Main Menu   Go to extras

You are here

About Citizen Science

Researchers at Oregon State University are building a network of trained citizen scientists to aid in monitoring the environment.  This program was developed as a result of a symposium and workshop for community stakeholders, researchers and policy makers held at Louisiana State University in January of 2013 titled "Response, Recovery, and Resilience to Oil Spills and Environmental Disasters: Engaging Experts and Communities"

Resilient communities are able to self-organize, share information about environmental risks, and adapt to changing conditions.  The purpose of this meeting was to enhance communication between experts and citizens to encourage better monitoring and sharing of information concerning local environmental conditions following disasters.

Workshops at this event included three topics:

  1. Response and Characterizing Exposure
    1. Focus : prioritizing contaminants to be monitored
  2. Recovery and the Role of “Citizen Scientists”
    1. Focus : define and enhance the utility of citizen contributions to science
  3. Resilience and Community Participation
    1. Focus : options for self-organizing and sharing technical information among local groups

Each small interactive group focused on one topic & included representatives of environmental groups, regulatory agencies and academia. One of the many outcomes from this symposia was the need for trained citizen scientists equipped with a  "disaster response kit" to ensure that data generation would be useful to policy makers.

This highlighted the need for all citizen Scientists engaged in sample collection to have documented training on:

  • Safety
  • Sampling Equipment
  • Sampling Techniques
  • Quality Assurance/Quality Control
    • Sample Chain of Custody

This network enables the growth of trained Citizen Scientists that can aid in the vital collection of monitoring data.  This network will mobilize citizen scientists in the event of a disaster with proper training and preparation.  The data generation will be of high value and shared with all.