New tools for interdisciplinary researchers
December 8, 2015—New York, NY and Seattle, WA—The JSTOR Labs team recently partnered with Dr. Jevin West’s team at the University of Washington DataLab to test and develop tools to help researchers introduce themselves to key topics and publications from other fields. The results of their work have been incorporated into JSTOR Sustainability—a new site, currently in beta, that contains a broad range of scholarly articles and research reports dealing with environmental stresses and their impact on human society.
Dr. West’s lab created the Eigenfactor, which uses citation networks to evaluate the impact of scholarly publications, and recently introduced Eigenfactor Recommends, which helps identify the classic and foundational papers within a field as well as the latest innovations.
JSTOR has created a semantic index which identifies more than 1,500 key terms in Sustainability. By uniting this semantic index with the algorithms that drive Eigenfactor Recommends, the two labs were able to create a series of topic pages that provide overviews of key areas in Sustainability, including a topic description, important journals and authors, and related topics. One of the key features of the topic pages is Influential Articles—an interactive timeline of the articles that have most influenced the topic. The Eigenfactor Recommends functionality also powers the “background reading” recommendations that appear alongside each article on JSTOR Sustainability.
JSTOR Labs employed a process called “flash builds” for the collaboration, which allowed the two teams to go from ideation to a tested prototype in only a week’s time. “The process was really instructive,” said Dr. West, Assistant Professor at the University of Washington School of Information. “I’ve never seen something progress from an idea to a viable, production-grade feature in such a short amount of time.”
“During a flash build, we get continual feedback from our users on what we’ve developed, and the feedback we received on the topic pages, and on the Influential Articles in particular, was really positive,” said Alex Humphreys, Director of JSTOR Labs. “I think part of the reason it resonated with researchers is because they help tell the story of a particular subject—you can see the progression of important discoveries over time, which is key when you’re delving into a new or unfamiliar research topic.” Humphreys noted, “One professor saw value in directing her students to the topic pages at the start of their research, as the topic overviews would help point them to the long history of content on JSTOR.”
“As someone whose work spans many disciplines, I can see first-hand the value of these tools. Eigenfactor Recommends helps give me a head start on assimilating a new field, and I’m excited to see how we’ve been able to adapt and strengthen it to power the topic pages on JSTOR Sustainability,” said Dr. West. “I think that the results of this project can serve a broad range of disciplines, and we’re looking forward to more collaborations with JSTOR to continue exploring new tools for researchers.”
A video of the collaboration is also available.
About JSTOR Labs
Launched in 2014, JSTOR Labs is a part of JSTOR (www.jstor.org), the not-for-profit digital library of academic journals, books, and primary sources. JSTOR Labs works with partner publishers, libraries and labs to create tools for researchers, students and teachers.
About the University of Washington DataLab
The DataLab is the nexus for research on Data Science and Analytics at the University of Washington School of Information. The DataLab studies large-scale, heterogeneous human data in an effort to understand why individuals, consumers, and societies behave the way they do. The DataLab’s goal is to use data for the social good, in an ethical manner that can inform policy and impact lives for the better. As the focal point for industry partnerships related to “big data” and business analytics, the DataLab also provides infrastructure and support for student training and engagement in projects that involve the analysis of large datasets.
JSTOR VP, Communications