Tso, C.-H.M.

State tagging application for environmental data quality assurance

Screenshot This R application is an implementation of state tagging approach for improved quality assurance of environmental data. The application returns state-dependent prediction intervals on input data. The states are determined based on clustering of auxiliary inputs (such as meteorological data) made on the same day. The method provides contextual information to assess the quality of observational data and is applicable to any point-based, daily time series observational data.

To use this application, the user will need to input two separate csv files: one for state variables and the other for observations.

This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

Publication date: 2020-04-22

Provenance & quality

This application comprises the source code of a R shiny app (app.R) that implements the method described in https://doi.org/10.3389/fenvs.2020.00046. It can be run on any machine with R and the required packages installed. The clustering is based on the k means function in the {stats} package in R. It has been tested up to R version 3.5.3. More testing info can be found in session_info.txt.


Tso, C-H. M.; Henrys, P; Rennie, S.;, Watkins, J. (2020). State tagging for improved earth and environmental data quality assurance. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2020.00046
Tso, M., Henrys, P., Rennie, S., & Watkins, J. (2020). State tagging for improved earth and environmental data quality assurance. https://doi.org/10.5194/egusphere-egu2020-4613

Correspondence/contact details

Dr. Chak Hau Michael Tso
UK Centre for Ecology & Hydrology
Lancaster Environment Centre, Library Avenue, Bailrigg


Tso, C.-H.M.
UK Centre for Ecology & Hydrology

Other contacts

NERC EDS Environmental Information Data Centre
NERC Environmental Information Data Centre
Rights Holder
UK Centre for Ecology & Hydrology

Additional metadata

Topic categories
Environment , Geoscientific Information
cluster analysis,  Data analytics,  Data Labs Data science,  Environmental Change Network Environmental informatics,  Environmental Monitoring,  Lakes Monitoring Modelling Quality assurance,  R shiny,  UK-SCAPE
Natural Environment Research Council Award: NE/R016429/1
Last updated
18 May 2022 12:37