The document outlines eight common mistakes made in research data collection and provides solutions to avoid them. Key issues include insufficient metadata collection, improper sensor installation, and failure to regularly review and back up data, which can compromise experimental outcomes. Recommendations include using cloud storage, consulting experts, and implementing robust monitoring practices.

2. 8 RESEARCH-RUINING
DATA MISTAKES –
AND HOW TO AVOID THEM
Colin Campbell
METER Group, Inc. USA

3. CLIMATE CHANGE IMPACTS ON
SPECIES DIVERSITY
Rush Valley,UT
Impact of fire, rodents, and
environment on invasive species
dynamics in an arid landscape
Adjust precipitation according to
modeled climate change

4. 1. FAILING TO COLLECT
ENOUGH METADATA
Problem
• Manuscript requires details no longer remembered
• Sensor installation depths are not known
• Wires from buried sensors contain no information
Solutions
• Brainstorm everything you would want to know
• ask friend to review your ideas
• Keep a site (e-)notebook
• be rigorous in your notetaking
• edit sheet right in the field
• note things like sensor serial numbers
• record how deep sensors are buried
• store in cloud… or at least record where data can be found
Motto:Shortest pencil is longer than the longest memory

5. DIGITAL FIELD NOTEBOOK

6. METADATA IN SOFTWARE

7. SITE PICTURES/MONITORING

8. 2. INSTALLING SENSORS IN
THE WRONG PLACE TO TEST
HYPOTHESIS
Problem
• How do you know you’re in the right spot?
Solutions
• Historical satellite data
• Soil sampling
• Separate areas into “statically interesting” units to
monitor
• Yield over time
• Expert advice
• draw detailed diagram of experiment
• articulate project goals

9. EXAMPLE
2019 SITE SELECTION
In situ measurement sites
selected by satellite estimation
of seasonal wetness across
each field
Locations for:
• average moisture across season
• driest moisture location across season

10. 3. FAILING TO ADD LAB DATA
TO GET A COMPLETE PICTURE
Problem
• Not enough information to fully characterize
experimental site
Solutions
• Soil type
• data for publishing papers
• sensor behavior
• Moisture release curve
• air entry point
• understanding field MRCs with in situ sensors
• Hydraulic conductivity
Water potential (-kPa)

11. LAB TESTING EQUIPMENT

12. 4. NOT INSTALLING ENOUGH
SENSORS TO CAPTURE
VARIABILITY
Problem
• Budget limits number of monitoring sites
• Correlations questioned because of lack of statistical
significance
Solutions
• Propose robust instrumentation budget
• Use other sources of data
• satellite
• soil sampling and testing to show field variability manually –
connect with remotely sensed data
• drone remote sensing
• above-ground fixed remote sensing for large footprint
• Carefully consider experimental design

13. 5. FAILING TO REVIEW
DATA REGULARLY
Problem
• Issues go undetected for months
• Data on server but no on looks at them until the end of
the season
• Statistics are run at the end of the experiment so
adjustments are impossible
• Logger batteries die so nothing is collected
• Criteria for good data is not well understood
• Data pile up on logger for occasional download during
site visits
• Key sampling moments are missed like emergence,
flowering, senescence, floods, droughts, etc.
• Mice chew through wires
Solutions
• Use cloud-based visualization software
• Data graphed according to expectation
• API integration into favorite data software (R, MatLab,
Excel)
• Set up alerts to email you when things are wrong

14. FAULTY SENSOR?

15. 6. LOSING DATA
Problem
• Data stored on field loggers, local drives, or obscure
servers are easily lost
• local HD is not backed up/fails
• Catastrophe at field site
• logger flooded
• animal savages system
• ants attack electronics
• Data saved to unknown server location
• Student moves on and and takes system knowledge
Solutions
• Save data on cloud with shared folders to all
stakeholders
• Use ZENTRA Cloud system so all can easily find,
interact with, and download data
• Think:“Hardware can be replaced, data cannot.”

16. 7. FAILING TO INSTALL
SENSORS PROPERLY
Problem
• Improper installation dooms experimental findings
• Many assume they know how to install properly
• fail to make good soil contact
• poorly levelled
• protective covers in place
• Compromises because of experimental realities
• rocky soils
• challenging to reach expected depth
• hard to install met stand

17. IMPROVED INSTALLATIONS
Solutions
• Use installation tool
• reach depth quickly
• press sensors into the soil consistently
• Use online aids
• videos
• step-by-step guides

18. 8. FORGETTING TO VERIFY
EVERYTHING BEFORE LEAVING
THE INSTALLATION SITE
Problem
• Things seldom work the first time we do them
• Field work is exhausting; it’s hard to take time to check
• Acceptable range of sensor output is poorly
understood; errors are not spotted
• System isn’t configured correctly
• logger not reading; reading at the wrong interval
• Poorly configured sensor ports
Solutions
• Setup system in lab before going to field
• Prepare checklist of plausible readings for each sensor
• Use friend to check values online with ZENTRA Cloud
to verify everything works
• Mentally plan time for verification; hard to do after
exhausting day in field
• New ZSC Bluetooth reader to check individual sensor
readings
• ZENTRA Utility Mobile – plug-and-play system avoids
setup errors

19. FIELD TOOLS TO VERIFY
SENSOR BEHAVIOR

20. SUMMARY
1. Failing to collect enough metadata
2. Installing sensors in the wrong place to test hypothesis
3. Failing to add lab data to get a complete picture
4. Not installing enough sensors to capture variability
5. Failing to review data regularly
6. Losing data
7. Failing to install sensors properly
8. Forgetting to verify everything before leaving the
installation site

21. QUESTIONS?