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Thanks everyone for your contributions so far! We're now going to discuss data quality- perhaps one of the biggest issues that #citizenscience researchers aim to tackle... #citscichat
By its very nature, #citizenscience relies on the contributions of laypeople, who- depending on how participant recruitment is targeted- may vary widely in experience and background knowledge... #citscichat
This means there is the potential for random error and/or systematic bias to be introduced into #citizenscience datasets... #citscichat
What strategies have you adopted to minimise these effects when collecting data? For example, does detailed metadata help assess data quality/reliability? #citscichat
Check out this #PLOSONE article from Torre et al. for a recent study on the value of including an "I don't know" option to register uncertainty #citscichat https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0211907
How important is it to build mechanisms for data validation into study designs? Does anyone have favourite examples of how this can be achieved? #citscichat
As an example, @StevenFalk1 & colleagues used expert verification of contributions to @BumblebeeTrust surveys to evaluate citizen scientist ID accuracy #citscichat https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218614
On the subject of data, how should the issue of data credit be tackled in #citizenscience? How should the contributions of citizen scientists be recognised? Are existing mechanisms appropriate? #citscichat
