Citizen science in environmental and ecological sciences

Nature Reviews Methods Primers, Journal Year: 2022, Volume and Issue: 2(1)

Published: Aug. 25, 2022

Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental ecological sciences, which non-professional participants contribute to data collection advance research. We present contributory citizen as a valuable method scientists practitioners focusing on full life cycle of practice, from design implementation, evaluation management. highlight key issues how address them, such participant engagement retention, quality assurance bias correction, well ethical considerations regarding sharing. also provide range examples illustrate diversity applications, biodiversity research land cover assessment forest health monitoring marine pollution. The aspects reproducibility sharing are considered, placing encompassing open perspective. Finally, we discuss its limitations challenges outlook for application multiple domains. Contributory whole or part This Primer outlines use discussing engagement, correction.

Language: Английский

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Citizen science’s transformative impact on science, citizen empowerment and socio-political processes

Socio-Ecological Practice Research, Journal Year: 2023, Volume and Issue: 5(1), P. 11 - 33

Published: Jan. 12, 2023

Abstract Citizen science (CS) can foster transformative impact for science, citizen empowerment and socio-political processes. To unleash this impact, a clearer understanding of its current status challenges development is needed. Using quantitative indicators developed in collaborative stakeholder process, our study provides comprehensive overview the CS Germany, Austria Switzerland. Our online survey with 340 responses focused on through (1) scientific practices, (2) participant learning empowerment, (3) With regard to we found that data quality control an established component practice, while publication results has not yet been achieved by all project coordinators (55%). Key benefits scientists were experience collective (“making difference together others”) as well gaining new knowledge. For scientists’ outcomes, different forms social learning, such systematic feedback or personal mentoring, essential. While majority respondents attributed important value decision-making, only few confident indeed utilized evidence decision-makers. Based these results, recommend researchers strengthen fostering management publications, enhance promoting opportunities initiators networks early engagement decision-makers alignment ongoing policy In way, evolve impact.

Language: Английский

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Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

Earth-Science Reviews, Journal Year: 2023, Volume and Issue: 241, P. 104438 - 104438

Published: April 27, 2023

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which gradually, yet fundamentally influenced way people interact with in digital physical world. Many human activities now not only operate geographical (physical) space but also cyberspace. Such changes triggered a paradigm shift geographic information science (GIScience), as cyberspace brings new perspectives for roles played by spatial temporal dimensions, e.g., dilemma placelessness possible timelessness. As discipline at brink even bigger made machine learning artificial intelligence, this paper highlights challenges opportunities associated relation to cyberspace, particular focus analytics visualization, including extended AI capabilities virtual reality representations. Consequently, encourage creation synergies between processing analysis cyber improve sustainability solve complex problems geospatial applications other advancements urban environmental sciences.

Language: Английский

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Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan

Sustainability, Journal Year: 2024, Volume and Issue: 16(1), P. 454 - 454

Published: Jan. 4, 2024

Marine biodiversity underpins the formation of marine protected areas (MPAs), necessitating detailed surveys to account for dynamic temporal and spatial distribution species influenced by tidal patterns microhabitats. The reef rock intertidal zones adjacent urban centers, such as Taiwan’s Cape Santiago, exhibit significant biodiversity, yet they are increasingly threatened tourism-related activities. This study introduces an artificial intelligence (AI)-empowered citizen science (CS) approach within local community address these challenges. By integrating CS with AI, we establish a hybrid (HI) system that conducts in situ biological educational programs focused on ecological conservation. initiative not only facilitates collective gathering AI-assisted analysis critical data but also uses machine-learning outputs gauge quality, thus informing subsequent collection refinement strategies. resulting collectivity iterative enhancement foster mutual continuous HI learning environment. Our model proves instrumental fostering engagement public involvement endeavors, cultivating skills necessary documenting rocky shifts. These efforts pivotal design governance future MPAs, ensuring their efficacy sustainability

Language: Английский

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Algorithmic management in scientific research

Research Policy, Journal Year: 2024, Volume and Issue: 53(4), P. 104985 - 104985

Published: March 15, 2024

Language: Английский

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Empowering local communities using artificial intelligence

Patterns, Journal Year: 2022, Volume and Issue: 3(3), P. 100449 - 100449

Published: March 1, 2022

Language: Английский

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Mapping Citizen Science through the Lens of Human-Centered AI

Human Computation, Journal Year: 2022, Volume and Issue: 9(1), P. 66 - 95

Published: Nov. 16, 2022

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value agency alongside productivity, we discuss categorize the potential of solving Citizen Science (CS) tasks with Hybrid (HI), a synergetic mixture artificial intelligence. Due unique participant-centered set values abundance drawing upon both common sense complex 21st century skills, believe that field CS offers an invaluable testbed for development human-centered AI including HI, while also benefiting CS. In order investigate this potential, first relate adjacent computational disciplines. Then, demonstrate projects be grouped according their HI-enhancement examining two key dimensions: level digitization amount knowledge or experience required participation. Finally, propose framework types human-AI interaction in based on established criteria HI. This “HI lens” provides community overview ways utilize combination intelligence projects. For researchers, work highlights opportunity presents engage real-world data sets explore new methods applications.

Language: Английский

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The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102065 - 102065

Published: March 13, 2023

There is a need for monitoring biodiversity at multiple spatial and temporal scales to aid conservation efforts. Autonomous recording units (ARUs) can provide cost-effective, long-term systematic species data sound-producing wildlife, including birds, amphibians, insects mammals over large areas. Modern deep learning efficiently automate the detection of occurrences in these sound with high accuracy. Further, citizen science be leveraged scale up deployment ARUs collect reference vocalizations needed training validating models. In this study we develop convolutional neural network (CNN) acoustic classification pipeline detecting 54 bird Sonoma County, California USA, vocalization collected by scientists within Soundscapes Landscapes project (www.soundscapes2landscapes.org). We trained three ImageNet-based CNN architectures (MobileNetv2, ResNet50v2, ResNet100v2), which function as Mixture Experts (MoE), evaluate usefulness several methods enhance model Specifically, we: 1) quantify accuracy fully-labeled 1-min soundscapes an assessment real-world conditions; 2) assess effect on precision recall additional pre-training external archive (xeno-canto) prior fine-tuning from our domain; and, 3) how detections errors are influenced presence coincident biotic non-biotic sounds (i.e., soundscape components). evaluating (n = 37 species) across probability thresholds models, found followed improved average 10.3% relative no pre-training, although there was small 0.8% reduction recall. selecting optimal architecture each based maximum F(β 0.5), MoE approach had total 84.5% 85.1%. Our exhibit issues arising applying county scale, relatively low fidelity recordings background noise overlapping vocalizations. particular, human significantly associated more incorrect (false positives, decreased precision), while physical interference (e.g., recorder hit branch) geophony wind) classifier missing negatives, recall). process surmounted obstacles, final predictions allowed us demonstrate applied low-cost paired valuable diversity

Language: Английский

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Recent progress of artificial intelligence for liquid-vapor phase change heat transfer

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: March 30, 2024

Language: Английский

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AI-driven surveillance of the health and disease status of ocean organisms: a review

Aquaculture International, Journal Year: 2023, Volume and Issue: 32(1), P. 887 - 898

Published: July 6, 2023

Language: Английский

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