Cite this dataset as
Tso, C.-H.M. (2020). State tagging application for environmental data quality assurance. NERC Environmental Information Data Centre. (Model). https://doi.org/10.5285/1de712d3-081e-4b44-b880-b6a1ebf9fcd8
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This model code is available under the terms of the Open Government Licence 
State tagging application for environmental data quality assurance
DISCLAIMER: Publication of this model code by the EIDC does not signify any endorsement or approval. By accessing and using the resource, you acknowledge that it is entirely at your own risk and you are solely responsible for any loss or liability that may arise.
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.
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
View record as
Format
R
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.
Related
Citations
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
Lancaster
Lancashire
LA1 4AP
UNITED KINGDOM
enquiries@ceh.ac.uk
Lancaster
Lancashire
LA1 4AP
UNITED KINGDOM
Author
https://orcid.org/0000-0002-2415-0826 Other contacts
- Rights holder
-
UK Centre for Ecology & Hydrology
- Custodian
-
NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk
- Publisher
-
NERC Environmental Information Data Centreinfo@eidc.ac.uk
Additional metadata
- Topic categories
- environment
geoscientificInformation - Keywords
- cluster analysis , Data analytics , Data science , Environmental informatics , Environmental Monitoring , Modelling , Quality assurance , R shiny , UK-SCAPE
- Funding
- Natural Environment Research Council Award: NE/R016429/1
- Last updated
- 08 February 2024 17:24