What is the difference between opportunistic and standardized data collection? Why do I need to follow specific protocols to contribute data to MDMAP?
Knowing more about debris found on the beach, or even on your street, can be the first step towards preventing it. In order to better understand the different types and amounts of marine debris in our environment, we need to collect data. Marine debris data is often collected through two methods: opportunistic and standardized.
Opportunistic data is collected and reported without using a consistent field method, or protocol, while standardized data collection utilizes consistent field methods and specific scientific study designs to gather debris observations or measurements. Many smartphone apps use opportunistic data collection methods to count debris informally on the go. Alternatively, the NOAA Marine Debris Monitoring and Assessment Project (MDMAP), uses standardized data collection methods. Collection through this systematic method allows NOAA and MDMAP partners and volunteers to compare the type and amount of debris they record over time and across geographies.
There are benefits and challenges to each type of data collection as outlined in the table below. The choice of which type of data collection to use depends on desired goals and the intended use of the data.
|
|
Opportunistic Data |
Standardized Data |
|---|---|---|
Uses |
Calculate the proportions of different debris types |
Calculate the proportions of different debris types Make comparisons of the amounts of debris over time, or across geographies |
Method |
No consistent protocol, enter data as you find debris/litter Sometimes uses a set debris category list |
Consistent protocol followed by all participants Uses a set debris category list |
When and Where |
Whenever, wherever data is desired or debris is encountered |
Collection frequency depends on data uses Sites chosen through scientific survey design |
Benefits |
Less time required, participate when you have time and/or find debris Often simple and easy to pair with cleanup events |
Data collected in a consistent manner Comparable to other datasets using the same method |
Challenges |
Data is not easily comparable Large amounts of data required for meaningful analyses Complicated statistics required to analyze data |
Time intensive Long-term data collection (~ 5 years) needed for analysis of patterns over time Sometimes the debris is not removed |
