Flourish is a data-driven web app and API enabling users to discover relevant and reputable Open Access (OA) publications in order to maximize publishing impact. By aggregating price information and impact data, we empower researchers to identify credible OA journals that best fit their publication needs. Our goal is to provide the OA community with the tools they need to separate legitimate OA publications from unethical and predatory publishers. Additionally, we believe that adding transparency to OA markets will produce downward price pressure, further lowering economic barriers to entry in scholarly publishing.

The Open Access (OA) movement grew from the traditional idea of research and scholarship as a public good. As the rapid growth of the Internet revolutionized our ability distribute and consume information, a set of principles began forming around the notion of "free and unrestricted access" to scholarly literature. On February 14, 2002, the Budapest Open Access Initiative codified these principles in a formal statement, laying out a framework for what we now know as the OA movement. Broadly speaking, Open Access is the free and immediate, online distribution of research and scholarship, along with unrestricted access and usage rights for the end-user. These principles stand athwart traditional academic publishing models that lock scholarship behind increasingly cost-prohibitive paywalls, restricting access and limiting participation in scholarly discourse.

Bolstered by early adopters like the Public Library of Science (PLoS), Biomed Central, and Creative Commons, as well as broad support from a number of research institutions, academic associations, funding bodies and governments, the OA movement has grown into a robust and innovative community of practice. Publishers are experimenting with business models that incorporate OA principles, and governments are increasingly looking to OA policies as a way to improve stewardship of taxpayer-funded research. In October 2016, the Directory of Open Access Journals (DOAJ) listed 9,210 OA journals from 129 countries, indexing over 2.3 million OA articles.

The emerging OA marketplace presents new opportunities and challenges for scholars, funding bodies and publishers looking to thrive in this new environment. Author-pay publishing models shift the financial burden from the consumer to the funding body, usually in the form of article processing charges (APCs). An influx of new OA publications, combined with a paucity of clear economic information has created a great deal of uncertainty in this market. Capitalizing on this uncertainty are a number of "predatory" publishers that employ unethical and deceptive practices to extract fees from researchers, offering only a pretense of peer-review in return. The Eigenfactor Project uses network analysis to examine scholarly impact, along with economic data from OA publications, to produce quantifiable metrics for cost-effectiveness. Flourish provides users with a practical implementation of these data models, creating an information-rich marketplace of OA publications.


The success of the Open Access publishing model is highly dependent on a thriving, transparent marketplace. Researchers need to quickly and reliably identify high-quality, relevant and reputable Open Access publications that will maximize their publishing impact. Conversely, quality Open Access publishers must be able to distinguish themselves from the fray of “predatory” journals and unproven startups. By aggregating price information and impact data we empower researchers to identify credible Open Access journals that best fit their publication needs. We also alert the community to unreliable and predatory publishers. Our goal is to increase the use, reliability, and influence of OpenAccess publishing, while decreasing the motivation of predatory publishers by exposing them to the community.


Our article processing charges are scraped directly from several publisher’s websites. We maintain a database consisting of the publishers, publication names, and article processing charges, as well as a handful of other metrics. We then calculate each publication’s influence score using Jevin West’s ArticleInfluence metric. Our database has journal prices for some journals as far back as 2012, and more historical prices are being added for more effective analysis. We believe in sharing our data openly and have created a well-documented REST API so that you can easily access this data.


This research is generously supported by the Sloan Foundation's program on scholarly communication.

Types of Journal

A hybrid open access journal is a subscription journal in which some of the articles are open access. This status typically requires the payment of a publication fee (also called an article processing charge or APC) to the publisher, in addition to the subscription fee paid by the reader. (Found on Wikipedia). A "fully" open access journal contains exclusively articles that are open access. Such journals usually have an article processing charge paid by the author, but do not require a subscription to be accessed.


Jevin West

Jevin is an assistant professor in the Information School at the University of Washington. He co-founded the Eigenfactor project, which aims to rank and map science, in hopes of building better tools for navigating the ever expanding set of scholarly literature.

Carl Bergstrom

Carl is a professor of biology at the University of Washington. He co-founded the Eigenfactor project with Jevin West.

Ashley Farley

Ashley is the Associate Officer of Knowledge and Research Services at the Bill & Melinda Gates Foundation. She can be found on LinkedIn.

Bree Norlander

Bree is a graduate student in Library and Information Sciences at the University of Washington and works as a Data Services Specialist for the University of Washington Libraries. Follow her on Twitter or LinkedIn.

Dale Coleman

Dale is a reference librarian at Tacoma Community College. He can be found on LinkedIn.

Patrick Spieker

Patrick is a computer science undergrad at the University of Washington. He likes to work at the intersection of data science and software engineering. He can be found on Github.