Estimating article influence scores for open access journals

, ,
1 Information School, University of Washington, Seattle, USA
2 FlourishOA
3 University of Washington, Seattle, Washington, United States
DOI
10.7287/peerj.preprints.26586v1
Published
Accepted
Subject Areas
Algorithms and Analysis of Algorithms, Data Mining and Machine Learning, Data Science, Databases
Keywords
scholarly publishing, bibliometrics
Copyright
© 2018 Norlander et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Norlander B, Li P, West JD. 2018. Estimating article influence scores for open access journals. PeerJ Preprints 6:e26586v1

Abstract

Motivated by a desire to curb "predatory" publishing, we created FlourishOA, a one-stop shop for authors, publishers, funders, librarians, and policy makers to find high-quality, cost-effective Open Access (OA) journals. FlourishOA provides Article Processing Charge and Article Influence (AI) score data for OA journals. AI scores are retrieved from InCites Journal Citations Reports (JCR). However, the FlourishOA database contains thousands of journals not indexed in JCR. In order to provide users with more data, our team gathered five years of citation counts from the Microsoft Academic Graph database via Microsoft Cognitive Services Academic Knowledge API and used a log-transformed linear regression to predict over 2,500 additional 2015 AI scores.

Author Comment

This is a preprint submission to PeerJ Preprints.