Got in here a few minutes late due to the tweetup.
Temporality – cyberinfrastructure =ci
ci is any everyday problem, over a long period of time, it’s not about a one-off tool. Theoretical questions: Institutionalization, the long now, rhythms of collaboration (biographical – life outside of work, peoples careers when the project is really long). Methodological: studies over decades, handoff of longitudinal data, working across – how do you handoff qualitative ethnographic data.
Jillian C. Wallis (UCLA)
Data sharing – how do research in collaborative science relate to data? How can we share our qualitative and quantitative data? How can we tame shimmering data to support data sharing?
Who’s responsible for putting data into the repository? the PI? the researcher? the institution? the public??
How can we share our own data – our data as LIS researchers looking at collaboration in science? How do we share over large distributed qualitative research (see similar discussion in presentation from SSS). IRB commitments and commitments to research participants – strip the data before sharing, but without that context is it useful?
Shimmering – Paul Edwards – seeing data clearly that has been analyzed and reanalyzed. Contextual metadata. How do we deal with the dynamic data.
Geoff Bowker (Pitt)
meta theoretical issue – why study collaborative science. We’re trying to develop new science, or new ways of seeing world. We now can get a the push of a button the data that required traveling around the world to find, but we aren’t asking new research questions.
The publishing industry is calling the shots. The university management don’t appreciate new types of scholarship – we’re straitjacketing people into types of research that aren’t the best for learning about the world (paraphrase for sure)
Data structures – co-data, but can’t mandate data structures, projects on top of projects, standards and data all the way up and all the way down. Why are we building CI in only one country? For example, dealing with climate change – why is that being done in only one country. Institution by institution, locally, “open methodological warfare”. We’re embedded in the project – we see the sausage being made and its messy. We need to tell the NSF when there are problems, but these friends with whom we’ve been embedded, what does that mean for them?
Archer L. Batcheller (Michigan)
sociotechnical design – design patterns. Common solution to common problems in CS. Extend that type of toolbox to sociotechnical design. Example: earth system modeling framework - mostly production, not a large research interest but building robust software. They spin off smaller projects to do research and then import the results back in. This is better than thinking about “best practices” because it provides context. Design patterns also talk about the tradeoffs.
good ethnography = good design … (maybe… hmm.)
Q: (K Fleischmann)the public and trust. also with social science, we want to be critical but not trash science as political people do
A: (AB)participants are very sensitive about the public particularly since climategate. Can we be more transparent? Where can we have more frank discussions. They are talking about having closed meetings so not to have to couch things for bloggers or members of the public. They don’t know exactly what to do about it
(GB) strategic xxessentialism? talk that we believe in something while working to undermine it. Professionalization strategy – come up with arcane language that is only understood within the profession. There still needs to be that. Privacy needs to be rethought and reunderstood. Need to teach children that there is uncertainty, no one single answer.
Q: (Julian Warner) sausage being made, end product may have traces of the process by which it was made, but might be difficult to get at how it was made
Q (missed name, UT Austin) about controversies – a way that scientists respond is through dipping themselves back into their disciplines and gathering more data, but this is much slower than congress pulling funding
A: (GB) scientists are not good at talking to the public beyond gee wiz stories. when talking to congress, talk like I am the voice of God. Need to represent uncertainties
() NSF is tailored to short term 2-3 year projects, so there are issues with longer
Another plug for Paul Edwards A Vast Machine – about climate data over time
long term research -
HIV studies – people change and age, the view of the disease changes, treatments change… the studies have to adjust continuously
Long term ones with sensors – by the end they want to re-do the beginning… years before data available on large long term projects