - Chapman,M
AU  - Fairweather,E
AU  - Khan,A
AU  - Curcin,V
DO  - 10.1007/978-3-030-80960-7_22
EP  - 262
PY  - 2021///
SN  - 0302-9743
SP  - 256
TI  - COVID-19 Analytics in Jupyter: Intuitive Provenance Integration Using ProvIt
UR  - http://dx.doi.org/10.1007/978-3-030-80960-7_22
ER  -
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