Data Management Documentation in Citizen Science Projects: Bringing Formalisation and Transparency Together

Gefion Thuermer, Esteban González Guardia, Neal Reeves, Oscar Corcho, Elena Simperl

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Citizen science (CS) is a way to open up the scientific process, to make it more accessible and inclusive, and to bring professional scientists and the public together in shared endeavours to advance knowledge. Many initiatives engage citizens in the collection or curation of data, but do not state what happens with such data. Making data open is increasingly common and compulsory in professional science. To conduct transparent, open science with citizens, citizens need to be able to understand what happens with the data they contribute. Data management documentation (DMD) can increase understanding of and trust in citizen science data, improve data quality and accessibility, and increase the reproducibility of experiments. However, such documentation is often designed for specialists rather than amateurs.

This paper analyses the use of DMD in CS projects. We present analysis of a qualitative survey and assessment of projects’ DMD, and four vignettes of data management practices. Since most projects in our sample did not have DMD, we further analyse their reasons for not doing so. We discuss the benefits and challenges of different forms of DMD, and barriers to having it, which include a lack of resources, a lack of awareness of tools to support DMD development, and the inaccessibility of existing tools to citizen scientists without formal scientific education. We conclude that, to maximise the inclusivity of citizen science, tools and templates need to be made more accessible for non-experts in data management.
Original languageEnglish
Article number25
JournalCitizen Science: Theory & Practice
Issue number1
Publication statusPublished - 5 Jun 2023


  • citizen science
  • data management
  • data management documentation
  • data management plans
  • data quality


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