Troubleshooting Co-Authorship Analysis
Scopus, a widely used bibliographic database, has released a new version that introduces some changes to the format of the data. If you encounter an error while trying to perform co-authorship analysis using VOSviewer with the new version of Scopus, it is likely due to a specific formatting issue in the Author's Name data. This guide will provide step-by-step instructions on how to resolve this issue and successfully conduct co-authorship analysis.
Step 1: Open the Scopus.csv File To begin, locate and open the Scopus.csv file using either Microsoft Excel or OpenRefine. If the Scopus.csv file contains a references column that is truncated, it is recommended to use Excel. However, if the references column is not truncated, OpenRefine can be used instead.
Step 2: Correct the Formatting in the Authors Column Once you have opened the Scopus.csv file, focus on the Authors column. In the new version of Scopus, authors' names are separated by a semi-colon (;) instead of a comma (,).
To resolve this issue, use the Find and Replace function in your chosen software (Excel or OpenRefine) to replace all instances of ";" with "," in the Authors column. This will ensure that the co-authorship analysis in VOSviewer can proceed without any errors.
Step 3: Save the Modified File After making the necessary formatting changes, save the modified file under a new name. This step is crucial to ensure that you retain the original Scopus.csv file for future use and preserve the integrity of your data.
By following these simple steps, you can successfully use Scopus (New Version) in conjunction with VOSviewer for co-authorship analysis. Correcting the formatting in the Authors column, which involves replacing semi-colons (;) with commas (,), will resolve the error that arises due to the formatting differences between the new and original versions of Scopus. With the modified file, you can now proceed with your analysis and uncover valuable insights from your co-authorship network.
Before conducting the co-authorship analysis, it is highly recommended for researchers to ensure the cleanliness and standardization of the author's names. This step is crucial to avoid any ambiguities or inaccuracies in the analysis. There are several important considerations to keep in mind:
- Identify Authors Who Are the Same Person: It is important to identify authors who are the same person but may have variations in their names. By standardizing the names, you can accurately attribute the publications to the correct authors. This can be achieved by cross-referencing multiple sources and verifying the author's affiliation, research interests, or unique identifiers such as ORCID.
- Ensure Unique Author Names: In cases where two or more authors share the same name, it is essential to differentiate them by assigning unique identifiers. This can involve adding initials, middle names, or using institutional affiliations to disambiguate authors. By assigning unique identifiers, you can accurately attribute publications and avoid any confusion in the co-authorship analysis.
- Consistent Format for Author Names: To maintain consistency in the analysis, it is important to format all author names consistently. This includes including the last name and initials, rather than just the last name. Consistent formatting ensures that authors are correctly identified and prevents any potential misinterpretation of the data.
- Combine Multiple Author IDs: In some cases, an author may have multiple author IDs or profiles across different platforms or databases. It is recommended to combine these multiple IDs to ensure that the author's work is accurately represented and counted in the co-authorship analysis. By consolidating author IDs, you can avoid underrepresentation or duplication of their contributions.
By adhering to these guidelines and cleaning the author's names prior to conducting co-authorship analysis, researchers can ensure the accuracy and reliability of the results. This attention to detail will enable a more robust analysis and facilitate the extraction of meaningful insights from the co-authorship network.
To simplify the process of cleaning and standardizing author information, researchers can utilize a tool called biblioMagika®. This comprehensive application offers various features that address the common issues encountered in author and affiliation data processing.
Author and Affiliation Data Processing: biblioMagika® provides a platform specifically designed for splitting and harmonizing author names, affiliations, and country data. By utilizing this feature, researchers can ensure that the processed data is clean, consistent, and standardized, effectively reducing discrepancies and errors in the analysis.
Missing Data Identification: One of the significant advantages of biblioMagika® is its ability to detect missing data within the dataset. This feature enables users to identify and rectify any incomplete or inaccurate information before proceeding with further analysis. By ensuring the reliability and completeness of the dataset, researchers can have greater confidence in the accuracy of their results.
Automatic Institution and Country Recognition: biblioMagika® incorporates an automatic recognition system that can identify institution names and countries associated with authors. This functionality is crucial for standardizing and harmonizing the data, as it eliminates variations in the representation of institutions and countries. By automating this process, researchers can save time and effort while ensuring consistency in the analysis.
For more information about biblioMagika, please visit, https://aidi-ahmi.com/index.php/bibliomagika