Journal articles on the topic 'Crowd science.Citizen science'
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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 <i>the crowd</i> 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 <i>the crowd</i>.
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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. <br><br> 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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Bröring, Arne, Albert Remke, Christoph Stasch, Christian Autermann, Matthes Rieke, and Jakob Möllers. "enviroCar: A Citizen Science Platform for Analyzing and Mapping Crowd-Sourced Car Sensor Data." Transactions in GIS 19, no. 3 (June 2015): 362–76. http://dx.doi.org/10.1111/tgis.12155.
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Moore, Ashley C., Ashley A. Anderson, Marilee Long, Lauralynn T. McKernan, and John Volckens. "The power of the crowd: Prospects and pitfalls for citizen science in occupational health." Journal of Occupational and Environmental Hygiene 16, no. 3 (February 22, 2019): 191–98. http://dx.doi.org/10.1080/15459624.2019.1566733.
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Parés, M. E., and F. Vázquez-Gallego. "C-AQM: A CROWD-SOURCED AIR QUALITY MONITORING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 491–97. http://dx.doi.org/10.5194/isprs-archives-xlii-4-491-2018.
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<p><strong>Abstract.</strong> European cities are currently facing one of the main evolutions of the last fifty years. “Cities for the citizens” is the new leitmotiv of modern societies, and citizens are demanding, among others, a greener environment including non-polluted air. Improved sensors and improved communication systems open the door to the design of new systems based on citizen science to better monitor the air quality. In this paper, we present a system that relies on the already available Copernicus Environment Service, on Air Quality Monitoring reference stations and on a cluster of new low-cost, low-energy sensor nodes that will improve the resolution of air quality maps. The data collected by this system will be stored in a time series database, and it will be available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main challenges imposed by Air Quality Monitoring systems, our proposal to overcome those challenges, and the results of our preliminary tests.</p>
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TAKADA, Yo. "Failed citizen science project “Crow Walking Survey”." Journal of the Japanese Society of Revegetation Technology 47, no. 4 (May 31, 2022): 447–48. http://dx.doi.org/10.7211/jjsrt.47.447.
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Prudic, Kathleen, Jeffrey Oliver, Brian Brown, and Elizabeth Long. "Comparisons of Citizen Science Data-Gathering Approaches to Evaluate Urban Butterfly Diversity." Insects 9, no. 4 (December 6, 2018): 186. http://dx.doi.org/10.3390/insects9040186.
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By 2030, ten percent of earth’s landmass will be occupied by cities. Urban environments can be home to many plants and animals, but surveying and estimating biodiversity in these spaces is complicated by a heterogeneous built environment where access and landscaping are highly variable due to human activity. Citizen science approaches may be the best way to assess urban biodiversity, but little is known about their relative effectiveness and efficiency. Here, we compare three techniques for acquiring data on butterfly (Lepidoptera: Rhopalocera) species richness: trained volunteer Pollard walks, Malaise trapping with expert identification, and crowd-sourced iNaturalist observations. A total of 30 butterfly species were observed; 27 (90%) were recorded by Pollard walk observers, 18 (60%) were found in Malaise traps, and 22 (73%) were reported by iNaturalist observers. Pollard walks reported the highest butterfly species richness, followed by iNaturalist and then Malaise traps during the four-month time period. Pollard walks also had significantly higher species diversity than Malaise traps.
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Spiers, Helen. "From stars to cells – harnessing the power of the crowd for research." Biochemist 39, no. 6 (December 1, 2017): 40–41. http://dx.doi.org/10.1042/bio03906040.
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Modern research techniques allow more data to be generated than can be easily analyzed by the scientists who produce it. An original solution to this problem is to recruit volunteers to help with data analysis through online citizen science projects such as the Zooniverse.
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Crumley, Ryan L., David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, and Christopher Cosgrove. "Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations." Hydrology and Earth System Sciences 25, no. 9 (August 31, 2021): 4651–80. http://dx.doi.org/10.5194/hess-25-4651-2021.
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Abstract. A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/, last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.
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Mao, Andrew, Ece Kamar, and Eric Horvitz. "Why Stop Now? Predicting Worker Engagement in Online Crowdsourcing." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 103–11. http://dx.doi.org/10.1609/hcomp.v1i1.13076.
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We present studies of the attention and time, or engagement, invested by crowd workers on tasks. Consideration of worker engagement is especially important in volunteer settings such as online citizen science. Using data from Galaxy Zoo, a prominent citizen science project, we design and construct statistical models that provide predictions about the forthcoming engagement of volunteers. We characterize the accuracy of predictions with respect to different sets of features that describe user behavior and study the sensitivity of predictions to variations in the amount of data and retraining. We design our model for guiding system actions in real-time settings, and discuss the prospect for harnessing predictive models of engagement to enhance user attention and effort on volunteer tasks.
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Wood, Sarah A., Patrick W. Robinson, Daniel P. Costa, and Roxanne S. Beltran. "Accuracy and precision of citizen scientist animal counts from drone imagery." PLOS ONE 16, no. 2 (February 22, 2021): e0244040. http://dx.doi.org/10.1371/journal.pone.0244040.
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Repeated counts of animal abundance can reveal changes in local ecosystem health and inform conservation strategies. Unmanned aircraft systems (UAS), also known as drones, are commonly used to photograph animals in remote locations; however, counting animals in images is a laborious task. Crowd-sourcing can reduce the time required to conduct these censuses considerably, but must first be validated against expert counts to measure sources of error. Our objectives were to assess the accuracy and precision of citizen science counts and make recommendations for future citizen science projects. We uploaded drone imagery from Año Nuevo Island (California, USA) to a curated Zooniverse website that instructed citizen scientists to count seals and sea lions. Across 212 days, over 1,500 volunteers counted animals in 90,000 photographs. We quantified the error associated with several descriptive statistics to extract a single citizen science count per photograph from the 15 repeat counts and then compared the resulting citizen science counts to expert counts. Although proportional error was relatively low (9% for sea lions and 5% for seals during the breeding seasons) and improved with repeat sampling, the 12+ volunteers required to reduce error was prohibitively slow, taking on average 6 weeks to estimate animals from a single drone flight covering 25 acres, despite strong public outreach efforts. The single best algorithm was ‘Median without the lowest two values’, demonstrating that citizen scientists tended to under-estimate the number of animals present. Citizen scientists accurately counted adult seals, but accuracy was lower when sea lions were present during the summer and could be confused for seals. We underscore the importance of validation efforts and careful project design for researchers hoping to combine citizen science with imagery from drones, occupied aircraft, and/or remote cameras.
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Palmer, Meredith S., Sarah E. Huebner, Marco Willi, Lucy Fortson, and Craig Packer. "Citizen science, computing, and conservation: How can “Crowd AI” change the way we tackle large-scale ecological challenges?" Human Computation 8, no. 2 (July 27, 2021): 54–75. http://dx.doi.org/10.15346/hc.v8i2.123.
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Camera traps - remote cameras that capture images of passing wildlife - have become a ubiquitous tool in ecology and conservation. Systematic camera trap surveys generate ‘Big Data’ across broad spatial and temporal scales, providing valuable information on environmental and anthropogenic factors affecting vulnerable wildlife populations. However, the sheer number of images amassed can quickly outpace researchers’ ability to manually extract data from these images (e.g., species identities, counts, and behaviors) in timeframes useful for making scientifically-guided conservation and management decisions. Here, we present ‘Snapshot Safari’ as a case study for merging citizen science and machine learning to rapidly generate highly accurate ecological Big Data from camera trap surveys. Snapshot Safari is a collaborative cross-continental research and conservation effort with 1500+ cameras deployed at over 40 eastern and southern Africa protected areas, generating millions of images per year. As one of the first and largest-scale camera trapping initiatives, Snapshot Safari spearheaded innovative developments in citizen science and machine learning. We highlight the advances made and discuss the issues that arose using each of these methods to annotate camera trap data. We end by describing how we combined human and machine classification methods (‘Crowd AI’) to create an efficient integrated data pipeline. Ultimately, by using a feedback loop in which humans validate machine learning predictions and machine learning algorithms are iteratively retrained on new human classifications, we can capitalize on the strengths of both methods of classification while mitigating the weaknesses. Using Crowd AI to quickly and accurately ‘unlock’ ecological Big Data for use in science and conservation is revolutionizing the way we take on critical environmental issues in the Anthropocene era.
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Özdemir, Vural, Kamal F. Badr, Edward S. Dove, Laszlo Endrenyi, Christy Jo Geraci, Peter J. Hotez, Djims Milius, et al. "Crowd-Funded Micro-Grants for Genomics and “Big Data”: An Actionable Idea Connecting Small (Artisan) Science, Infrastructure Science, and Citizen Philanthropy." OMICS: A Journal of Integrative Biology 17, no. 4 (April 2013): 161–72. http://dx.doi.org/10.1089/omi.2013.0034.
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Noel-Storr, Anna, Gordon Dooley, Robin Featherstone, Susanna Wisniewski, Ian Shemilt, James Thomas, Gerald Gartlehner, Barbara Nußbaumer-Steit, and Christopher Mavergames. "Crowdsourcing and COVID-19: a case study of Cochrane Crowd." Journal of EAHIL 17, no. 2 (June 24, 2021): 27–31. http://dx.doi.org/10.32384/jeahil17467.
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Cochrane has used crowdsourcing effectively to identify health evidence since 2014. To date, over 175,000 trialshave been identified for Cochrane’s Central Register of Controlled Trials via Cochrane Crowd (https://crowd.cochrane.org), Cochrane’s citizen science platform, engaging a Crowd of over 20,000 people from 166 countries. The COVID-19 pandemic presented the evidence synthesis community with the enormous challenge of keeping up with the exponential output of COVID-19 research. This case study will detail the new tasks we developed to aid the production of COVID-19 rapid reviews and supply the Cochrane COVID-19 study register. The pandemic initially looked set to disrupt the Crowd team’s plans for 2020 but has in fact served to further our understanding of the potential role crowdsourcing can play in the health evidence ecosystem.
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Bonaventura, Filippo. "Science journalism in the age of crowd: interviews." Journal of Science Communication 09, no. 04 (December 21, 2010): C01. http://dx.doi.org/10.22323/2.09040301.
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The purpose of this commentary is extending and enriching the discussion raised in the “Science Journalism and Power in the 21st Century” workshop, held last month in the context of MAPPE project at SISSA, Trieste. We collected three interviews of authors expert in communication and media on different fields strongly influenced by participatory communication practices: Anabela Carvalho (global warming and climate change), Pieter Maeseele (technological risks) and Denise Silber (‘eHealth’ and ‘Health 2.0’). The interviews therefore analyze three different perspectives of a more general issue: How is the ecosystem of scientific information changing by means of a new concept of ‘public’? Which are the new ways in which citizens produce and manage scientific information? What could be a new role for science journalism? These three interviews aim to delve, from a theoretical point of view, into the sociological framework of an ecosystem of information driven by active public participation in the communicative practices. Emphasis will be put on the way in which scientific knowledge is reconstructed and negotiated in the Web 2.0 arena: democracy in the knowledge society intrinsically depends on a fair outcome of this process. Nevertheless, the crisis of traditional media and journalist’s figure is threatening the democratization of science. In this sense, the social function of journalism is still – and will be – unescapable. The re-distribution of social power by means of Web 2.0 is a key issue, and new sensible communication practices and professionals are needed.
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Hagen, Niclas. "Scaling up and rolling out through the Web." Nordic Journal of Science and Technology Studies 8, no. 1 (May 16, 2020): 4–15. http://dx.doi.org/10.5324/njsts.v8i1.3320.
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The purpose of this paper is to investigate online public participation and engagement in science through crowdsourcing platforms. In order to fulfil this purpose, this paper will use the crowdsourcing platform Zooniverse as a case study, as it constitutes the most prominent and established citizen science platform today. The point of departure for the analysis is that Zooniverse can be seen as a “platformization” of citizen science and scientific citizenship. The paper suggests that the mobilisation of individuals who participate and engage in science on the Zooniverse platform takes place through an epistemic culture that emphasises both authenticity and prospects of novel discoveries. Yet, in the process of turning “raw” data into useable data, Zooniverse has implemented a framework that structures the crowd, something that limits the sort of participation that is offered on the platform. This limitation means that the platform as a whole hardly be seen as fostering a more radical democratic inclusion, for example in the form of a co-production of scientific knowledge, that dissolves the institutional borders between scientists and non-professional volunteers.
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Chamberlain, Jon. "Groupsourcing: Problem Solving, Social Learning and Knowledge Discovery on Social Networks." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 (September 5, 2014): 65–66. http://dx.doi.org/10.1609/hcomp.v2i1.13136.
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Increasingly social networks are being used for citizen science, where members of the public contribute knowledge to scientific endeavours. Tasks can be presented and solved using human computation, termed groupsourcing, with users benefiting from community tuition and experts gaining knowledge from the crowd. This paper gives details of a prototype that utilises groupsourcing to solve image classification tasks, to support social learning and to facilitate knowledge discovery in the domain of marine biology.
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Hsu, Yen-Chia, Ting-Hao (Kenneth) Huang, Ting-Yao Hu, Paul Dille, Sean Prendi, Ryan Hoffman, Anastasia Tsuhlares, Jessica Pachuta, Randy Sargent, and Illah Nourbakhsh. "Project RISE: Recognizing Industrial Smoke Emissions." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 14813–21. http://dx.doi.org/10.1609/aaai.v35i17.17739.
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Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for Social Impact.
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van Meerveld, H. J. Ilja, Marc J. P. Vis, and Jan Seibert. "Information content of stream level class data for hydrological model calibration." Hydrology and Earth System Sciences 21, no. 9 (September 28, 2017): 4895–905. http://dx.doi.org/10.5194/hess-21-4895-2017.
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Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.
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Väli, Ülo, and Ana Magalhães. "Web-based citizen science as a tool in conservation research: A case study of prey delivery by the Lesser Spotted Eagle." PLOS ONE 17, no. 1 (January 26, 2022): e0261655. http://dx.doi.org/10.1371/journal.pone.0261655.
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Citizen science is increasingly contributing to ecology and conservation research, mostly by the extensive collection of field data. Although webcams attract numerous observers, they have been underused in this respect. We used prey delivery records deposited by citizen scientists in an internet forum linked to webcams to explore the diet composition and food provisioning in a forest-dwelling raptor of conservation concern, the Lesser Spotted Eagle (Clanga pomarina). Four pairs were studied throughout the breeding season. Most of the identified prey items were mammals (62.1%), followed by frogs (31.2%), birds (6.6%) and fish (0.1%). Among mammals, voles accounted for 84.6%, moles 12.1%, water voles 2.4% and weasels 0.4%. Frogs were the most frequently detected prey item in the spring, with a slight increase towards the end of the season, the proportion of mammals increased during the breeding season, and birds were hunted mostly in the middle of the breeding season. However, exact temporal patterns differed between nests. The food delivery rate of males increased over time but decreased somewhat before fledging the young. Females started hunting in mid-summer and their rapidly increasing effort compensated for a reduced male hunting intensity. The data collected by citizen scientists via webcams reflected the general patterns detected in earlier studies, supporting the reliability of crowd-sourced web-based data collection in avian foraging ecology.
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Elmore, Kimberly L., Z. L. Flamig, V. Lakshmanan, B. T. Kaney, V. Farmer, Heather D. Reeves, and Lans P. Rothfusz. "MPING: Crowd-Sourcing Weather Reports for Research." Bulletin of the American Meteorological Society 95, no. 9 (September 1, 2014): 1335–42. http://dx.doi.org/10.1175/bams-d-13-00014.1.
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The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among the expectations that have resulted from the upgrade is the ability to discriminate between different precipitation types in winter precipitation events. To know how well any such algorithm performs and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple “app” called mobile Precipitation Identification Near the Ground (mPING) was developed to run on “smart” phones or, more generically, web-enabled devices with GPS location capabilities. Using mPING, anyone with a smartphone can pass observations to researchers at no additional cost to their phone service or to the research project. Deployed in mid-December 2012, mPING has proven to be not only very popular, but also capable of providing consistent, accurate observational data.
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Ulahannan, Jijo Pulickiyil, Nikhil Narayanan, Nishad Thalhath, Prem Prabhakaran, Sreekanth Chaliyeduth, Sooraj P. Suresh, Musfir Mohammed, et al. "A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India." Journal of the American Medical Informatics Association 27, no. 12 (November 5, 2020): 1913–20. http://dx.doi.org/10.1093/jamia/ocaa203.
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Abstract Objective India reported its first coronavirus disease 2019 (COVID-19) case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, these unstructured data limit upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets. Materials and Methods Through a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. This was further visualized as a dashboard through a front-end Web application and a JSON (JavaScript Object Notation) repository, which serves as an application programming interface for the front end. Results From the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for nonspecialists. To ensure longevity and reusability, the dataset was deposited in an open-access public repository for future analysis. Finally, we provide outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak. Discussion We anticipate that our dataset can form the basis for future studies, supplemented with clinical and epidemiological data from the individuals affected with COVID-19 in Kerala. Conclusions We reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals.
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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." Informing Science: The International Journal of an Emerging Transdiscipline 19 (2016): 125–39. http://dx.doi.org/10.28945/3500.
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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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Oremland, Laura, Abigail Furnish, Julia Byrd, and Richard Cody. "How Fishery Managers Can Harness the Power of the Crowd: Using Citizen Science and Nontraditional Data Sources in Fisheries Management." Fisheries 47, no. 11 (November 2022): 459–62. http://dx.doi.org/10.1002/fsh.10858.
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Noël, Alain. "Studying Your Own Country: Social Scientific Knowledge for Our Times and Places." Canadian Journal of Political Science 47, no. 4 (December 2014): 647–66. http://dx.doi.org/10.1017/s0008423914001085.
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AbstractPolitical science is both a generalizing and an anchored, nationally defined, discipline. Too often, the first perspective tends to crowd out the latter, because it appears more prestigious, objective, or scientific. Behind the international/national dichotomy, there are indeed rival conceptions of social science and important ontological, epistemological and methodological assumptions. This article discusses these assumptions and stresses the critical contribution of idiographic, single-outcome studies, the importance of producing relevant, usable knowledge and the distinctive implications of studying one's own country, where a scholar is also a citizen, involved in more encompassing national conversations. The aim is not to reject the generalizing, international perspective, or even the comparative approach, but rather to reaffirm the importance of maintaining as well, and in fact celebrating, the production of social scientific knowledge directly relevant for our own times and places.
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Skarlatidou, Artemis, and Marcos Moreu. "Maps in Citizen Science: A Preliminary Analysis of Use and User Issues." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-339-2019.
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<p><strong>Abstract.</strong> Citizen Science involves a collaboration or partnership between scientists and amateur volunteers, which may take various forms; from simple data collection to a close collaboration where both parts jointly define their aims, methodologies and analysis approaches in the scientific endeavour. Although citizen science has existed for more than two centuries (Silvertown, 2009), the widespread use of information and communication technology (ICT) now plays a significant role in the way citizen science is currently shaped and utilised. At present, there are hundreds of citizen science applications available which engage thousands of volunteers in the disciplines of astronomy, environmental conservation, biology, marine science, geography and many others. A relatively recent analysis of 388 citizen science projects revealed that they have been used to engage 1.3 million volunteers, contributing up to US$2.5 billion in-kind annually (Theobald et al. 2015).</p><p>Web 2.0 and its associated technologies, which have existed for almost 15 years now, have enabled the development of websites which supported content generation by their end users (aka crowdsourcing; Howe, 2008) and multiple interactions amongst them. Examples include web-based communities, social-networking sites, wikis, mashups, and others (Batty et al., 2010). In this context the term ‘Neogeography’ was coined (Eisnor, 2006) and since then it has been used within the geographic and cartographic circles to describe the multi-directional generation of geospatial contents and interactions, which enables non-GIS professionals to create and share maps and other geographic information online “on their own terms” simply using the “elements of an existing toolset” (Eisnor, 2006). Map mashups started to not only be used for disseminating spatial information to a wider user audience, but applications have been created which enabled the crowdsourcing of geographic information for the production of geospatial knowledge; a trend, which is also known under the term Volunteered Geographic Information (Goodchild, 2007). OpenStreetMap (OSM) is perhaps one of the earliest examples that the literature cites to demonstrate how harnessing the power of the crowds for the collection of geographic information can result in the creation of a free, open source of map of the world (Goodchild, 2007; Haklay et al., 2008; Batty et al., 2010).</p><p>We argue in this paper that the above developments from the geospatial context have massively contributed to the current state of citizen science. While interactive web maps made their appearance as mainly “way-finding” tools (Skarlatidou and Haklay, 2006), they quickly became part of digital interactions in a much broader context and they are currently a basic component of most citizen science projects. The relevance and significance of space has been fully exploited by technological features such as geotagging, GPS-enabled mobile devices fully integrated with other sensors, which has made the collection and sharing of data much easier (Haklay, 2013). Sinton (2018) argues that it is such the power of maps in citizen science that “it would be difficult to pursue a project in biological conservation, for example, without incorporating mapping”. The breadth of citizen science applications is so wide that we observe an extremely wide range of potential users, with very different skill sets, backgrounds, literacy levels and user needs.</p>
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Manik, Lindung Parningotan, Hatim Albasri, Reny Puspasari, Aris Yaman, Shidiq Al Hakim, Al Hafiz Akbar Maulana Siagian, Siti Kania Kushadiani, et al. "Usability and acceptance of crowd-based early warning of harmful algal blooms." PeerJ 11 (March 1, 2023): e14923. http://dx.doi.org/10.7717/peerj.14923.
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Crowdsensing has become an alternative solution to physical sensors and apparatuses. Utilizing citizen science communities is undoubtedly a much cheaper solution. However, similar to other participatory-based applications, the willingness of community members to be actively involved is paramount to the success of implementation. This research investigated factors that affect the continual use intention of a crowd-based early warning system (CBEWS) to mitigate harmful algal blooms (HABs). This study applied the partial least square-structural equation modeling (PLS-SEM) using an augmented technology acceptance model (TAM). In addition to the native TAM variables, such as perceived ease of use and usefulness as well as attitude, other factors, including awareness, social influence, and reward, were also studied. Furthermore, the usability factor was examined, specifically using the System Usability Scale (SUS) score as a determinant. Results showed that usability positively affected the perceived ease of use. Moreover, perceived usefulness and awareness influenced users’ attitudes toward using CBEWS. Meanwhile, the reward had no significant effects on continual use intention.
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Comber, A., P. Mooney, R. S. Purves, D. Rocchini, and A. Walz. "COMPARING NATIONAL DIFFERENCES IN WHAT PEOPLE PERCEIVE TO BE THERE: MAPPING VARIATIONS IN CROWD SOURCED LAND COVER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 71–75. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-71-2015.
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This paper describes a simple comparison of the distributions of land cover features identified from volunteered data contributed by different social groups – in this case comparing two groups of Geo-Wiki campaigns. Understanding the impacts on analyses of citizen science data contributed by different groups is critical to ensure robust scientific outputs and to fully realise the potential benefits to formal scientific research. It is well known that different people, with different backgrounds and subject to different cultural factors, hold varying landscape conceptualisations. This paper analyses volunteered geographical information on land cover to generate land cover maps. It uses a geographically weighted approach to generate land cover mappings. The mappings generated by different groups (in this case a from a specific unnamed country) are compared and the results show how the predicted land cover distributions vary, with large differences in some classes (e.g. Barren land, Shrubland, Wetland) and little difference in others (e.g. Tree cover). This suggests that for some landscape features cultural and national differences matter when it comes to using crowdsourced data in formal scientific analyses and highlights the potential problems of not considering contributor backgrounds in citizen science. This is important because such data re now routinely being used to develop global land cover data, to generate uncertainty estimates of existing global land cover products and to generate global forest inventories. These in turn are being suggested as suitable inputs to such things as global climate models. A number of critical research directions arising from these findings are discussed.
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McKay, Derek, and Andreas Kvammen. "Auroral classification ergonomics and the implications for machine learning." Geoscientific Instrumentation, Methods and Data Systems 9, no. 2 (July 9, 2020): 267–73. http://dx.doi.org/10.5194/gi-9-267-2020.
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Abstract. The machine-learning research community has focused greatly on bias in algorithms and have identified different manifestations of it. Bias in training samples is recognised as a potential source of prejudice in machine learning. It can be introduced by the human experts who define the training sets. As machine-learning techniques are being applied to auroral classification, it is important to identify and address potential sources of expert-injected bias. In an ongoing study, 13 947 auroral images were manually classified with significant differences between classifications. This large dataset allowed for the identification of some of these biases, especially those originating as a result of the ergonomics of the classification process. These findings are presented in this paper to serve as a checklist for improving training data integrity, not just for expert classifications, but also for crowd-sourced, citizen science projects. As the application of machine-learning techniques to auroral research is relatively new, it is important that biases are identified and addressed before they become endemic in the corpus of training data.
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Dhonju, H. K., W. Xiao, B. Shakya, J. P. Mills, and V. Sarhosis. "DOCUMENTATION OF HERITAGE STRUCTURES THROUGH GEO-CROWDSOURCING AND WEB-MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 17–21. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-17-2017.
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Heritage documentation has become increasingly urgent due to both natural impacts and human influences. The documentation of countless heritage sites around the globe is a massive project that requires significant amounts of financial and labour resources. With the concepts of volunteered geographic information (VGI) and citizen science, heritage data such as digital photographs can be collected through online crowd participation. Whilst photographs are not strictly geographic data, they can be geo-tagged by the participants. They can also be automatically geo-referenced into a global coordinate system if collected via mobile phones which are now ubiquitous. With the assistance of web-mapping, an online geo-crowdsourcing platform has been developed to collect and display heritage structure photographs. Details of platform development are presented in this paper. The prototype is demonstrated with several heritage examples. Potential applications and advancements are discussed.
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Capel, Horacio. "Ciencia ciudadana, ética y política para viejos y nuevos problemas." Locale 1, no. 1 (March 21, 2017): 29–69. http://dx.doi.org/10.14409/rl.v1i1.6264.
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Los retos a los que se enfrentan los países iberoamericanos son numerosos. Algunos son viejos y siguen estando vigentes, pero a ellos se han unido otros nuevos, como resultado de las transformaciones del mundo actual.Para estudiarlos y enfrentarse a ellos, hoy es posible asociar a los ciudadanos a las investigaciones científicas que se realizan. No solo se puede contar con la colaboración de no profesionales en la recolección de datos. lo que ya es muy importante, sino que éstos también pueden hacer preguntas, expresar críticas o dudas, y sugerir nuevas caminos en los trabajos científicos que se realizan. Se alude hoy a ello con el término «ciencia ciudadana», y con calificativos tales como ciencia cívica, ciencia en red, ciencia colaborativa, crowd science, crowd– source science, y otras. Se trata de las investigaciones científicas que se llevan a cabo por científicos con la colaboración de no profesionales, aficionados y gente común, a menudo como forma de colaboración abierta y de micromecenazgo. Lo cual contribuye a extender la práctica científica, en un mundo en el que la población tiene una formación cultural cada vez más elevada.La necesidad de comportamientos éticos se reivindica cada vez más en la crisis actual. En este artículo se defiende que la ética está vinculada a las costumbres y a la política. Aunque las alusiones a la ética son bienvenidas, lo que se necesitan son medidas políticas. En el artículo se abordan los problemas de la ética y su relación con las costumbres y con la política, lo que ya fue teorizado por Aristóteles en su Ética Nicomáquea y por Kant en su Metafísica de las costumbres. La coacción jurídica es fundamental y prioritaria, y los principios religiosos no deben afectar a las leyes que se elaboran y se aprueban democráticamente con validez para todos.Palabras clave: política y ética; problemas en países iberoamericanos; problemas viejos y nuevos; ciencia en colaboración. AbstractCITIZEN SCIENCE, ETHICS AND POLITICAL TO OLD AND NEW PROBLEMSThe challenges facing Ibero American countries are numerous. Some are old and are still in force; but they have been joined by new ones as a result of current world's change.To study and face them, it is possible to associate citizens to scientific research which are being conducted. Not only it is posible to count on the collaboration non–professional in data collection, which is a key issue, but they can also ask questions, express criticism or doubts, and suggest new ways in scientific work done. It is being referred today with the terms citizen science, civic science, networked science, collaborative science, crowd science, and others. It is about the scientific investigations conducted by scientists with the help of non–professionals, amateurs and ordinary people (often as crowdsourcing and crowdfunding), thus contributing to widespread scientific practice, in a world in which the population has a high and increasing cultural training. The need for ethical behaviours are increasingly calling with the current crisis. In this paper it is argued that ethics is linked to the customs and politics.Although allusions to ethics are welcome, what is needed are political measures. This paper addresses the problems of ethics and its relationship with the customs and which politics, which was already theorized by Aristotle in his Nicomachean Ethics and by Kant in his Metaphysics of Morals. Legal coercion is fundamental and a priority, and religious principles should not affect the laws that are developed and approved democratically whith validity for everyone.Keywords: politics and ethics; issues in Ibero American countries; old and new problems; science in collaboration; collaborative science. ResumoCIÊNCIA CIDADÃ, ÉTICA E POLÍTICA PARA PROBLEMAS VELHOS E NOVOSCiência cidadã, ética e política para problemas velhos e novos Os desafíos enfrentados pelos países ibero–americanos são inúmeros. Alguns são antigos, porém ainda vigentes. E a eles se uniram outros novos, como resultado das tranformações do mundo atual.Para estudá–los e enfrentá–los, hoje é possível associar os cidadãos às pesquisas científicas que se realizam. Não somente contar com a colaboração de não–profissionais na coleta de dados, o que já é muito importante, mas os mesmos também podem fazer perguntas, expressar suas críticas e dúvidas, além de sugerir novos caminhos aos trabalhos científicos. O termo «ciência cidadã» hoje é usado para referir–se a este cenário, com denominações tais como ciência cívica, ciência em rede, ciência colaborativa, crowd science, crowd source science, entre outros. Tratam–se de pesquisas científicas realizadas por cientistas com o apoio de não–profissionais, estusiastas e gente comum, muitas vezes através de colaboração aberta e financiamento coletivo. O que acaba contribuindo para estender a prática científica, em um mundo no qual a população tem uma formação cultural cada vez mais elevada.Urge a necessidade de comportamentos éticos na atual crise. Neste artigo defende–se que a ética está vinculada aos costumes e à política, abordando seus problemas e relações, o que já foi teorizado por Aristóteles em Ética a Nicômaco e por Kant em Metafísica dos Costumes. Apesar das alusões à ética serem bem–vindas, o que se necessita na realidade são medidas políticas. A força jurídica é fundamental e prioritária, e os princípios religiosos não devem afetar as leis que são elaboradas e aprovadas democraticamente, válida para todos.Palavras–chave: política e ética; desafios em países ibero americanos; problemas velhos e novos; ciência colaborativa.
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Kamar, Ece, Ashish Kapoor, and Eric Horvitz. "Identifying and Accounting for Task-Dependent Bias in Crowdsourcing." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 92–101. http://dx.doi.org/10.1609/hcomp.v3i1.13238.
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Models for aggregating contributions by crowd workers have been shown to be challenged by the rise of task-specific biases or errors. Task-dependent errors in assessment may shift the majority opinion of even large numbers of workers to an incorrect answer. We introduce and evaluate probabilistic models that can detect and correct task-dependent bias automatically. First, we show how to build and use probabilistic graphical models for jointly modeling task features, workers' biases, worker contributions and ground truth answers of tasks so that task-dependent bias can be corrected. Second, we show how the approach can perform a type of transfer learning among workers to address the issue of annotation sparsity. We evaluate the models with varying complexity on a large data set collected from a citizen science project and show that the models are effective at correcting the task-dependent worker bias. Finally, we investigate the use of active learning to guide the acquisition of expert assessments to enable automatic detection and correction of worker bias.
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Al Duhayyim, Mesfer, Eatedal Alabdulkreem, Khaled Tarmissi, Mohammed Aljebreen, Bothaina Samih Ismail Abou El Khier, Abu Sarwar Zamani, Ishfaq Yaseen, and Mohamed I. Eldesouki. "Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities." Applied Sciences 12, no. 21 (November 4, 2022): 11187. http://dx.doi.org/10.3390/app122111187.
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Video surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd density analysis is a widely used application of object detection, while crowd density classification techniques face complications such as inter-scene deviations, non-uniform density, intra-scene deviations and occlusion. The convolution neural network (CNN) model is advantageous. This study presents Aquila Optimization with Transfer Learning based Crowd Density Analysis for Sustainable Smart Cities (AOTL-CDA3S). The presented AOTL-CDA3S technique aims to identify different kinds of crowd densities in the smart cities. For accomplishing this, the proposed AOTL-CDA3S model initially applies a weighted average filter (WAF) technique for improving the quality of the input frames. Next, the AOTL-CDA3S technique employs an AO algorithm with the SqueezeNet model for feature extraction. Finally, to classify crowd densities, an extreme gradient boosting (XGBoost) classification model is used. The experimental validation of the AOTL-CDA3S approach is tested by means of benchmark crowd datasets and the results are examined under distinct metrics. This study reports the improvements of the AOTL-CDA3S model over recent state of the art methods.
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Pel, Bonno, and Julia Backhaus. "Realizing the Basic Income." Science & Technology Studies 33, no. 2 (May 14, 2020): 83–101. http://dx.doi.org/10.23987/sts.60871.
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Current social innovation initiatives towards societal transformations bring forward new ways of doing and organizing, but new ways of knowing as well. Their efforts towards realizing those are important sites for the investigation of contemporary tensions of expertise. The promotion of new, transformative ways of knowing typically involves a large bandwidth of claims to expertise. The attendant contestation is unfolded through the exemplar case of the Basic Income, in which the historically evolved forms of academic political advocacy are increasingly accompanied by a new wave of activism. Crowd-funding initiatives, internet activists, citizen labs, petitions and referenda seek to realize the BI through different claims to expertise than previous attempts. Observing both the tensions between diverse claims to expertise and the overall co-production process through which the Basic Income is realized, this contribution concludes with reflections on the politics of expertise involved in transformative social innovation.
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Samulowska, Marta, Szymon Chmielewski, Edwin Raczko, Michał Lupa, Dorota Myszkowska, and Bogdan Zagajewski. "Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping." ISPRS International Journal of Geo-Information 10, no. 2 (January 22, 2021): 46. http://dx.doi.org/10.3390/ijgi10020046.
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Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions.
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Martín-Santamaría , Raúl, Ana D. López-Sánchez , María Luisa Delgado-Jalón , and J. Manuel Colmenar . "An Efficient Algorithm for Crowd Logistics Optimization." Mathematics 9, no. 5 (March 2, 2021): 509. http://dx.doi.org/10.3390/math9050509.
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Crowd logistics is a recent trend that proposes the participation of ordinary people in the distribution process of products and goods. This idea is becoming increasingly important to both delivery and retail companies, because it allows them to reduce their delivery costs and, hence, to increase the sustainability of the company. One way to obtain these reductions is to hire external drivers who use their own vehicles to make deliveries to destinations which are close to their daily trips from work to home, for instance. This situation is modelled as the Vehicle Routing Problem with Occasional Drivers (VRPOD), which seeks to minimize the total cost incurred to perform the deliveries using vehicles belonging to the company and occasionally hiring regular citizens to make just one delivery. However, the integration of this features into the distribution system of a company requires a fast and efficient algorithm. In this paper, we propose three different implementations based on the Iterated Local Search algorithm that are able to outperform the state-of-art of this problem with regard to the quality performance. Besides, our proposal is a light-weight algorithm which can produce results in small computation times, allowing its integration into corporate information systems.
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Isa, Wan Abdul Rahim Wan Mohd, Indah Mohd Amin, and Norhidayah Saubiran. "Mobile Application on Malay Medicinal Plants based on Information Crowdsourcing." Alinteri Journal of Agriculture Sciences 36, no. 2 (August 16, 2021): 208–29. http://dx.doi.org/10.47059/alinteri/v36i2/ajas21135.
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Mobile Application on Malay Medicinal Plants Based on Information Crowdsourcing is an application that provides information on Malay medicinal plants. The information in this application is obtained from a crowd of people including researchers, Malay villagers, traditional medical practitioners, and the public who are willing to share their knowledge and information on Malay medicinal plants. This project focuses on the use of Malay medicinal plants that contain nutrients which is good for human health. There are a lot of Malay medicinal plants founded by the researcher that can help to treat human illnesses. This project involves crowdsourcing. Crowdsourcing is the best way for people to get information from the researchers and crowd people. This project is related to crowdsourcing information systems. Crowdsourcing information systems are information systems that produce informational products or services for internal or external customers by utilizing the potential of crowd people. This project promotes knowledge sharing and awareness among researchers, Malay villagers, traditional medical practitioners, and local herbs entrepreneurs, and the public towards Malay medicinal plants. This project applies the concept of Wikipedia whereby the information is obtained from a crowd of people. It allows the researchers, Malay villagers, traditional medical practitioners, local herbs entrepreneurs, and the public to share their knowledge and findings on Malay medicinal plants on the internet easily. This project also focuses on motivating the public that there are a lot of Malay medicinal plants that can be used for health care. This project is developed in the Malay language as it provides information on Malay medicinal plants and the target user is Malaysia’s citizens. For future enhancement, this project plan to be developed in English and wider target users from other countries.
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Gogola, Marián. "Analysing the Vibration of Bicycles on Various Road Surfaces in the City of Zilina." Archives of Automotive Engineering – Archiwum Motoryzacji 88, no. 2 (June 30, 2020): 77–97. http://dx.doi.org/10.14669/am.vol88.art6.
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Over recent years, cycling has emerged as a particularly desirable mode of transport in Europe. Many local authorities, together with cycling lobbyist and advocating groups strive to get cycling to accepted perceived level of urban mobility. Some movements have already introduced the citizens’ science approach aimed at improving the local conditions for cyclists with crowd-sourced data collection. With this in mind, this paper presents an entry-level analysis of vibration from various surfaces which might affect the comfort of cyclists in the city of Zilina. The emphasis is focused on analysis of the road surface with a smartphone application and a so-called instrumental or probe bicycle. The results of testing are explained in the context of the problematic issues that occur in the infrastructure. The results are aimed at drawing attention to the fact that not all infrastructure is properly built, designed or maintained. There is a relationship between properly planned and built cycling infrastructure and the cycling traffic. An Android smartphone with the Phyphox application was used in the analysis as the example of citizens science.
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Muller, Jan-Peter, Yu Tao, Panagiotis Sidiropoulos, Klaus Gwinner, Konrad Willner, Lida Fanara, Marita Waehlisch, et al. "EU-FP7-iMARS: ANALYSIS OF MARS MULTI-RESOLUTION IMAGES USING AUTO-COREGISTRATION, DATA MINING AND CROWD SOURCE TECHNIQUES: PROCESSED RESULTS – A FIRST LOOK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 14, 2016): 453–58. http://dx.doi.org/10.5194/isprs-archives-xli-b4-453-2016.
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Understanding planetary atmosphere-surface exchange and extra-terrestrial-surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to overlay image data and derived information from different epochs, back in time to the mid 1970s, to examine changes through time, such as the recent discovery of mass movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. <br><br> Within the EU FP-7 iMars project, we have developed a fully automated multi-resolution DTM processing chain, called the Coregistration ASP-Gotcha Optimised (CASP-GO), based on the open source NASA Ames Stereo Pipeline (ASP) [Tao et al., this conference], which is being applied to the production of planetwide DTMs and ORIs (OrthoRectified Images) from CTX and HiRISE. Alongside the production of individual strip CTX & HiRISE DTMs & ORIs, DLR [Gwinner et al., 2015] have processed HRSC mosaics of ORIs and DTMs for complete areas in a consistent manner using photogrammetric bundle block adjustment techniques. A novel automated co-registration and orthorectification chain has been developed by [Sidiropoulos & Muller, this conference]. Using the HRSC map products (both mosaics and orbital strips) as a map-base it is being applied to many of the 400,000 level-1 EDR images taken by the 4 NASA orbital cameras. In particular, the NASA Viking Orbiter camera (VO), Mars Orbiter Camera (MOC), Context Camera (CTX) as well as the High Resolution Imaging Science Experiment (HiRISE) back to 1976. A webGIS has been developed [van Gasselt et al., this conference] for displaying this time sequence of imagery and will be demonstrated showing an example from one of the HRSC quadrangle map-sheets. <br><br> Automated quality control [Sidiropoulos & Muller, 2015] techniques are applied to screen for suitable images and these are extended to detect temporal changes in features on the surface such as mass movements, streaks, spiders, impact craters, CO2 geysers and Swiss Cheese terrain. For result verification these data mining techniques are then being employed within a citizen science project within the Zooniverse family. Examples of data mining and its verification will be presented.
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Santana, Juan Ramon, Luis Sanchez, Pablo Sotres, Jorge Lanza, Tomas Llorente, and Luis Munoz. "A Privacy-Aware Crowd Management System for Smart Cities and Smart Buildings." IEEE Access 8 (2020): 135394–405. http://dx.doi.org/10.1109/access.2020.3010609.
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Abhilash, Purushothaman Chirakkuzhyil. "Restoring the Unrestored: Strategies for Restoring Global Land during the UN Decade on Ecosystem Restoration (UN-DER)." Land 10, no. 2 (February 17, 2021): 201. http://dx.doi.org/10.3390/land10020201.
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Restoring the health of degraded land is critical for overall human development as land is a vital life-supporting system, directly or indirectly influencing the attainment of the UN Sustainable Development Goals (UN-SDGs). However, more than 33% of the global land is degraded and thereby affecting the livelihood of billions of people worldwide. Realizing this fact, the 73rd session of the UN Assembly has formally adopted a resolution to celebrate 2021–2030 as the UN Decade on Ecosystem Restoration (UN-DER), for preventing, halting, and reversing degradation of ecosystems worldwide. While this move is historic and beneficial for both people and the planet, restoration of degraded land at different scales and levels requires a paradigm shift in existing restoration approaches, fueled by the application of applied science to citizen/community-based science, and tapping of indigenous and local knowledge to advanced technological breakthroughs. In addition, there is a need of strong political will and positive behavioral changes to strengthen restoration initiatives at the grassroot level and involvement of people from all walks of life (i.e., from politicians to peasants and social workers to scientists) are essential for achieving the targets of the UN-DER. Similarly, financing restoration on the ground by the collective contribution of individuals (crowd funding) and institutions (institutional funding) are critical for maintaining the momentum. Private companies can earmark lion-share of their corporate social responsibility fund (CSR fund) exclusively for restoration. The adoption of suitable bioeconomy models is crucial for maintaining the perpetuity of the restoration by exploring co-benefits, and also for ensuring stakeholder involvements during and after the restoration. This review underpins various challenges and plausible solutions to avoid, reduce, and reverse global land degradation as envisioned during the UN-DER, while fulfilling the objectives of other ongoing initiatives like the Bonn Challenge and the UN-SDGs.
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Dorner, W., L. Ramirez Camargo, and P. Hofmann. "CAN GEOINFORMATION HELP TO BETTER PROTECT INFORMAL SETTLEMENTS? - A CONCEPT FOR THE CITY OF MEDELLIN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 20, 2019): 115–20. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-115-2019.
Full textAbstract:
<p><strong>Abstract.</strong> New contributions to disaster research need to address the increasing vulnerability of informal settlements in a changing climate situation. Informal settlements are frequently built in hazardous areas and are often left out of traditional disaster risk management concepts. Hence, formal and informal societal structures, as well as technical systems to warn against, handle or mitigate natural hazards, need to evolve. Within the project Inform@Risk we are addressing these issues based on a case study in Medellín (Colombia). Here, as a result of civil conflicts informal dwellings were partly constructed by people displaced from rural areas. They are mainly located in the urban peripheral areas along steep and unstable slopes, where the resettlement of all inhabitants at risk of landslides is unfeasible. This contribution presents the technical infrastructure and the concept to incorporate geodata from different sources in an integrated landslide early warning system for some selected informal settlements of Medellin. Special attention is given to possibilities on how building societal institutions, supported by information systems, increases local resilience. Using geoformation as a basis, we will combine classical participatory planning methods with digitally assisted concepts. These include combining satellite and UAS based remote sensing data with terrestrial sensor networks, crowd sourcing and citizen science to collect volunteered geographic information about the settlement and its environmental parameters, as well as distribute this information and disseminate warnings to the local population.</p>