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Добірка наукової літератури з теми "Crowd science.Citizen science"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Crowd science.Citizen science"

Oesterlund, Carsten S., Gabriel Mugar, Corey Jackson, Katie DeVries Hassman, and Kevin Crowston. "Socializing the Crowd: Learning to Talk in Citizen Science." Academy of Management Proceedings 2014, no. 1 (January 2014): 16799. http://dx.doi.org/10.5465/ambpp.2014.16799abstract.

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Mooney, P., and L. Morgan. "HOWMUCH DO WE KNOWABOUT THE CONTRIBUTORS TO VOLUNTEERED GEOGRAPHIC INFORMATION AND CITIZEN SCIENCE PROJECTS?" ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 20, 2015): 339–43. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-339-2015.

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In the last number of years there has been increased interest from researchers in investigating and understanding the characteristics and backgrounds of citizens who contribute to Volunteered Geographic Information (VGI) and Citizen Science (CS) projects. Much of the reluctance from stakeholders such as National Mapping Agencies, Environmental Ministries, etc. to use data and information generated and collected by VGI and CS projects grows from the lack of knowledge and understanding about who these contributors are. As they are drawn from the crowd there is a sense of the unknown about these citizens. Subsequently there are justifiable concerns about these citizens’ ability to collect, generate and manage high quality and accurate spatial, scientific and environmental data and information. This paper provides a meta review of some of the key literature in the domain of VGI and CS to assess if these concerns are well founded and what efforts are ongoing to improve our understanding of the crowd.

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Mueller, Johannes, Hangxin Lu, Artem Chirkin, Bernhard Klein, and Gerhard Schmitt. "Citizen Design Science: A strategy for crowd-creative urban design." Cities 72 (February 2018): 181–88. http://dx.doi.org/10.1016/j.cities.2017.08.018.

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Cappa, Francesco. "Big data from customers and non-customers through crowdsourcing, citizen science and crowdfunding." Journal of Knowledge Management 26, no. 11 (August 12, 2022): 308–23. http://dx.doi.org/10.1108/jkm-11-2021-0871.

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Purpose The unprecedented growth in the volume, variety and velocity with which data is generated and collected over the last decade has led to the spread of big data phenomenon. Organizations have become increasingly involved in the collection and analysis of big data to improve their performance. Whereas the focus thus far has mainly been on big data collected from customers, the topic of how to collect data also from those who are not yet customers has been overlooked. A growing means of interacting with non-customers is through crowd-based phenomena, which are therefore examined in this study as a way to further collect big data. Therefore, this study aims to demonstrate the importance of jointly considering these phenomena under the proposed framework. Design/methodology/approach This study seeks to demonstrate that organizations can collect big data from a crowd of customers and non-customers through crowd-based phenomena such as crowdsourcing, citizen science and crowdfunding. The conceptual analysis conducted in this study produced an integrated framework through which companies can improve their performance. Findings Grounded in the resource-based view, this paper argues that non-customers can constitute a valuable resource insofar as they can be an additional source of big data when participating in crowd-based phenomena. Companies can, in this way, further improve their performance. Originality/value This study advances scientific knowledge of big data and crowd-based phenomena by providing an overview of how they can be jointly applied to further benefit organizations. Moreover, the framework posited in this study is an endeavour to stimulate further analyses of these topics and provide initial suggestions on how organizations can jointly leverage crowd-based phenomena and big data.

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Albers, B., N. de Lange, and S. Xu. "AUGMENTED CITIZEN SCIENCE FOR ENVIRONMENTAL MONITORING AND EDUCATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 1–4. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1-2017.

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Environmental monitoring and ecological studies detect and visualize changes of the environment over time. Some agencies are committed to document the development of conservation and status of geotopes and geosites, which is time-consuming and cost-intensive. Citizen science and crowd sourcing are modern approaches to collect data and at the same time to raise user awareness for environmental changes. Citizen scientists can take photographs of point of interests (POI) with smartphones and the PAN App, which is presented in this article. The user is navigated to a specific point and is then guided with an augmented reality approach to take a photo in a specific direction. The collected photographs are processed to time-lapse videos to visualize environmental changes. Users and experts in environmental agencies can use this data for long-term documentation.

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Thilker, David A. "A Citizen-Science-enabled Comprehensive Search for XUV-disk Galaxies." Proceedings of the International Astronomical Union 11, S321 (March 2016): 298. http://dx.doi.org/10.1017/s1743921316011352.

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AbstractInitial efforts to identify extended UV disk (XUV-disk) galaxies were confined to nearby targets using image products from early in the GALEX mission. We developed a beta Zooniverse-based citizen science project to address this issue, specifically (1) allowing a dramatically larger galaxy sample by crowd-sourcing blink comparison UV-optical image inspection to volunteers, and (2) incorporating all archived GALEX data for each target considered. We aim to widely deploy this project to the public within the upcoming year.

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Chen, Shun-Ling. "How Empowering Is Citizen Science? Access, Credits, and Governance for the Crowd." East Asian Science, Technology and Society 13, no. 2 (June 1, 2019): 215–34. http://dx.doi.org/10.1215/18752160-7497711.

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Caley, Peter, Marijke Welvaert, and Simon C. Barry. "Crowd surveillance: estimating citizen science reporting probabilities for insects of biosecurity concern." Journal of Pest Science 93, no. 1 (June 11, 2019): 543–50. http://dx.doi.org/10.1007/s10340-019-01115-7.

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Wiederkehr, Stefan. "Open data for the crowd: an account of citizen science at ETH Library." LIBER Quarterly 29, no. 1 (December 23, 2019): 1. http://dx.doi.org/10.18352/lq.10294.

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Getschmann, Christopher, and Florian Echtler. "DesPat: Smartphone-Based Object Detection for Citizen Science and Urban Surveys." i-com 20, no. 2 (August 1, 2021): 125–39. http://dx.doi.org/10.1515/icom-2021-0012.

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Abstract Data acquisition is a central task in research and one of the largest opportunities for citizen science. Especially in urban surveys investigating traffic and people flows, extensive manual labor is required, occasionally augmented by smartphones. We present DesPat, an app designed to turn a wide range of low-cost Android phones into a privacy-respecting camera-based pedestrian tracking tool to automatize data collection. This data can then be used to analyze pedestrian traffic patterns in general, and identify crowd hotspots and bottlenecks, which are particularly relevant in light of the recent COVID-19 pandemic. All image analysis is done locally on the device through a convolutional neural network, thereby avoiding any privacy concerns or legal issues regarding video surveillance. We show example heatmap visualizations from deployments of our prototype in urban areas and compare performance data for a variety of phones to discuss suitability of on-device object detection for our usecase of pedestrian data collection.

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Більше джерел

Дисертації з теми "Crowd science.Citizen science"

Munke, Martin. "Citizen Science/Bürgerwissenschaften: Projekte, Probleme, Perspektiven (am Beispiel Sachsen)." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2018. https://slub.qucosa.de/id/qucosa%3A21204.

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Unter dem englischen Begriff Citizen Science und seiner deutschen Entsprechung Bürgerwissenschaften werden eine Reihe von Konzepten gefasst, die eine Beteiligung von Laien bei der Generierung wissenschaftlicher Erkenntnisse bezeichnen. Diese Konzepte sind eng verbunden mit der Vorstellung einer Offenen Wissenschaft (Open Science) und ihrem Ziel, 'Wissenschaft einer größeren Zahl von Menschen einfacher zugänglich zu machen' (Wikipedia). Der Vortrag im Rahmen der Konferenz 'Forschungsdesign 4.0. Datengenerierung und Wissenstransfer in interdisziplinärer Perspektive' des Instituts für Sächsische Geschichte und Volkskunde e.V. vom 19. bis 21. April 2018 an der Sächsischen Landesbibliothek - Staats- und Universitätsbibliothek Dresden untersuchte unterschiedliche Definitionsansätze zusammengeführt und skizzierte am Beispiel aktueller Projekte aus Sachsen Probleme und Perspektiven von Citizen Science allgemein.

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Ruotsalainen, Marcus. "VALIDERINGSMETODER I CITIZEN SCIENCE : Sex stycken fallstudier av valideringsmetoder i citizen science projekt." Thesis, Umeå universitet, Sociologiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130683.

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This research looks at some of the different methods of validation used in the growing phenomenon citizen science. Citizen science is discussed and a small range of different typologies is used to define it. To find some of the common themes of validation six case studies are performed. The case studies examine the following six citizen science projects: GLOBE at Night, eBird, Citclops, Foldit, Galaxy Zoo and EyeWire. These projects a divided equally in to two types based on previous typological research: data collection projects and analysis projects. All projects are international in scope but differ greatly in actions and so in what type of validation they use. It is showed that some validation is made in comparison to data made by professionals or machine data or some other external source. In two cases the results of the project are self-validating and one case validation is made by experts on data that seems out of the ordinary. A few projects use consensus data i.e. the average of observations or analyses made by the citizen scientists either for validation or as a measure of probable correctness. A short discussion of the results and some suggestions of future research finishes of this research.

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Roth, Hannah Michelle. "Smartphone Privacy in Citizen Science." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78360.

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Group signature schemes enable anonymous-yet-accountable communications. Such a capability is extremely useful for modern applications such as smartphone-based crowdsensing and citizen science. A prototype named GROUPSENSE was developed to support anonymous-yet-accountable crowdsensing with SRBE in Android devices. From this prototype, an Android crowdsensing application was implemented to support privacy in citizen science. In this thesis, we will evaluate the usability of our privacy-preserving crowdsensing application for citizen science projects. An in person user study with 22 participants has been performed showing that participants understood the importance of privacy in citizen science and were willing to install privacy-enhancing applications, yet over half of the participants did not understand the privacy guarantee. Based on these results, modifications to the crowdsensing application have been made with the goal of improving the participants' understanding of the privacy guarantee.
Master of Science

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Bernstein, Michael Scott. "Crowd-powered systems." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/74888.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 217-237).
Crowd-powered systems combine computation with human intelligence, drawn from large groups of people connecting and coordinating online. These hybrid systems enable applications and experiences that neither crowds nor computation could support alone. Unfortunately, crowd work is error-prone and slow, making it difficult to incorporate crowds as first-order building blocks in software systems. I introduce computational techniques that decompose complex tasks into simpler, verifiable steps to improve quality, and optimize work to return results in seconds. These techniques develop crowdsourcing as a platform so that it is reliable and responsive enough to be used in interactive systems. This thesis develops these ideas through a series of crowd-powered systems. The first, Soylent, is a word processor that uses paid micro-contributions to aid writing tasks such as text shortening and proofreading. Using Soylent is like having access to an entire editorial staff as you write. The second system, Adrenaline, is a camera that uses crowds to help amateur photographers capture the exact right moment for a photo. It finds the best smile and catches subjects in mid-air jumps, all in realtime. Moving beyond generic knowledge and paid crowds, I introduce techniques to motivate a social network that has specific expertise, and techniques to data mine crowd activity traces in support of a large number of uncommon user goals. These systems point to a future where social and crowd intelligence are central elements of interaction, software, and computation.
by Michael Scott Bernstein.
Ph.D.

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Mehran, Ramin. "Analysis of behaviors in crowd videos." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4801.

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In this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of information typically employed for applications such as event detection, behavior recognition, and activity recognition. We present new representations of human group motion for static cameras, and propose algorithms for their application to variety of problems. We first propose a method to model and learn the scene activity of a crowd using Social Force Model for the first time in the computer vision community. We present a method to densely estimate the interaction forces between people in a crowd, observed by a static camera. Latent Dirichlet Allocation (LDA) is used to learn the model of the normal activities over extended periods of time. Randomly selected spatio-temporal volumes of interaction forces are used to learn the model of normal behavior of the scene. The model encodes the latent topics of social interaction forces in the scene for normal behaviors. We classify a short video sequence of $n$ frames as normal or abnormal by using the learnt model. Once a sequence of frames is classified as an abnormal, the regions of anomalies in the abnormal frames are localized using the magnitude of interaction forces. The representation and estimation framework proposed above, however, has a few limitations. This algorithm proposes to use a global estimation of the interaction forces within the crowd. It, therefore, is incapable of identifying different groups of objects based on motion or behavior in the scene. Although the algorithm is capable of learning the normal behavior and detects the abnormality, but it is incapable of capturing the dynamics of different behaviors. To overcome these limitations, we then propose a method based on the Lagrangian framework for fluid dynamics, by introducing a streakline representation of flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position. This approach is advantageous over the previous representations in two aspects: first, its rich representation captures the dynamics of the crowd and changes in space and time in the scene where the optical flow representation is not enough, and second, this model is capable of discovering groups of similar behavior within a crowd scene by performing motion segmentation. We propose a method to distinguish different group behaviors such as divergent/convergent motion and lanes using this framework. Finally, we introduce flow potentials as a discriminative feature to recognize crowd behaviors in a scene. Results of extensive experiments are presented for multiple real life crowd sequences involving pedestrian and vehicular traffic. The proposed method exploits optical flow as the low level feature and performs integration and clustering to obtain coherent group motion patterns. However, we observe that in crowd video sequences, as well as a variety of other vision applications, the co-occurrence and inter-relation of motion patterns are the main characteristics of group behaviors. In other words, the group behavior of objects is a mixture of individual actions or behaviors in specific geometrical layout and temporal order. We, therefore, propose a new representation for group behaviors of humans using the inter-relation of motion patterns in a scene. The representation is based on bag of visual phrases of spatio-temporal visual words. We present a method to match the high-order spatial layout of visual words that preserve the geometry of the visual words under similarity transformations. To perform the experiments we collected a dataset of group choreography performances from the YouTube website. The dataset currently contains four categories of group dances.
ID: 031001560; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Title from PDF title page (viewed August 26, 2013).; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 100-104).
Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering

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Casey, Leanne Maura. "Using citizen science to monitor bumblebee populations." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/68403/.

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Bumblebees are important pollinators of crops and wildflowers and are currently in global decline. The main drivers of decline include agricultural intensification, climate change, invasive species, pesticides, parasites and pathogens and it is thought that these multiple stressors act together to impact populations. However, their relative importance is unknown and there are wide knowledge gaps in relation to the current status of species populations and their response to environmental variables such as climate, habitat and land use change. Citizen science offers a potential method of collecting data at a broad enough scale to measure species population responses to environmental stressors and it has successfully been applied to other taxa, particularly UK birds and butterflies. This thesis investigates the use of citizen science to address the current knowledge gaps in the status of UK bumblebee populations by analysing volunteer-collected data on current distribution and abundance trends in relation to habitat and climate change. Results are compared to previous studies to infer long-term changes in population dynamics. The value of applying citizen science methods to bumblebee monitoring is highlighted, revealing evidence for decline of some common species and the recent retraction of rare species to their climatic optima. The main findings reveal a potential impact of climate on the distribution of winter-active bumblebees. They also indicate that, while urban parks and gardens provide refuge for bumblebees in an otherwise impoverished landscape, urbanisation may favour short-tongued generalist species over long-term specialists. The outcomes of this thesis have important management implications for UK bumblebee populations including the need for reassessment of the conservation status of B. soroeensis and the sympathetic management of urban parks and gardens for longtongued specialists through the provision of suitable forage material.

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Zilli, Davide. "Smartphone-powered citizen science for bioacoustic monitoring." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/382943/.

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Citizen science is the involvement of amateur scientists in research for the purpose of data collection and analysis. This practice, well known to different research domains, has recently received renewed attention through the introduction of new and easy means of communication, namely the internet and the advent of powerful “smart” mobile phones, which facilitate the interaction between scientists and citizens. This is appealing to the field of biodiversity monitoring, where traditional manual surveying methods are slow and time consuming and rely on the expertise of the surveyor. This thesis investigates a participatory bioacoustic approach that engages citizens and their smartphones to map the presence of animal species. In particular, the focus is placed on the detection of the New Forest cicada, a critically endangered insect that emits a high pitched call, difficult to hear for humans but easily detected by their mobile phones. To this end, a novel real time acoustic cicada detector algorithm is proposed, which efficiently extracts three frequency bands through a Goertzel filter, and uses them as features for a hidden Markov model-based classifier. This algorithm has permitted the development of a cross-platform mobile app that enables citizen scientists to submit reports of the presence of the cicada. The effectiveness of this approach was confirmed for both the detection algorithm, which achieves an F1 score of 0.82 for the recognition of three acoustically similar insects in the New Forest; and for the mobile system, which was used to submit over 11,000 reports in the first two seasons of deployment, making it one of the largest citizen science projects of its kind. However the algorithm, though very efficient and easily tuned to different microphones, does not scale effectively to many-species classification. Therefore, an alternative method is also proposed for broader insect recognition, which exploits the strong frequency features and the repeating phrases that often occur in insects songs. To express these, it extracts a set of modulation coefficients from the power spectrum of the call, and represents them compactly by sampling them in the log-frequency space, avoiding any bias towards the scale of the phrase. The algorithm reaches an F1 score of 0.72 for 28 species of UK Orthoptera over a small training set, and an F1 score of 0.92 for the three insects recorded in the New Forest, though with higher computational cost compared to the algorithm tailored to cicada detection. The mobile app, downloaded by over 3,000 users, together with the two algorithms, demonstrate the feasibility of real-time insect recognition on mobile devices and the potential of engaging a large crowd for the monitoring of the natural environment.

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CESARANO, CINZIA. "Citizen Science approaches for beach litter monitoring." Doctoral thesis, Università Politecnica delle Marche, 2022. https://hdl.handle.net/11566/305901.

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Questa tesi dal titolo "Approcci della scienza dei cittadini per il monitoraggio dei rifiuti da spiaggia" si concentra sui rifiuti marini delle spiagge (di seguito MBL). MBL rappresenta un enorme problema che riguarda le aree scientifiche, economiche e sociali. Durante il primo anno di dottorato è stata organizzata e realizzata un'attività pilota di citizen science per il monitoraggio dell'ambiente balneare con studenti delle scuole primarie e secondarie, utilizzando il protocollo MAC-Emerso. Le osservazioni raccolte sono state incluse nel database ufficiale MAC-Emerso. Durante il secondo anno, è stata completata un'analisi bibliometrica sul MBL e i risultati raggiunti sono stati organizzati per la pubblicazione in una rivista peer-reviewed. Inoltre, è stata effettuata un'analisi preliminare del database nazionale MAC-Emerso disponibile. Il terzo anno è stato dedicato alla compilazione di studi e programmi precedenti incentrati sul monitoraggio MBL e sulle campagne di bonifica lungo la costa mediterranea. Tutte le metodologie applicate fino ad oggi sono state analizzate e confrontate nel dettaglio per identificare i punti di forza e di debolezza dei protocolli attuali, il coinvolgimento dei cittadini e le lacune esistenti. La presente tesi è composta da otto capitoli e due articoli e si apre con un'introduzione generale che descrive MBL (Capitolo 1). Il capitolo 2 discute l'obiettivo generale della ricerca di dottorato e riassume gli articoli inclusi nella tesi di dottorato. Il capitolo 3 esamina in dettaglio la strategia marina, mentre il capitolo 4 si concentra sulla scienza dei cittadini e sul protocollo MAC-Emerso. Il capitolo 5 descrive i principali risultati raggiunti, inclusa l'attività pilota di citizen science organizzata e realizzata per il monitoraggio dell'ambiente balneare con gli studenti delle scuole primarie e secondarie che utilizzano il protocollo MAC-Emerso. Il capitolo 6 comprende la raccolta dei due articoli scientifici su MBL realizzati durante le attività di dottorato. Il primo paper (Cesarano et al., 2021) è stato pubblicato su Marine Pollution Bulletin (con ranking Q1), mentre il secondo paper è stato recentemente inviato alla stessa rivista. Il primo esplora la letteratura scientifica globale sull'MBL attraverso un'accurata analisi bibliometrica. Quest'ultimo presenta una revisione sistematica della letteratura corrente sul monitoraggio MBL lungo le coste del Mediterraneo. Insieme, forniscono una revisione completa delle conoscenze scientifiche sull'MBL nella regione mediterranea e offrono spunti interessanti per capire dove si trovano le lacune attuali e cosa sarebbe necessario per sviluppare un monitoraggio più efficiente su scala di bacino a sostegno dei nostri sforzi per affrontare il Sfida MBL. Infine, un'osservazione conclusiva dei risultati complessivi raggiunti nel presente studio è elaborata nel Capitolo 7. Segue una nota sugli altri prodotti non inclusi in questa tesi, ma eseguiti durante il mio periodo di dottorato. Quindi, un elenco di riferimento degli studi menzionati attraverso la tesi conclude questo documento.
This thesis entitled “Citizen Science approaches for beach litter monitoring” focuses on Marine Beach Litter (hereafter MBL). MBL represents a huge problem that concerns scientific, economic, and social areas. During the first year of my PhD, a pilot citizen science activity was organized and realized for monitoring beach environment with primary and secondary school students, using the MAC-Emerso protocol. The collected observations were included in the official MAC-Emerso database. During the second year, a bibliometric analysis on the MBL topic has been completed and the achieved results have been organized for publication in a peer-reviewed journal. Furthermore, a preliminary analysis of the available national MAC-Emerso database was carried out. The third year was devoted to compile previous studies and programs focusing on MBL monitoring and cleanup campaigns along the Mediterranean coastline. All the methodologies applied to date have been analysed and compared in detail to identify strengths and weaknesses of current protocols, citizen involvement, and existing gaps. The present thesis consists of eight chapters and two papers and opens with a general introduction describing MBL (Chapter 1). Chapter 2 discusses the overall aim of the PhD research, and summarizes the papers included in the PhD thesis. Chapter 3 examines in detail the Marine Strategy, while Chapter 4 focuses on Citizen Science and the MAC-Emerso protocol. Chapter 5 describes the main results achieved, including the pilot citizen science activity organized and realized for monitoring beach environment with primary and secondary school students using the MAC-Emerso protocol. Chapter 6 includes the collection of the two scientific papers on MBL realized during the PhD activities. The first paper (Cesarano et al., 2021) has been published in Marine Pollution Bulletin (with Q1 ranking), while the second paper has been recently submitted to the same journal. The former explores the global scientific literature on MBL through an accurate bibliometric analysis. The latter presents a systematic review of current literature concerning MBL monitoring along the Mediterranean coasts. Together, they do provide a comprehensive review of the scientific knowledge on MBL in the Mediterranean region and offer interesting insights to understand where current gaps lie, and what would be needed to develop a basin-scale more efficient monitoring in support of our efforts to tackle the MBL challenge. Finally, a concluding remark of the overall results achieved in the present study is elaborated in Chapter 7. A note about the other products not included in this thesis, but performed during my PhD period, follows. Then, a reference list of the studies mentioned through the thesis ends this document.

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Wang, Sunrise. "Evolving controllable emergent crowd behaviours with Neuro-Evolution." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/20015.

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Crowd simulations have become increasingly popular in films over the past decade, appearing in large crowd shots of many big name block-buster films. An important requirement for crowd simulations in films is that they should be directable both at a high and low level, and be believable. As agent-based techniques allow for low-level directability and more believable crowds, they are typically used in this field. However, due to the bottom-up nature of these techniques, achieving high level direct ability requires the modification of agent-level parameters until the desired crowd behaviour emerges. As manually adjusting parameters is a time consuming and tedious process, this thesis investigates a method for automating this, using Neuro-Evolution (NE). This is achieved by using Artificial Neural Networks as the agent controllers within an animated scene, and evolving these with an Evolutionary Algorithm so that the agents behave as desired. To this end, this thesis proposes, implements, and evaluates a system that allows for the low-level control of crowds using NE. Overall, this approach shows very promising results, with the time taken to achieve the desired crowd behaviours being either on par or faster than previous methods.

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Benavides, Aerin Benavides. "Meanings teachers make of teaching science outdoors as they explore citizen science." Thesis, The University of North Carolina at Greensboro, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10123698.

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This descriptive case study examined the meanings public elementary school teachers (N = 13) made of learning to enact citizen science projects in their schoolyards in partnership with a local Arboretum. Utilizing Engeström’s (2001) framework of cultural-historical activity theory (CHAT), the Arboretum’s outreach program for area Title 1 schools was viewed as an activity system composed of and acting in partnership with the teachers. The major finding was that teachers designed and mastered new ways of teaching (expansive learning) and transformed their citizen science activity to facilitate student engagement and learning. I highlight four important themes in teachers’ expansive learning: (a) discussion, (b) inclusion, (c) integration, and (d) collaboration. Teacher learning communities formed when colleagues shared responsibilities, formed mentor/mentee relationships, and included student teachers and interns in the activity. This program could serve as a model for elementary school citizen science education, as well as a model for professional development for teachers to learn to teach science and Environmental Education outdoors.

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Книги з теми "Crowd science.Citizen science"

Vohland, Katrin, Anne Land-Zandstra, Luigi Ceccaroni, Rob Lemmens, Josep Perelló, Marisa Ponti, Roeland Samson, and Katherin Wagenknecht, eds. The Science of Citizen Science. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58278-4.

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Pandya, Rajul, and Kenne Ann Dibner, eds. Learning Through Citizen Science. Washington, D.C.: National Academies Press, 2018. http://dx.doi.org/10.17226/25183.

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Kovacs, Vic. Get Into Citizen Science. Edited by Marcia Abramson and Petrice Custance. St. Catherines, Ontario, Canada: Crabtree Publishing Company, 2017.

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Cavalier, Darlene, and Eric B. Kennedy, eds. The Rightful Place of Science: Citizen Science. Charleston, South Carolina, USA: Consortium for Science, Policy, & Outcomes, 2016.

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Citizen scientist. New York, NY: American Institute of Physics, 1991.

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Be a Citizen Scientist! New York, New York, USA: Gareth Stevens Publishing, 2018.

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Landgraf, Greg. Citizen science guide for families: Taking part in real science. Chicago, Illinois, USA: Huron Street Press, 2013.

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Mueller, Michael P., and Deborah J. Tippins, eds. EcoJustice, Citizen Science and Youth Activism. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11608-2.

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Bat Count: A Citizen Science Story. Mt. Pleasant, SC, USA: Arbordale Publishing, 2017.

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Citizen Machiavelli. New Brunswick: Transaction Publishers, 2014.

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Частини книг з теми "Crowd science.Citizen science"

Lukyanenko, Roman, Jeffrey Parsons, and Yolanda Wiersma. "Citizen Science 2.0: Data Management Principles to Harness the Power of the Crowd." In Service-Oriented Perspectives in Design Science Research, 465–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20633-7_34.

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Dickinson, Janis L., and Rhiannon L. Crain. "Socially Networked Citizen Science and the Crowd-Sourcing of Pro-Environmental Collective Actions." In Lecture Notes in Social Networks, 133–52. Vienna: Springer Vienna, 2014. http://dx.doi.org/10.1007/978-3-7091-1340-0_8.

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Dickel, Sascha. "Technoscientific Citizenship in Citizen Science. Assembling Crowds for Biomedical Research." In TechnoScienceSociety, 251–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43965-1_14.

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Madry, Scott. "The Emerging World of Crowd Sourcing, Social Media, Citizen Science, and Remote Support Operations in Disasters." In Space Systems for Disaster Warning, Response, and Recovery, 117–21. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1513-2_9.

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Kersting, Norbert, and Yimei Zhu. "Crowd Sourced Monitoring in Smart Cities in the United Kingdom." In Communications in Computer and Information Science, 255–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02843-5_20.

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Gitahi, Joseph, and Michael Hahn. "Evaluation of Crowd-Sourced PM2.5 Measurements from Low-Cost Sensors for Air Quality Mapping in Stuttgart City." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 225–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_14.

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AbstractExposure to particulate matter (PM) pollution poses a major risk to the environment and human health. Monitoring PM pollution is thus crucial to understand particle distribution and mitigation. There has been rapid development of low-cost PM sensors and advancement in the field of Internet of Things (IoT) that has led to the deployment of the sensors by technology-aware people in cities. In this study, we evaluate the stability and accuracy of PM measurements from low-cost sensors crowd-sourced from a citizen science project in Stuttgart. Long-term measurements from the sensors show a strong correlation with measurements from reference stations with most of the selected sensors achieving Pearson correlation coefficients of r > 0.7. We investigate the stability of the sensors for reproducibility of measurements using five sensors installed at different height levels and horizontal distances. They exhibit minor variations with low correlation of variation (CV) values of between 10 and 14%. A CV of ≤10% is recommended for low-cost sensors. In a dense network, the sensors enable extraction pollution patterns and trends. We analyse PM measurements from 2 years using space-time pattern analysis and generate two clusters of sensors that have similar trends. The clustering shows the relationship between traffic and pollution with most sensors near major roads being in the same cluster.

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Heinisch, Barbara, Kristin Oswald, Maike Weißpflug, Sally Shuttleworth, and Geoffrey Belknap. "Citizen Humanities." In The Science of Citizen Science, 97–118. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58278-4_6.

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AbstractCitizen humanities is the term for citizen ‘science’ in the humanities. It has a long tradition and, since the object of investigation is human culture, raises questions about values, cultural significance, and deeper meaning of phenomena related to human culture.The development of digital technologies not only led to the emergence of digital humanities but also to new ways of involving citizens in the activities of cultural heritage institutions and academic research. Participants’ contributions to academic research and to the preservation of cultural heritage range from uncovering treasures hidden in archives and digital environments to tapping local knowledge. Their tasks have included tagging, transcribing, or cataloguing artefacts, through which they acquire specialist knowledge and competences, while assisting scholars and researchers to gain new insights. Challenges in the citizen humanities include biases, participant training and retention, as well as the advancement of digital technologies, such as artificial intelligence.Citizen humanities can combine topical issues in society with academic knowledge, demonstrate the relevance of the humanities for society, and establish a direct link to its members. In addition to the advancement of knowledge, the citizen humanities can unlock the potential of embedded, diverse, and culturally sensitive knowledge and play a crucial role in preserving and enriching cultural heritage.

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Bonney, Rick, Jennifer Shirk, and Tina B. Phillips. "Citizen Science." In Encyclopedia of Science Education, 1–3. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6165-0_291-1.

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Hall, James A. "Citizen Science." In Astronomers' Universe, 225–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-20636-3_14.

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Oesterle, Justine, Bhaskar Upadhyay, Julie C. Brown, and Matthew Vernon. "Citizen Science." In Handbook of Theory and Research in Cultural Studies and Education, 779–808. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-56988-8_28.

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Тези доповідей конференцій з теми "Crowd science.Citizen science"

Gorokhova, Tatiana. "Citizen science and its role in the sustainable development of Ukraine." In First International Conference "Open Science and Innovation in Ukraine 2022". State Scientific and Technical Library of Ukraine, 2022. http://dx.doi.org/10.35668/978-966-479-129-5-5-1.

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The research analyses the role of citizen science in the sustainable development (SD) of Ukraine, including case studies of citizen science implementation based on principles of SD. The author examines the consistent aspects that underlie citizen science and stimulate the effective participation of citizens and their contribution to citizen science. Keywords: citizen science, sustainable development, research, crowd science, citizen science program.

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Crowston, Kevin, Erica Mitchell, and Carsten Østerlund. "Coordinating Advanced Crowd Work: Extending Citizen Science." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2018. http://dx.doi.org/10.24251/hicss.2018.212.

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Rughinis, Cosima. "Citizen science, gallaxies and tropes: Knowledge creation in impromptu crowd science movements." In 2016 15th RoEduNet Conference: Networking in Education and Research. IEEE, 2016. http://dx.doi.org/10.1109/roedunet.2016.7753210.

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Guo, Jiayan, Jian Tang, Xinxue Zhou, and Jing Li. "How "Task-Individual Fit" Influences User Contribution Behaviors in Citizen Science." In ICCSE'19: The 4th International Conference on Crowd Science and Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3371238.3371254.

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Ono, Eiri, Yuko Ikkatai, and Teruaki Enoto. "Encouraging Citizen Motivation of Crowd Science: A Case Study of Kyoto Open Science Activities." In 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2017. http://dx.doi.org/10.1109/iiai-aai.2017.175.

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Minkman, E., P. J. van Overloop, and M. C. A. van der Sanden. "Citizen Science in Water Quality Monitoring: Mobile Crowd Sensing for Water Management in the Netherlands." In World Environmental and Water Resources Congress 2015. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479162.138.

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J Storer, Jeremy, Joseph T. Chao, Andrew T Torelli, and Alexis D Ostrowski. "KnoWare: A System for Citizen-based Environmental Monitoring." In InSITE 2016: Informing Science + IT Education Conferences: Lithuania. Informing Science Institute, 2016. http://dx.doi.org/10.28945/3510.

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Non-expert scientists are frequently involved in research requiring data acquisition over large geographic areas. Despite mutual benefits for such “citizen science”, barriers also exist, including 1) difficulty maintaining user engagement with timely feedback, and 2) the challenge of providing non-experts with the means to generate reliable data. We have developed a system that addresses these barriers. Our technologies, KnoWare and InSpector, allow users to: collect reliable scientific measurements, map geo-tagged data, and intuitively visualize the results in real-time. KnoWare comprises a web portal and an iOS app with two core functions. First, users can generate scientific ‘queries’ that entail a call for information posed to a crowd with customized options for participant responses and viewing data. Second, users can respond to queries with their GPS-enabled mobile device, which results in their geo- and time-stamped responses populating a web-accessible map in real time. KnoWare can also interface with additional applications to diversify the types of data that can be reported. We demonstrate this capability with a second iOS app called InSpector that performs quantitative water quality measurements. When used in combina-tion, these technologies create a workflow to facilitate the collection, sharing and interpretation of scientific data by non-expert scientists.

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Rosser, Holly, and Andrea Wiggins. "Crowds and Camera Traps: Genres in Online Citizen Science Projects." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2019. http://dx.doi.org/10.24251/hicss.2019.637.

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"Knowledge Collaboration Between Professionals and Non-Professionals: A Systematic Mapping Review of Citizen Science, Crowd Sourcing and Community-driven Research." In 2th European Conference on Game Based Learning. ACPI, 2019. http://dx.doi.org/10.34190/gbl.19.185.

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Zappatore, Marco, Antonella Longo, and Mario A. Bochicchio. "Using mobile crowd sensing for noise monitoring in smart cities." In 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech). IEEE, 2016. http://dx.doi.org/10.1109/splitech.2016.7555950.

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Звіти організацій з теми "Crowd science.Citizen science"

Wagenknecht, Katherin, Sarah Klemisch, and Kamila Labuda. Towards Citizen Science Communication : How can citizen science enhance science communication? Technische Hochschule Wildau, 2021. http://dx.doi.org/10.15771/innohub_2.

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Science communication has shifted significantly in recent decades. From an early, widespread understanding that scientific findings were disseminated in a linear, closed pathway, there is now widespread acknowledgement of the need for more comprehensive and inclusive participation in science [cf. Massarani et al., 2017; Schäfer et al., 2015]. The project “Wir forschen”, which is part of the project “Innovation Hub 13 – fast track to transfer” coordinated by Technical University of Applied Science Wildau and Brandenburg University of Technology Cottbus-Senftenberg, explores methodological and practical characteristics of citizen science as a form of science communication. In this project, we outline an argumentation of understanding citizen science as science communication and furthermore introduce the term citizen science communication. In the processual course of the projects, different instruments of science communication come into play, which establish a dialog between the actors and initiate exchange with different intentions and approaches. In doing so, the project contributes to the science of science communication.

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Monika Krach, Monika Krach. Rocky Intertidal Citizen Science. Experiment, June 2015. http://dx.doi.org/10.18258/5405.

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Reynolds, Christian, Libby Oakden, Sarah West, Rachel Pateman, and Chris Elliott. Citizen Science and Food: A Review. Food Standards Agency, March 2021. http://dx.doi.org/10.46756/sci.fsa.nao903.

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Citizen science and food is part of a new programme of work to explore how we can involve the communities we serve when building the evidence-base on which policy decisions are made. Citizen science is an approach that can provide high volumes of data with a wide geographic spread. It is relatively quick to deploy and allows access to evidence we would ordinarily have difficulty collating. This methodology has been endorsed by the European Commission for Research, Science and Innovation. There is no one size fits all definition, but citizen science projects involves engaging with communities and asking them to be part of the project, either through engaging them in data collection or through other ways of co-creation. For participants, citizen science offers learning opportunities, the satisfaction of contributing to scientific evidence and the potential to influence policy. It can also give us data which is high in volume, has wide geographical spread, is relatively quick to deploy and that we couldn’t access any other way. Projects using these methods often involve engaging with communities and asking them to be part of the project. This can be either through working with them in data collection, or through co-creation. This report demonstrates that the research community are already undertaking numerous pieces of research that align with FSA’s evidence needs. This includes examples from the UK and other global communities. Participants in such research have collected data on topics ranging from food preparation in the home to levels of chemical contaminant in foods. The findings of this report outline that citizen science could allow the FSA to target and facilitate more systematic engagement with UK and global research communities, to help address key research priorities of the FSA.

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Hemment, Drew, Mel Woods, and Raquel Ajates Gonzalez. Massive Online Open Citizen Science: Use of MOOCs to scale rigorous Citizen Science training and participation. University of Dundee, November 2018. http://dx.doi.org/10.20933/100001122.

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Christian, Siderius, Uddin Nasir, Singh Prasoon, Virk Zeeshan, and Syed Abu. Citizen-driven science climate adaptation ateliers - CISCAAs. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2018. http://dx.doi.org/10.53055/icimod.861.

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Woods, Mel, Saskia Coulson, Raquel Ajates, Angelos Amditis, Andy Cobley, Dahlia Domian, Gerid Hager, et al. Citizen Science Projects: How to make a difference. WeObserve, 2020. http://dx.doi.org/10.20933/100001193.

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Citizen Science Projects: How to make a difference, is a massive open online course (MOOC). It was developed by the H2020 WeObserve project and ran on the FutureLearn platform from 2019. The course was designed to assist learners from all backgrounds and geographical locations to discover how to build their own citizen science project to address global challenges and create positive change. It also helped learners with interpreting the information they collected and using their findings to educate others about important local and global concerns. The main learning objectives for the course were: * Discover what citizen science and citizen observatories are * Engage with the general process of a citizen science project, the tools used and where they can be accessed * Collect and analyse data on relevant issues such as environmental challenges and disaster management, and discuss the results of their findings * Explore projects happening around the world, what the aims of these projects are and how learners could get involved * Model the steps to create their own citizen science project * Evaluate the potential of citizen science in bringing about change This course also provided five open-source, downloadable tools which have been tested in previous citizen science projects and created for the use of a wider range of projects. These tools are listed below and available in the research repository: * Empathy timeline tool * Community-level indicators tool * Data postcards tool * Future newspaper tool * Co-evaluation tool

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Joey Hulbert, Joey Hulbert. Discovering Plant Destroyers in South Africa with Citizen Science. Experiment, February 2014. http://dx.doi.org/10.18258/2066.

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Kreofsky, Tess. Isn’t Citizen Science a Hoot? A Case-study Exploring the Effectiveness of Citizen Science as an Instrument to Teach the Nature of Science through a Local Nocturnal Owl-Monitoring Project. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2641.

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Joey Hulbert, Joey Hulbert. Engage Kayamandi Youth in Cape Citizen Science with Vision Afrika. Experiment, December 2016. http://dx.doi.org/10.18258/8690.

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Mattijssen, Thomas, Maarten Visscher, Wessel Ganzevoort, and Marcel Pleijte. Monitoring van burgerbetrokkenheid bij natuur : Citizen science en doelgroep-panels. Wageningen: Wettelijke Onderzoekstaken Natuur & Milieu, 2022. http://dx.doi.org/10.18174/558907.

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