Proceedings Scholar Metrics 2017: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2012-2016)

Emilio Delgado López-Cózar, Enrique Orduña-Malea, Proceedings Scholar Metrics 2017: H Index of proceedings on  Computer Science, Electrical & Electronic Engi
neering, and Communications according to Google Scholar Metrics (2012-2016). 
EC3 Reports, 22. Granada, 27th of December, 2017.. 

https://doi.org/10.13140/RG.2.2.11387.41768 

INTRODUCTION
PROCEEDINGS SCHOLAR METRICS is a ranking that displays proceedings (conferences, workshops, symposia, meetings) indexed in Google Scholar Metrics (GSM) on the areas of Computer Science, Electrical & Electronic Engineering, and Communications, which in this fourth edition corresponds to the period 2012-2016.

It is a well-known fact that conference proceedings play a major role as a means of scientific communication in all areas concerning Computer Engineering, Electronics, and Communications. The rapid rate at which knowledge is generated in these fields required the creation of a more dynamic system to communicate and publish research results. Conferences have historically fulfilled this role. 


The development of Google Scholar Metrics, launched on April 2012 with the goal of providing a ranking of scientific publications indexed on Google Scholar (journals, proceedings, repositories), provided that they had published at least 100 papers and received at least one citation in the last five years, has been a crucial step towards knowing the impact of conferences, which are so important in these areas.

Therefore, the system interface does not allow us to effectively determine which and how many conference proceedings GSM has indexed. In order to overcome this limitation, the objective of PROCEEDINGS SCHOLAR METRICS is to compile an inventory of all the conferences present in GSM concerning these fields of knowledge, and after that, rank them according to their scientific impact, as measured by the H index.

METHODOLOGY

Subject areas covered

Proceedings concerning Computer Science (theoretical, information theory, artificial intelligence, evolutionary computation, fuzzy systems, human computer interaction, computer vision & pattern recognition, computer hardware design, computing systems, signal processing, computer networks & wireless communication, robotics, automation & control theory, software systems, computer security & cryptography, computer graphics, databases & information systems, data mining & analysis, multimedia, bioinformatics & computational biology, biomedical technology, medical informatics, computational linguistics, education technology), Electrical & Electronic Engineering and Communications (telecommunications, remote sensing, antennas, radar, microware).

Search strategy

In order to identify the proceedings we followed two different strategies:

1. We collected all the conferences displayed in the subcategories concerning Computer Science, Electrical & Electronic Engineering, and Communications.
2. We collected the data about the conferences we had already found in previous editions of this report.
3. We carried out various searches using descriptive and pertinent keywords in order to locate the rest of relevant conferences. These searches took place on the second week of December, 2017.

The results of each search were downloaded (including the name of the conference, the h5-index, and the h5-median) and duplicates were removed. A manual check was carried out in order to filter out any irrelevant entries (journals, repositories, and conferences outside the scope of our study). A total of 1,918 conference proceedings were identified.

Criteria for the inclusion of Google Scholar Metrics proceedings

Proceedings that published at least 100 papers and received at least one citation in the last five years (2012-2016)

Sorting criteria and fields displayed

The proceedings are sorted by their H Index. In case of a tie, the discriminate value is the h5-median (the median number of citations for the articles that make up its h-index)

The information displayed for each conference is: H5-Index; H5-Median; Quartile 

This work is an update to the reports published in previous years:

Martín-Martín, A., Ayllón, J. M., Orduña-Malea, E., Delgado López-Cózar, E. (2016). Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2011-2015). EC3 reports 19. Granada, 13th of December, 2016, 
Available http://doi.org/10.13140/RG.2.2.16076.41605

Martín-Martín, A., Ayllón, J. M., Orduña-Malea, E., Delgado López-Cózar, E. (2015). Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2010-2014). EC3 reports 15. Granada, 14th of December, 2015. 
Available http://doi.org/10.13140/RG.2.1.4504.9681 

Martín-Martín, A.; Orduña-Malea, E.; Ayllón, J.M.; Delgado López-Cózar, E. Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2009-2013). EC3 Reports, 12.  Granada, 7th of January, 2015.

Available http://hdl.handle.net/10481/34148

MADAP: A method for depicting academic disciplines through Google Scholar Citations

Alberto Martín-Martín, Enrique Orduna-Malea, 

Emilio Delgado López-Cózar 

A novel method for depicting academic disciplines through Google Scholar Citations: 

The case of Bibliometrics

Scientometrics, in press. 

https://doi.org/10.1007/s11192-017-2587-4  

This article describes a procedure to generate a snapshot of the structure of a specific scientific community and their outputs based on the information available in Google Scholar Citations. We call this method MADAP (Multifaceted Analysis of Disciplines through Academic Profiles). 
The international community of researchers working in Bibliometrics, Scientometrics, Informetrics, Webometrics, and Altmetrics was selected as a case study. The records of the top 1,000 most cited documents by these authors were manually checked to fill any missing information and deduplicate fields like the journal names and book publishers. The results suggest that it is feasible to use Google Scholar Citations and the MADAP method to produce an accurate depiction of the community of researchers working in Bibliometrics (both specialist and occasional) and their publication habits (main publication venues such as journals and book publishers). 
Additionaly, the wide document coverage of Google Scholar (especially books and book chapters) enables more comprehensive analyses of the documents published in a specific discipline than were previously possible with other citation indexes, finally shedding light on what until now had been a blind spot in most citation analyses

Top 25 influential specialist/occasional authors in Bibliometrics according to Google Scholar Citations

Top 25 most influential documents in Bibliometrics according to Google Scholar Citations

 Top 25 most influential journals in Bibliometrics according to Google Scholar Citations

Network of the Bibliometrics discipline through the MADAP method in Google Scholar (author-journal)

Visibility in Google scholar of 48 Institutional Repositories of Peruvian Universities

Alhuay-Quispe, J., Quispe-Riveros, D., Bautista-Ynofuente, L., Pacheco-Mendoza, J. (2017). 

Metadata Quality and Academic Visibility Associated with Document Type Coverage in Institutional Repositories of Peruvian Universities. 

Journal of Web Librarianship, 1-14.

• https://doi.org/10.1080/19322909.2017.1382427 

This article analyzes level of metadata quality (MQ ratio) and level of academic visibility in Google Scholar (IGS ratio) associated with coverage of four types of documents (theses, articles, books, and conferences) in repositories of Peruvian universities. 
This research is a cross-sectional descriptive and correlational study with intentional non-probabilistic sampling that analyzes 48 repositories from national (n = 10) and private (n = 38) universities integrated in the Peruvian National Digital Repository Alicia (alicia.concytec.gob.pe). 
Regarding the MQ ratio, we found a median of 0.67 [RIC: 0.552–0.891] for national universities and a median of 0.65 [RIC: 0.407–0.838] for private universities (p = .542). Regarding the IGS ratio, we found a median of 0.32 [RIC: 0.241–0.596] for national universities and a median of 0.62 [RIC: 0.464–0.749] for private universities (p = .054). The p value in Spearman's rank correlation shows a moderate correlation (ρ = 0.594; p < .01) between MQ ratio and the thesis coverage indicator, and a low correlation (ρ = 0.157) between the index of document indexing in Google Scholar and the proportion of documents harvested in Alicia. 
We conclude that the highest proportion of academic visibility is concentrated in private universities, and the metadata quality number of items integrated in Alicia favors public universities.

Google Scholar as a source for scholarly evaluation: a bibliographic review of database errors

Enrique Orduna-Malea, Alberto Martín-Martín, 
Emilio Delgado López-Cózar
Google Scholar as a source for scholarly evaluation: a bibliographic review of database errors
Revista Española de Documentación Científica 40.4 (2017): e185.
http://dx.doi.org/10.3989/redc.2017.4.1500

The launch of Google Scholar back in 2004 meant a revolution not only in the scientific information search market but also in research evaluation processes. Its dynamism, unparalleled coverage, and uncontrolled indexing make Google Scholar an unusual product, especially when compared to traditional bibliographic databases. Conceived primarily as a discovery tool for academic information, it presents a number of limitations as a bibliometric tool.  The main objective of this chapter is to show how Google Scholar operates and how its core database may be used for bibliometric purposes. To do this, the general features of the search engine (in terms of document typologies, disciplines, and coverage) are analyzed. Lastly, several bibliometric tools based on Google Scholar data, both official (Google Scholar Metrics, Google Scholar Citations) and some developed by third parties (H Index Scholar, Publishers Scholar Metrics, Proceedings Scholar Metrics, Journal Scholar Metrics, Scholar Mirrors), as well as software to collect and process data from this source (Publish or Perish, Scholarometer), are introduced, aiming to illustrate the potential bibliometric uses of this source.