10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21 <sup>st</sup> century
Bibliographic record
Abstract
Abstract Citizen science plays an increasingly important role in generating scientific knowledge and supporting environmental and social action. However, its potential to address complex global challenges remains underutilised. This study explores how to improve citizen science by involving the public in all stages of scientific research. Using participatory research methods, we conducted online surveys and group discussions with researchers, citizen scientists, and Indigenous people. Thematic coding was used to identify key challenges, opportunities, and best practices to enhance citizen science initiatives. Additionally, nine case studies were reported using the Standardised Data on Initiatives (STARDIT) reporting tool. The study identifies key strategies for enhancing participant engagement and retention in citizen science initiatives. Findings underscore the importance of inclusive, evidence-informed approaches such as targeted outreach, fair compensation, tailored support, and co-creation practices. Ensuring data quality and fostering trust require adherence to FAIR data principles, transparent validation and sharing processes, and the establishment of ethical research partnerships. Persistent challenges include short-term funding, which undermines long-term project sustainability, and the lack of centralized support for ethics and project management. Formal recognition of citizen scientists through co-authorship, standardized training, and professional development opportunities can further strengthen involvement and build capacity. Finally, emerging technologies—including AI and open data platforms—present opportunities to scale and improve efficiency, provided they are implemented with appropriate ethical safeguards and investment. Drawing together these insights, we provide 10 actionable recommendations for citizen science in the 21 st century. These highlight the importance of embedding citizen science in national research infrastructure, education, and policy, alongside consistent evaluation and reporting, to improve its inclusivity, longevity, and impact. We conclude by arguing that as the world confronts climate change, public health crises, and biodiversity loss, broader public involvement in science is key for equitable, efficient and evidence-informed responses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.209 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.039 | 0.032 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".