@InProceedings{FariaAmar:2017:MuAnCM,
               author = "Faria, Bruno Lopes and Amaral, Hugo Prado",
                title = "Manipulation of netCDF data with R for climate change research: 
                         Multi-model analysis for CMIP5 models",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1289--1297",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Geoscientists now live in a world with an exponential growth in 
                         digital data and methods. Climate change studies usually describe 
                         computational methods informally. Climate scientists seek to share 
                         their information, the justification of reproducible research has 
                         received increasing attention in geosciences. To have it in an 
                         open-source format makes it easier to interchange not only with 
                         fellow scientists but also a variety of sources including funders, 
                         publishers, and journalists. R is a open-source computer language 
                         powerful and highly extensible that can promotes reproductive 
                         science techniques in a easier way. R is highly accessible for 
                         non-computational scientists when coupled with packages like 
                         raster'', netcdf'', ´rgdal`and rasterVis'', R enables scientists 
                         to make sense of their data and to carry out complex data 
                         analysis. In this paper we have assessed the power of R language 
                         for manipulating climate data from a huge dataset: the Coupled 
                         Model Intercomparison Project Phase 5 (CMIP5). Moreover we have 
                         proposed an example of best practices to handle model ensembles. 
                         This is the first study to our knowledge to promote best practices 
                         for CMIP5 ensemble. The NetCDF data accessible to R via raster 
                         package capabilities provides efficient access to the multi-model, 
                         with crucial applications in climate change research. In recent 
                         years more than 100 peer-reviewed scientific publications have 
                         used the CMIP5 data sets. We envision that in the near future 
                         (5-10 years), scientists will use radically new tools to author 
                         papers and disseminate information about the process and products 
                         of their research.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59557",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS4GES",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS4GES",
           targetfile = "59557.pdf",
                 type = "Meteorologia e climatologia",
        urlaccessdate = "10 maio 2024"
}