ChaomeiChen的个人博客分享 http://blog.sciencenet.cn/u/ChaomeiChen

博文

Google Scholar,ResearcherID,ResearchGate,Mendeley,Twitter

已有 4381 次阅读 2016-1-22 00:08 |系统分类:科研笔记

向对书目计量感兴趣,或使用Google Scholar, ResearchGate, Mendeley等的各位推荐这篇论文。


The counting house, measuring those who count: Presence of Bibliometrics, Scientometrics, Informetrics, Webometrics and Altmetrics in Google Scholar Citations, ResearcherID, ResearchGate, Mendeley, & Twitter

Alberto Martín-Martín1, Enrique Orduna-Malea2, Juan M. Ayllón1 & Emilio Delgado López-Cózar1

1 EC3 Reseach Group: Evaluación de la Ciencia y de la Comunicación Científica, Universidad de Granada (Spain)

2 EC3 Reseach Group: Evaluación de la Ciencia y de la Comunicación Científica, Universidad Politécnica de Valencia (Spain)


http://doi.org/10.13140/RG.2.1.4814.4402


DOI: 10.13140/RG.2.1.4814.4402

ABSTRACT

Following in the footsteps of the model of scientific communication, which has recently gone through a metamorphosis (from

the Gutenberg galaxy to the Web galaxy), a change in the model and methods of scientific evaluation is also taking place. A

set of new scientific tools are now providing a variety of indicators which measure all actions and interactions among

scientists in the digital space, making new aspects of scientific communication emerge. In this work we present a method for

“capturing” the structure of an entire scientific community (the Bibliometrics, Scientometrics, Informetrics, Webometrics, and

Altmetrics community) and the main agents that are part of it (scientists, documents, and sources) through the lens of

Google Scholar Citations (GSC).

Additionally, we compare these author “portraits” to the ones offered by other profile or social platforms currently used by

academics (ResearcherID, ResearchGate, Mendeley, and Twitter), in order to test their degree of use, completeness,

reliability, and the validity of the information they provide. A sample of 814 authors (researchers in Bibliometrics with a public

profile created in GSC) was subsequently searched in the other platforms, collecting the main indicators computed by each

of them. The data collection was carried out on September, 2015. Spearman correlation (α= 0.05) was applied to these

indicators (a total of 31), and a Principal Component Analysis was applied in order to reveal the relationships among metrics

and platforms as well as the possible existence of metric clusters.

We found that it is feasible to depict an accurate representation of the current state of the Bibliometrics community using

data from GSC (the most influential authors, documents, journals, and publishers). Regarding the number of authors found

in each platform, GSC takes the first place (814 authors), followed at a distance by ResearchGate (543), which is currently

growing at a vertiginous speed. The number of Mendeley profiles is high, although 17.1% of them are basically empty.

ResearcherID is also affected by this issue (34.45% of the profiles are empty), as is Twitter (47% of the Twitter accounts have published less than 100 tweets). Only 11% of our sample (93 authors) have created a profile in all the platforms

analyzed in this study. From the PCA, we found two kinds of impact on the Web: first, all metrics related to academic

impact. This first group can further be divided into usage metrics (views and downloads) and citation metrics. Second, all

metrics related to connectivity and popularity (followers). ResearchGate indicators, as well as Mendeley readers, present a high correlation to all the indicators from GSC, but only a moderate correlation to the indicators in ResearcherID. Twitter indicators achieve only low correlations to the rest of the indicators, the highest of these been to GSC (0.42-0.46), and to Mendeley (0.41-0.46).

Lastly, we present a taxonomy of all the errors that may affect the reliability of the data contained in each of these platforms,

with a special emphasis in GSC, since it has been our main source of data. These errors alert us to the danger of blindly using any of these platforms for the assessment of individuals, without verifying the veracity and exhaustiveness of the data.

In addition to this working paper, we also have made available a website where all the data obtained for each author and the results of the analysis of the most cited documents can be found: Scholar Mirrors.

KEYWORDS

Google Scholar; Social media metrics; Bibliometrics; Altmetrics; Mendeley; ResearchGate, ResearcherID, Twitter; Academic profiles.

60 pages, 12 tables, 35 figures




https://wap.sciencenet.cn/blog-496649-951762.html

上一篇:CiteSpace使用中的8个如何。。。
下一篇:CiteSpace用户所处理的网络的大小

3 胡小洋 李杰 zguodong2006

该博文允许注册用户评论 请点击登录 评论 (0 个评论)

数据加载中...

Archiver|手机版|科学网 ( 京ICP备07017567号-12 )

GMT+8, 2021-12-7 19:10

Powered by ScienceNet.cn

Copyright © 2007- 中国科学报社

返回顶部