ReflectionsByBen

From Data-Intensive Collaboration in Science and Engineering
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As a relatively new person to this way of framing collaboration, I'm thankful that policy is recognised as a realm in which this group could have some influence. This is a challenge in other science communities. I'm still looking for more theory, but it's reassuring that lessons learned through comparison are important ways to intervene and contribute back to the communities we visit.

I am appreciative of the feedback that I received about my preliminary ideas and questions concerning big data, and that they are relevant to the thinking about this space. It's good to know that even though some concepts are foreign, I have been able to contribute perspectives valuable to at least the several individuals whom thanked me for raising some issues.

Let's consider not calling a potential special issue "data-intensive collaboration" or its variants. Perhaps it should just be "collaboration" to recognise that one of the goals is to make the clueful approach to big data the norm among future scientific collaborations.

Remaining open questions: How do we understand the high-level positioning of this big data effort as a whole, as well as how its core concepts and ideas are internally stratified. There could be more links from this into the history/philosophy of science, since this idea of how we do science using big data today is fundamentally important.

Why do we conceptually treat data, software, processes, and infrastructure as drastically different? We seem aware that each of those lose value over time if they are not used or documented, but we don't discuss "software-intensive collaboration" or the like.

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