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PyProBE: Python Processing for Battery Experiments

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 12 Jan 2025Publisher:Zenodo
Authors: Holland, Thomas; Cummins, Daniel; Marinescu, Monica;
Code repository: https://github.com/ImperialCollegeLondon/PyProBE/tree/v2.3.1

doi: 10.5281/zenodo.16745409 , 10.5281/zenodo.14782550 , 10.5281/zenodo.14635070 , 10.5281/zenodo.14720989 , 10.5281/zenodo.17855205 , 10.5281/zenodo.16680549 , 10.5281/zenodo.14845131 , 10.5281/zenodo.14796110 , 10.5281/zenodo.15089626 , 10.5281/zenodo.14635071 , 10.5281/zenodo.14947540 , 10.5281/zenodo.17803483

PyProBE: Python Processing for Battery Experiments

Abstract

PyProBE (Python Processing for Battery Experiments) is a Python package designed to simplify and accelerate the process of analysing data from battery cyclers.

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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.
BIP!Impulse provided by BIP!
0
Average
Average
Average