Dataset from the Upper Mississippi River Restoration Program (1993-2019) to reconstruct missing data by comparing interpolation techniques
June 27, 2023
The dataset accompanies the scientific article, "Reconstructing missing data by comparing interpolation techniques: applications for long-term water quality data." Missingness is typical in large datasets, but intercomparisons of interpolation methods can alleviate data gaps and common problems associated with missing data. We compared seven popular interpolation methods for predicting missing values in a long-term water quality data set from the upper Mississippi River, USA.
Citation Information
Publication Year | 2023 |
---|---|
Title | Dataset from the Upper Mississippi River Restoration Program (1993-2019) to reconstruct missing data by comparing interpolation techniques |
DOI | 10.5066/P9ZR7BWL |
Authors | Danelle M Larson, Wako Bungula, Amber Lee, Alaina Stockdill, Casey McKean, Frederick Miller, Killian Davis, Richard A Erickson, Enrika J Hlavacek |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Upper Midwest Environmental Sciences Center |
Rights | This work is marked with CC0 1.0 Universal |
Related
Reconstructing missing data by comparing interpolation techniques: Applications for long-term water quality data
Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a lon
Authors
Danelle M. Larson, Wako Bungula, Amber Lee, Alaina Stockdill, Casey McKean, Frederick Miller, Killian Davis, Richard A. Erickson, Enrika Hlavacek
Danelle Marie Larson, PhD
Research Ecologist
Research Ecologist
Email
Phone
Richard Erickson, PhD
Research Ecologist
Research Ecologist
Email
Phone
Related
Reconstructing missing data by comparing interpolation techniques: Applications for long-term water quality data
Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a lon
Authors
Danelle M. Larson, Wako Bungula, Amber Lee, Alaina Stockdill, Casey McKean, Frederick Miller, Killian Davis, Richard A. Erickson, Enrika Hlavacek
Danelle Marie Larson, PhD
Research Ecologist
Research Ecologist
Email
Phone
Richard Erickson, PhD
Research Ecologist
Research Ecologist
Email
Phone