Unexpected aberrant data patterns on slope graphs to examine article characteristics: Say good-bye to the burst bar chart in bibliometrics.

The rise of bibliometrics is closely linked to visualization tools. However, visuals produced by these tools often lack clarity and offer limited valuable information. There is a need for a comprehensive method that concisely describes key article metadata and highlights unexpected aberrant data patterns (UADPs) on slope graphs instead of traditional burst bar charts in CiteSpace. This study examined metadata from 26,555 articles in the journal Heliyon'' sourced from the Web of Science Core Collection. It assessed metrics and impacts across 10 critical metadata aspects, using the Rasch model to identify UADP displayed on slope graphs. The performance analytics, summary reports, and visual validations model, with performance analytics, summary reports, and visual validations, was applied to display findings. The slope graph, offering more valuable information, was proposed to replace traditional burst bar charts in CiteSpace. The analysis focused on the top 10 elements from 10 different areas, providing a detailed understanding of the journal and its significant contributors in performance analytics. China emerged as the leading country in research contributions to "Heliyon" with a distinct UADP in summary reports. The visual validations showed that: the UADPs were highlighted in China and Covenant University (Nigeria) by the outfit mean square error (MNSQs = 5.28 and 2.22, respectively, >2.0), and the keyword PERFORMANCE'' has a lower outfit MNSQ (=1.95, with a data pattern similar to other top 9 keywords plus). The study emphasized slope graph with UADPs over traditional burst bar chart in bibliometrics. The study highlights China's dominance in research output for the journal "Heliyon," identified through advanced slope graphs showing a significantly higher outfit MNSQ (=5.28). Future bibliometric analyses should emphasize UADPs alongside top-ranked elements to provide deeper insights into article characteristics.