Package: pyBibX
pyBibXIntroductionA Bibliometric and Scientometric python library that uses the raw files generated byScopus(.bib files),WOS (Web of Science)(.bib files), andPubMed(.txt files) scientific databases. Also, Powered with Advanced AI Technologies for Analyzing Bibliometric, Scientometric Outcomes, and Textual DataTo export the correct file formats from Scopus, Web of Science, and PubMed, follow these steps:a)Scopus: Search, select articles, click "Export," choose "BibTeX," select all fields, click "Export" again.b)WoS: Search, select articles, click "Export," choose "Save to Other File Formats," select "BibTeX," select all fields, click "Send."c)PubMed: Search, select articles, click "Save," choose "PubMed" format, click "Save" to download a .txt file.General Capabilities:a) Works withScopus(.bib files),WOS(.bib files) andPubMed(.txt files) databasesb) Identification and Removal of duplicatesc) Identification of documents per typed) Generates anEDA (Exploratory Data Analysis)Report: Publications Timespan, Total Number of Countries, Total Number of Institutions, Total Number of Sources, Total Number of References, Total Number of Languages (and also the number of docs for each language), Total Number of Documents, Average Documents per Author, Average Documents per Institution, Average Documents per Source, Average Documents per Year, Total Number of Authors, Total Number of Authors Keywords, Total Number of Authors Keywords Plus, Total Single-Authored Documents, Total Multi-Authored Documents, Average Collaboration Index, Max H-Index, Total Number of Citations, Average Citations per Author, Average Citations per Institution, Average Citations per Document, Average Citations per Sourcee) Creates anID (Identification)for each Document, Authors, Sources, Institutions, Countries, Authors' Keywords, Keywords Plus. The IDs can be used in graphs/plots to obtain a cleaner visualizationf) Creates aWordCloudfrom the Abstracts, Titles, Authors Keywords or Keywords Plusg) Creates aN-Gram Bar Plot (interactive plot)from the Abstracts, Titles, Authors Keywords or Keywords Plush) Creates aProjection (interactive plot)of the documents based on the Abstracts, Titles, Authors Keywords or Keywords Plusi) Creates anEvolution Plot (interactive plot)based on Abstracts, Titles, Sources, Authors Keywords or Keywords Plusj) Creates aSankey Diagram (interactive plot)with any combination of the following keys: Authors, Countries, Institutions, Journals, Auhors_Keywords, Keywords_Plus, and/or Languagesk) Creates aTreeMapfrom the Authors, Countries, Institutions, Journals, Auhors_Keywords, or Keywords_Plusl) Creates anAuthors Productivity Plot (interactive plot)It informs for each year the documents (IDs) published for each authorm) Creates aBar Plotfor the following statistics: Documents per Year, Citations per Year, Past Citations per Year, Lotka's Law, Sources per Documents, Sources per Citations, Authors per Documents, Authors per Citations, Authors per H-Index, Bradford's Law (Core Sources 1, 2 or 3), Institutions per Documents, Institutions per Citations, Countries per Documents, Countries per Citations, Language per Documents, Keywords Plus per Documents and Authors' Keywords per DocumentsNetwork Capabilities:a)Citation Analysis (interactive plot)between Documents (Blue Nodes) and Citations (Red Nodes). Documents and Citations can be highlighted for better visualizationb)Collaboration Analysis (interactive plot)between Authors, Countries, Institutions orAdjacency Analysis (interactive plot)between Authors' Keywords or Keywords Plus. Collaboration and Adjacency can be highlighted for better visualizationc)Similarity Analysis (interactive plot)can be performed using coupling or cocitation methodsd)World Map Collaboration Analysis (interactive plot)between Countries in a MapArtificial Intelligence Capabilities:a)Topic Modellingusing BERTopic to cluster documents by topicb) Visualize topics distributionc) Visualize topics by the most representative wordsd) Visualize documents projection and clusterization by topice) Visualize topics heatmapf) Find the most representative documents from each topicg) Find the most representative topics according to a wordh) CreatesEmbeddingsfrom Abstracts, Titles, Authors Keywords or Keywords Plusi)Abstractive Text SummarizationusingPEGASUSon a set of selected documents or all documentsj)Abstractive Text SummarizationusingchatGPTon a set of selected documents or all documents. Requires the user to have anAPI key(https://platform.openai.com/account/api-keys)k)Extractive Text SummarizationusingBERTon a set of selected documents or all documentsl)Ask chatGPTto analyze the following results: EDA Report, WordCloud, N-Grams, Evolution Plot, Sankey Diagram, Authors Productivity Plot, Bar Plots, Citation Analysis, Collaboration Analysis, Similarity Analysis, and World Map Collaboration Analysis (consultExample 08). Requires the user to have anAPI key(https://platform.openai.com/account/api-keys)Correction and Manipulation Capabilities:a) Filter the .bib or .txt file by Year, Sources, Bradford Law Cores, Countries, Languages and/or Abstracts (Documents with Abstracts)b) Merge Authors, Institutions, Countries, Languages and/or Sources that have multiple entriesc) Merge different or the same database files one at a time. The preference for information preservation is given to the old database, so the order of merging matters (consultExamples 04 and 05)UsageInstallpipinstallpyBibXTry it inColab:Example 01: Scopus                (Colab Demo)Example 02: WOS                   (Colab Demo)Example 03: PubMed                (Colab Demo)Example 04: Scopus + WOS          (Colab Demo)Example 05: WOS + Scopus          (Colab Demo)Example 06: Scopus + WOS + Pubmed (Colab Demo)Example 07: Your Own              (Colab Demo)Example 08:Ask chatGPTAnalysis  (Colab Demo)AcknowledgementThis section indicates the libraries that inspired pyBibXBERT (https://smrzr.io/)a) Github:https://github.com/dmmiller612/bert-extractive-summarizerb) Paper: DEREK, M. (2019). Leveraging BERT for Extractive Text Summarization on Lectures. arXiv. doi:https://doi.org/10.48550/arXiv.1906.04165BERTopic (https://maartengr.github.io/BERTopic/index.html)a) Github:https://github.com/MaartenGr/BERTopicb) Paper: GROOTENDORST, M. (2022). BERTopic: Neural Topic Modeling with a Class-based TF-IDF Procedure. arXiv. doi:https://doi.org/10.48550/arXiv.2203.05794Bibliometrix (https://www.bibliometrix.org/home/)a) Github:https://github.com/massimoaria/bibliometrixb) Paper: ARIA, M.; CUCCURULLO, C. (2017). Bibliometrix: An R-tool for Comprehensive Science Mapping Analysis. Journal of informetrics, 11(4), 959-975. doi:https://doi.org/10.1016/j.joi.2017.08.007chatGPT (https://chat.openai.com/chat)a) Github:https://github.com/openaib) Paper: OPENAI. (2023). GPT-4 Technical Report. arXiv. doi:https://doi.org/10.48550/arXiv.2303.08774Metaknowledge (http://www.networkslab.org/metaknowledge)a) Github:https://github.com/UWNETLAB/metaknowledgeb) Paper: McILROY-YOUNG, R.; McLEVEY, J.; ANDERSON, J. (2015). Metaknowledge: Open Source Software for Social Networks, Bibliometrics, and Sociology of Knowledge Research.SentenceTransformers (https://www.sbert.net/)a) Github:https://github.com/UKPLab/sentence-transformersb) Paper: REIMERS, N.; GUREVYCH, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv. doi:https://arxiv.org/abs/1908.10084PEGASUS (https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html?m=1)a) Github:https://github.com/huggingface/transformersb) Paper: ZHANG, J.; ZHAO, Y.; SALEH, M.; LIU, P.J. (2019). PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. arXiv. doi:https://doi.org/10.48550/arXiv.1912.08777And to all the people that helped to improve or correct the code. Thank you very much!Fabio Ribeiro von Glehn (29.DECEMBER.2022) - UFG - Federal University of Goias (Brazil)