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Emilio Delgado López-Cózar

Publications and source records attributed to Emilio Delgado López-Cózar.

At least 19 recordsLinked to original sources

Large coverage fluctuations in Google Scholar: a case study

Unlike other academic bibliographic databases, Google Scholar intentionally operates in a way that does not maintain coverage stability: documents that stop being available to Google Scholar's crawlers are removed from the system. This can also affect Google Scholar's citation graph (citation counts can decrease). Furthermore, because Google Scholar is not transparent about its coverage, the only way to directly observe coverage loss is through regular monitorization of Google Scholar data. Because of this, few studies have empirically documented this phenomenon. This study analyses a large decrease in coverage of documents in the field of Astronomy and Astrophysics that took place in 2019 and its subsequent recovery, using longitudinal data from previous analyses and a new dataset extracted in 2020. Documents from most of the larger publishers in the field disappeared from Google Scholar despite continuing to be available on the Web, which suggests an error on Google Scholar's side. Disappeared documents did not reappear until the following index-wide update, many months after the problem was discovered. The slowness with which Google Scholar is currently able to resolve indexing errors is a clear limitation of the platform both for literature search and bibliometric use cases.

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Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations' COCI: a multidisciplinary comparison of coverage via citations

New sources of citation data have recently become available, such as Microsoft Academic, Dimensions, and the OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI). Although these have been compared to the Web of Science (WoS), Scopus, or Google Scholar, there is no systematic evidence of their differences across subject categories. In response, this paper investigates 3,073,351 citations found by these six data sources to 2,515 English-language highly-cited documents published in 2006 from 252 subject categories, expanding and updating the largest previous study. Google Scholar found 88% of all citations, many of which were not found by the other sources, and nearly all citations found by the remaining sources (89%-94%). A similar pattern held within most subject categories. Microsoft Academic is the second largest overall (60% of all citations), including 82% of Scopus citations and 86% of Web of Science citations. In most categories, Microsoft Academic found more citations than Scopus and WoS (182 and 223 subject categories, respectively), but had coverage gaps in some areas, such as Physics and some Humanities categories. After Scopus, Dimensions is fourth largest (54% of all citations), including 84% of Scopus citations and 88% of WoS citations. It found more citations than Scopus in 36 categories, more than WoS in 185, and displays some coverage gaps, especially in the Humanities. Following WoS, COCI is the smallest, with 28% of all citations. Google Scholar is still the most comprehensive source. In many subject categories Microsoft Academic and Dimensions are good alternatives to Scopus and WoS in terms of coverage.

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Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories

Despite citation counts from Google Scholar (GS), Web of Science (WoS), and Scopus being widely consulted by researchers and sometimes used in research evaluations, there is no recent or systematic evidence about the differences between them. In response, this paper investigates 2,448,055 citations to 2,299 English-language highly-cited documents from 252 GS subject categories published in 2006, comparing GS, the WoS Core Collection, and Scopus. GS consistently found the largest percentage of citations across all areas (93%-96%), far ahead of Scopus (35%-77%) and WoS (27%-73%). GS found nearly all the WoS (95%) and Scopus (92%) citations. Most citations found only by GS were from non-journal sources (48%-65%), including theses, books, conference papers, and unpublished materials. Many were non-English (19%-38%), and they tended to be much less cited than citing sources that were also in Scopus or WoS. Despite the many unique GS citing sources, Spearman correlations between citation counts in GS and WoS or Scopus are high (0.78-0.99). They are lower in the Humanities, and lower between GS and WoS than between GS and Scopus. The results suggest that in all areas GS citation data is essentially a superset of WoS and Scopus, with substantial extra coverage.

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Unbundling Open Access dimensions: a conceptual discussion to reduce terminology inconsistencies

The current ways in which documents are made freely accessible in the Web no longer adhere to the models established Budapest/Bethesda/Berlin (BBB) definitions of Open Access (OA). Since those definitions were established, OA-related terminology has expanded, trying to keep up with all the variants of OA publishing that are out there. However, the inconsistent and arbitrary terminology that is being used to refer to these variants are complicating communication about OA-related issues. This study intends to initiate a discussion on this issue, by proposing a conceptual model of OA. Our model features six different dimensions (prestige, user rights, stability, immediacy, peer-review, and cost). Each dimension allows for a range of different options. We believe that by combining the options in these six dimensions, we can arrive at all the current variants of OA, while avoiding ambiguous and/or arbitrary terminology. This model can be an useful tool for funders and policy makers who need to decide exactly which aspects of OA are necessary for each specific scenario.

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Evidence of Open Access of scientific publications in Google Scholar: a large-scale analysis

This article uses Google Scholar (GS) as a source of data to analyse Open Access (OA) levels across all countries and fields of research. All articles and reviews with a DOI and published in 2009 or 2014 and covered by the three main citation indexes in the Web of Science (2,269,022 documents) were selected for study. The links to freely available versions of these documents displayed in GS were collected. To differentiate between more reliable (sustainable and legal) forms of access and less reliable ones, the data extracted from GS was combined with information available in DOAJ, CrossRef, OpenDOAR, and ROAR. This allowed us to distinguish the percentage of documents in our sample that are made OA by the publisher (23.1%, including Gold, Hybrid, Delayed, and Bronze OA) from those available as Green OA (17.6%), and those available from other sources (40.6%, mainly due to ResearchGate). The data shows an overall free availability of 54.6%, with important differences at the country and subject category levels. The data extracted from GS yielded very similar results to those found by other studies that analysed similar samples of documents, but employed different methods to find evidence of OA, thus suggesting a relative consistency among methods.

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Coverage of highly-cited documents in Google Scholar, Web of Science, and Scopus: a multidisciplinary comparison

This study explores the extent to which bibliometric indicators based on counts of highly-cited documents could be affected by the choice of data source. The initial hypothesis is that databases that rely on journal selection criteria for their document coverage may not necessarily provide an accurate representation of highly-cited documents across all subject areas, while inclusive databases, which give each document the chance to stand on its own merits, might be better suited to identify highly-cited documents. To test this hypothesis, an analysis of 2,515 highly-cited documents published in 2006 that Google Scholar displays in its Classic Papers product is carried out at the level of broad subject categories, checking whether these documents are also covered in Web of Science and Scopus, and whether the citation counts offered by the different sources are similar. The results show that a large fraction of highly-cited documents in the Social Sciences and Humanities (8.6%-28.2%) are invisible to Web of Science and Scopus. In the Natural, Life, and Health Sciences the proportion of missing highly-cited documents in Web of Science and Scopus is much lower. Furthermore, in all areas, Spearman correlation coefficients of citation counts in Google Scholar, as compared to Web of Science and Scopus citation counts, are remarkably strong (.83-.99). The main conclusion is that the data about highly-cited documents available in the inclusive database Google Scholar does indeed reveal significant coverage deficiencies in Web of Science and Scopus in several areas of research. Therefore, using these selective databases to compute bibliometric indicators based on counts of highly-cited documents might produce biased assessments in poorly covered areas.

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Google Scholar as a data source for research assessment

The launch of Google Scholar (GS) marked the beginning of a revolution in the scientific information market. This search engine, unlike traditional databases, automatically indexes information from the academic web. Its ease of use, together with its wide coverage and fast indexing speed, have made it the first tool most scientists currently turn to when they need to carry out a literature search. Additionally, the fact that its search results were accompanied from the beginning by citation counts, as well as the later development of secondary products which leverage this citation data (such as Google Scholar Metrics and Google Scholar Citations), made many scientists wonder about its potential as a source of data for bibliometric analyses. The goal of this chapter is to lay the foundations for the use of GS as a supplementary source (and in some disciplines, arguably the best alternative) for scientific evaluation. First, we present a general overview of how GS works. Second, we present empirical evidences about its main characteristics (size, coverage, and growth rate). Third, we carry out a systematic analysis of the main limitations this search engine presents as a tool for the evaluation of scientific performance. Lastly, we discuss the main differences between GS and other more traditional bibliographic databases in light of the correlations found between their citation data. We conclude that Google Scholar presents a broader view of the academic world because it has brought to light a great amount of sources that were not previously visible.

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A novel method for depicting academic disciplines through Google Scholar Citations: The case of Bibliometrics

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 (GSC). 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 according to GSC were manually processed to fill any missing information and deduplicate fields like the journal titles and book publishers. The results suggest that it is feasible to use GSC and the MADAP method to produce an accurate depiction of the community of researchers working in Bibliometrics (both specialists and occasional researchers) and their publication habits (main publication venues such as journals and book publishers). Additionally, the wide document coverage of Google Scholar (specially 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.

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Can we use Google Scholar to identify highly-cited documents?

The main objective of this paper is to empirically test whether the identification of highly-cited documents through Google Scholar is feasible and reliable. To this end, we carried out a longitudinal analysis (1950 to 2013), running a generic query (filtered only by year of publication) to minimise the effects of academic search engine optimisation. This gave us a final sample of 64,000 documents (1,000 per year). The strong correlation between a document's citations and its position in the search results (r= -0.67) led us to conclude that Google Scholar is able to identify highly-cited papers effectively. This, combined with Google Scholar's unique coverage (no restrictions on document type and source), makes the academic search engine an invaluable tool for bibliometric research relating to the identification of the most influential scientific documents. We find evidence, however, that Google Scholar ranks those documents whose language (or geographical web domain) matches with the user's interface language higher than could be expected based on citations. Nonetheless, this language effect and other factors related to the Google Scholar's operation, i.e. the proper identification of versions and the date of publication, only have an incidental impact. They do not compromise the ability of Google Scholar to identify the highly-cited papers.

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Author-level metrics in the new academic profile platforms: The online behaviour of the Bibliometrics community

The new web-based academic communication platforms do not only enable researchers to better advertise their academic outputs, making them more visible than ever before, but they also provide a wide supply of metrics to help authors better understand the impact their work is making. This study has three objectives: a) to analyse the uptake of some of the most popular platforms (Google Scholar Citations, ResearcherID, ResearchGate, Mendeley and Twitter) by a specific scientific community (bibliometrics, scientometrics, informetrics, webometrics, and altmetrics); b) to compare the metrics available from each platform; and c) to determine the meaning of all these new metrics. To do this, the data available in these platforms about a sample of 811 authors (researchers in bibliometrics for whom a public profile Google Scholar Citations was found) were extracted. A total of 31 metrics were analysed. The results show that a high number of the analysed researchers only had a profile in Google Scholar Citations (159), or only in Google Scholar Citations and ResearchGate (142). Lastly, we find two kinds of metrics of online impact. First, metrics related to connectivity (followers), and second, all metrics associated to academic impact. This second group can further be divided into usage metrics (reads, views), and citation metrics. The results suggest that Google Scholar Citations is the source that provides more comprehensive citation-related data, whereas Twitter stands out in connectivity-related metrics.

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2016 Google Scholar Metrics released: a matter of languages... and something else

The 2016 edition of Google Scholar Metrics was released on July 15th 2016. There haven't been any structural changes respect to previous versions, which means that most of its limitations still persist. The biggest changes are the addition of five new language rankings (Russian, Korean, Polish, Ukrainian, and Indonesian) and elimination of two other language rankings (Italian and Dutch). In addition, for reasons still unknown, this new edition doesn't include as many working paper and discussion paper series as previous editions.

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Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2010-2014)

The objective of this report is to present a list of proceedings (conferences, workshops, symposia, meetings) in the areas of Computer Science, Electrical & Electronic Engineering, and Communications covered by Google Scholar Metrics and ranked according to their h-index. Google Scholar Metrics only displays publications that have published at least 100 papers and have received at least one citation in the last five years (2010-2014). The searches were conducted between the 8th and 10th of December, 2015. A total of 1501 proceedings have been identified

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Improvements in Google Scholar Citations are for the summer: creating an institutional affiliation link feature

This report describes the feature introduced by Google to provide standardized access to institutional affiliations within Google Scholar Citations. First, this new tool is described, pointing out its main characteristics and functioning. Next, the coverage and precision of the tool are evaluated. Two special cases (Google Inc. and Spanish Universities) are briefly treated with the purpose of illustrating some aspects about the accuracy of the tool for the task of gathering authors within their appropriate institution. Finally, some inconsistencies, errors and malfunctioning are identified, categorized and described. The report finishes by providing some suggestions to improve the feature. The general conclusion is that the standardized institutional affiliation link provided by Google Scholar Citations, despite working pretty well for a large number of institutions (especially Anglo-Saxon universities) still has a number of shortcomings and pitfalls which need to be addressed in order to make this authority control tool fully useful worldwide, both for searching purposes and for metric tasks

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Does Google Scholar contain all highly cited documents (1950-2013)?

The study of highly cited documents on Google Scholar (GS) has never been addressed to date in a comprehensive manner. The objective of this work is to identify the set of highly cited documents in Google Scholar and define their core characteristics: their languages, their file format, or how many of them can be accessed free of charge. We will also try to answer some additional questions that hopefully shed some light about the use of GS as a tool for assessing scientific impact through citations. The decalogue of research questions is shown below: 1. Which are the most cited documents in GS? 2. Which are the most cited document types in GS? 3. What languages are the most cited documents written in GS? 4. How many highly cited documents are freely accessible? 4.1 What file types are the most commonly used to store these highly cited documents? 4.2 Which are the main providers of these documents? 5. How many of the highly cited documents indexed by GS are also indexed by WoS? 6. Is there a correlation between the number of citations that these highly cited documents have received in GS and the number of citations they have received in WoS? 7. How many versions of these highly cited documents has GS detected? 8. Is there a correlation between the number of versions GS has detected for these documents, and the number citations they have received? 9. Is there a correlation between the number of versions GS has detected for these documents, and their position in the search engine result pages? 10. Is there some relation between the positions these documents occupy in the search engine result pages, and the number of citations they have received?

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Reviving the past: the growth of citations to old documents

In this Digest we review a recent study released by the Google Scholar team on the apparently increasing fraction of citations to old articles from studies published in the last 24 years (1990-2013). First, we describe the main findings of their article. Secondly, we conduct an analogue study, using a different data source as well as different measures which throw very similar results, thus confirming the phenomenon. Lastly, we discuss the possible causes of this phenomenon.

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About the size of Google Scholar: playing the numbers

The emergence of academic search engines (Google Scholar and Microsoft Academic Search essentially) has revived and increased the interest in the size of the academic web, since their aspiration is to index the entirety of current academic knowledge. The search engine functionality and human search patterns lead us to believe, sometimes, that what you see in the search engine's results page is all that really exists. And, even when this is not true, we wonder which information is missing and why. The main objective of this working paper is to calculate the size of Google Scholar at present (May 2014). To do this, we present, apply and discuss up to 4 empirical methods: Khabsa & Giles's method, an estimate based on empirical data, and estimates based on direct queries and absurd queries. The results, despite providing disparate values, place the estimated size of Google Scholar in about 160 million documents. However, the fact that all methods show great inconsistencies, limitations and uncertainties, makes us wonder why Google does not simply provide this information to the scientific community if the company really knows this figure.

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Google Scholar Metrics 2014: a low cost bibliometric tool

We analyse the main features of the third edition of Google Scholar Metrics (GSM), released in June 2014, focusing on its more important changes, strengths, and weaknesses. Additionally, we present some figures that outline the dimensions of this new edition, and we compare them to those of previous editions. Principal among these figures are the number of visualized publications, publication types, languages, and the maximum and minimum h5-index and h5-median values by language, subject area, and subcategory. This new edition is marked by continuity. There is nothing new other than the updating of the time frame (2009-2013) and the removal of some redundant subcategories (from 268 to 261) for English written publications. Google has just updated the data, which means that some of the errors discussed in previous studies still persist. To sum up, GSM is a minimalist information product with few features, closed (it cannot be customized by the user), and simple (navigating it only takes a few clicks). For these reasons, we consider it a 'low cost' bibliometric tool, and propose a list of features it should incorporate in order to stop being labeled as such. Notwithstanding the above, this product presents a stability in its bibliometric indicators that supports its ability to measure and track the impact of scientific publications.

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