| Abstract | Research on FLOSS has relied on several different kinds of scientific evidence, such as the archives created by the FLOSS developers, versioned code repositories, mailing list messages and bug and issue tracking repositories [1]. FLOSS teams retain and make public archives of many of their activities as by-products of their open technology-supported collaboration. However, the easy availability of primary data provides a misleading picture of ease of conducting research on FLOSS. Precisely because these data are by-products, they are generally not in a form that is useful for researchers. Instead potentially useful data is locked up in HTML pages, CVS log files, text-only mailing list archives or dumps of website databases. FLOSS research projects, therefore, expend significant energy collecting and re-structuring these archives for their research, which is repetitive and wasteful [2]. Furthermore, different researchers will extract different data at different points in time, take different approaches to processing and cleaning data and make different decisions about analyses, but without all of these decisions being visible, auditable or reproducible. In principle, these latter problems can be addressed by individual researchers better documenting what they have done. However, research publications typically have restrictions on publication lengths that make complete discussion impossible. Furthermore, published papers are just the tip of the iceberg, and knowing what others have done does not necessarily make it any easier to replicate the results.
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Data and analyses sharing
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