Strategic Choices: IP Exploitation vs. Knowledge Sharing

Many academic institutions house valuable intellectual property (IP) that is generated through the research activity of its community. While U.S. institutions have been allowed to pursue ownership of inventions based on federally-funded research since passage of the Bayh-Dole Act in 1980, few research universities have successfully reaped rewards, despite the enormous potential value.¹

The emergence of “big data” and text and data mining has opened up new possibilities for research universities to exploit their IP in profitable ways. Articles and datasets can be mined for insights that can be used by industry, for example to improve the odds of profitable investments in R&D or venture capital. Such activities could generate substantial value for academic research institutions, particularly at a time when the future of government funding is clouded by budget constraints and international competition among academic institutions is rising, driving the need for larger budgets.

On the other hand, vigorous IP exploitation would likely raise a number of ethical issues around partnering with specific industries and companies, as well as concerns that prioritizing IP exploitation could shift resources away from disciplines with less commercial value. Moreover, any decision to exploit data and knowledge for commercial and financial purposes must be weighed against the benefits of Open Data for accelerating the pace of discovery and increasing the integrity of the scientific and scholarly record.

This debate is not necessarily mutually exclusive. For example, it may be possible to maintain an Open Data policy for baseline-quality datasets, while setting up a second flow for datasets that have been processed, cleaned, and standardized for IP exploitation. This structure could provide a “best of both worlds” scenario, where grant funding could support the first flow and commercial services could pay for access to the second flow.

While SPARC is known for advocating for Open Data when possible, we recognize that different institutions can legitimately adjudicate this issue differently. Our goal in this document is not to prescribe answers, but to encourage institutions to hold a broad and thoughtful debate to decide this issue for themselves.