Evidence map›Paper›PMID 32287409›Full record

ArticleTechnological forecasting and social change2011

Quantitative mapping of scientific research-The case of electrical conducting polymer nanocomposite.

Pei-Chun Lee, Hsin-Ning Su

Abstract read
In one paragraph

Article in Technological forecasting and social change, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Fatemeh Makkizadeh Ph.D., Esmaeil Bigdeloo M.A.International journal of reproductive biomedicine · 2019
    Article
  2. Article
  3. Article
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

2 authors.

Pei-Chun LeeSPRU, Science and Technology Policy Research, The Freeman Centre, University of Sussex, Brighton, BN1 9QE, UK.
Hsin-Ning SuScience and Technology Policy Research and Information Center, National Applied Research laboratories, 14 F., No. 106, Sec. 2, He-Ping E. Rd., Taipei, 106, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to understand knowledge structure both quantitatively and visually by integrating keyword analysis and social network analysis of scientific papers. The methodology proposed in this study is capable of creating a three-dimensional "Research focus parallelship network" and a "Keyword Co-occurrence Network", together with a two-dimensional knowledge map. The network and knowledge map can be depicted differently by choosing different information for the network actor, i.e. country, institute, paper and keyword, to reflect knowledge structures from macro, to meso, to micro-levels. A total of 223 highly cited papers published by 142 institutes and 26 countries are analyzed in this study. China and the US are the two countries located at the core of knowledge structure and China is ranked no. 1. This quantitative exploration provides a way to unveil important or emerging components in scientific development and also to visualize knowledge; thus an objective evaluation of scientific research is possible for quantitative technology management.

Indexed as

Electrical conductionKeywordKnowledge structureNetwork theoryPolymer nanocomposite

Identifiers

PMID32287409
PMCPMC7126463

What OpenQuestion holds

Textmetadata
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.