A historical review and Bibliometric analysis of research on Weak
measurement research over the past decades based on Biblioshiny
- URL: http://arxiv.org/abs/2108.11375v1
- Date: Wed, 25 Aug 2021 03:07:37 GMT
- Title: A historical review and Bibliometric analysis of research on Weak
measurement research over the past decades based on Biblioshiny
- Authors: Jing-Hui Huang, Xue-Ying Duan, Fei-Fan He, Guang-Jun Wang and
Xiang-Yun Hu
- Abstract summary: We used bibliometric methods to evaluate the global scientific output of research on Weak measurement from 2000 to 2020.
The number of publications has increased substantially with time.
The focus has evolved to study quantum information and amplify weak signals.
- Score: 4.278591555984394
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Weak measurement has enabled fundamental studies in both experiment and
theory of quantum measurement. Numerous researches have indicated that weak
measurements have a wide range of application and scientific research value. In
our work, we used bibliometric methods to evaluate the global scientific output
of research on Weak measurement and explore the current status and trends in
this field from 2000 to 2020. The R bibliometric package was used for
quantitative and qualitative analyses of publication outputs and author
contributions. In total, 636 related articles and reviews were included in the
final analysis. The main results were as follows: The number of publications
has increased substantially with time. Physical Review A was the most active
journal. The country and institution contributing the most to this field were
The United States and University Rochester respectively. The analysis of the
conceptual structure of keywords indicated that weak measurements were involved
a variety of metrology, quantum communication, and nonclassical features of
quantum mechanics. Our bibliometric analysis shows that weak measurement
research continues to be a hot-spot. The focus has evolved to study quantum
information and amplify weak signals.
Related papers
- Quantum Machine Learning: Unveiling Trends, Impacts through Bibliometric Analysis [1.1510009152620668]
Quantum Machine Learning (QML) is the intersection of two revolutionary fields: quantum computing and machine learning.
This research endeavors to conduct a comprehensive bibliometric analysis of scientific information pertaining to QML covering the period from 2000 to 2023.
arXiv Detail & Related papers (2025-04-10T13:18:48Z) - Optimizing Research Portfolio For Semantic Impact [55.2480439325792]
Citation metrics are widely used to assess academic impact but suffer from social biases.
We introduce rXiv Semantic Impact (XSI), a novel framework that predicts research impact.
XSI tracks the evolution of research concepts in the academic knowledge graph.
arXiv Detail & Related papers (2025-02-19T17:44:13Z) - Teaching Software Metrology: The Science of Measurement for Software Engineering [10.23712090082156]
This chapter reviews key concepts in the science of measurement and applies them to software engineering research.
A series of exercises for applying important measurement concepts to the reader's research are included.
arXiv Detail & Related papers (2024-06-20T16:57:23Z) - A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
Large language models (LLMs) have revolutionized the way text and other modalities of data are handled.
We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
arXiv Detail & Related papers (2024-06-16T08:03:24Z) - Mapping the Increasing Use of LLMs in Scientific Papers [99.67983375899719]
We conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals.
Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers.
arXiv Detail & Related papers (2024-04-01T17:45:15Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - A Comprehensive Study of Groundbreaking Machine Learning Research:
Analyzing highly cited and impactful publications across six decades [1.6442870218029522]
Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields.
It is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far.
arXiv Detail & Related papers (2023-08-01T21:43:22Z) - A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and
Why? [84.46288849132634]
We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
We define three variables to encompass diverse facets of the evolution of research topics within NLP.
We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
arXiv Detail & Related papers (2023-05-22T11:08:00Z) - Artificial Intelligence in Concrete Materials: A Scientometric View [77.34726150561087]
This chapter aims to uncover the main research interests and knowledge structure of the existing literature on AI for concrete materials.
To begin with, a total of 389 journal articles published from 1990 to 2020 were retrieved from the Web of Science.
Scientometric tools such as keyword co-occurrence analysis and documentation co-citation analysis were adopted to quantify features and characteristics of the research field.
arXiv Detail & Related papers (2022-09-17T18:24:56Z) - Change Summarization of Diachronic Scholarly Paper Collections by
Semantic Evolution Analysis [10.554831859741851]
We demonstrate a novel approach to analyze the collections of research papers published over longer time periods.
Our approach is based on comparing word semantic representations over time and aims to support users in a better understanding of large domain-focused archives of scholarly publications.
arXiv Detail & Related papers (2021-12-07T11:15:19Z) - A Measure of Research Taste [91.3755431537592]
We present a citation-based measure that rewards both productivity and taste.
The presented measure, CAP, balances the impact of publications and their quantity.
We analyze the characteristics of CAP for highly-cited researchers in biology, computer science, economics, and physics.
arXiv Detail & Related papers (2021-05-17T18:01:47Z) - Early Indicators of Scientific Impact: Predicting Citations with
Altmetrics [0.0]
We use altmetrics to predict the short-term and long-term citations that a scholarly publication could receive.
We build various classification and regression models and evaluate their performance, finding neural networks and ensemble models to perform best for these tasks.
arXiv Detail & Related papers (2020-12-25T16:25:07Z) - A bibliometric analysis of research based on the Roy Adaptation Model: a
contribution to Nursing [0.0]
To perform a modern bibliometric analysis of the research based on the Roy Adaptation Model, a founding nursing model proposed by Sor Callista Roy in the1970s.
We used information from the two dominant scientific databases, Web Of Science and SCOPUS.
arXiv Detail & Related papers (2020-03-29T14:02:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.