@InProceedings{SanchezVQSGALC:2017:ExDaAn,
               author = "Sanchez, Alber and Vinhas, Lubia and Queiroz, Gilberto Ribeiro and 
                         Sim{\~o}es, Rolf and Gomes, Vitor and Assis, Luiz Fernando F. G. 
                         and Llapa, Eduardo and Camara, Gilberto",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Reproducible geospatial data science: exploratory data analysis 
                         using collaborative analysis environments",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Davis Jr., Clodoveu A. (UFMG) and Queiroz, Gilberto R. de (INPE)",
                pages = "7--16",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 18. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The answers to current our planets problems could be hidden in gi- 
                         gabytes of satellite imagery of the last 40 years, but scientists 
                         lack the means for processing such amount of data. To answer this 
                         challenge, we are build- ing a scientific platform for handling 
                         big Earth observation data. We organized decades of satellite 
                         images into data cubes in order to put together data and analysis. 
                         Our platform allows to scale-up analysis to larger areas and 
                         longer periods of time. However, we need to provide scientists 
                         with tools and mecha- nisms to test and refine their routines 
                         before interacting with the Big data hosted in our platform. We 
                         believe that web services along collaborative analysis 
                         environments fit the hypothesis-test pattern followed by 
                         researchers while writing scientific computer code. Web services 
                         enable us to embed our platforms data and algorithms into 
                         collaborative analysis environments such as Jupyter notebooks. To 
                         make our case, we prepared a Jupyter notebook where Earth 
                         observation scientists can interact with our platform through web 
                         services and the analytic capabilities of the programming language 
                         Python.",
  conference-location = "Salvador",
      conference-year = "04-06 dez. 2017",
                 issn = "2179-4820",
             language = "pt",
                  ibi = "8JMKD3MGPDW34P/3Q5DK88",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3Q5DK88",
           targetfile = "1sanchez_camara.pdf",
        urlaccessdate = "27 abr. 2024"
}