Harnessing and Sharing the Benefits of State-Sponsored Research: Intellectual Property Rights and Data Sharing in California's Stem Cell Initiative

Document Type



The most up-to-date version of this piece can be found in the Duke Law Scholarship


In recent years data-sharing has been a recurring focus of struggle within the scientific research community as improvements in information technology and digital networks have expanded the ways that data can be produced, disseminated, and used. Information technology makes it easier to share data in publicly accessible archives that aggregate data from multiple sources. Such sharing and aggregation facilitate observations that would otherwise be impossible. But data disclosure poses a dilemma for scientists. Data have long been the stock in trade of working scientists, lending credibility to their claims while highlighting new questions that are worthy of future research funding. Some disclosure is necessary in order to claim these benefits, but data disclosure may also benefit one's research competitors. Scientists who share their data promptly and freely may find themselves at a competitive disadvantage relative to free riders in the race to make future observations and thereby to earn further recognition and funding. The possibility of commercial gain further raises the competitive stakes. This article discusses data sharing in California's stem cell initiative against the background of other data sharing efforts and in light of the competing interests that the California Institute for Regenerative Medicine (“CIRM”) is directed to balance. We begin by considering how IP law affects data-sharing. We then assess the strategic considerations that guide the IP and data policies and strategies of federal, state, and private research sponsors. With this background, we discuss four specific sets of issues that public sponsors of data-rich research, including CIRM, are likely to confront: (1) how to motivate researchers to contribute data; (2) who may have access to the data and on what conditions; (3) what data get deposited and when do they get deposited; and (4) how to establish database architecture and curate and maintain the database.

Date of Authorship for this Version

November 2006