• Overview
• Reality Check
• how to
• resources
• tools
• questions
• cases

• use proper study design to meet scientific objectives
• understand and account for potential biases of research design
• ensure study design also facilitates other objectives (e.g., educational)
• develop a clearly written protocol
  

• choose protocols within capacity of volunteers (be realistic)
• is the project overly ambitious (too tedious or complex)?
• is it underestimating volunteer skills (not interesting/challenging)?
  
• keep protocols transparent (observers should know why they're being asked to do things a certain way)
• plan for reasonable equipment intensity (cost, maintenance, usability)
  

• users filling in all fields
  
• geo-referencing data
• temporally referencing data
• collection of negative data
• identification of observer for each data point
• early identification of "outliers"
  
• allows differentiation of typos from unusual sightings
• provides an opportunity to request confirmation
  

• choose protocols within capacity of volunteers
• ensure the protocols are clearly written and accessible to all participants
  
• build ID guides and data confirmation steps into data collection tools
  
• consult with a statistician and:
  
• determine the necessary sample size for your study
• determine the necessary number of study sites
  
• address the potential for sampling bias
  
• decide up front how to handle questionable data
• pilot test all stages of project development
  

• minimize errors by making data collection electronic at the earliest stage
  
• but, consider that a pencil and paper datasheet can be sufficient
• identify particular needs of different users/clients (scientists, volunteers, etc.)
• provide different data forms for different audiences that collect the same data (e.g., paper and electronic)
• assess appropriate technology options (see tech tools)
  
• customizable forms (one example)
  
• "smart" phones and software
• measurement tools and data loggers (one example)
  
• be creative and opportunistic

• compare how much it costs to collect...
• X records of quality A vs...
  
• Y records of quality B
  
• account for necessary training and support expenses
• remember to also value and account for other goals (e.g., educational)
• use efficiency measures to guide protocol design, equipment choices, etc. 

• respond to pilot tests, formative evaluations, and participant feedback regarding:
• data forms
• use of protocols
• technology tools
  
• available training and support
  
• make improvements in a timely, transparent, and coherent manner
  

Know of any FTWs for this step?  Soon you will be able to share them through our discussion forum.

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(form a team)		(recruit participants)