Data quality, and accuracy, is a fundamental concern when utilizing citizen science data. Although many methods can be used to assess quality, and accuracy, these methods may not be sufficient to qualify citizen science data for widespread use in scientific research. While Data Fitness For Use (DFFU) does not provide a blanket assessment of data quality, it does assesses the ability of the data to be used for a specific application, within a given area. The STAAq (Spatial, Temporal, Aptness, and Accuracy) assessment was developed to assess the fitness for use of citizen science data, this assessment can be used on a standalone dataset, or be used to compare multiple datasets.
The STAAq assessment was tested by comparing citizen science data to two authoritative datasets. The citizen science data used in this assessment was collected by volunteers of the Map of Life- Denali project, which is a tourist-centric citizen science project developed through a partnership with Arizona State University, Map of Life at Yale University, and Denali National Park and Preserve. Volunteers use the offline version of the Map of Life mobile application to record their wildlife, insect, and plant observations while touring the park.
The STAAq assessment was tested in two example scenarios. Both scenarios involved utilizing these data for species distribution modeling within Denali National Park and Preserve. The first scenario examined bear distribution, during the summer time in the park. The second scenario examined annual caribou distribution in the park. Data from three different sources– Map of Life- Denali, Ride Observe and Record (ROAR), and NPS wildlife surveys– were compared to determine which dataset was the most “Fit For Use” in each scenario. These datasets were compared and ranked according to how well they performed in each of the components of the STAAq assessment. These components include spatial scale, temporal scale, aptness, and application. The Map of Life- Denali data and the ROAR program data were most “Fit For Use” in the first scenario, involving summer bear distribution, while the NPS wildlife survey data was the most “Fit For Use” for the second scenario, annual caribou distribution in Denali. This data fitness for use assessment provides a means to assess data fitness, instead of simply data quality, for citizen science data sets.