Variation Across Time and Space in Grazing within Thunder Basin National Grassland, Wyoming
Additional Funding Sources
The project described was supported by the Pacific Northwest Louis Stokes Alliance for Minority Participation through the National Science Foundation under Award No. HRD-1410465.
Abstract
Data of interest to human-environment systems scientists are often recorded in very different ways in different places and times, resulting in datasets that are complex, messy, and hierarchical. As a result, researchers using these datasets often develop ad-hoc methods for accessing these data for any given project. But ad-hoc methods can be problematic for the advance of science because they are prone to error, difficult to reproduce, and too often obscure the provenance, or source, of the original data. In this project, we developed a workflow to transform highly complex and messy data into a usable system of relational datasets. Our approach is highly automated, reproducible, robust to errors in data-entry, and makes data provenance clear. We applied our workflow to the transformation of decades of hand-recorded data on US Forest Service grazing allotment usage in Thunder Basin National Grassland, Wyoming. Our workflow creates an opportunity to make the use (and reuse) of decades of messy, highly complex, and variable data more systematic and efficient, allowing human-environment systems scientists to confront old questions with new data.
Variation Across Time and Space in Grazing within Thunder Basin National Grassland, Wyoming
Data of interest to human-environment systems scientists are often recorded in very different ways in different places and times, resulting in datasets that are complex, messy, and hierarchical. As a result, researchers using these datasets often develop ad-hoc methods for accessing these data for any given project. But ad-hoc methods can be problematic for the advance of science because they are prone to error, difficult to reproduce, and too often obscure the provenance, or source, of the original data. In this project, we developed a workflow to transform highly complex and messy data into a usable system of relational datasets. Our approach is highly automated, reproducible, robust to errors in data-entry, and makes data provenance clear. We applied our workflow to the transformation of decades of hand-recorded data on US Forest Service grazing allotment usage in Thunder Basin National Grassland, Wyoming. Our workflow creates an opportunity to make the use (and reuse) of decades of messy, highly complex, and variable data more systematic and efficient, allowing human-environment systems scientists to confront old questions with new data.