This file includes an annotated R script used for data analysis for this project. Data files called in this script are also uploaded. Annotations within the script equate to metadata. This dataset is associated with the following publication: Wick, M., T. Angradi, M. Pawlowski, D. Bolgrien, R. Debbout, J. Launspach, and M. Nord. Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(5): 1469-1478, (2020).

We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

These data were exported from Deep Lake Explorer and include metadata about the subjects (video clips) analyzed in DLE. A data dictionary is included as a separate sheet within this spreadsheet. See R script for data analysis. This dataset is associated with the following publication: Wick, M., T. Angradi, M. Pawlowski, D. Bolgrien, R. Debbout, J. Launspach, and M. Nord. Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(5): 1469-1478, (2020).

This file contains the individual classifications done by users on Zooniverse for the video clip subjects. Metadata is in the file as a separate spreadsheet. See R script for data analysis. This dataset is associated with the following publication: Wick, M., T. Angradi, M. Pawlowski, D. Bolgrien, R. Debbout, J. Launspach, and M. Nord. Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(5): 1469-1478, (2020).

This sheet correlates the subject_id (assigned by Zooniverse) to the SiteID, Video filename and DropSiteID. See R script for analysis. This dataset is associated with the following publication: Wick, M., T. Angradi, M. Pawlowski, D. Bolgrien, R. Debbout, J. Launspach, and M. Nord. Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(5): 1469-1478, (2020).

These data were exported from Deep Lake Explorer and include all comments made by volunteers on Deep Lake Explorer's Talk forum prior to 9/12/2019. A data dictionary is included as a separate sheet within this spreadsheet. See R script for data analysis. This dataset is associated with the following publication: Wick, M., T. Angradi, M. Pawlowski, D. Bolgrien, R. Debbout, J. Launspach, and M. Nord. Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(5): 1469-1478, (2020).