About this Project

What are Large Data Sets?

Across science and engineering fields, the analysis and synthesis of large datasets is increasingly common. In many ways, the environmental sciences, including earth science and ecology, are undergoing an "informatics" revolution, with networks of sensors and people generating unprecedented amounts of data at a range of spatial and temporal scales

Both long-term and high frequency datasets are typically large and complex, containing many variables, multiple sites, missing data points, and incorrect sensor readings. Large datasets can be long-term data collected manually over many years. High-frequency data generated by automated sensor-based systems (Schimel & Keller, 2015, Benson et al. 2009), are increasingly being used to measure and record data for multiple parameters at high frequencies (readings every 15 minutes or even more frequently) and over long time spans (years). These sensors provide records of change that are essential research and monitoring tools. Sensor technologies are now used to collect high-frequency data on ecologically relevant variables ranging from soil moisture to stream conditions to correlating animal movements with environmental conditions. From a practical perspective, large datasets are ones for which there is more data than can be easily viewed on a single computer screen, thus necessitating the use of software keyboard commands and graphing as ways to conduct initial explorations of these data. Thus young scientists should have opportunities to learn how to manage, analyze, and interpret large datasets.

Why is working with large datasets important for developing Quantitative Reasoning

Open-ended exploration through the analysis and interpretation of large datasets can have substantial benefits as students explore the stochastic nature of environmental and Earth systems (Brewer and Gross, 2003; Ellwein et al., 2014; Gougis et al., 2016). Using authentic and publicly-accessible online datasets to address real-world questions reinforces the need and rationale for developing quantitative reasoning skills, leading to an increased appreciation for large complex datasets associated with basic environmental monitoring (Ellwein et al., 2014; O'Reilly et al., 2017). In addition, spatially resolved datasets allow students to find place-based data that are meaningful to them, and real-time data allow students to see immediate relevance.

Quantitative reasoning (QR) refers to the ability to interpret data and to reason with numbers in real-world situations (Steen, 2004), and working with large authentic datasets can provide the contextualization needed for meaningful student engagement. Graphing is a key element of QR and scientific literacy because data visualization is a critical step for initial exploration, for fostering scientific knowledge, and for effective communication of complex information (AAAS, 2011). Undergraduate students' ability to comprehend and conceptualize data need improvement and summarizing, condensing, displaying, interpreting, and communicating quantitative data remains a persistent challenge in science

Macrosystems ecology and ecological forecasting

Macrosystems ecology is the study of ecological dynamics at multiple interacting spatial and temporal scales (e.g., Heffernan et al. 2014). For example, global climate change can interact with local land-use activities to control how an ecosystem changes over the next decades. Macrosystems ecology recently emerged as a new sub-discipline of ecology to study ecosystems and ecological communities around the globe that are changing at an unprecedented rate because of human activities (IPCC 2013). The responses of ecosystems and communities are complex, non-linear, and driven by feedbacks across local, regional, and global scales (Heffernan et al. 2014). These characteristics necessitate novel approaches for making predictions about how systems may change to improve both our understanding of ecological phenomena as well as inform resource management.

Forecasting is a tool that can be used for understanding and predicting macrosystems dynamics. To anticipate and prepare for increased variability in populations, communities, and ecosystems, there is a pressing need to know the future state of ecological systems across space and time (Dietze et al. 2018). Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasts are a powerful test of the scientific method because ecologists make a hypothesis of how an ecological system works; embed their hypothesis in a model; use the model to make a forecast of future conditions; and then when observations become available, assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated. Forecasts that are effectively communicated to the public and managers will be most useful for aiding decision-making. Consequently, macrosystems ecologists are increasingly using ecological forecasts to predict how ecosystems are changing over space and time. Our interdisciplinary team is developing flexible classroom modules that Each module.

The macrosystems modules introduce undergraduate students to the core concepts of macrosystems ecology and ecological forecasting. They utilize long-term, high-frequency, and sensor-based datasets from diverse sources, including the Global Lakes Ecological Observatory Network, the United States Geological Survey, the Long Term Ecological Research Network, and the National Ecological Observatory Network. Each module can be adapted for use in introductory, intermediate, and advanced courses in ecology and related fields, in order to enhance students' understanding of macrosystems ecology and ecological forecasting, their computational skills, and their ability to conduct inquiry-based studies.