SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN

Information and Knowledge for Data Reuse

Lessons from Ecology

Ann Zimmerman

What do ecologists and organizations have in common when it comes to sharing data? A lot!

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

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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

Understanding Data Limitations

What frog species live here? How many frogs live here?

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

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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