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Perovskite Solar Cells Ageing Dataset
- data.niaid.nih.gov
Updated Jul 26, 2023
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EmailClick to copy linkLink copiedCiteSchlatmann, Rutger (2023). Perovskite Solar Cells Ageing Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8185882Dataset updatedJul 26, 2023Dataset provided byAbate, Antonio
Hartono, Noor Titan Putri
Khenkin, Mark
Ulbrich, Carolin
Köbler, Hans
Graniero, Paolo
Schlatmann, RutgerLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis dataset contains cleaned 2,245 ageing test traces (time vs. MPPT PCE/ maximum power point tracking power conversion efficiency) for perovskite solar cells with various device stacks and architectures in the pickle (.pkl) format.
The dataset can be loaded with the following commands on Python.
import pickle5 as pickle import pandas as pd import numpy as np
with open('20230303_mySeriesDrop.pkl', "rb") as fh: mySeriesDrop = pickle.load(fh)
The following command can be used to call a specific row (row 0) within the dataset.
mySeriesDrop[0]
The next steps to use the dataset is using scaling/ normalisation (for instance using sklearn.preprocessing.MaxAbsScaler) and smoothing (for instance using Savitzky-Golay filter).
The code to run the complete analysis, including self-organising map clustering, can be accessed here: https://doi.org/10.5281/zenodo.8181602.
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FacebookTwitterEmailHartono, Noor Titan Putri
Khenkin, Mark
Ulbrich, Carolin
Köbler, Hans
Graniero, Paolo
Schlatmann, Rutger
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains cleaned 2,245 ageing test traces (time vs. MPPT PCE/ maximum power point tracking power conversion efficiency) for perovskite solar cells with various device stacks and architectures in the pickle (.pkl) format.
The dataset can be loaded with the following commands on Python.
import pickle5 as pickle import pandas as pd import numpy as np
with open('20230303_mySeriesDrop.pkl', "rb") as fh: mySeriesDrop = pickle.load(fh)
The following command can be used to call a specific row (row 0) within the dataset.
mySeriesDrop[0]
The next steps to use the dataset is using scaling/ normalisation (for instance using sklearn.preprocessing.MaxAbsScaler) and smoothing (for instance using Savitzky-Golay filter).
The code to run the complete analysis, including self-organising map clustering, can be accessed here: https://doi.org/10.5281/zenodo.8181602.