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Harvard Forest REU Symposium Abstract 2011
- Title: Using Little-JIL to Model Complex Scientific Processes
- Author: Andrew Kaldunski (Ripon College)
- Abstract:
A scientific data analysis (or process) must be clearly definable by a list of steps or activities performed. A scientific process should also be reproducible, i.e. it should return the same results if the same input data are used. Scientific processes encapsulate every step used from the creation of data to the analysis of data. At Harvard Forest, we are tracking the water flux through the forest to better understand the interactions involving water and gases between the atmosphere and the forest. Measuring stream discharge is part of tracking the water flow. We used Little-JIL, a graphical language that defines a process with agents, to define the process of measuring stream discharge. Using Little-JIL, we were able to model the stream discharge process precisely. This may lead to a reproducible automation of stream discharge data processing throughout the forest. Little-JIL uses visual representations of steps to direct the agents that perform the scientific process. In this case the agents range from the collection of data, whether done by a human or by an electronic device, to the calculations needed to calculate the discharge of streams. The steps direct the execution of agents and the agents perform the desired tasks in the process. The current stream discharge model created in Little-JIL has steps that are optional, steps that are performed an unpredictable number of times, steps that can be executed during or after the process, and steps that depend on other steps. Modeling stream discharge in Harvard Forest with Little-JIL displays Little-JIL’s ability to represent a complex scientific process.
- Research Category: Ecological Informatics and Modelling
