UC Berkeley has unparalleled intellectual resources across the entire span of social science, encompassing over 550 social science faculty and researchers on the campus, working in a myriad of topical, methodological, and theoretical specializations. Within the Division of Social Science alone, there are 278 faculty and some 900 graduate students. Outside the division, there are as many faculty again, distributed across the professional schools and other colleges. This combination of scale and dispersion represents both a vast horizon of potential and a challenge. The challenge lies initially in the opacity of the research landscape: it is extremely difficult to know the full range of what is being done in Berkeley social sciences.
The goal of the Matrix Research Network is to render this research landscape visible, not through the lens of departments, divisions, or research centers, but by visualizing networks of collaboration along several lines. The faculty in the network are linked to one another based on their past co-advising PhD students, serving as co-principal investigators on grants, and co-authoring publications. At the level of individual social scientists on campus, we have rich profiles of 688 faculty based on publicly available data, including selected publications, social media and YouTube channels, and other web-based content. The Network showcases the phenomenal quality and scope of social science at Berkeley, while making it more readily available to the Berkeley community itself, and to all who wish to learn what is happening in research here.
The Matrix Research Network is an interactive tool for research bearing on the Berkeley social science landscape, and serves as a gateway to the many communities of researchers whose work defines that landscape. For any faculty member moving into a new area of research or simply looking for potential collaborators, the Network is searchable by key terms, and one can navigate across a wide range of factors and ways of organizing them. For new faculty, this tool can provide an invaluable resource for locating colleagues with common interests, getting to know the campus, and establishing networks and communities. For alumni or others with particular interests and who might wish to partner with Berkeley in some way, the Network allows them to learn quickly what research is happening in a chosen area of their interest.
Beyond these practical objectives lies the more basic conceptual question of whether and how one can read a pluridisciplinary research landscape in order to understand its propensity to shift. Our intuition at Matrix is that the Berkeley social science research landscape is brimming with collaborations, many of them across different social sciences or linking them to STEM (Science, Technology, Engineering and Medicine) fields in often novel ways. Looked at historically, some collaborations grow into enduring cross-disciplinary fields, such as behavioral economics, cognitive neuroscience, linguistic or biological anthropology, and psycholinguistics. Others are so new or so ephemeral that they have not and may never even grow proper names, let alone institutionalized recognition. To read a landscape would be to understand the shifting boundary zones among disciplines, to measure trends within and among them, and to identify areas of research on the threshold of growth.
The Matrix Network in its current shape is a work in progress, and we continue to work on several challenging problems. One is how to maintain current accuracy of the information in our databases, since the research landscape is always in flux. To date, the data have been gathered and curated by teams of students and Matrix staff. The artisanal character of the data selection, we believe, is part of its high quality, yet we are keenly aware that we must automate major aspects of the ongoing data acquisition, while also expanding the kinds of data we gather.
More generally, there are fascinating questions about how we manage the dynamic variability of the campus due to recruitments, separations, and retirements. We are also building out the range of researchers in the database. We initially focused on faculty in the Division of Social Sciences. From there, we expanded to include social scientists elsewhere on campus. Since many of these researchers collaborate with colleagues in STEM fields, we are organically expanding to encompass more of the campus and the broader research landscape. Ultimately, our ability to do this depends upon resources and enlisting the collaboration of colleagues from STEM fields, since it is they who best understand the fields they work in.
We have undertaken two projects that we offer as concrete evidence of the value of the network. The first is what we call Matrix White Papers, which are stand-alone documents aimed at profiling research at Berkeley on specific topics or areas of work. We have produced white papers on climate change, behavior change, human rights, and the future of politics. We are currently working on a paper on mind and brain. Written in accessible prose, these white papers serve as gateways and guides to sectors of the research landscape. Since we use data in the Network as our starting point, and then enter all new data from white papers back into the Network, the papers are both a proof of concept and a way of growing our database.
The second project seeks to demonstrate the power of the Network in revealing changing orientations in disciplines over time. We are using campus data to ask whether joint membership on a PhD advisory committee reveals changing orientations of the disciplines, and whether mutual collaboration raises the probability of further collaboration among the individuals or disciplines in question.