Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Leroux Agri2018 8-6.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Murtagh BigDataCogComp2018 2-2.jpg|240px]]</div>
'''"[[Journal:GeoFIS: An open-source decision support tool for precision agriculture data|GeoFIS: An open-source decision support tool for precision agriculture data]]"'''
'''"[[Journal:The development of data science: Implications for education, employment, research, and the data revolution for sustainable development|The development of data science: Implications for education, employment, research, and the data revolution for sustainable development]]"'''


The world we live in is an increasingly spatial and temporal data-rich environment, and the [[agriculture industry]] is no exception. However, data needs to be processed in order to first get [[information]] and then make informed management decisions. The concepts of "precision agriculture" and "smart agriculture" can and will be fully effective when methods and tools are available to practitioners to support this transformation. An open-source program called GeoFIS has been designed with this objective. It was designed to cover the whole process from spatial data to spatial information and decision support. The purpose of this paper is to evaluate the abilities of GeoFIS along with its embedded algorithms to address the main features required by farmers, advisors, or spatial analysts when dealing with precision agriculture data. Three case studies are investigated in the paper: (i) mapping of the spatial variability in the data, (ii) evaluation and cross-comparison of the opportunity for site-specific management in multiple fields, and (iii) delineation of within-field zones for variable-rate applications when these latter are considered opportune. ('''[[Journal:GeoFIS: An open-source decision support tool for precision agriculture data|Full article...]]''')<br />
In data science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the data science theme are described. The following are covered in this article: the rapidly growing post-graduate university course provisioning for data science; a preliminary study of employability requirements; and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of big data [[Data analysis|analytics]], relating to data aggregation and scale effects. ('''[[Journal:The development of data science: Implications for education, employment, research, and the data revolution for sustainable development|Full article...]]''')<br />
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Revision as of 19:43, 4 September 2018

Fig1 Murtagh BigDataCogComp2018 2-2.jpg

"The development of data science: Implications for education, employment, research, and the data revolution for sustainable development"

In data science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the data science theme are described. The following are covered in this article: the rapidly growing post-graduate university course provisioning for data science; a preliminary study of employability requirements; and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of big data analytics, relating to data aggregation and scale effects. (Full article...)

Recently featured:

GeoFIS: An open-source decision support tool for precision agriculture data
Technology transfer and true transformation: Implications for open data
Eleven quick tips for architecting biomedical informatics workflows with cloud computing