Types of Errors Hiding in Google Scholar Data
Google Scholar (GS) is a free tool that may be used by researchers to analyze citations; find appropriate literature; or evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding, or research grants. GS has become a major bibliographic and citatio...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-05-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2022/5/e28354 |
| _version_ | 1797735036750921728 |
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| author | Romy Sauvayre |
| author_facet | Romy Sauvayre |
| author_sort | Romy Sauvayre |
| collection | DOAJ |
| description |
Google Scholar (GS) is a free tool that may be used by researchers to analyze citations; find appropriate literature; or evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding, or research grants. GS has become a major bibliographic and citation database. For assessing the literature, databases, such as PubMed, PsycINFO, Scopus, and Web of Science, can be used in place of GS because they are more reliable. The aim of this study was to examine the accuracy of citation data collected from GS and provide a comprehensive description of the errors and miscounts identified. For this purpose, 281 documents that cited 2 specific works were retrieved via Publish or Perish software (PoP) and were examined. This work studied the false-positive issue inherent in the analysis of neuroimaging data. The results revealed an unprecedented error rate, with 279 of 281 (99.3%) examined references containing at least one error. Nonacademic documents tended to contain more errors than academic publications (U=5117.0; P<.001). This viewpoint article, based on a case study examining GS data accuracy, shows that GS data not only fail to be accurate but also potentially expose researchers, who would use these data without verification, to substantial biases in their analyses and results. Further work must be conducted to assess the consequences of using GS data extracted by PoP. |
| first_indexed | 2024-03-12T12:52:10Z |
| format | Article |
| id | doaj.art-0c48c625dd874a50a83ff7fedf2df9e6 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:52:10Z |
| publishDate | 2022-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-0c48c625dd874a50a83ff7fedf2df9e62023-08-28T21:51:54ZengJMIR PublicationsJournal of Medical Internet Research1438-88712022-05-01245e2835410.2196/28354Types of Errors Hiding in Google Scholar DataRomy Sauvayrehttps://orcid.org/0000-0003-0806-6234 Google Scholar (GS) is a free tool that may be used by researchers to analyze citations; find appropriate literature; or evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding, or research grants. GS has become a major bibliographic and citation database. For assessing the literature, databases, such as PubMed, PsycINFO, Scopus, and Web of Science, can be used in place of GS because they are more reliable. The aim of this study was to examine the accuracy of citation data collected from GS and provide a comprehensive description of the errors and miscounts identified. For this purpose, 281 documents that cited 2 specific works were retrieved via Publish or Perish software (PoP) and were examined. This work studied the false-positive issue inherent in the analysis of neuroimaging data. The results revealed an unprecedented error rate, with 279 of 281 (99.3%) examined references containing at least one error. Nonacademic documents tended to contain more errors than academic publications (U=5117.0; P<.001). This viewpoint article, based on a case study examining GS data accuracy, shows that GS data not only fail to be accurate but also potentially expose researchers, who would use these data without verification, to substantial biases in their analyses and results. Further work must be conducted to assess the consequences of using GS data extracted by PoP.https://www.jmir.org/2022/5/e28354 |
| spellingShingle | Romy Sauvayre Types of Errors Hiding in Google Scholar Data Journal of Medical Internet Research |
| title | Types of Errors Hiding in Google Scholar Data |
| title_full | Types of Errors Hiding in Google Scholar Data |
| title_fullStr | Types of Errors Hiding in Google Scholar Data |
| title_full_unstemmed | Types of Errors Hiding in Google Scholar Data |
| title_short | Types of Errors Hiding in Google Scholar Data |
| title_sort | types of errors hiding in google scholar data |
| url | https://www.jmir.org/2022/5/e28354 |
| work_keys_str_mv | AT romysauvayre typesoferrorshidingingooglescholardata |