Engaging students with real-time 'big data' from automated environmental sensors

Project Title: Engaging students with real-time 'big data’ from automated environmental sensors

Project Lead's Name: Michael J Vanni

Project Lead's Email: vannimj@MiamiOH.edu

Project Lead's Phone: 513-529-3192

Project Lead's Division: CAS

Primary Department: Biology

Other Team Members and their emails:

  • David Berg, Biology/Biological Sciences, bergdj@MiamiOH.edu
  • Annette Bollman, Microbiology, bollmaa@MiamiOH.edu
  • María González, Biology, gonzalmj@MiamiOH.edu
  • Bartosz Grudzinski, Geography, grudzibp@MiamiOH.edu
  • Jonathan Levy, IES/Geology & Env Earth Sciences, levyj@MiamiOH.edu
  • Karsten Maurer, Statistics, maurerkt@MiamiOH.edu
  • Mary Henry, Geography, mary.henry@MiamiOH.edu
  • David Russell, Biology, russeld@MiamiOH.edu
  • Craig Williamson, willia85@MiamiOH.edu

List Departments Benefiting or Affected by this proposal:

  • Biology
  • Microbiology
  • Geography
  • Geology & Environmental Earth Sciences
  • Institute for the Environment and Sustainability
  • Statistics

Estimated Number of Under-Graduate students affected per year (should be number who will actually use solution, not just who is it available to): 200

Estimated Number of Graduate students affected per year (should be number who will actually use solution, not just who is it available to): 30

Describe the problem you are attempting to solve and your approach for solving that problem: Scientific data sets are growing rapidly in size. In the environmental sciences, this growth is largely driven by an increased use of automated sensors, which can collect large amounts of data at high frequency. A couple of national initiatives illustrate these trends. The National Ecological Observatory Network (NEON, http://www.neonscience.org), funded by the National Science Foundation (NSF), is a continental scale ‘geographically distributed observatory’ consisting of next-generation sensor networks and instrumentation in all climate regions in the USA. The Global Lake Ecological Observatory Network (GLEON, http://gleon.org) is similar, but focused on lakes. One goal of both NEON and GLEON is to provide high frequency data on environmental parameters (temperature, carbon dioxide, oxygen, etc.) that can be used to help forecast and solve environmental problems such as climate change, land use change, and water quality degradation.

A group of Miami faculty has been training students to collect, use, and analyze large environmental data sets derived from automated sensors, with the help of previous Tech Fee awards and other funding. We request funds here that will allow us to install necessary upgrades to our environmental monitoring systems so that students can continue to be trained in these areas, and specifically to provide students access to real-time data. We have two automated sensor systems, both at Hueston Woods State Park. Our ‘on-land’ system is a weather station located on the shore of Acton Lake, which provides data every 15 minutes on air temperature, wind speed and direction, precipitation, solar radiation, relative humidity, and barometric pressure. In addition, we have an ‘underwater’ system installed on a buoy on Acton Lake. The buoy houses a ‘sonde’ that provides data every 15 minutes (April – October) on water temperature, dissolved oxygen concentration, conductivity, chlorophyll (a measure of the concentration of algae) and colored dissolved organic matter. Both the weather station and lake buoy have been in operation since April 2010, and were initially funded by an Integrated Education and Research Training Grant (IGERT) from NSF. In 2017, we replaced the original lake sonde, with funding from a 2016 Tech Fee grant.

Our request here is to upgrade both monitoring systems to provide real-time data for student use. Both monitoring systems are already equipped with a data logger, modem, and cell phone connection that allow for real-time data collection and transmission. In addition, we already have a website and an smartphone app, which can be used to view real-time data from these sites. The website and app are hosted by NexSens, a company in the Dayton area that specializes in real-time environmental data collection; the fee for the website is $900 per year and is paid for by the Department of Biology (thus we do not request Tech Fee funds for this). The intent of our last Tech Fee grant was for both archived and real-time data to be used in various environmentally-oriented classes, and for student projects. (Data going back to 2010 are archived in a data base maintained by Tera Ratliff, Research Associate for the Center for Aquatic and Watershed Sciences, CAWS, and are freely available). The data generated by these systems have, in fact, been used in various classes and by several undergraduate and graduate students for research projects. However, we are no longer able to reliably transmit real-time data because our data loggers and modems are out of date (they are the originals purchased in 2010). The main reason for this is that Verizon is converting all of its cell towers to 4G, whereas the modems on the weather station and the lake sonde are 3G and thus cannot transmit 4G data. Verizon plans to convert all towers to 4G sometime in 2019, so it is critical that we modernize our modems so they can handle such data. Our data loggers are also very old (also originals from 2010) and for both systems the modems are housed within the data loggers. Finally, upgrading our data logger for the lake sonde requires that we purchase a new buoy and solar panels (which power the sensors) because the new data loggers are designed specifically to be housed with the new buoys. Therefore, we request funds that will allow us to upgrade 1) the data logger/modem for the weather station, 2) the data logger/modem for the lake sonde, 3) the buoy that houses the lake sonde.

Upgrading our sensor systems will allow us to achieve our goal of providing real time environmental data to Miami students. Although one could argue that real-time environmental data are already available from sites all over the world, our systems are valuable for student training because they are from local sites. Thus, our sensors provide excellent data on local ecosystems, which can be used by students either as stand-alone data sets, or in conjunction with field-based (‘on the ground’) research they are conducting at these sites.

How would you describe the innovation and/or the significance of your project: Many environmental variables change rapidly over time, and these dynamics are important for understanding ecosystems and how they are impacted by humans. The sensors allow students to quantify these dynamics, which is not possible using other methods. Our current set-ups for the weather station and lake sonde already provide excellent high-frequency data, but we can no longer transmit these data in real time. Upgrading our data loggers and modems will permit real-time data transmission, which will allow students to engage in innovative class exercises and research projects. For example, our local weather changes rapidly due to frontal systems, high winds, thunderstorms, and even hurricanes in the Gulf of Mexico that bring rain to our area. In lakes, the concentration of dissolved oxygen changes rapidly on a daily cycle, reflecting the oxygen sources (diffusion from the atmosphere an photosynthesis) and oxygen consumption (via organism respiration). Analyses of real-time data will allow students to investigate these and other dynamics, and thus improve their understanding of ecosystems and their ability to work with large data sets.

The real-time data we generate from these sensors will be used in class or lab exercises in numerous courses across several departments (Table 1). We have identified 12 courses in 6 departments/programs (BIO, GEO, GLG, IES, MBI and STA), with a total annual enrollment of over 800 students, which will utilize the real-time data. In all likelihood, additional courses will use the data once more people are aware of the data. Courses include large classes such as Earth’s Physical Environment (GEO 121), which is offered in multiple sections to 150-200 students every semester, to advanced courses such as Microbial Ecology (MBI 475/575), which is offered to ~18 students on average each year.

The real-time data will also be used by undergraduate and graduate students for their research projects (Table 2). Nine faculty mentors have stated that their students will use the data in research projects; again, this number is likely to be larger once more faculty and students become aware of the data. Several undergraduate and graduate students have already used data from our sensors in their research projects, and access to real-time data will certainly increase this number.

In total we expect 150-200 undergraduate students to use the data each year as part of class exercises (although the combined enrollments of relevant courses listed in Table 1 exceeds this number, not all sections of all courses will use the data every year). In addition, we expect that 10-20 undergraduates will use the data for research projects each year. Finally, we expect about 30 graduate students to use the data annually, in courses as well as in thesis/dissertation research.

Miami students who have experience using sensors and handling large amounts of real-time data, whether from class exercises or their own research projects, will be highly competitive for graduate school and for jobs requiring experience in data analysis. For example, construction is nearly complete on the National Ecological Observatory Network (NEON), and they are hiring for many positions that require knowledge of advanced sensors and quantitative skills.

How will you assess the success of the project: Student usage and learning will be assessed in a few ways. In class exercises, we will survey students before and after specific assignments and ask them several questions regarding the pros and cons of the exercise, and how use of the sensors and analysis of data contributed to their learning. For example, we will ask to what extent the exercise improved their skills at analyzing large data sets and in using sensor technology. In addition, we will test students' abilities in certain skills before and after exercises. We will also collect data on the number of courses and students (in both courses and independent research projects) using the sensor data, as well as presentations and publications by students who use the sensors. Long-term, we can assess the project by the number of students trained and their products. For example, several students who used the sensors we purchased in our previous Tech Fee grant have presented their work in posters and are authors on publications that present these data.

Total Amount Requested: $14,187.34

Budget Details: We request funds to upgrade our weather station ($3,484) and lake sonde ($10,703.34), and we attach a quote from Fondriest Environmental detailing the expenditures. Upgrades to the weather station include funds for a new datalogger, solar panels, and associated components. Funds to upgrade the underwater lake sonde include are for a new datalogger ($2,995) and a new buoy with solar panels ($5,995), and associated components. Fondriest, located in the Dayton area, will provide on-site system support and training, at no cost (see quote).

Is this a multi-year request: No

Please address how, if at all, this project impacts any of Miami's BCSAE, 2020, or divisional plans: One of the goals of the Boldly Creative Strategic Academic Enrichment Initiative (BCSAE) is to “offer an education that prepares students with the knowledge to succeed at the cutting edge of a technologically and data-driven world, while instilling the traditional liberal education capacities to think critically, communicate effectively…” (https://miamioh.edu/academic-affairs/faculty-affairs/boldly%20creative/index.html). We feel that our project will enhance our students’ abilities to understand and manipulate data. Class exercises and student research projects will certainly foster critical thinking by teaching students to use large data sets to study and solve environmental problems.