Methodology data of "Twenty years of research in Digital Humanities: a topic modeling study"

This document contains the datasets created in the thesis "Twenty years of research in Digital Humanities: a topic modeling study". The methodological approach of the work is based on two datasets built by web scraping DH journals’ official web pages and API requests to popular academic databases (Crossref, Datacite). The datasets constitute a corpus of DH research and include research papers abstracts and abstract papers from DH journals and international DH conferences published between 2000 and 2020. Probabilistic topic modeling with latent Dirichlet allocation is then performed on both datasets to identify relevant research subfields. <strong>Data</strong> Folder <strong>"<em>data/</em>" </strong>contains four folders which relate to two datasets: The first dataset, which will be referred to as the journals dataset, contains original research papers published in journals exclusively devoted to digital humanities scholarshipis [1] and is composed of 2,464 articles from 26 journals. The second dataset, the conference dataset, contains abstract papers available in ADHO conference archives and is composed of 2,160 articles from 15 years of ADHO conferences and 4 conferences promoted by journals Both datasets are provided with: URL (if available); identifier and related scheme (if available); abstract or abstract paper; title; authors’ given name, family name; author’s affiliation name, found within the document metadata or text; normalized affiliation name, country of the affiliation, identifiers of the affiliation provided by the Research Organization Registry Community (ROR, https://ror.org); publisher (if available); publishing date (complete date when provided or only the year); keywords (if available); journal title; volume and issue (if available); electronic and/or print ISSN (if available). The two folders <strong>"data/no_abstracts..."</strong> are licensed under a Creative Commons public domain dedication (CC0), while the others keep their original license (the one provided by their publisher) because they contain full abstracts of the papers. These latter datasets are provided in order to favor the reproducibility of the results obtained in our work. <strong>Topic modeling</strong><br> <br> <em><strong>"topic_modeling/"</strong></em> directory contains input and output data used within MITAO, a tool for mashing up automatic text analysis tools, and creating a completely customizable visual workflow [2]. The topic modeling results are divided in two folders, one for each of the datasets. <strong>Note:</strong> It's necessary to unzip the file to get access to all the files and directories listed below. <strong>References</strong> Spinaci, G., Colavizza, G., Peroni, S., <em>Preliminary Results on Mapping Digital Humanities Research</em>, in: Atti del IX Convegno Annuale AIUCD. La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica, Milan, Università Cattolica del Sacro Cuore, 2020, pp. 246 - 252 (atti di: IX Convegno Annuale AIUCD. La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica, Milano, Italy, 15-17 gennaio 2020) Ferri, P., Heibi, I., Pareschi, L., &amp; Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. https://doi.org/10.19245/25.05.pij.5.2.3