Data Cleaning, Translation &amp; Split of the Dataset for the Automatic Classification of Documents for the Classification System for the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft

Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer. Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset. ger_train.csv – The German training set as CSV file. ger_validation.csv – The German validation set as CSV file. en_test.csv – The English test set as CSV file. en_train.csv – The English training set as CSV file. en_validation.csv – The English validation set as CSV file. splitting.py – The python code for splitting a dataset into train, test and validation set. DataSetTrans_de.csv – The final German dataset as a CSV file. DataSetTrans_en.csv – The final English dataset as a CSV file. translation.py – The python code for translating the cleaned dataset.

Keywords

Automatic Classification

Impact byBIP!

	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).	0
	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.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average