Information and Knowledge for Data Reuse

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!

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

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)

Factors that Influence Research Methods • The scientific question • The environment of the study • The taxa to be studied • Practical considerations: time, money, and skill

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

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

Understanding Data Using a clinometer to measure tree height Image from: http://www.greenhouse.gov.au/land/bush_workbook_a3/part02/section03/3.6/

Understanding Data Limitations What frog species live here? How many frogs live here?

Identifying Points of Potential Error Images from: http://www.glerl.noaa.gov/seagrant/GLWL/Zooplankton/ Copepods/Copepods.html

Identifying Points of Potential Error Brachionus variabilis Hempel, 1896 Brachionus calyciflorus Pallas, 1766 Images from: http://data.acnatsci.org/biodiversity_databases/rotifer.php/ familyBrachionidae

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.”

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

Information and Knowledge for Data Reuse