Archive for the 'collaboration' category

Authorship in various fields

Sep 17 2011 Published by under collaboration, scholarly communication

DrugMonkey's been on an interesting run on order of authorship (his view is distinctly from his discipline as in Econ and some other fields alphabetical order is the norm)... so this reminds me of a couple incidents at work.

One project I'm on mostly has people from the geosciences or planetary sciences area. Abstracts sent to AGU have everyone on the team's name on them. The presenter is the primary author, but we're all collaborators even if the piece emphasizes something that's not really on our part of the project. The presenter might not be the first author, btw, as each first author can only submit one thing.

Another project just submitted a conference abstract and I didn't get the opportunity to co-author, even though my role has probably been a lot larger and the team is a lot smaller. Also, I wrote about 60% of our project documentation and my name wasn't added as an author (only the person who edited my work and the person who wrote the other 40%). I, of course, raised a fuss and then got my name put on it... but sigh. This is in an area of CS.

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Early thoughts about a collaborative meeting organized by mpow

Apr 15 2011 Published by under collaboration, scholarly communication

This will hopefully be reported in a while in some peer reviewed venue or at least a conference, but I wanted to get some of my thoughts down early on.  I’m part of an internally funded research project at MPOW (not linked from here, but you can easily google me) that is incredibly ambitious and has some risk. What are we trying to do? We’re trying “To provide actionable knowledge to decision makers, build capacity for communicating ideas, and create resilience through discovery.” That’s a pretty broad statement. Here’s the nutshell version: get scientists and decision makers together to address issues related to climate disruption, its mitigation, and adaptation to it. Our specialty is systems engineering, so that’s our approach.

The first way we’re starting on this is to hold a few conferences to develop prioritized lists of needs in a few key areas and to start building a community of interest supported by an online community. The conferences aren’t the standard sit-there-and-be-lectured-at type of thing. They involve breakout sessions with read aheads, polls, surveys, online comments, and a notetaker recording spoken discussion. The first of these was held this week with climate scientists and public health researchers and practitioners. It was pretty intensive with scheduling from 7:30am to as late as 8pm and with all the breakfasts, lunches, and one dinner provided there at the meeting location.

My concerns were facilitating communication across different areas of science and to decision makers as well as how a software tool can support this type of meeting. I really didn’t get to help much with the communication bit, unfortunately, but I did work pretty hard on the platform, alongside a web developer, a couple of software engineers, a noted psychologist who studies collaboration in science, a cosmologist, an astronomer, an atmospheric physicist, and others.

There are many platforms that did some of the things we wanted to do, but other future work is to provide a type of virtual observatory for climate data, so we were looking for something very flexible. We were also working on a shoestring, so we couldn’t just outsource a consultant to build what we needed. What we ended up with is a Drupal 6.2 installation. There were some plenary sessions but it’s the breakout sessions and the digital library that are probably the most interesting. For the breakout sessions, we had a session description, a bio of the moderator, room for notes from the note taker (with a scroll bar), and a forum-like threaded place for comments. The comments was also where files could be uploaded. In the sidebar, we had a listing of the accounts who were looking at the page (custom built), a list of the files that had been uploaded (custom built), and a place for polls. The concept was that the moderator would introduce the topic, but the conversation would involve all attendees (we expected up to about 20 per room). Attendees could share an image or a file to illustrate their point. The comments would be used to get input from the shy participants. The polls could be used to establish a starting place or rank whatever came out of the discussion. (I’m not including an image on purpose – I don’t have a blank one to show).

The digital library is pretty cool. The module we’re using will fill in the details if you give it a PMID. Since this first conference was about public health, we used that quite a bit. It also allows us to use the node number to quickly add a footnote or the whole citation wherever we want with a short macro.

As you might expect, there was a learning curve not only in using the site, but also in understanding what the site was useful for. Some of the moderators were all about getting the most out of the site while others didn’t get it or were afraid it would be distracting. In addition to the notetaker, each breakout had a facilitator to help everyone get logged in, to upload files, and to create polls on the fly. I was the facilitator for 4 sessions with 3 different moderators. Each session was different. Creating a poll to rank the 8 things that came out in discussion was tough to do on the fly, even for those who type fast.

The attendees, once they got used to not being on the internet (no checking e-mail!), and got logged in, seemed to do ok. A lot of them said they enjoyed that part of it. One part that got great feedback was the feedback session itself! That was essentially a threaded forum, but then we discussed the feedback and I took notes.

Attendees would be interested in receiving an occasional e-mail, but didn’t seem to really want to contribute to the site really. It was also suggested that we have regular virtual meetings using the site.

Anyway, lots more detail needed, lots more to say, but that’s it for now.

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Has the online search displaced the friend as the preferred first information source?

ResearchBlogging.orgBased on this article, apparently not. There is an entire body of work talking about how scientists, engineers, people in business, etc., all consult friends (acquaintances, weak ties, strong ties, etc) first before looking at their files or consulting a reference or heaven forfend, trying the library. Even though some people do some research before asking other people (Wolek, 1972), it's a well documented thing that people like to use other people as sources of information (just a few examples not cited by the authors include Borgatti & Cross, 2003, Hertzum & Pejtersen, 2000, Constant Sproull, & Kiesler, 1996) .

Now with knowledge workers being hyperconnected with easy access to fast internet at work, I've been wondering if running a quick search in a search engine would displace asking people as the top thing. For me it's sort of a toss-up. If I can ask someone who's right here, I might do that before searching online. But what did the authors find?

  • Quick questions > internet.
  • Identifying plants or insects > internet.
  • Complex questions > people.
  • Evaluations of products > people.
  • Specific information (e.g., state-specific)> people.
  • Efficiency/cost > depends.

But that was just  one of their research questions. They also looked at weak ties/strong ties (a la Granovetter). Their participants liked to ask friends first (and do not like this one woman in the office although they are willing to ask her stuff because she's connected and knowledgeable!). The participants worry about the credibility of information online, particularly that coming from .com sites.

Methods.

They did semi-structured interviews with 14 individuals working at various locations in an agricultural education and outreach organization affiliated with a large research university in the Northeastern US. They also did a social network analysis survey asking about friendship and knowledge seeking networks. They used grounded theory methods for coding.

Some minor critiques.

The whole knowledge thing. People in KM and business areas of information science - and the authors - say that knowledge includes information and data. They say that you can basically just use knowledge seeking in place of information seeking or whatever. Baloney.

There are other stylistic issues that make me think this was originally headed for an MIS journal instead of a LIS journal, but it ended up in a LIS journal so should use those conventions.

So much detail about a woman participant who everyone dislikes but asks for information anyway. Wow. That's probably pretty recognizable if you're the woman!

There are some papers about the aspects of relevance related to searching for people. It seems like some of the results are really *about* judging relevance when selecting people, but do not go in depth.

In Sum.

Not a fabulous article, but decent. I think they could have skipped a lot of what we already know, and spent more time on the electronic vs. people question - since that's what I'm interested in!

References.

(post is about:)
Yuan, Y., Rickard, L., Xia, L., & Scherer, C. (2010). The interplay between interpersonal and electronic resources in knowledge seeking among co-located and distributed employees Journal of the American Society for Information Science and Technology DOI: 10.1002/asi.21472

(others)

Borgatti, S. P., & Cross, R. (2003). A relational view of information seeking and learning in social networks. Management Science, 49(4), 432.

Constant, D., Sproull, L., & Kiesler, S. (1996). The Kindness of Strangers: The Usefulness of Electronic Weak Ties for Technical Advice. Organization Science, 7(2), 119-135.

Hertzum, M., & Pejtersen, A. M. (2000). The information-seeking practices of engineers: searching for documents as well as for people. Information Processing & Management, 36(5), 761-778.

Wolek, F. W. (1972). Preparation for interpersonal communication. Journal of the American Society for Information Science, 23(1), 3-10. doi:10.1002/asi.4630230104

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ASIST2010: Structure and evolution of scientific collaboration networks in a modern research collaboratory

Alberto Pepe, UCLA (dissertation award winner)

:) hasn’t looked at his dissertation for 4 months so he might be rusty, ask his advisor

look at collaboration in a collaboratory where work is interdiscplinary and distributed. Physical and virtual spaces… At CENS, sensor network research, about 300 researchers, multidisciplinary (ee, cs, stats, bio, environ sci, urban planners, sociology, media). Multi-sited within southern California. He was a participant observer.

Important to study relations instead of things bcs of connectedness of sci collabs. Looked at co-authorship, communication (mailing lists), acquaintanceship

What is the topology of the network, what is the structure of CENS and how has it evolved, how are the networks related to each other – what is the role of communication and acquaintanceship on co-authorship?

Authorship data – CENS annual reports are the official listing of all of the publications from the collaboration (lucky this exists – doesn’t for many orgs). 600 publications over 7 annual reports. 400 conf papers. Yr 2 was most productive, dip yr 5 then rise. Most papers have 2-3 authors. Hard to define the boundaries of the system are since there is co-authorship across institutions, countries – used co-authorship to draw boundaries.

87 mailing lists, 30k e-mails, 1500 threads.

Social survey: who do you know?

(opt out – each has a name and picture from a public database…. 300 people from co-authors).

How do you know them? When did you meet, how often do you communicate?

10-30 acquaintances – 191 of 373 responded to the survey.

he didn’t look at betweenness (or apparently like Bonaicich or eigenfactor)

Community structure:

Used Newman-Girvan method. (would have been nice if he had drawn a line around them or something – hard to see – as he did on a later slide)

some communities around country of origin, academic affiliation, position (staff,faculty,phd).

co-auth and acq overlap and are one institution and one discipline (i think he said) but are becoming more interdisciplinary and less inter-institutional

cens collab  communities are open fluid, inclusive, and small-worldish (but not based on prestige).

hubs bring different communities together not as interdisciplinary as we have heard

these q occurred to me: the survey – ethical issues with opt out? ethical issues with pictures? did they shift the order? order alphabetical? fatigue? sort in groups

a: they were ordered by institution and discipline, tried other things but this was what worked

In CS they co-author without knowing each other !?!?! that’s cool. In bio they know each other if they co-auth

wrt ethical questions – director of CENS was on the dissertation committee so was all approved.

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ASIST2010: Collaborative Science

Got in here a few minutes late due to the tweetup.

Ribes (Georgetown)

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

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More on proximity to industry – and similar ideas

In a recent post on openness and sharing in chemistry, I briefly touched on proximity to industry. This is actually somewhat nuanced and a few research studies have looked into it.

As I mentioned Birnholtz, in his dissertation [1] and subsequent JASIST article [2] describes proximity to industry as both/either being funded by a commercial or industrial organization and/or "the extent to which there is an interest by researchers or others in commercializing or otherwise profiting financially from research discoveries" (dissertation, p27). There's the myth that the research university gets all of its money in grants from the government (NSF, NIH, mostly), foundations, and large non-profits. In reality, some money comes in from licensing patents, money comes in from DOD and NASA, and some comes from companies. In the LIS world, Microsoft and Google sponsor a lot of research (AOL used to).

So even though the money might come from a commercial organization, typically the intellectual property belongs to the researcher - in a University. Since the Bayh-Dole act, there's been a lot more effort to patent in universities but as Bill Hooker found when he researched it, the licensing income isn't all that. Kleinberg [3] comes up with some other reasons university folks patent, but I've discussed that before, so no real need to go through that again now. In industry the intellectual property is "work for hire" and belongs to the company.

So what's the difference between a chemist working in industry and one working in a university?

Typically, being employed in industry means fewer journal publications. There are several reasons for this. First, publishing may not be valued or used for any type of promotion or reward (in some parts of MPOW journal articles have to be written at home in your own time and then still passed through 3 different review offices - which might take weeks if not months). A second reason is that the information can't really be shared except maybe in patent applications and then it's obfuscated.

Folks in industry will ask for help from others in the company before or instead of going outside because there's more shared context - it's easier to get to common ground [4]

There might be more standardized record keeping practices to support intellectual property claims. In other words, lab notebooks are checked out from a central office, are bound and numbered, and are locked up or there are centralized electronic lab notebook servers.

On the other hand, lots of studies show that the general information seeking of industrial scientists and engineers is similar to that of academic scientists and engineers (as an example, [5])

 

Added 12/6/2009

I forgot that Walsh and Bayma [6] talk about "market penetration" - they found that researchers in fields with high market penetration (chemistry and experimental biology) were less likely to use ICTs for informal scholarly communication than market buffered fields like math. I was reminded when browsing Fry [7]

References

[1] Birnholtz, J. P. (2005). When Do Researchers Collaborate? Toward a Model of Collaboration Propensity in Science and Engineering Research. Unpublished Doctor of Philosophy (Information), The University of Michigan. 3186579. (this is also downloadable from his Cornell website)

[2] Birnholtz, J. P. (2007). When do researchers collaborate? Toward a model of collaboration propensity. Journal of the American Society for Information Science and Technology, 58(14),2226-2239. doi: 10.1002/asi.20684

[3] Kleinman, D. L. (1998). Untangling Context: Understanding a University Laboratory in the Commercial World. Science, Technology, & Human Values, 23(3), 285-314.

[4] Hertzum, M., & Pejtersen, A. M. (2000). The information-seeking practices of engineers: searching for documents as well as for people. Information Processing & Management, 36(5), 761-778.

[5] Ellis, D., & Haugan, M. (1997). Modelling the information seeking patterns of engineers and research scientists in an industrial environment. Journal of Documentation, 53., 384-403.

[6] Walsh, J. P., & Bayma, T. (1996). Computer Networks and Scientific Work. Social Studies of Science, 26(3), 661-703.

[7] Fry, J. (2006). Scholarly research and information practices: a domain analytic approach. Information Processing & Management, 42(1), 299-316. doi: 10.1016/j.ipm.2004.09.004

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Balancing open & collaboration with private & individual

Aug 02 2009 Published by under collaboration, open science

A quick note on the tension between sharing everything as quickly as possible and keeping things for yourself.

The thrill of collaboration when like minds come together to brainstorm and solve big problems and the egoboo of having something you created "liked" or reused should not exclude or overshadow the value of figuring things out for yourself and having something you can point to as your own.

Recent posts from Sabine and Cameron got me thinking about this a little more. There are also some excellent comments on Sabine's post.

I think it's important to go offline for a bit and to work things out for yourself. Certainly, if you're reading something in math or science, you might try to work through the problem on your own prior to reading how the authors say to do it.  I'm an extreme extrovert so I think by talking and writing (that's why you - like my husband - might be maddened by the apparent drift in my "convictions" or point of view). Others get the data, then go off somewhere and come back with an idea fully formed.  What seems like ages ago now, I proposed that blogs were good to help people of these two groups work together, but I wonder about the pace of friendfeed and/or polymath projects and the necessity to feed the beast.  How does that work for the introvert types?

Likewise with open science, perhaps, for theoretical scientists or for folks who need to go offline and then present ideas fully formed.  Having someone jump in to their thoughts and tell them that they made a misstep in their proof or to tell them the answer instead of letting them figure it out for themselves, might throw them off their game. 

Seems like for some projects the ideal limit of openness might not be real-time, complete, but at turning points when various milestones are passed... showing the work, but only after it's done and cleaned up.  What do you think?

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