Super size my ecology!

Well, I’ve finally made it into a news release for the University of Wisconsin:

The University of Wisconsin-Madison, home of pioneering ecologists who studied lakes, forests, wetlands and prairies, is playing a key role in the next wave of ecological research: large teams of scientists confronting the dilemma of a changing climate on a shrinking planet.

The article summarizes work of two NSF Macrosystems funded projects, GLEON and PalEON (obviously, borrowing on the gold standard of Neon Inc.) and features a quote from me that sounds like something I might have said slightly tongue in cheek: “We’re pollen whisperers,” erm, yeah. . .

Figure 1.  That time that Science Hall was in a very famous movie.
Figure 1. That time that Science Hall was in a very famous movie.

Regardless, I like the news releases’ thread between the history of the University of Wisconsin and our modern work.  As put by Jack Williams:

“Reid Bryson was one of the first to look seriously at climate change, and John Kutzbach produced a groundbreaking set of studies identifying the key causes of past climate change. Thompson Webb, my advisor at Brown, got his Ph.D. here in Madison in 1971 and has been studying paleoclimate ever since.”

Working in Science Hall I’ve always felt well connected to the history of the University, even if I’m only here temporarily. Reid Bryson, John Curtis (Bray-Curtis anyone?), Tom Webb III, and many other people central to the intersection of climate and ecology, shared these halls at some point in the last century. The walls have stayed the same but the ideas have flowed on like pine pollen on a spring breeze.

Much of our work, and the work I’m quoted on (pollen quote aside), has been deeply influenced by David Mladenoff and his lab group who have been working with Public Land Survey data for Wisconsin and the Upper Midwest for some time now.  He’s been an invaluable collaborator, even if he’s not in Science Hall.

Anyway, back to prepping for our June Pollen/R course at UMaine. I’ll update soon with some R tricks that experienced users wish they had learned early on.

EarthCube webinars and the challenges of cross-disciplinary Big Data.

EarthCube is a moon shot.  It’s an effort to bring communities broadly supported through the NSF Geosciences Directorate and the Division of Advanced Cyberinfrastructure together to create a framework that will allow us to understand our planet (and solar system) in space and in time using data and models generated across a spectrum of disciplines, and spanning scales of space and time.  A lofty goal, and a particularly complex one given the fragmentation of many disciplines, and the breadth of researchers who might be interested in participating in the overall project.

To help support and foster a sense of community around EarthCube the directorate has been sponsoring a series of webinars as part of a Research Coordination Network called “Collaboration and Cyberinfrastructure for Paleogeosciences“, or, more simply C4P.  These webinars have been held every other Tuesday from 4 – 5pm Eastern, but are archived on the webinar website (here).

The Neotoma Paleoecological Database was featured as part of the first webinar.  Anders Noren talked about the cyber infrastructure required to support LacCore‘s operations, and Shanan Peters talks about an incredible text mining initiative (GeoDeepDive) in one of the later webinars.

Fig 1. The flagship for the Society for American Pedologists has run aground and it is now a sitting duck for the battle machine controlled by the Canadian Association for Palynologists in the third war of Data Semantics.

It’s been interesting to watch these talks and think about both how unique each of these paleo-cyberinfrastructure projects is, but also how much overlap there is in data structure, use, and tool development.  Much of the struggle for EarthCube is going to be developing a data interoperability structure and acceptable standards across disciplines.  In continuing to develop the neotoma package for R I’ve been struggling to understand how to make the data objects we pull from the Neotoma API interact well with standard R functions, and existing R packages for paleoecological data.  One of the key questions is how far do we go in developing our own tools before that tool development creates a closed ecosystem that cuts off outside development?  If I’m struggling with this question in one tiny disciplinary nook, imagine the struggle that is going to occur when geophysicists and paleobotanists get together with geochonologists and pedologists!

Interoperability of these databases needs to be a key goal.  Imagine the possibilities if we could link modern biodiversity databases with Pleistocene databases such as Neotoma, and then to deep time databases like the Paleobiology Database in a seamless manner.  Big data has clearly arrived in some disciplines, but the challenges of creating big data across disciplines is just starting.