Pubmed Journal Recommendation System dataset

<p>Dataset for Journal recommendation, includes title, abstract, keywords, and journal.</p> <p>We extracted the journals and more information of:</p> <p>Jiasheng Sheng. (2022). PubMed-OA-Extraction-dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6330817.</p> <p>Dataset Components:</p> <ul> <li> <p><strong>data_pubmed_all:</strong> This dataset encompasses all articles, each containing the following columns: 'pubmed_id', 'title', 'keywords', 'journal', 'abstract', 'conclusions', 'methods', 'results', 'copyrights', 'doi', 'publication_date', 'authors', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract', 'AKE_keywords', 'File_Name'.</p> </li> <li> <p><strong>data_pubmed:</strong> To focus on recent and relevant publications, we have filtered this dataset to include articles published within the last five years, from January 1, 2018, to December 13, 2022&mdash;the latest date in the dataset. Additionally, we have exclusively retained journals with more than 200 published articles, resulting in 262,870 articles from 469 different journals.</p> </li> <li> <p><strong>data_pubmed_train, data_pubmed_val, and data_pubmed_test:</strong> For machine learning and model development purposes, we have partitioned the 'data_pubmed' dataset into three subsets&mdash;training, validation, and test&mdash;using a random 60/20/20 split ratio. Notably, this division was performed on a per-journal basis, ensuring that each journal's articles are proportionally represented in the training (60%), validation (20%), and test (20%) sets. The resulting partitions consist of 157,540 articles in the training set, 52,571 articles in the validation set, and 52,759 articles in the test set.</p> </li> </ul>