Tuesday, April 26, 2011

Reinventing Research? Information Practices in the Humanities

[re-blogged from Resource Connection : April 26, 2011]

A project of the Research Information Network (RIN) focuses on the behaviours and needs of researchers working in  the humanities.The goal of RIN study is to:
  • "develop an in-depth understanding of humanities researchers’ approaches to discovering, accessing, analysing, managing, creating, reļ¬ning and disseminating information resources;
  • "provide comparisons between the behaviours and needs of researchers in different subjects/disciplines, research teams or institutional contexts;
  • "identify barriers to more effective performance in using, creating, managing and exchanging information resources, and suggest how they might be overcome."

The report is based on interviews and focus groups with academics responsible for digital humanities projects such as Old Bailey Online, Digital Image Archive of Medieval Music, and The Digital Republic of Letters, projects they've arrayed in a two dimensional attribute space defined by computational complexity and collaborative complexity:

 The report is available to download from Information use: case studies in the humanities - Report

Friday, April 22, 2011

No Such Thing as Evanescent Data

Pretty good coverage of the "iphone keeps track of where you've been" story in today's NYT "Inquiries Grow Over Apple’s Data Collection Practices" and in David Pogue's column yesterday ("Your iPhone Is Tracking You. So What?"). Not surprisingly, devices that have GPS capability (or even just cell tower triangulation capability) write the information down. Given how cheap and plentiful memory is, not surprising that they do so in ink.

This raises a generic issue: evanescent data (information that is detected, perhaps "acted" upon, and then discarded) will become increasingly rare.  We should not be surprised that our machines rarely allow information to evaporate and it is important to note that this is not the same as saying that any particular big brother (or sister) is watching.  Like their human counterparts, a machine that can "pay attention" is likely to remember -- if my iPhone always know where it is, why wouldn't it remember where it's been? 

It's the opposite of provenience that matters -- not where the information came from but where it might go to.  Behavior always leaves traces -- what varies is the degree to which the trace can be tied to its "author" and how easy or difficult it is to collect the traces and observe or extract patterns they may contain.  These reports suggest that the data has always been there, but was relatively difficult to access.  It's only recently that, ironically, due to the work of the computer scientists who "outed" Apple, that there is an easy way to get at the information.

Setting aside the issue of nefarious intentions, we are reminded of the time-space work of the human geographers such as Nigel Thrift and Tommy Carlstein who did small scale studies of the space-time movements of people in local communities in the 1980s and since. And, too, we are reminded of the 2008 controversy stirred up when some scientists studying social networks used anonymized cell phone data on 100,000 users in an unnamed country.

Of course, the tracking of one's device is not the same as the tracking of oneself.  We can imagine iPhones that travel the world like that garden gnome in Amelie and people being proud not just of their own travels but where there phone has been. 

See also
  1. Technologically Induced Social Alzheimers
  2. Information Rot

Data Exhaust and Informational Efficiency

Heard an interesting talk by Paul Kedrosky a few weeks ago at PARC titled Data Exhaust, Ladders, and Search.

The gist of the talk is that human behaviors of all kinds leave traces which constitute latent datasets about that activity. Social scientists have long had a name for gathering this type of data: unobtrusive observation. Perhaps the most famous example is looking at carpet wear in a museum as a way of figuring out which exhibits captured the most visitor attention or garbology and related "trace measures used by anthropologist W. Rathje in the 70s and 80s.

One of Kedrosky's nicer examples was comparing aerial view of Wimbledon center court at the end of a recent tournament with one from the 1970s. The total disappearance of the net game from professional tennis was clearly visible in the wear patterns on the grass court.

In addition to a number of neat examples (ladders found on highways as indicator of housing bubble was a favorite) of using various techniques to capture "data exhaust" (indeed, he suggests, it's the entire principle behind google), he asks the question: What are the consequences of an instrumented planet? That is, a planet on which more and more data exhaust is captured and analyzed, permitting better decisions and more efficient choices.

In fact, one of the comments on Kedrosky's blog post about the talk (by one J Slack) suggests a continuing move toward "informational efficiency" -- with more and more instrumentation generating data and more and more connectivity, he suggests, "we'll be continuously approaching an asymptotic efficiency, though never quite getting there."

A standard definition of informational efficiency is "the speed and accuracy with which prices reflect new information" (TheFreeDictionary.com).  But there is some circularity here -- in this context it's only information if it does affect the price, otherwise it's mere noise.  And so we're still left with the challenge of sorting out the signal from the noise even after the data has been extracted from the exhaust.  And the more of everything the more of a job it is.

Bottom line: I think "data exhaust" is a great concept, but I don't think perfecting its capture and analysis gets you to a fully efficient use of information about the world (even asymptotically).  The second law of thermodynamics kicks in along the way for starters, but the boundedness of human cognition finishes the job.

Somebody is probably going to point out that evolution already does this (that is, it's the most unobtrusive data collection method of all).  But it takes big numbers and lots of time to do it and the result, though beautiful, is messy.

More to think about here, to be sure.

See Also (2014)

Johnson, Steven. "What a Hundred Million Calls to 311 Reveal About New York." Wired Magazine 11.01.10