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Dataset: Active learning streamlines development of high performance catalysts for higher alcohol synthesis

<p>In this repository there are five Excel files, three Jupyter notebook files, and a zip archive containing Origin files.&nbsp;</p> <p>The Full_catalytic_performance_data.xlsx file comprises all the experimental and computational catalytic data complied as a part of the research work titled "Active learning streamlines development of high performance catalysts for higher alcohol synthesis" carried out at the Advanced Catalysis Engineering group, ETHZ. The Source_data.xlsx file and .opju files contain the raw data used to create the display items in the manuscript.</p> <p>The repository contains three additional files "Modelling_Data_Phase_1.xlsx", "Modelling_Data_Phase_2.xlsx", "Modelling_Data_Phase_3.xlsx" which contain the curated data to run the Gaussian process -Bayesian Optimization algrotihm across three specific active learning Phases devised in this study. The python codes necessary to run the model are provided as Jupyter Notebook (.ipynb) files and are also available on GitHub in the link provided below.</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

Dataset details

Repository
zenodo
Published
May 3, 2024
License
mit-license
DOI
10.5281/zenodo.11113829

Scored with release 0.9.0

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