A very broad category of data that we’ve talked about before is everything that simply fades from common usage because of changes in convention. This is certain an obvious reason why something might not be there but is in someways the reverse of information that is so commonly used that it is no longer explicitly noted, i.e., evolving standards.
In a subsequent post I’ll talk about a specfic examples such as evolving knowledge and frames of reference in science and the shift in standards for research.
Searching on “Tacit” along with data terms provides a wealth of links to discussions that fit in this domain. However, tacit knowledge has a meaning that is a bit off from what we are going after.
The below site offers a look into research about the culturally derived assumptions we make when designing systems.
Cornell University Faculty of Information Science and Department of Science & Technology Studies
We analyze, design, build, and evaluate computing devices as they relate to their cultural context. We analyze the ways in which technologies reflect and perpetuate unconscious cultural assumptions, and design, build, and test new computing devices that reflect alternative possibilities for technology. We have a focus on reflective design, or design practices that help both users and designers reflect on their experiences and the role technology plays in those experiences.
Our primary focus is the environment; we are exploring the role IT could play to reconfigure our cultural relationship to the environment. We have worked extensively on affective computing, to develop approaches in which the full complexity of human emotions and relationships as experienced by users in central to design (rather than the extent to which computers can understand and process those emotions).
We draw from, contribute to, and mutually inform the technical fields of human computer interaction and artificial intelligence and the humanist/sociological fields of cultural and science & technology studies.
I recently did a Clusty search on “missing data”. The focus of the results turns out to be the statistical science of imputing missing data in experiments, etc.
Knowing about this is important for grounding our work and contrasting it with semantically data missing for reasons such as neuropsych or semantic assumptions. The generic statistical limits on outliers don’t really apply to what we are doing unless the context is bounded. By that I mean we may be in an open-world context, not a clinical measurement.
Missign Data: A Gentle Introduction
http://www.spss.com/missing_value/ http://www.statistics.com/courses/missing http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Missing_Data/Missing.html http://www.fields.utoronto.ca/programs/scientific/04-05/missing-data/
http://www.lshtm.ac.uk/msu/missingdata/index.html —–Original Message—–From: M. David Allen [mailto:firstname.lastname@example.org]Sent: Thursday, July 26, 2007 10:15 AMTo: email@example.com
Subject: Interesting missing value case: neuropsych evaluations
They’re trying to take neuropsych measurements on people with behavioral disorders due to dementia — the disorder causes them to be unable to gather the information.The actual lack of information (or inability to gather
it) is an indicator of the seriousness of the disorder they’re studying.
In many processes, assumptions are made and information is stored in advance of hard facts or the actual event/instance. Afterwards, revision of the data may be flawed, overlooked or not deemed worthwhile. In such circumstances, information can be inaccurate or may have been left out.
An example is names of people attending a seminar. An atendee preregisters himself and one additional colleague, to-be-determined. At the seminar organizers may or may not capture the actual name into their system. A registration sytem might be designed in many different ways to handle this use case.