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The number of scientific publications continues to rise exponentially,especially in Computer Science (CS). However, current solutions to analyzethose publications restrict access behind a paywall, offer no features forvisual analysis, limit access to their data, only focus on niches orsub-fields, and/or are not flexible and modular enough to be transferred toother datasets. In this thesis, we conduct a scientometric analysis to uncoverthe implicit patterns hidden in CS metadata and to determine the state of CSresearch. Specifically, we investigate trends of the quantity, impact, andtopics for authors, venues, document types (conferences vs. journals), andfields of study (compared to, e.g., medicine). To achieve this we introduce theCS-Insights system, an interactive web application to analyze CS publicationswith various dashboards, filters, and visualizations. The data underlying thissystem is the DBLP Discovery Dataset (D3), which contains metadata from 5million CS publications. Both D3 and CS-Insights are open-access, andCS-Insights can be easily adapted to other datasets in the future. The mostinteresting findings of our scientometric analysis include that i) there hasbeen a stark increase in publications, authors, and venues in the last twodecades, ii) many authors only recently joined the field, iii) the most citedauthors and venues focus on computer vision and pattern recognition, while themost productive prefer engineering-related topics, iv) the preference ofresearchers to publish in conferences over journals dwindles, v) on average,journal articles receive twice as many citations compared to conference papers,but the contrast is much smaller for the most cited conferences and journals,and vi) journals also get more citations in all other investigated fields ofstudy, while only CS and engineering publish more in conferences than journals.

Analyzing the State of Computer Science Research with the DBLP Discovery Dataset