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Since open research is about publishing data which are accessible to all, what kind of actions should I take to prevent open research from unauthorised copying or its data being plagiarised without referring to its sources?

What kind of policy, terms or copyright statements should the open project have to make it clear in order to prevent any abuse?

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There's no guarantee that a work won't be plagiarized. However, once you have a version that you're willing to make public, deposit it in an OA repository. That will give the work an authoritative public time-stamp. If you're plagiarized, you can use it to prove your priority. 

Don't buy the myth that OA somehow authorizes plagiarism. For example, a CC-BY license requires attribution (that's what the "BY" means). All other other CC licenses also require attribution as well. OA licenses are enforceable.

Don't buy the myth that OA somehow invites plagiarism. As I put it in my book < http://bit.ly/oa-book > (pp. 23-24), not all plagiarists are smart, but the smart ones will not steal from a work indexed in every search engine. OA makes plagiarism easier to detect and expose. In that sense, OA deters plagiarism.


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Also note that scientific conduct and proper provenance are largely orthogonal to licensing issues.   Published science is, regardless of licensing, by definition public and hence open to plagiarism, which is the reason why rules on proper attribution are (or should be) holy to the scientific community.  They were introduced ages ago when people realised open knowledge is beneficial to science and impediments to publishing (valid) results should be removed as far as possible.

Plagiarism, if detected, is scientific suicide regardless of the license of the plagiarised work.  People shouldn't cite because of CC-BY, they should cite because it's good scientific conduct.

There are some aspects where licensing and scientific conduct are not altogehter orthogonal -- clear licenses make proper reuse easier, and, as Peter already pointed out, publicly accessible texts may help identifying fraud, in particular if bulk analysis of large  bodies of text and/or data is covered by the license.

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