Visualization and analysis of SCImago Journal &amp; Country Rank structure via journal clustering

Gómez-Núñez, Antonio J.; Vargas-Quesada, Benjamín; Chinchilla‐Rodríguez, Zaida; Batagelj, Vladimir; Anegón, Félix de Moya

doi:10.1108/ajim-12-2015-0205

Cited by 12 publications

(7 citation statements)

References 45 publications

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“…At large, scientograms show relationships that occur between disciplines; and they may be used more specifically as an index to comprehend to what extent certain research lines or fields are connected with others (Vargas-Quesada and Moya-Anegón, 2007). They enable one to explore the sequential evolution of research, identify research fronts, detect emerging or decadent topics, and locate areas of interdisciplinary efforts (Gómez-Núñez et al, 2016).…”

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confidence: 99%

Identification and Visualization of the Intellectual Structure in Graphene Research

Vargas-Quesada

Chinchilla‐Rodríguez

Rodríguez

2017

Front. Res. Metr. Anal.

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Since the discovery of the promising properties of graphene, research in the field has attracted numerous grants and sponsors, leading to an exponential rise in the number of papers and applications. This article presents a global map of graphene research and its intellectual structure, drawn using the terms of more than 50,000 documents extracted from Scopus database, years 1998-2015. The unit of analysis consisted of descriptors (including Author Keywords and Indexed Keywords), with the co-occurrence of descriptor as the unit of measure, using fractional counting. The main research lines identified are: Fundamental Research, Functionalization and Biomedical Applications, Technology and Devices, Materials Science, Energy Storage, Optics and Chemical Properties and Sensors. Using overlay maps, we depict the graphene research efforts of the United States, the European Union (Europe-28), and China, and project their evolution through longitudinal maps to facilitate comparison. The United States was initially at the head of world output in graphene research, but was surpassed by China in 2011 and by Europe in 2014, as a result of their respective scientific policies and financial support. The output of China has since been so intense that it can be said to mark graphene research trends. We believe this information may be valuable for the core community involved in this scientific field, as it offers a large-scale analysis showing how research has changed over time. It is therefore also helpful for policy makers and research planners. The resulting maps are a useful and attractive tool for the graphene research community, as they reveal the main lines of exploration at a glance. The methodology described here could be re-created in any other field of science to uncover and display its intellectual structure and evolution over time.

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confidence: 99%

Identification and Visualization of the Intellectual Structure in Graphene Research

Vargas-Quesada

Chinchilla‐Rodríguez

Rodríguez

2017

Front. Res. Metr. Anal.

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“…However, we have to face a general disadvantage stemming from the problematic use of integer vs. fractional counting approaches. For example, the SCImago Institutions Rankings (SIR) 5 (used by Bornmann and de Moya-Anegón (2018) and Gómez-Núñez et al (2016) in their studies classified 31 institutions into New York City, an additional 28 institutions into settlements of the New York MSA and 14 institutions into settlements located in the New York CSA but outside the New York MSA. That is, based on information provided by the SIR, 73 top research institutions are located in the New York CSA, and most possibly this number is only a fraction of the real number of research institutions situated in the New York CSA.…”

Section: Challenges Stemming From Lack Of Standardized Methods Of Biblmentioning

confidence: 99%

On the challenges ahead of spatial scientometrics focusing on the city level

Csomós

2019

AJIM

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Since the mid-1970s, it has become highly acknowledged to measure and evaluate changes in international research collaborations and the scientific performance of institutions and countries through the prism of bibliometric and scientometric data. Spatial bibliometrics and scientometrics (henceforward spatial scientometrics) have traditionally focused on examining both country and regional levels; however, in recent years, numerous spatial analyses on the city level have been carried out. While citylevel scientometric analyses have gained popularity among policymakers and statistical/economic research organizations, researchers in the field of bibliometrics are divided regarding whether it is possible to observe the spatial unit 'city' through bibliometric and scientometric tools. After systematically scrutinizing relevant studies in the field, three major problems have been identified: 1) there is no standardized method of how cities should be defined and how metropolitan areas should be delineated, 2) there is no standardized method of how bibliometric and scientometric data on the city level should be collected and processed and 3) it is not clearly defined how cities can profit from the results of bibliometric and scientometric analysis focusing on them. This paper investigates major challenges ahead of spatial scientometrics, focusing on the city level and presents some possible solutions.

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“…In order to use the leading journals in the economics field, we used the Scimago Journal and Country rank website (https://www.scimagojr.com/; accessed on 24 October 2023), using the search terms "economics and econometrics". We ultimately chose the following top 10 journals [43][44][45]: Quarterly Journal of Economics, American Economic Review, Journal of Political Economy, Journal of Finance, Review of Economic Studies, Econometrica, Journal of Economic Literature, Review of Financial Studies, Journal of Marketing, and Journal of Financial Economics. For each journal, we mainly counted how many manuscripts the journal had published in the last two years and then asked the computer to randomly pick 10 indices.…”

Section: Economic Manuscripts Use Casementioning

confidence: 99%

Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?

Lazebnik,

Gorlitsky

2023

Data

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The reproducibility of academic research has long been a persistent issue, contradicting one of the fundamental principles of science. Recently, there has been an increasing number of false claims found in academic manuscripts, casting doubt on the validity of reported results. In this paper, we utilize an adapted version of Benford’s law, a statistical phenomenon that describes the distribution of leading digits in naturally occurring datasets, to identify the potential manipulation of results in research manuscripts, solely using the aggregated data presented in those manuscripts rather than the commonly unavailable raw datasets. Our methodology applies the principles of Benford’s law to commonly employed analyses in academic manuscripts, thus reducing the need for the raw data itself. To validate our approach, we employed 100 open-source datasets and successfully predicted 79% of them accurately using our rules. Moreover, we tested the proposed method on known retracted manuscripts, showing that around half (48.6%) can be detected using the proposed method. Additionally, we analyzed 100 manuscripts published in the last two years across ten prominent economic journals, with 10 manuscripts randomly sampled from each journal. Our analysis predicted a 3% occurrence of results manipulation with a 96% confidence level. Our findings show that Benford’s law adapted for aggregated data, can be an initial tool for identifying data manipulation; however, it is not a silver bullet, requiring further investigation for each flagged manuscript due to the relatively low prediction accuracy.

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Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

Cited by 12 publications

References 45 publications

Identification and Visualization of the Intellectual Structure in Graphene Research

Identification and Visualization of the Intellectual Structure in Graphene Research

On the challenges ahead of spatial scientometrics focusing on the city level

Can We Mathematically Spot the Possible Manipulation of Results in Research Manuscripts Using Benford’s Law?

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