10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century

Jack Nunn; Håkon da Silva Hyldmo; Lauren McKnight; Heather McCulloch; Jennifer L. Lavers; Julie M. Old; Laura Smith; N.J. Grobler; Cheryl Tan Kay Yin; Wing Yan Chan; Candice B. Raeburn; Nittya S. M. Simard; Adam Kingsley Smith; Sam Van Holsbeeck; Eleanor Drinkwater; Kit Stasia Prendergast; Emma L. Burrows; Christopher L. Lawson

doi:10.1371/journal.pone.0331161

PLoS ONEJul 1, 2026Scientific Communication & EducationPeer-reviewed journal article

10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century

Jack Nunn iD, Håkon da Silva Hyldmo iD, Lauren McKnight iD, Heather McCulloch iD, Jennifer L. Lavers iD, Julie M. Old iD, Laura Smith, N.J. Grobler iD, Cheryl Tan Kay Yin, Wing Yan Chan iD, Candice B. Raeburn iD, Nittya S. M. Simard iD, Adam Kingsley Smith iD, Sam Van Holsbeeck iD, Eleanor Drinkwater iD, Kit Stasia Prendergast iD, Emma L. Burrows iD, Christopher L. Lawson iD

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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 21st 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.

In-depth summary

Claude Opus 5.5 · xhigh effort · 5 Nov 2026 · from the full text of version 2 (24 Sept 2025) · CC BY 4.0

Co-produced analysis of 46 survey responses and nine STARDIT case studies identifies eleven core challenges in citizen science and formulates 10 recommendations for infrastructure, FAIR data, and contributor support.

Citizen science can expand scientific knowledge and environmental or health action, yet initiatives struggle with volunteer retention, concerns over data quality, short-term funding, and lack of centralized ethics or project management support. Co-designing surveys and standardized case-study reports with researchers, citizen scientists, and Indigenous participants yields actionable guidelines to embed citizen science into research infrastructure and policy.

Key results

ShownInductive thematic coding of two survey rounds identified eleven main themes dominated by recruitment, data confidence, and project support.
Across 46 respondents (28 in Round One, 18 in Round Two; 16,247 words total, ~350 words per respondent, 80% in Australia), top themes were Recruitment and Awareness (43 responses), Data Collection and Confidence (38), Project Support and Capacity (26), Attitudes (25), and Involvement and Retention (24). · Demographic data; Main themes from data
ShownContributors frequently perform co-authorship, dissemination, and grant writing unpaid, while supporting financial compensation for specialized management and analytical tasks.
Among 17 of 18 Round Two respondents reporting payment history, project management, planning, and design were most often paid whereas co-authorship and dissemination were mostly unpaid; respondents most strongly endorsed paying for data analysis, project management, and ethics applications. · Data on payment for expertise in citizen science…; Funding for inclusive and efficient ways of…; Figure legends
ShownCross-case analysis of nine STARDIT-reported initiatives shows that artificial intelligence and interactive platforms aid scalability, but open data sharing remains inconsistent.
From 49 candidate initiatives and 17 STARDIT reports, nine selected case studies showed that AI image analysis (Great Reef Census, NOBURN) reduced expert training needs and manual processing errors, while only 4 of the 9 case studies shared data according to FAIR principles and 5 did not. · Illustrative case studies; Recommendations
ShownParticipants positively evaluated the co-production process and STARDIT reporting framework, synthesizing 10 recommendations for 21st-century citizen science.
Contributors volunteered an estimated 1385 hours across 20 online meetings; in feedback surveys (n = 29), 55% (15/27) felt able to join all project tasks, 45% some tasks, and 90% (9/10) recommended using STARDIT. · Online surveys; Demographic data; Participation experience data; Recommendations

Methods, data and reproducibility

Using a participatory action research framework, the authors conducted two rounds of open-ended online Google Forms surveys (67 and 21 days), 20 co-creation meetings, and a post-project survey. Responses underwent six-stage manual inductive thematic coding alongside Python 3.13 keyword frequency and LDA topic modeling, complemented by cross-case analysis of nine initiatives using STARDIT reports.

Two-round online survey responses and post-project participation feedback from researchers, citizen scientists, and Indigenous participants · human · n = 46 survey responses (28 Round 1, 18 Round 2; 16,247 words) + 29 feedback replies
Citizen science best-practice initiatives and standardized STARDIT case study reports · n = 49 initiatives identified, 17 STARDIT reports, 9 illustrative case studies

Methods & tools: Standardised Data on Initiatives (STARDIT), Google Forms, Google Docs, Python (v3.13), Natural Language Toolkit (NLTK), scikit-learn (LatentDirichletAllocation, CountVectorizer), pandas, matplotlib, seaborn, Jupyter notebook
Data & accessions: https://osf.io/wbv9s http://stardit.wikimedia.org.au/wiki/0202407220511
Reagents & environment: Python 3.13 in a Jupyter notebook environment using NLTK, scikit-learn, pandas, matplotlib, and seaborn.
Reproducibility gaps: Personally identifiable information was redacted from the public dataset, Jupyter analysis notebooks are not linked separately, and 7 of 9 case-study initiatives did not respond to validate their STARDIT reports.

Limitations (2)

The sample size of 46 survey respondents was too small for statistical inference, and 80% of participants lived in Australia. · stated by the authors
Respondents were skewed toward academia (76% with academic writing experience, 65% paid researchers) and ecology, while health, weather, climate, air pollution, industry, policymakers, and critics of citizen science were underrepresented or absent. · stated by the authors

Read this if you design, fund, or evaluate citizen science and participatory research programs and want standardized reporting practices and policy recommendations.