1. Water quality data - providing information for management
Philipp Saile
UNEP GEMS/Water Data Centre
International Centre for Water Resources and Global Change

2. Data Management in Monitoring Programme Design
Set objective
Preliminary surveys
Monitoring design
Field operations
Quality control and assurance
Programme redesign
Laboratory operations
Data storage
Data analysis and presentation
Interpretation and assessment
Management action

3. Water quality monitoring data management
Plan
Collect
Process
Analyze
Preserve
Publish/
Report
Describe
Manage Quality: Quality Control and Assurance
Backup & Secure

4. Data Management Planning
Data collection
Responsibilities
Information about data and metadata
Schedule and Budget
Quality management
Usage, users and outputs
Storage & preservation
Data management plan
What data are you collecting? Do the data already exist?
How will data be collected?
Electronic vs analog
In-house vs contractors
Who is responsible for managing the data and the data management plan?
Clearly define responsibilities for data management, collection, metadata
Information about data and metadata
What format will be used to collect data?
Existing standards?
Different formats for data archiving, publishing and reporting?
What metadata will be compiled, which format and standard?
Version control
Schedule and budget:
Program start, end, lifetime
Available funding
Data quality control and assurance
How will data be checked?
IN-house vs external
Is all data checked or random samples?
What are the uses for the data, who are the users and what outputs are required to meet different stakeholder objectives
Data access regulations
Will data be published?
=> Effects on data storage, access and protection
Storage and preservation
Is data stored in different places and media?
Who is handling backups, archiving
Are backups checked and by whom?
How long are backups being kept?
Plan for data archiving?

5. Quality management: Quality Control and Assurance
Plan
Collect
Process
Analyze
Preserve
Publish/
Report
Describe
Manage Quality: Quality Control and Assurance
Backup & Secure

6. Data Quality Control and Assurance
Sampling & Analytical QA/QC
Data QA/QC
QA/QC before collection
QA/QC during data entry
QA/QC after data entry
Data quality assurance is an important requirement for subsequent data analysis and use and depends on proper sampling & analytical quality assurance. It comprises all activities that prevent errors from entering or staying in a data set. These activities ensure the quality of the data before it is collected, entered, or analyzed, and monitoring and maintaining the quality of data throughout the program.
We will look at different activities before data collection, during and after data entry.

7. QA/QC Before Collection
Define & enforce standards
Formats
Codes & Controlled Vocabularies
Units of Measure
Analytical methods
Metadata
Design data storage “fit for purpose”
Minimize number of times items that must be entered repeatedly
Use consistent terminology
Atomize data: one cell per piece of information
Assign responsibility for data quality
Be sure assigned person is educated in QA/QC
The remainder of the module will cover best practices for quality control and quality assurance for the different stages of a research project.
First, before data collection, a researcher should think about defining and enforcing standards that will be used during the project. Consider formats that will be used for the data tables or data entry forms. Also, if abbreviations or codes are used, they should be defined up front. Measurement units should also be specified and relevant metadata should be identified before collection.
Second, you should assign responsibility for data quality before collection begins. Ideally, the person responsible for data quality assurance and data quality control is the person collecting the data, and is educated in quality control and assurance methods.

8. QA/QC During Data Entry
Double entry
Data keyed in by two independent people
Check for agreement with computer verification
Document changes to data
Avoids duplicate error checking
Allows undo if necessary
=> Version control
Consider using techniques that help eliminate mistakes during data entry. Examples are using Double data entry, where non-digital data are keyed in by two people independently. Differences in entries can then be detected via computer programs and examined further for mistakes. Another way to reduce data entry error is to record yourself reading off the data, and then transcribe it from the recording. You may also use a text-to-speech program reads the data to you while you type it into the computer.
CC image by weskriesel on Flickr

9. Basic data checks QA/QC After Data Entry Transcription error checks
Wrong decimal position
Double entries
Omission error checks
Validity & feasibility checks
Outliers
Major ion balance error
Statistical feasibility
Analytical feasibility
Transcription errors such as incorrect positioning of the decimal point or
transcription of data relating to the wrong sample, double reporting
Ommission errors e.g nitrate is often ommitted
Validity and feasibility checks
Outlier checks:
Identify potential data contamination
Graphical checks: Normal probability plots, regression, scatter plots
Maps
Statistical tests
Statistical feasibility: Totals must be greater than component parts
Analytical feasibility: Result need to be in analytical range of method applied
Major ion balance

10. Data Storage and Preservation
Plan
Collect
Process
Analyze
Preserve
Publish/
Report
Describe
Manage Quality: Quality Control and Assurance
Backup & Secure

11. Data collection and storage
Information System
Database
Spreadsheet
Textfile
Data analysis
Reporting/sharing
Data storage
Data processing
Data entry*
Data exchange
Data archiving
Features
Complexity

12. Backups Archiving Data rescue Data Preservation
Periodic snapshots of data in case current version is destroyed or lost
Stored for near to near-long-term
Backup control checks!
Archiving
Used to preserve data for historical reference or potentially during disasters
Final version of data stored for long-term
Frequency: major milestones and
Data rescue
Convert historical data in usable formats

13. Data Analysis – Overview
Analysis of data distribution
Testing assumptions about data sets
Specifying magnitudes and variability
Estimating reliability of statistics
Comparison of data sets
Associations between data sets
Identifying trends and seasonality
Testing theories relating to the water quality data
Analysis of data distribution

14. Data Analysis – Analysis of data distribution
Histogram
Cumulative frequency
distribution
Test for normal distribution
Chi-Square
G-Test
Kolmogorov-Smirnov (n > 50)
Shapiro-Wilk (n < 50)

15. Data Analysis – Specifying magnitudes and variability
Descriptive Statistics
Mean
Standard deviation
Coefficient of variation
Percentiles
Boxplots
Parameter variability
Temporal variability
Spatial variability

16. Data analysis - Comparison of data sets
Scatter Plots
Statistical tests to detect
significant differences
Student‘s t-test
Mann-Witney U-test
Kruskal-Wallis test
Student t-test can be used in water quality management to test for compliance with water quality objectives (or standards or
guidelines) or for assessing the effectiveness of water pollution control measures.
E.g compares different time periods of same parameter,

17. Statistical software packages
Data Analysis Tools
Spreadsheet software
Excel, OpenOffice
Statistical software packages
SPSS, Matlab, SAS
R, Octave, (Python)

18. Data backup and archival at defined intervals including checks
Summary
Data management planning is essential to ensure data integrity, to maximize use of data and to meet information requirements of monitoring program
Plan and ensure quality control and assurance measures through entire data life cycle
Data backup and archival at defined intervals including checks
Selection of analyis methods depending on underlying data and scope