This resource was created for the 2024 New Zealand Hydrological Society Data Workshop in Queenstown, NZ. This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package to detect anomalies. This resource consists of 3 example notebooks and associated data files. For more information, see the original resource from which this was derived: http://www.hydroshare.org/resource/451c4f9697654b1682d87ee619cd7924.

Notebooks:
1. Example 1: Import and plot data
2. Example 2: Perform rules-based quality control
3. Example 3: Perform model-based quality control (ARIMA)
4. Example 4: Model-based quality control (ARIMA) with user data

Data files:
Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are:
- temp: water temperature, degrees C
- cond: specific conductance, μS/cm
- ph: pH, standard units
- do: dissolved oxygen, mg/L
- turb: turbidity, NTU
- stage: stage height, cm

For each variable, there are 3 columns:
- Raw data value measured by the sensor (column header is the variable abbreviation).
- Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor').
- Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

There is also a file "data.csv" for use with Example 4. If any user wants to bring their own data file, they should structure it similarly to this file with a single column of datetime values and a single column of numeric observations labeled "raw".