Anecdata.org: An online citizen science platform for Building Climate Resilient Communities

Disney, Jane; Bailey, Duncan; Farrell, Anna; Taylor, Ashley; McGreavy, Bridie

doi:10.1109/oceans.2018.8604515

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…There is potential in the future to blend this approach with additional machine learning techniques to enable even larger comparisons or more fine-grained data extraction. These methods complement the emerging citizen science approaches, which show potential for documentation of adaptation responses not readily captured in the published literature [124][125][126][127] . Our study highlights that new approaches to evidence synthesis are increasingly necessary to take stock of the current conditions and to inform interdisciplinary climate solutions.…”

Section: Improve Inclusion Of Timescale and Dynamics Of Responsesmentioning

confidence: 99%

A systematic global stocktake of evidence on human adaptation to climate change

et al. 2021

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A systematic global stocktake of evidence on human adaptation to climate changeAssessing global progress on human adaptation to climate change is an urgent priority. Although the literature on adaptation to climate change is rapidly expanding, little is known about the actual extent of implementation. We systematically screened >48,000 articles using machine learning methods and a global network of 126 researchers. Our synthesis of the resulting 1,682 articles presents a systematic and comprehensive global stocktake of implemented human adaptation to climate change. Documented adaptations were largely fragmented, local and incremental, with limited evidence of transformational adaptation and negligible evidence of risk reduction outcomes. We identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses.

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Section: Improve Inclusion Of Timescale and Dynamics Of Responsesmentioning

confidence: 99%

A systematic global stocktake of evidence on human adaptation to climate change

et al. 2021

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“…By utilizing citizen science data in tandem with edge computing, there are new avenues that can be opened for the mapping and tracking of data real time. For example, crowdsourcing data can be effectively transferred over time with the use of edge computing with faster relay speeds [27]. On a similar note, the developments within IoT are being used to help shape citizens to be more digitally apt, resulting in a smarter city overall.…”

Section: Internet Of Things For Citizen Sciencementioning

confidence: 99%

MOSQUITO EDGE: An Edge-Intelligent Real-Time Mosquito Threat Prediction Using an IoT-Enabled Hardware System

Polineni¹,

Shastri²,

Bagchi³

et al. 2022

Sensors

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Species distribution models (SDMs) that use climate variables to make binary predictions are effective tools for niche prediction in current and future climate scenarios. In this study, a Hutchinson hypervolume is defined with temperature, humidity, air pressure, precipitation, and cloud cover climate vectors collected from the National Oceanic and Atmospheric Administration (NOAA) that were matched to mosquito presence and absence points extracted from NASA’s citizen science platform called GLOBE Observer and the National Ecological Observatory Network. An 86% accurate Random Forest model that operates on binary classification was created to predict mosquito threat. Given a location and date input, the model produces a threat level based on the number of decision trees that vote for a presence label. The feature importance chart and regression show a positive, linear correlation between humidity and mosquito threat and between temperature and mosquito threat below a threshold of 28 °C. In accordance with the statistical analysis and ecological wisdom, high threat clusters in warm, humid regions and low threat clusters in cold, dry regions were found. With the model running on the cloud and within ArcGIS Dashboard, accurate and granular real-time threat level predictions can be made at any latitude and longitude. A device leveraging Global Positioning System (GPS) smartphone technology and the Internet of Things (IoT) to collect and analyze data on the edge was developed. The data from the edge device along with its respective date and location collected are automatically inputted into the aforementioned Random Forest model to provide users with a real-time threat level prediction. This inexpensive hardware can be used in developing countries that are threatened by vector-borne diseases or in remote areas without cloud connectivity. Such devices can be linked with citizen science mosquito data platforms to build training datasets for machine learning based SDMs.

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“…The references also highlight the challenges described above and/or provide recommendations for RDM. Several identified platforms are directed at CS projects (Bonn et al, 2016;Disney et al, 2017;Greshake Tzovaras et al, 2019;Heigl et al, 2018;Wang et al, 2015) or are scientific project platforms that also can accommodate CS projects (Wolf et al, 2019). The possibilities for handling RDM aspects on these platforms vary widely from simply being a place to store and share data (Anecdata.org (Disney et al, 2017)) to the Ocean Network Canada that provides a complete system for RDM that simultaneously FAIRifies data (Wolf et al, 2019).…”

Section: Existing Tools and Guidelinesmentioning

confidence: 99%

Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study

Hansen

Gadegaard

Hansen

et al. 2021

CODATA

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Citizen science (CS) projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. Increasing the value and reuse of CS data has received growing attention with the appearance of the FAIR principles and systematic research data management (RDM) practises, which are often promoted by university libraries. However, RDM initiatives in CS appear diversified and if CS have special needs in terms of RDM is unclear. Therefore, the aim of this article is firstly to identify RDM challenges for CS projects and secondly, to discuss how university libraries may support any such challenges.A scoping review and a case study of Danish CS projects were performed to identify RDM challenges. 48 articles were selected for data extraction. Four academic project leaders were interviewed about RDM practices in their CS projects.Challenges and recommendations identified in the review and case study are often not specific for CS. However, finding CS data, engaging specific populations, attributing volunteers and handling sensitive data including health data are some of the challenges requiring special attention by CS project managers. Scientific requirements or national practices do not always encompass the nature of CS projects.Based on the identified challenges, it is recommended that university libraries focus their services on 1) identifying legal and ethical issues that the project managers should be aware of in their projects, 2) elaborating these issues in a Terms of Participation that also specifies data handling and sharing to the citizen scientist, and 3) motivating the project manager to good data handling practises. Adhering to the FAIR principles and good RDM practices in CS projects will continuously secure contextualisation and data quality. High data quality increases the value and reuse of the data and, therefore, the empowerment of the citizen scientists.

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Anecdata.org: An online citizen science platform for Building Climate Resilient Communities

Cited by 4 publications

References 16 publications

A systematic global stocktake of evidence on human adaptation to climate change

A systematic global stocktake of evidence on human adaptation to climate change

MOSQUITO EDGE: An Edge-Intelligent Real-Time Mosquito Threat Prediction Using an IoT-Enabled Hardware System

Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study

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