SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Information and Knowledge for Data Reuse Lessons from Ecology Ann Zimmerman
Dec 20, 2015
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Information and Knowledge for Data Reuse
Lessons from Ecology
Ann Zimmerman
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Ecology
Ecology is a “craft” science Single investigators conduct small scale
studies Data sets are highly diverse Standard methods are difficult to achieve There is a high level of data ownership
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Standards as Distance Spanners
Theodore Porter (1992, 1995)– Quantification as a technology of distance
– Standards as a substitute for trust
Bruno Latour (1999)
– Standard measurements involve a loss of information (reduction)
– Reduction turns local knowledge into public knowledge (amplification)
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Factors that Influence Research Methods
The scientific question The environment of the study The taxa to be studied Practical considerations: time, money, and skill
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Gathering One’s Own Data Helps with Reuse
Ecologists’ experiences as collectors of their own data in the field or laboratory plays an important role in their secondary use of data
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Data Gathering Provides:
The ability to understand data The ability to recognize data limitations The ability to visualize potential points of error A ‘sense’ for data
Image from: http://www.greenhouse.gov.au/land/bush_workbook_a3/part02/section03/3.6/
Using a clinometer to measure tree height
Understanding Data
Images from: http://www.glerl.noaa.gov/seagrant/GLWL/Zooplankton/ Copepods/Copepods.html
Identifying Points of Potential Error
Images from: http://data.acnatsci.org/biodiversity_databases/rotifer.php/familyBrachionidae
Brachionus variabilis Hempel, 1896 Brachionus calyciflorus Pallas, 1766
Identifying Points of Potential Error
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Gaining a ‘Sense’ for Data
Nancy: “When you’re in the field, most of what you learn is not the data points you’re collecting – it’s just that sense.”
Michael: “The more you actually go out and do these things the more critical you are of the data.”
SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN
Relevance of Findings to Settings Outside of Science
Reusing data is hard, and it requires a lot of knowledge
Standardization of methods is only part of the solution to address challenges of data sharing
It’s important to find ways to incorporate articulated tacit knowledge into data sharing systems