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Evaluating community science.

Karen Kovaka1

  • 1Virginia Tech, 229 Major Williams Hall, 220 Stanger St, Blacksburg, VA, 24060, United States.

Studies in History and Philosophy of Science
|June 14, 2021
PubMed
Summary

Community science, involving non-professional scientists, offers advantages like cost-effective data collection. Evaluating its data quality requires assessing adequacy-for-purpose, not just expert accuracy.

Area of Science:

  • Scientific methodology
  • Citizen science
  • Research ethics

Background:

  • Community science engages non-professional scientists, offering benefits like large-scale data collection at reduced costs.
  • Volunteer participation enhances public understanding and appreciation of science.
  • Concerns exist regarding the data quality and trustworthiness of community science projects.

Purpose of the Study:

  • To challenge the conventional evaluation of community science data quality based on expert accuracy standards.
  • To propose an alternative framework for assessing community science data quality.
  • To highlight the diverse and sometimes conflicting aims within scientific research.

Main Methods:

  • Critically analyzing the assumptions underlying traditional methods for evaluating scientific data.
Keywords:
Aims of scienceCitizen scienceCommunity scienceData evaluation

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  • Examining the diverse aims of science and their implications for data quality assessment.
  • Proposing a new standard for evaluating community science data: adequacy-for-purpose.

    • Main Results:

    • The standard of expert accuracy is inappropriate for evaluating community science due to a flawed assumption about science's singular aim.
    • Community science projects may have different, equally valid aims that do not prioritize maximal accuracy.
    • Data quality in community science should be judged by its fitness for the specific research purpose.

    Conclusions:

    • Evaluating community science data solely on expert accuracy is misguided.
    • A more appropriate metric for community science data quality is its adequacy-for-purpose.
    • Recognizing the diverse aims of science is crucial for fairly assessing community science contributions.