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This open access book discusses how the involvement of citizens into scientific endeavors is expected to contribute to solve the big challenges of our time, such as climate change and the loss of biodiversity, growing inequalities within and between societies, and the sustainability turn. The field of citizen science has been growing in recent decades. Many different stakeholders from scientists to citizens and from policy makers to environmental organisations have been involved in its practice. In addition, many scientists also study citizen science as a research approach and as a way for science and society to interact and collaborate. This book provides a representation of the practices as well as scientific and societal outcomes in different disciplines. It reflects the contribution of citizen science to societal development, education, or innovation and provides and overview of the field of actors as well as on tools and guidelines. It serves as an introduction for anyone who wants to get involved in and learn more about the science of citizen science.
Communication Studies --- User Interfaces and Human Computer Interaction --- Science, Humanities and Social Sciences, multidisciplinary --- Environment Studies --- Multimedia Information Systems --- Media and Communication --- Humanities and Social Sciences --- Environmental Sciences --- Citizen science tools and guidelines --- Data quality, standardization, interoperability --- Evaluation and quality criteria in citizen science --- Interdisciplinarity and transdisciplinarity --- Knowledge transfer and science communication --- Open Access --- User interface design & usability --- Interdisciplinary studies --- Society & Social Sciences --- The environment --- Graphical & digital media applications
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heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.
rough set theory --- water quality --- attribute reduction --- core attribute --- rule extraction --- climate extreme indices (CEIs) --- ClimPACT --- GLDAS --- Expert Team on Climate Change Detection and Indices (ETCCDI) --- Expert Team on Sector-specific Climate Indices (ET-SCI) --- Dataset Licensedatabase --- geophysical monitoring --- magnetotelluric monitoring --- processing --- arthropod vector --- invasive species --- microhabitat --- species distribution modeling --- remote sensing --- data assimilation --- 3D-Var --- multi-class classification --- soil texture calculator --- k-Nearest Neighbors --- support vector machines --- decision trees --- ensemble learning --- earth-science data --- data scarcity --- missing data --- data quality --- data imputation --- statistical methods --- machine learning --- environmental modeling --- environmental observations
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The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science.
volunteer geographic information --- positional accuracy --- land administration systems --- location-based social networks (LBSNs) --- clustering --- user preference --- social relationship effect --- spatial proximity --- crowdsourcing --- volunteered geographic information (VGI) --- ensemble --- classification accuracy --- latent class analysis --- OpenStreetMap --- VGI --- community mapping --- data analysis --- GIS education --- data import --- citizen science --- marine mammal --- opportunistic data --- Alaska --- spatial bias --- sample size --- volunteer --- education --- recruitment --- Pentatomidae --- Environmental niche modeling --- citizen science --- crowdsourcing --- MaxEnt --- QGIS --- brown marmorated stink bug --- air quality estimation --- air pollution --- citizen science --- sky images --- social media --- data fusion --- citizen science --- volunteered geographic information (VGI) --- toponym --- crowdsourced data collection --- data quality --- GIS --- digital cartography --- algorithms --- spatial accuracy --- analysis --- OpenStreetMap --- citizen science --- geoinformatics --- projects survey --- geoinformation in citizen science --- VGI in citizen science --- crowdsourced geoinformation collection and analysis
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