Posts tagged ‘Collaborations’

Nov 17

Near Real-Time Data Analysis

We are entering an era of cheap data. Sensor technology has advanced to the point where it has become easy to collect large amounts of measurement data at high spatiotemporal resolution.

Hydroserver map screen: Using an off-the-shelf open source informatics system like Hydroserver kept us from reinventing what’s already out there, but allowed flexibility to program to our own needs.

We are now to the point where we have gigabytes worth of data on soil moisture, plant canopy processes, precipitation, wind speed, and temperature, but the amount of data is so overwhelming that we are having a difficult time dealing with it. The cost of measurement data is dropping so quickly, people are forced to change from a historical mindset where they analyzed individual data points to the mindset of turning gigabytes of data into knowledge.

Because Bioinformatics students are used to working with DNA data, they understand how to write computer programs that analyze large amounts of data in near real-time.

One approach suggested by my colleague Rick Gill, a BYU Ecologist, is to collaborate with bioinformatics students. Because they are used to working with DNA data, these students understand how to write computer programs that analyze large amounts of data in near real-time. Rick came up with the idea to tap these students’ expertise in order to analyze the considerable information he anticipates collecting in our Desert FMP Project, an experiment which will use TEROS 21 and SRS sensors to determine the role of varying environmental and biological factors involved in rangeland fire recovery.

Rick and I are predicting that near real-time data analysis will give us several advantages. First, we need readily available information so we can tell that sensors and systems are working at the remote site. Large gaps in data are common for sites that aren’t visited often, and sensor failures are missed when data are collected but never analyzed. With our new approach, all data are databased instantly, and the results are visualized as we go. Not only that, we’ll be able to control what’s being analyzed as we see what’s happening. We can tell the bioinformatics students what we need as we begin to see the results come in. If we see important trends, we can assign them to analyze new data that may be relevant right away.

These techniques have the potential to help scientists from all disciplines become more efficient at collection and analysis of large data streams. Although we’ve started the process, we have yet to determine its effectiveness. I will post more information as we see how well it is working and as new developments arise.

Watch Dr. Gill’s data analysis webinar: Finding Insights in Big Data Sets

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Nov 10

Solving the Problem of Disappearing Science Lab Technicians

One of the hardest issues university researchers face today is the lack of funding for lab technicians. Although it’s frustrating that universities are no longer able to support this type of personnel, can technology close the gap? This is a question we’ve tried to answer in our Desert FMP project in collaboration with BYU.

Source: Simplyhired.com. Job listings for Science Lab Technicians have decreased 38% from March 2013-March 2014

I was talking to my colleague, Rick Gill, several weeks ago, and he had this to say about the disappearance of the previously indispensable lab technician: “We have fewer people in the lab, and the people we have are more expensive. We need to be deliberate in how we use their time. If we can make the entire system more efficient using technology, we’ll use the people we have in a way that is meaningful. In ecology right now, one of the things that we’re beginning to recognize is that the typical process where the lab tech would go out and take ten samples and average them is not what’s interesting. What’s interesting is when it’s been dry for four weeks, and you get a big rain event. This is because the average for four weeks is really low for almost all processes, but the data three days after it rains swamps the previous four weeks. So the average condition means almost nothing in terms of the processes we’re studying for global change. We need technology to take the place of the technician who would be monitoring the weather and trying to guess when the big events will occur.”

To capture these pulses in the Desert FMP project, we’re using a continuous monitoring system that communicates feedback directly to us as the principal investigators. Using advanced analysis techniques, we can painlessly assure that data are being collected properly and important events are never missed. Although we don’t have a technician, the goals of the project are still being met.

What do you think? How have you dealt with the disappearance of the lab tech?

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Nov 1

Spectral Reflectance and Water Content in the Wasatch Plateau Experiment

We chose to collaborate with Brigham Young University in an experiment on the Wasatch Plateau in 2009 because a scientist friend of ours had been working in that area the previous five years, and he noticed there were big grazing responses. The plants growing in the long-term grazed areas were all drought tolerant, while ungrazed plots had plants that were often found only in wetter areas. The only difference was the fence that kept sheep on one side and not on the other. The big question was: how does water influence plants in this ecosystem that we understand relatively well? The story had always been the influence of grazers, when in fact, maybe the indirect consequence of grazing was mediated by water.

The Wasatch Plateau above Ephraim Canyon, UT, USA.

METER donated some sensors in order to set up an experiment where we changed the amount of water in various plots of land. We had rain exclusion plots, and we had treatments where we collected all incoming rainfall and reapplied it either once a week or every three weeks. This allowed us to say to what extent this system was controlled by water during the growing season. To do this, we took measurements with our prototype NDVI Spectral Reflectance Sensor to measure canopy greenness. We also used our prototype volumetric water content sensors to measure soil moisture (this was a few years ago and the sensors were prototypes at the time). Using these sensors, we found that water is critical in a system people have dismissed as being climate-controlled because it’s at the top of a mountain.

A very early prototype of a NDVI sensor measuring canopy greenness in experimental plots on the Wasatch Plateau.

It turns out the amount and timing of precipitation makes a big difference. We were able to directly connect plant survival, not just to the grazing treatment, but to the actual amount of water that was in the soil. Also, using continuous NDVI data, we were able to look closely at the role of grazing on plant canopies. When we looked at our NDVI data, we were able to see a seasonal signal, not just a single snapshot sample in time. So by having the richer data from the data loggers, we obtained a more nuanced understanding of the impact of land use on these important ecological processes.

One of the mistakes we made was failure to include redundancy in the system. We only had two replicates, so when one of them went down we ended up having just one little case study. However, that mistake gave us new ideas on how to set up a better system using the right sensors for the job, and it generated a new idea on how to get real-time analysis of data. In our new Desert FMP project, we have a much better-replicated system where more is invested in the number of sensors that we’re putting out. Each treatment combination will have five to ten water potential sensors. We are also developing a system where we can analyze data in real-time, so this time we will know when a sensor goes out if a student accidentally kicks it.

For more details on the Wasatch Plateau Experiment, watch for our published paper that we’ll link to when it comes out.

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