GScholarLENS
GScholarLENS is a browser extension designed to scrape key publication statistics from Google Scholar profiles, providing insights into author contributions in academic work.
GScholarLENS is a browser extension designed for academics, researchers, and enthusiasts who want deeper insights into induvidual contributions. By seamlessly integrating with Google Scholar profiles, GScholarLENS can:
Note:
GScholarLENS fetches data from the already loaded Google Scholar profile page. The extension scans each publication entry, retrieving:
The extension handles asynchronous data requests to fetch additional author details for entries with abbreviated author lists.
After installing the extension, visit a Google Scholar profile page and click on the "Run GScholarLENS" button beneath the profile header. Once the data is scraped the plots are generated and the data can be downloaded as a TSV. Click the "Run GScholarLENS" button below to get more information about how GScholarLENS works.
GScholarLENS classifies author contributions into four categories:
Authors marked with caret(^) share the same position in the list.
Using custom regular expressions, GScholarLENS filters author names based on their position in the author list and specific markers (like `*` or `^`). If the author's name contains multiple spellings or abbreviations, GScholarLENS fetches additional data for clarity.
GScholarLENS generates plots using Chart.js, to provide a visual summary of author contributions and their impact on research.
A Stacked Bar chart is used to visualize contributions by author positions from all the listed publications. Journals in the field of biology, bioinformatics and biotechnology majorly consider the author positions to be a direct measure of their contribution in a publication. Each stacked bar in the chart displays the total counts for each role and each stacked section displays the counts of available journal ranks:
Available citation counts for each publication are collected and categorized by author positions to understand the impact of publications based on author contributions. Each authorship class is sectioned based on the Q* rank of the publishing journal. This helps visualize how the publications have impacted research based on the authors contribution alongside the quality of the journals that they have published in.
Distribution of citations in each authorship class is visualized using a violinplot. This chart gives insights on how the citations for publications are distributed in each authorship category.
A stacked bar plot is used to visualize the contributions per authorship for all the publications considered. Some publications might be omitted due to missing or escaped author information. In such cases the total contribution would not add up to 100%.
A stacked bar plot is displays citations per authorship in percentages out of total citation count.
Sh-Index is an adjusted version of h-index, that is normalized to the citation contributions, per authorship position. H-indices are calculated for each author position individually by adjusting citations with weights. Finally Sh-Index is calculated using the weight adjusted citations.
| HFirst - First Author | HSecond- Second Author | HOther - Co Author | HCo - Corresponding Author |
|---|---|---|---|
| ${hFirst} | ${hSecond} | ${hOther} | ${hCO} |
GScholarLENS is built using JavaScript, HTML, and CSS, along with the following libraries:
GScholarLENS uses a semaphore to manage data retrieval tasks sequentially. This prevents overlap, ensuring each data fetch completes before starting the next. Content is scraped with the request limits in mind and only for one author/profile at a time.
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GScholarLENS is available on major browser extension stores: