<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0121-5612</journal-id>
<journal-title><![CDATA[Colombia Internacional]]></journal-title>
<abbrev-journal-title><![CDATA[colomb.int.]]></abbrev-journal-title>
<issn>0121-5612</issn>
<publisher>
<publisher-name><![CDATA[Departamento de Ciencia Política y Centro de Estudios Internacionales. Facultad de Ciencias Sociales, Universidad de los Andes]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-56122020000200041</article-id>
<article-id pub-id-type="doi">10.7440/colombiaint102.2020.03</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Ciencia de datos y estudios globales: aportaciones y desafíos metodológicos]]></article-title>
<article-title xml:lang="en"><![CDATA[Data Science and Global Studies: Contributions and Methodological Challenges]]></article-title>
<article-title xml:lang="pt"><![CDATA[Ciência de dados e estudos globais: contribuições e desafíos metodológicos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lemus-Delgado]]></surname>
<given-names><![CDATA[Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Navarro]]></surname>
<given-names><![CDATA[Ricardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico de Monterrey  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico de Monterrey  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>01</day>
<month>04</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>01</day>
<month>04</month>
<year>2020</year>
</pub-date>
<numero>102</numero>
<fpage>41</fpage>
<lpage>62</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-56122020000200041&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-56122020000200041&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-56122020000200041&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN.  Objetivo/Contexto:  La ciencia de datos es considerada un enfoque novedoso y promisorio empleado en la obtención y análisis de información en múltiples disciplinas científicas. A pesar de su amplia difusión y aceptación, el uso de técnicas basadas en la ciencia de datos en el campo de los estudios globales está en ciernes. En este contexto, este artículo tiene como objetivo discutir las contribuciones y desafíos metodológicos que la ciencia de datos puede aportar a la disciplina de los estudios globales.  Metodología:  Este artículo parte del análisis crítico de información sobre los fundamentos conceptuales de la ciencia de datos y lo contrasta con sus posibles aplicaciones en la disciplina de los estudios globales. De esta manera, se examinan los elementos conceptuales que definen la ciencia de datos, se establecen las concepciones paradigmáticas de los estudios globales, se analizan los vínculos posibles entre la ciencia de datos y los estudios globales y se discuten los límites y alcances que la ciencia de datos puede aportar como enfoque metodológico.  Conclusiones:  Este artículo concluye que las herramientas tecnológicas basadas en big data pueden enriquecer nuestra comprensión sobre los fenómenos globales, siempre y cuando se asuma una actitud crítica que reconozca que tanto la elección de los datos como su análisis están embebidos en contextos históricos y sociales. Por lo tanto, como cualquier otro enfoque, la ciencia de datos representa una manera parcial de capturar y analizar información.  Originalidad:  La originalidad de este artículo recae en asumir una reflexión crítica sobre los alcances y límites de la ciencia de datos, más allá de una visión positivista del conocimiento científico, reconociendo los sesgos que existen detrás de las explicaciones supuestamente neutrales que encierra el empleo de técnicas de investigación basadas en la ciencia de datos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT.  Objective/Context:  Big Data is a novel and promising methodological approach to acquire and analyze information from an extensive range of scientific disciplines. Despite the fact that Big Data has wide dissemination and acceptance in different scientific disciplines, its use in the field of Global Studies is still incipient. Taking this into account, this paper discusses the methodological challenges and presumable contributions that Big Data offers to Global Studies.  Methodology: The starting point for this paper is the critical analysis of the conceptual bases of Big Data and its relationships with Global Studies. So, this paper proposes the possible applications of Big Data in the Global Studies contrasting some key concept. In this way, the conceptual elements that define Data Science are examined and compared with the paradigmatic conceptions of Global Studies. On these conceptual fundaments, the article discusses links, limits and, scopes of Big Data and how it can contribute to Global Studies.  Conclusions:  This paper concludes that technological tools based on Big Data can enrich our understanding of global phenomena only if we position ourselves in a critical attitude that recognizes that the choice of data and its analysis are decisions embedded in historical and social contexts. In this way, as with any other methodological approach, Big Data is only a partial way to obtain and analyze information.  Originality:  The originality of this paper is to shed critical light on the scope and limits of Big Data, beyond a positivist vision of scientific knowledge. In other words, this article recognizes the biases behind the supposed neutral explanations regarding the use of techniques based on Big Data.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[RESUMO.  Objetivo/Contexto:  A ciência de dados é considerada uma abordagem nova e promissora, utilizada por várias disciplinas científicas para a obtenção e análise de informações. Apesar de sua ampla disseminação e aceitação, o uso de técnicas baseadas na ciência de dados no campo de estudos globais está em estágio inicial. Nesse contexto, este artigo tem como objetivo discutir as contribuições e desafios metodológicos que a ciência de dados pode trazer para a disciplina de estudos globais.  Metodologia:  Este artigo analisa críticamente os fundamentos conceituais da ciência de dados e os compara com as possíveis aplicações na área de estudos globais. Desse modo, este artigo examina os elementos conceituais que definem a ciência de dados, estabelece os paradigmas conceituais dos estudos globais, analisa as possíveis ligações entre ciência de dados e estudos globais, e discute como os limites e alcance da ciência de dados pode contribuir como abordagem metodológica.  Conclusões:  este artigo conclui que as ferramentas tecnológicas baseadas em big data podem enriquecer nossa compreensão sobre os fenômenos globais, desde que seja assumida uma atitude crítica que reconheça que a seleção dos dados e sua análise estão incorporadas em contextos históricos e sociais. Deste modo, como em qualquer outra abordagem, a ciência de dados representa um modo tendencioso de capturar e analisar informações.  Originalidade:  A originalidade deste artigo consiste em assumir uma reflexão crítica sobre o alcance e os limites da ciência de dados, além de fornecer uma visão positivista do conhecimento científico, reconhecendo a parcialidade que existe por trás das explicações supostamente neutras sobre o emprego de técnicas de pesquisa baseadas em ciência de dados.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Estudios globales]]></kwd>
<kwd lng="es"><![CDATA[ciencias de datos]]></kwd>
<kwd lng="es"><![CDATA[investigación científica]]></kwd>
<kwd lng="es"><![CDATA[metodología.]]></kwd>
<kwd lng="en"><![CDATA[Global Studies]]></kwd>
<kwd lng="en"><![CDATA[Big Data]]></kwd>
<kwd lng="en"><![CDATA[Scientific Research]]></kwd>
<kwd lng="en"><![CDATA[Methodology.]]></kwd>
<kwd lng="pt"><![CDATA[Estudos globais]]></kwd>
<kwd lng="pt"><![CDATA[ciências de dados]]></kwd>
<kwd lng="pt"><![CDATA[Pesquisa científica]]></kwd>
<kwd lng="pt"><![CDATA[metodologia]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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<person-group person-group-type="author">
<name>
<surname><![CDATA[Van der Aalst]]></surname>
<given-names><![CDATA[Wil]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Data Scientist: The Engineer of the Future&#8221;]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Mertins]]></surname>
<given-names><![CDATA[Kai]]></given-names>
</name>
<name>
<surname><![CDATA[Bénaben]]></surname>
<given-names><![CDATA[Frédérick]]></given-names>
</name>
<name>
<surname><![CDATA[Poler]]></surname>
<given-names><![CDATA[Raúl]]></given-names>
</name>
<name>
<surname><![CDATA[Bourrières]]></surname>
<given-names><![CDATA[Jean-Paul]]></given-names>
</name>
</person-group>
<source><![CDATA[Enterprise Interoperability IV]]></source>
<year>2014</year>
<page-range>13-28</page-range><publisher-loc><![CDATA[Nueva York ]]></publisher-loc>
<publisher-name><![CDATA[Springer]]></publisher-name>
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<ref id="B56">
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<person-group person-group-type="author">
<name>
<surname><![CDATA[Ware]]></surname>
<given-names><![CDATA[Colin]]></given-names>
</name>
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<source><![CDATA[Information Visualization: Perception for Design]]></source>
<year>2012</year>
<publisher-loc><![CDATA[Ámsterdam ]]></publisher-loc>
<publisher-name><![CDATA[Elsevier]]></publisher-name>
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</article>
