Bibliometric Analysis of Forensic Human Remains Identification Literature from 1991 to 2022.
Fa Yi Xue Za Zhi
; 40(3): 245-253, 2024 Jun 25.
Article
in En, Zh
| MEDLINE
| ID: mdl-39166305
ABSTRACT
OBJECTIVES:
To describe the current state of research and future research hotspots through a metrological analysis of the literature in the field of forensic anthropological remains identification research.METHODS:
The data retrieved and extracted from the Web of Science Core Collection (WoSCC), the core database of the Web of Science information service platform (hereinafter referred to as "WoS"), was used to analyze the trends and topic changes in research on forensic identification of human remains from 1991 to 2022. Network visualisation of publication trends, countries (regions), institutions, authors and topics related to the identification of remains in forensic anthropology was analysed using python 3.9.2 and Gephi 0.10.RESULTS:
A total of 873 papers written in English in the field of forensic anthropological remains identification research were obtained. The journal with the largest number of publications was Forensic Science International (164 articles). The country (region) with the largest number of published papers was China (90 articles). Katholieke Univ Leuven (Netherlands, 21 articles) was the institution with the largest number of publications. Topic analysis revealed that the focus of forensic anthropological remains identification research was sex estimation and age estimation, and the most commonly studied remains were teeth.CONCLUSIONS:
The volume of publications in the field of forensic anthropological remains identification research has a distinct phasing. However, the scope of both international and domestic collaborations remains limited. Traditionally, human remains identification has primarily relied on key areas such as the pelvis, skull, and teeth. Looking ahead, future research will likely focus on the more accurate and efficient identification of multiple skeletal remains through the use of machine learning and deep learning techniques.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Bibliometrics
/
Forensic Anthropology
/
Body Remains
Limits:
Humans
Language:
En
/
Zh
Journal:
Fa Yi Xue Za Zhi
Journal subject:
JURISPRUDENCIA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
China