Biology subjects

Lawson, C. L.

Publications and source records attributed to Lawson, C. L..

3 recordsLinked to original sources

bioRxiv2025

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

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.

scientific communication and education↗

bioRxiv2024

Broadscale reconnaissance of coral reefs from citizen science and deep learning

Coral reef managers require various forms of data. While monitoring is typically the preserve of scientists, larger scale reconnaissance data that can be used to inform spatial decisions does not usually require such precise measurement. There is an increasing need to collect such broadscale, up-to-date environmental data at massive scale to prioritise limited conservation resources in the face of global disturbances. Citizen science combined with novel technology presents an opportunity to achieve data collection at the required scale, but the accuracy and feasibility of new tools must be assessed. Here we show that a citizen science program that collects seascape images and analyses them using a combination of deep learning and online citizen scientists can produce accurate benthic cover estimates of key coral groups. The deep learning and citizen scientist analysis methods had different but complementary strengths depending on coral category. When the best performing analysis method was used for each category in all images, mean estimates from 8086 images of percent benthic cover of branching Acropora, plating Acropora, and massive-form coral were [~]99% accurate compared to expert assessment of the same images, and >95% accurate at all coral cover ranges tested. The effort to achieve 95% accuracy at a site - our ecologically relevant target based on the accuracy of other tools - was attainable based on citizen scientist involvement in pilot years of the program, with 18-80 images needed depending on coral type and reef state. Power analyses showed that sampling up to 114 images per site was needed to detect a 10% absolute difference in coral cover per category (power = 0.8), accounting for natural heterogeneity. However, the benthic cover of all other coral groups as a single category could only be estimated with 95% accuracy at 60% of survey sites and for images with 10-30% coral cover. Disaggregating this other coral group into more distinct coral categories may improve accuracy. Overall, citizen science can provide an accuracy that is acceptable for many end-users for select coral morphologies. Such a combination of emerging technology and citizen science presents an attainable tool for collecting inexpensive, widespread reconnaissance data of coral reefs that can complement higher resolution survey programs or be an accessible tool for resource-poor locations.

ecology↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗