Evidence map›Paper›PMID 29782219›Full record

ArticleSoft robotics2018

Soft Robotics: Academic Insights and Perspectives Through Bibliometric Analysis.

Guanjun Bao, Hui Fang, Lingfeng Chen, Yuehua Wan, Fang Xu, Qinghua Yang, Libin Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Soft robotics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  5. Review
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  7. Article
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  13. Article
  14. Advances in artificial muscles: A brief literature and patent review.Frontiers in bioengineering and biotechnology · 2023
    Review
  15. Review
  16. Article
  17. Review
  18. Article
  19. Drug repositioning: A bibliometric analysis.Frontiers in pharmacology · 2022
    Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Guanjun Bao1 College of Mechanical Engineering, Zhejing University of Technology , Hangzhou, China .
Hui Fang2 Library, Zhejiang University of Technology , Hangzhou, China .
Lingfeng Chen1 College of Mechanical Engineering, Zhejing University of Technology , Hangzhou, China .
Yuehua Wan2 Library, Zhejiang University of Technology , Hangzhou, China .
Fang Xu1 College of Mechanical Engineering, Zhejing University of Technology , Hangzhou, China .
Qinghua Yang1 College of Mechanical Engineering, Zhejing University of Technology , Hangzhou, China .
Libin Zhang1 College of Mechanical Engineering, Zhejing University of Technology , Hangzhou, China .

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft robotics is of growing interest in the robot community as well as in public media, and there is an increase in the quality and quantity of publications related to this topic. To formally elaborate this growth, we have used a bibliometric analysis to evaluate the publications in the field from 1990 to 2017 based on the Science Citation Index Expanded database. We present a detailed overview and discussion based on keywords, citation, h-index, year, journal, institution, country, author, and review articles. The results show that the United States takes the leading position in this research field, followed by China and Italy. Harvard University has the most publications, high average number of citations per publication and the highest h-index. IEEE Transactions on Robotics ranks first among the top 20 academic journals publishing articles related to this field, whereas Soft Robotics holds the top position in journals categorized with "ROBOTICS." Actuator, fabrication, control, material, sensing, simulation, bionics, stiffness, modeling, power, motion, and application are the hot topics of soft robotics. Smart materials, bionics, morphological computation, and embodiment control are expected to contribute to this field in the future. Application and commercialization appear to be the initial driving force and final goal for soft robots.

Indexed as

artificial musclebibliometricsbioinspired robotmultidisciplinarysmart materialsoft robotics

Identifiers

PMID29782219
PMCPMC5995266

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.