@InProceedings{MoraisSantRadd:2014:NeNeBa,
               author = "Morais, Alessandra Marli M. and Santos, Rafael Duarte Coelho dos 
                         and Raddick, M. Jordan",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and Johns Hopkins 
                         University, Baltimore, MD, United States",
                title = "Neural network based visualization of collaborations in a citizen 
                         science project",
            booktitle = "Proceedings...",
                 year = "2014",
         organization = "Next-Generation Analyst, 2.",
            publisher = "SPIE",
              address = "Baltimore, MD",
             keywords = "Conformal mapping, Distributed computer systems, Flow 
                         visualization, Galaxies, User interfaces, Visualization, Citizen 
                         science, Data collecting system, Data collection system, 
                         Distributed data processing, Kohonen self-organizing maps, 
                         Pixel-based techniques, Visual identification, Visualization 
                         technique, Behavioral research.",
             abstract = "Citizen science projects are those in which volunteers are asked 
                         to collaborate in scientific projects, usually by volunteering 
                         idle computer time for distributed data processing efforts or by 
                         actively labeling or classifying information - shapes of galaxies, 
                         whale sounds, historical records are all examples of citizen 
                         science projects in which users access a data collecting system to 
                         label or classify images and sounds. In order to be successful, a 
                         citizen science project must captivate users and keep them 
                         interested on the project and on the science behind it, increasing 
                         therefore the time the users spend collaborating with the project. 
                         Understanding behavior of citizen scientists and their interaction 
                         with the data collection systems may help increase the involvement 
                         of the users, categorize them accordingly to different parameters, 
                         facilitate their collaboration with the systems, design better 
                         user interfaces, and allow better planning and deployment of 
                         similar projects and systems. Users behavior can be actively 
                         monitored or derived from their interaction with the data 
                         collection systems. Records of the interactions can be analyzed 
                         using visualization techniques to identify patterns and outliers. 
                         In this paper we present some results on the visualization of more 
                         than 80 million interactions of almost 150 thousand users with the 
                         Galaxy Zoo I citizen science project. Visualization of the 
                         attributes extracted from their behaviors was done with a 
                         clustering neural network (the Self-Organizing Map) and a 
                         selection of icon- and pixel-based techniques. These techniques 
                         allows the visual identification of groups of similar behavior in 
                         several different ways.",
  conference-location = "Baltimore",
      conference-year = "may 5, 2014",
                  doi = "10.1117/12.2050183",
                  url = "http://dx.doi.org/10.1117/12.2050183",
                 isbn = "9781628410594",
                 issn = "0277786X",
                label = "scopus 2014-11 MoraisSantRadd:2014:NeNeBa",
             language = "en",
           targetfile = "912207.pdf",
               volume = "9122",
        urlaccessdate = "20 abr. 2024"
}