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One of the most valuable actions taken by OSG was the deployment of grid testbeds and the experimentation that followed. Building and operating such “prototype cyberinfrastructures” provided experience that could be acquired in no other way. Some specific lessons are summarized here:

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Learning “facility thinking”: The mechanics of operating a prototype cyberinfrastructure immediately confronts the people running it with the essential fact that a cyberinfrastructure is a facility, requiring clear organization, defined personnel roles and constant attention to a wide assortment of details. This statement is even more relevant when the cyberinfrastructure is a production–level [39] facility.
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Fostering collaboration: Operating a distributed cyberinfrastructure testbed forces personnel at different sites — or in different roles — to communicate frequently with one another, building social bonds and good will that improve overall collaboration. This “social glue” is especially beneficial to collaborations containing subgroups from different disciplines or sub–disciplines, e.g. astronomy–physics, ocean sciences–atmospheric sciences, biology–computer science, etc.
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Developing common software or standards: Running a cyberinfrastructure testbed even for a short time exposes inefficiencies and troubleshooting difficulties that arise from heterogeneous software and error–prone manual processes. OSG’s predecessor projects adopted the VDT early, both for providing common tools and for installing and configuring them in simple and identical ways. Other communities might benefit further by adopting data standards.
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Designing for scalability: As testbeds grow (merging perhaps into the final cyberinfrastructure), it is important to identify as quickly as possible organizational structures or operations models that scale poorly. Fixing such problems early avoids painful and perhaps prohibitive restructuring costs later. For example, as

Open science proposes changing the culture without destroying the creative tension between the two ends of the science-for-innovation rope. And it predicts that the payoff – to human knowledge and to the economies of knowledge-intensive countries like Canada – will be much greater than any loss, by leveraging knowledge to everyone’s benefit.

“You can’t get rid of competition in science, nor do you want to because it feeds innovation,” says Bartha Maria Knoppers, Canada Research Chair in Law and Medicine and a law professor at Université de Montréal. Open science isn’t about throwing out the patent system, she insists. Rather, says open science, we need to distinguish between the research tools and basic knowledge that scientists need to do their work, and those ideas that are truly inventive and novel, with industrial application, that the patent system was designed to protect.

"But institutions are generic, and libraries are generic, even in more focused institutions like MIT. The library, the archive, the IR, in different ways, are about collecting elements of the scholarly discourse that contribute both globally and locally. So institutional repositories are about generic continuity of data, as libraries are about continuity of collections."

"So back to the "subject"; if there is a data repository here, it is likely staffed by "domain experts", capable of taking on a "community proxy" role. They know their stuff. They will treat their data in domain-specific ways; they will know where to seek out data to complement their collection, they will know how to make connections between different parts. They can describe it appropriately, they can develop standards with their colleagues. They will know how to help their colleague scientists extract maximum value."

"Claire Driscoll of the NIH describes the dilemma as follows:

It would be theoretically possible for an unscrupulous company or entity to add on a trivial amount of information to the published…data and then attempt to secure ‘parasitic’ patent claims such that all others would be prohibited from using the original public data."

The OAIS model seems to me (though some argue against this view) premised on a preservation or perhaps archival view: resources are ingested, preserved within the archive, and then disseminated at a later date. I’ve always had doubts about how easily this fits with a more continuous curation model, where the data are ingested, managed, preserved and disseminated simultaneously.

The work of the people that I see most often is in research and teaching. But the lesson is broader: this generation will be working collaboratively in tools like Facebook. In schools, in corporations, in small non-profits, in community centers - people will collaborate and work together in social applications. And that is going to be as natural to them as email and text messaging.