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		<issn>2179-4820</issn>
		<citationkey>SanchezVQSGALC:2017:ExDaAn</citationkey>
		<title>Reproducible geospatial data science: exploratory data analysis using collaborative analysis environments</title>
		<format>Pendrive, On-line.</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Sanchez, Alber,</author>
		<author>Vinhas, Lubia,</author>
		<author>Queiroz, Gilberto Ribeiro,</author>
		<author>Simões, Rolf,</author>
		<author>Gomes, Vitor,</author>
		<author>Assis, Luiz Fernando F. G.,</author>
		<author>Llapa, Eduardo,</author>
		<author>Camara, Gilberto,</author>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<editor>Davis Jr., Clodoveu A. (UFMG),</editor>
		<editor>Queiroz, Gilberto R. de (INPE),</editor>
		<e-mailaddress>lubia@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Geoinformática, 18 (GEOINFO)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>04-06 dez. 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>7-16</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Full papers</tertiarytype>
		<transferableflag>1</transferableflag>
		<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.</abstract>
		<area>SRE</area>
		<language>pt</language>
		<targetfile>1sanchez_camara.pdf</targetfile>
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