Citing data has been part of the LTER information management culture for several decades, albeit passively. The documentation for most LTER data sets contains information about how to cite or acknowledge the data in publication but journals vary widely on their acceptance and approach to citing data. Large search engines and unique identifiers have now made it possible to realize some real benefits from citing data sets and more journals are coming around to encouraging the idea.
It is a hot summer’s evening in 2050. Laura, a postdoctoral researcher, is leaving the Arizona State University campus in Tempe, Arizona. She climbs into her electric car and commands it to take her home. While the car pulls out onto the street Laura connects to her apartment computer. She explains her latest hypothesis that an ecological trend she thinks she has identified probably began in the early part of the century and possibly hit a tipping point around 2025. Her computer works with her to refine the details, making a couple of suggestions, until together they re-articulate Laura’s research question. Laura asks the computer to look for ecological datasets that she might use to test her hypothesis.
The Information Managers Committee (IMC) – one of the most active committees in the Network – met all day on Sunday, September 9, 2012. Attendance at the morning session was about 40, including site representatives and personnel from the LTER Network Office. In the afternoon, a dozen more participants from the iLTER community joined us.
Testing metadata, reproducing results, and training students for synthesis science
In February, 2011, Victoria C. Stodden (Columbia University) organized a symposium at the American Association for the Advancement of Science (AAAS) Conference that addressed Reproducibility and Interdisciplinary Knowledge Transfer (see http://aaas.confex.com/aaas/2011/webprogram/Session3166.html). That symposium dealt primarily with computational results, but her contention that the difficulty verifying published research results might be “leading to a credibility crisis affecting many scientific fields” sparked a debate on whether ecology data archives could be used to verify research results, and if so, what methods would be most effective for accomplishing this.