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Assessing data analysis performance in research contexts: An experiment on accuracy, efficiency, productivity and researchers' satisfaction

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Assessing data analysis performance in research contexts: An experiment on accuracy, efficiency, productivity and researchers' satisfaction

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Martín-Rodilla, P.; Panach Navarrete, JI.; González-Pérez, CA.; Pastor López, O. (2018). Assessing data analysis performance in research contexts: An experiment on accuracy, efficiency, productivity and researchers' satisfaction. Data & Knowledge Engineering. 116:177-204. https://doi.org/10.1016/j.datak.2018.06.003

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Title: Assessing data analysis performance in research contexts: An experiment on accuracy, efficiency, productivity and researchers' satisfaction
Author:
UPV Unit: Universitat Politècnica de València. Centro de Investigación en Métodos de Producción de Software - Centre d'Investigació en Mètodes de Producció de Software
Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà
Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Embargo end date: 2020-07-01
Abstract:
[EN] Any knowledge generation process involves raw data comprehension, evaluation and inferential reasoning. These practices, common to different disciplines, are known as data analysis, and represent the most important ...[+]
Subjects: Data-analysis , Software-assistance , Data-analysis measurement , Data-analysis performance , Cognitive processes
Copyrigths: Embargado
Source:
Data & Knowledge Engineering. (issn: 0169-023X )
DOI: 10.1016/j.datak.2018.06.003
Publisher:
Elsevier
Publisher version: http://doi.org/10.1016/j.datak.2018.06.003
Thanks:
This paper has the support of Generalitat Valenciana through project IDEO (PROMETEOII/2014/039) and Spanish Ministry of Science and Innovation through project DataME (ref: TIN2016-80811-P).
Type: Artículo

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