Data

ML_Data_Input.xlsx

RMIT University, Australia
Calum Drummond (Aggregated by) Dilek Yalcin (Aggregated by) Hank Han (Aggregated by) Ibrahim Orhan (Aggregated by) Kyle Hearn (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25439/rmt.21067732.v1&rft.title=ML_Data_Input.xlsx&rft.identifier=https://doi.org/10.25439/rmt.21067732.v1&rft.publisher=RMIT University, Australia&rft.description=The physicochemical characterisation of 50 ionic liquids prepared with descriptors for multiple linear regression machine learning analysis.&rft.creator=Calum Drummond&rft.creator=Dilek Yalcin&rft.creator=Hank Han&rft.creator=Ibrahim Orhan&rft.creator=Kyle Hearn&rft.creator=Shveta Pandiancherri&rft.creator=Stuart Brown&rft.creator=Tamar Greaves&rft.creator=Tu Le&rft.date=2022&rft_rights=CC-0&rft_subject=Ionic Liquids&rft_subject=Physicochemical Characterisation&rft_subject=Machine Learning&rft_subject=Multiple Linear Regression&rft_subject=Protic Ionic Liquids&rft_subject=Physical chemistry not elsewhere classified&rft_subject=Machine learning not elsewhere classified&rft.type=dataset&rft.language=English Access the data
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The physicochemical characterisation of 50 ionic liquids prepared with descriptors for multiple linear regression machine learning analysis.

Issued: 2022-09-09

Created: 2022-09-09

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Ionic Liquids | Machine Learning | Machine learning not elsewhere classified | Multiple Linear Regression | Physical chemistry not elsewhere classified | Physicochemical Characterisation | Protic Ionic Liquids |

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