IMHO this is 2 separate but related questions masquerading as one, and these questions are so related (by the nature of open science) that I'll add a third (since it's already been asked):
Q1. How do "open source" products (e.g., content (e.g., analyses, images, publications), data, software) relate to the practice of open science?
Open science is fundamentally (though not exclusively!) about increasing or optimizing reproducibility. (Reproducible science has several benefits, including
- verification: reproducible results are reasonably considered more reliable
- training: reproducing results of increasingly non-trivial studies is (or should be) a key part of scientific apprenticeship
- extension: partial reproduction, with intentional variation, is key to the accumulation of "normal science"
) In the best, most "open-science" case, any competent practitioner should be able to access the inputs, procedures, and outputs of a study (e.g., experiment, model run), and verify that applying the procedures to the inputs produces significantly-similar outputs. Obviously (IMHO--ICBW) a scientific study can be most generally reproducible, and (I claim) therefore most open, when
- its inputs are open data
- its procedures are FLOSS where computational and open content where not (e.g., descriptive text)
- its outputs are open content (e.g., images, text) and open data
Hence I assert the practice of open science will require (et al!) skills in using and generating open content, data, and software, in a manner similar to that which developing FLOSS draws upon more general informatic skills. Furthermore, "doing open science" will typically require specialization in
- generation of particular content, i.e., scientific publications
- use of particular infrastructure, e.g., data repositories, computational clusters
Q2. How would StackExchanges={Open Source, Open Science} overlap and will this be a problem?
Q3. How would StackExchanges={Open Data, Open Science} overlap and will this be a problem?
My answers to both are the same: IIUC an Open Science SE would often point back to (and, probably all too often, mark as duplicates) issues specific to SE=Open Data or (should it happen) SE=Open Source. But SE=Open Science will (I suspect)
- often involve "emergent" or "higher level" compositional and infrastructural issues. E.g., consider the way that IP networking has a vast, specialized problem domain conceptually distinct from the codes and OSes that enable it (i.e., that are the "platforms" for IP networking) and from the data transported by IP networks.
- involve or target a distinct (scientific) community (as pointed out by trichoplax here).