The Eigenfactor project began as an effort to quantify the value provided by academic journals, and this remains a core component of our mission. We consider citations to be the primary indicator of scholarly impact, and use network methods to improve upon simple citation counts as a way of quantifying influence.
Maps help us navigate by simplifying complex landscapes and highlighting the important structures therein. The best maps convey a great deal of relevant information while minimizing the bandwidth needed to communication by suppressing extraneous detail; the best maps efficiently compress of complex data. In our study of science, the terrain that we wish to map is the lattice of interrelated scientifc publications, linked first and foremost by scholarly citations. We have developed a suite of network analytic tools which, when coupled with data visualization techniques, allow us to map our the intellectual structure of science.
The digital revolution in scholarly publishing has largely solved the problem of document delivery. If you know which paper you need, you can usually obtain a copy in at most a couple of minutes (assuming you have access to pay-walled content). The next step is to make headway in the problem of document discovery. How can scholars become familiar with the intellectual landscape of a new field? How can scholars find the papers that they need to read but perhaps have heard nothing about?
Science is inherently social. Our activity as scholars is governed by extensive norms and institutions that govern our research activities, our publication patterns, and our interactions with one another. Game theory and economic theory can help us understand the strategic implications of academic conventions and institutions. Large data sets can help us understand the human element of science: for example, who does what, with whom, and where?