MDPI is pleased to announce the release of a new functionality giving the possibility for researchers and scholars to endorse, and formally recommend articles to their colleagues.
MDPI was an early signatory of the San Francisco Declaration on Research Assessment (https://sfdora.org/read/) which calls for improvement in how quality and impact of scholarly research outputs are evaluated, especially in moving beyond journal-based citation metrics (journal Impact Factor, Scopus Citescore, etc.).
MDPI supports the establishment of article-level impact metrics, including citations, views, downloads, and Altmetric scores. These measures serve as an impact indicator for research articles on a case–by-case basis, assessing paper on its own merit. However, these metrics are also subjective and can give a biased picture of the article impact: they do not directly reflect the quality or the intrinsic scientific value of the article.
In our view, community engagement with publications based on community-driven metrics can help to overcome this limitation. We have therefore launched an option for scholars to endorse articles, indicating their own assessment of its content and making a recommendation to their community. This follows our implementation of the open source Hypothesis commenting tool, which has been available for all articles published by MDPI for over a year (https://www.mdpi.com/about/announcements/1397). Both endorsement and commenting are available for all previously published and forthcoming MDPI articles.
In addition to potentially serving as a sustainable solution to article assessment, endorsements will help scientific communities to identify the most relevant articles, independently of the journal in which it was published.
The code for the endorsing functionality, which relies on DOIs and ORCIDs, will be made available on GitHub with an open source license.
Dr. Shu-Kun Lin, President and Founder
Dr. Franck Vazquez, Chief Scientific Officer
Dr. Martyn Rittman, Publishing Director