¿Por qué hay que buscar en varias bases de datos?
En Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources (2020) se puede leer:
To date, it remains unclear which search systems are most appropriate for evidence synthesis and why. Advice on which search engines and bibliographic databases to choose for systematic searches is limited and lacking systematic, empirical performance assessments.
Según este estudio, Google Scholar no es un buen buscador de artículos científicos:
Nevertheless, overall, Google Scholar's search precision has been found to be significantly lower than 1% for systematic searches [25].
This is not surprising, since our findings show that Google Scholar does not support many of the features required for systematic searches.
Our findings support the criticism of Bramer et al [33], Bramer et al [34], and Boeker et al [25] and indicate that Google Scholar's coverage
and recall is an inadequate reason to use it as principal search system in systematic searches [53].
If a system such as Google Scholar fails to deliver retrieval capabilities that allow a reviewer to search systematically with high levels of
recall, precision, transparency, and reproducibility, its coverage is irrelevant for query-based search.
Google Scholar's extraordinary coverage acting as a multidisciplinary compendium of scientific world knowledge should not blind users to the fact
that users' ability to access this compendium is severely limited, especially in terms of a systematic search.
En The journal coverage of Web of Science and Scopus: a comparative analysis (2016) también se alerta de la baja calidad de Google Scholar:
WoS had been the sole tool for citations analysis until the creation of Scopus and Google Scholar in 2004. However, the low data quality found in Google Scholar raises questions about its suitability for research evaluation. Thus, WoS and Scopus remain today the main sources for citation data.
En Improving the Reliability of Literature Reviews: Detection of Retracted Articles through Academic Search Engines (2022) se puede leer:
In summary, no database provides 100% coverage of the scientific production in a specific area, which makes it necessary to use several of them in order to obtain greater coverage of the information retrieved.
[...]
It is a recommended practice to use several scientific search engines that both allow
access to many scientific articles and provide information on whether these can be considered
as solid scientific evidence or, on the contrary, whether they have been retracted and
should not be used in scientific studies.
Qué bases de datos utilizar
¿Scopus? ¿Web of Science? ¿Google Scholar?
En general, Scopus incluye más revistas que Web of Science. En The journal coverage of Web of Science and Scopus: a comparative analysis (2016) se afirma:
Overall, except for Natural Sciences and Engineering, Scopus includes most of the journals indexed in WoS. Furthermore, Scopus has a larger number of exclusive journals than WoS in all fields, which can be explained by the fact that Scopus covers a lot more journals than WoS.
[...]
We also found that despite Scopus’s larger journal coverage in all fields, the database shows similar biases than those found in WoS.
Pero hay revistas que Web of Science incluye que no están en Scopus, y también ocurre al revés. En The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis (2021) se afirma:
This article aims to present a comparative analysis of the journal coverage of the three databases (Web of Science, Scopus and Dimensions), with the objective to describe, understand and visualize the differences in them. The most recent master journal lists of the three databases is used for analysis. The results indicate that the databases have significantly different journal coverage, with the Web of Science being most selective and Dimensions being the most exhaustive. About 99.11% and 96.61% of the journals indexed in Web of Science are also indexed in Scopus and Dimensions, respectively. Scopus has 96.42% of its indexed journals also covered by Dimensions. Dimensions database has the most exhaustive journal coverage, with 82.22% more journals than Web of Science and 48.17% more journals than Scopus.
[...]
First, the three databases are found to differ significantly in their journal coverage, with Web of Science having the most selective journal coverage, whereas Dimensions having the most exhaustive journal coverage. It is found that almost all journals indexed in Web of Science are also covered by Scopus and Dimensions. Scopus indexes 66.07% more unique journals as compared to Web of Science and Dimensions covers 82.22% and 48.17% more unique journals as compared to Web of Science and Scopus, respectively.
Por lo tanto, si se desean resultados completos sobre un tema, una única base de datos no es suficiente en la mayoría de los casos; se deben consultar varias bases de datos relevantes o incluso parcialmente. En Tale of Three Databases: The Implication of Coverage Demonstrated for a Sample Query (2018) se afirma:
Coverage is an important criterion when evaluating information systems. This exploratory study investigates this issue by submitting the same query to different databases relevant to the query topic. Data were retrieved from three databases: ACM Digital Library, Web of Science (with the Proceedings Citation Index) and Scopus. The search phrase was “information retrieval,” publication years were between 2013 and 2016. Altogether 8,699 items were retrieved, out of which 5,306 (61%) items were retrieved by a single database only, and only 977 (11%) items were located in all three databases.
[...]
The major goal of this paper was to highlight the importance of coverage for comprehensive data retrieval. Coverage is one of the parameters in information retrieval evaluation (Cleverdon, 1968), and it has major implications in research assessment as well. WoS and Scopus are selective databases and this is the reason for the varied coverage. However, the small overlap between the databases is worrying. When considering overlap based on journal titles (Gavel and Iselid, 2008; Mongeon and Paul-Hus, 2016), both papers report 45–50% overlap between WoS and Scopus, in this case study out of the 6,814 unique items retrieved by WoS or Scopus, only 2,319 appear in both databases (34%) (see Figure 1). Of course, it is not possible to generalize based on the results of a single query, but this issue should be further studied.
[...]
The results emphasize the need for searching in multiple databases in order to increase recall as recommended by previous studies (e.g., Ramos-Remus et al., 1994; Meho and Yang, 2007; De Groote et al., 2012).
Cómo buscar los artículos
Según el artículo Systematic mapping studies in software engineering (2008), un mapeo sistemático se compone de las siguientes etapas:
- Definition of Research Questions (Research Scope).
- Conduct Search for Primary Studies (All Papers).
- Screening of Papers for Inclusion and Exclusion (Relevant Papers).
- Keywording of Abstracts (Classification Scheme).
- Data Extraction and Mapping of Studies (Systematic Map).
El paso 2 del proceso de un mapeo sistemático es Conduct Search for Primary Studies (All Papers). En Systematic literature studies: database searches vs. backward snowballing (2012) se analizan y comparan dos de los métodos clásicos de búsqueda de artículos para realizar una revisión sistemática:
- Database searches: using search strings in a number of databases.
- Backward snowballing: start with the reference lists of a starting set of papers.
En Guidelines for snowballing in systematic literature studies and a replication in software engineering (2014) se explica la técnica de snowballing:
Snowballing refers to using the reference list of a paper or the citations to the paper to identify additional papers.
However, snowballing could benefit from not only looking at the reference lists and citations, but to complement it with a systematic way of looking at where papers are actually referenced and where papers are cited.
Using the references and the citations respectively is referred to as backward and forward snowballing.
La siguiente imagen muestra un proceso de selección de artículos en una revisión sistemática:
El proceso de selección de los artículos influye enormemente en los resultados obtenidos. En Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources (2005) se analizó la efectividad para seleccionar los diferentes métodos que se emplean normalmente:
- Protocol driven (search strategy defined at the outset of the study):
- Hand search
- Electronic search
- "Snowballing" (emerging as the study unfolded):
- Reference tracking (the reference lists of all full text papers and used judgment to decide whether to pursue these further)
- Citation tracking (using special citation tracking databases such as Science Citation Index, Social Science Citation Index, and Arts and Humanities Citation Index)
- Personal knowledge (what we knew and who we knew):
- Our existing knowledge and resources
- Our personal contacts and academic networks
- Serendipitous discovery (such as finding a relevant paper when looking for something else).