Frontiers and emerging topics in a century of Silkie chicken research: insights, challenges, and opportunities

Abstract

Silkie chickens are a unique breed renowned for their pigmentation, food and medicine homology properties, and distinctive appearance, making them highly valuable in exhibitions, as pets, in medicinal cuisine, and as a model for melanin research. Despite their vast potential, the growing volume of publications and patents related to Silkie chicken highlights the critical need for systematic organization, summarization, and analysis of this wealth of information. For the first time, this study employs bibliometric tools to summarize and analyze 114 years of research on Silkie chicken. Our study demonstrates that academic studies primarily focus on their nutritional value, melanin production, and genetic mechanisms, while patents emphasize food formulations, breeding methods, and purebred identification. Although there has been significant growth in publications and citations since 2001, international collaboration remains limited. This study presents the need for integrated and multidisciplinary research to unlock the full potential of Silkie chicken and provides a foundational framework for future studies and applications.

Keywords: Bibliometric; Nutrition; Patent; Pigmentation; Silkie chicken.

Fig. 1

Key morphological and functional traits of the Silkie chicken.

Fig. 2

Annual publication counts and citation frequency for research on Silkie chicken research. Notes: (A) Red bars represent publications from China, while purple bars represent publications from other countries. The combined height of the bars (red + purple) reflects the global publication volume for each year. The cumulative citation count is shown by the blue line, plotted on a secondary y-axis, to highlight the overall citation trajectory over time. (B) Geographical distribution of Silkie chicken research publications from 1911 to 2024, based on the Web of Science Core Collection database. The color intensity represents the number of publications per country, with darker shades indicating higher publication counts.

Fig. 3

Top 10 fields with the most publications in Silkie chicken research. Note: The fan-shaped segments represent the total publication volume for each field from 1911 to 2024. Inside each segment, the smaller bars closer to the center indicate the publication volume. The black circles mark the boundary for data corresponding to 1911–2000.

Fig. 4

Global and institutional collaboration networks in Silkie chicken research. Notes: (A) Collaborative relationships between countries involved in Silkie chicken research. The size of the nodes represents the research output, and the connections indicate the strength of international collaborations. (B) Represents institutional collaborations within Silkie chicken research. The size of the nodes corresponds to the volume of research contributions, and the links denote collaborative efforts between institutions.

Fig. 5

Authors' collaboration networks in Silkie chicken research.

Fig. 6

Research frontiers and keyword evolution in Silkie chicken studies. Notes: (A) Network visualization identifies ten primary research clusters, each representing a focused area of study in Silkie chicken research. (B) Keyword evolution over time, indicating the emergence and progression of high-impact keywords.

Fig. 7

Trends and geographical distribution of patents on Silkie chicken research. Notes: (A) Patent trends over time, indicating that the invention patents have consistently dominated, with a notable peak in 2014. The orange line represents the percentage of invention patents, showing a steady dominance over the years. Utility models and design patents appear less frequently, contributing to the overall fluctuation in patent types. (B) Represents the geographical distribution of the patents, with the bar representing the number of patents in each country.

Fig. 8

Innovation word cloud for Silkie chicken. Note: The key phrase extraction process used to generate the Word Cloud is as follows: First, Patsnap segments the text into tokens and removes the common stop words (using a stopword dictionary). Then, the phrases are grouped into clusters based on the Suffix Tree Clustering algorithm, which considers phrases as a collection of short phrases instead of single words (to yield a better clustering result). Each cluster is then scored, and the top 300-500 phrases are selected as key phrases for visualization.