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Archived updates for Friday, November 11, 2005

Introducing Science Commons

The good folks at Creative Commons have launched a new service called Science Commons in order "to encourage stakeholders to create areas of free access and inquiry using standardized licenses and other means; a 'Science Commons' built out of voluntary private agreements." The are currently working on the problems of "inaccessible journal articles, tools locked up behind complex contracts, socially irresponsible patent licensing, and data obscured by technology or end-user licensing agreements."

The Science Commons "Publishing Project" hopes to reduce the legal and logistical effort involved in managing copyrighted scientific publications. Our work includes an exploration of the unique needs of peer-reviewed literature, technical methods to encourage greater legally permissible information sharing, and the construction of a resource library for all stakeholders: commercial publishers, science and medical society publishers, universities, libraries and individual researchers.

The Science Commons "Licensing Project" is working with foundations, universities, corporations, non-profit institutes, and inventors on standard legal terms, educational materials and curricula, organizational designs (technology trusts, patent pools) and bringing together stakeholders. For example, although “standard” material transfer agreements exist (the Uniform Biological Material Transfer Agreement, or UBMTA, was developed in 1995) the licensing of materials remains a problem. A complex set of interlocking licenses covering dozens of different materials imposes significant transaction costs simply to gain the opportunity to begin research.

The Science Commons "Data Project" is exploring ways to assure broad access to scientific data. They see a wasteful data economy evolving in which raw data are not made accessible; scientists are either leery of the risks of losing control over their data or subject to institutional requirements that mandate a closed approach. Implicit in data sets are answers to questions the researcher perhaps did not specify - answers that are a consequence of the throughput of the experiment.

Click here to get involved in the Science Commons.
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