Authors: M. Lamba and F.A. Santosa
Textual analysis remains challenging for researchers, students and practitioners without programming expertise, creating barriers in social sciences and humanities research. This poster presents Coconut Libtool 2.0, an enhanced web-based application that makes advanced textual analysis and visualization accessible to everyone. Building upon version 1.0, Coconut Libtool 2.0 significantly expands data source integration to include HathiTrust digital library, Dimensions, and PubMed, alongside existing Scopus, Web of Science, Lens, and customized file support. Enhanced text and visualization techniques include established methods for topic modeling, network text analysis, and keyword stemming, in addition to new features such as burst detection, sentiment analysis, and scattertext visualization.
Authors: M. Lamba, Y. Peng, S. Nikolov, and J. S. Downie
Acknowledgments are the most overlooked part of a scientific publication that is often taken for granted. The acknowledgment section of dissertations is historically understudied and is considered as “Cinderella” of academic writing. Examining the acknowledgment sections of dissertations is crucial for understanding the cultural aspects embedded within academic practices and their impact on wider societal values and norms. In this data paper, we introduce an ongoing project involving a manually coded dataset consisting of 4603 acknowledgment sentences derived from 3737 dissertations sourced from the institutional repository of the University of Illinois Urbana-Champaign. A team of 12 coders, divided into 6 groups, used a tailor-made streamlit web-based tool developed specifically for qualitative coding, utilizing 17 support, 4 sentiment, and 2 non-support tags. This dataset will be an important asset for researchers in natural language processing, computational social science, and information science for various downstream analyses, including machine learning and named entity recognition, among others.
Authors: C. Barnett, M. FitzGerald, K. Krumbholz, and M. Lamba (Equal Contribution)
This application provides access to the data presented and discussed in Carolyn Barnett, Michael FitzGerald, Katie Krumbholz, and Manika Lamba, “Gender Research in Political Science Journals: A Dataset,” PS: Political Science and Politics, 2022. The article and its accompanying online appendix discuss in detail the methodology used to collect and code the articles in the dataset. The full dataset is also available in .rds format, with additional documentation, on the Harvard Dataverse at https://doi.org/10.7910/DVN/UVWRTV. The authors thank John Kim for creating this Shiny app. Any questions should be directed to one of the study authors listed above.