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Call for Papers | Data reuse: What new information can we learn from used data?

Henrique Castro Martins; Marcelo S. Perlin

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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3858031", 
  "container_title": "Revista de Administra\u00e7\u00e3o Contempor\u00e2nea", 
  "language": "eng", 
  "title": "Call for Papers | Data reuse: What new information can we learn from used data?", 
  "issued": {
    "date-parts": [
  "abstract": "<p>Call for papers for the Special Issue &quot;Data reuse: What new information can we learn from used data?&quot;&nbsp;</p>\n\n<p>One of the most critical tasks of executing empirical research in Business Administration is to collect reliable data. This is usually costly, time-consuming and, sometimes, demands legal agreements along the process to guarantee the rights of those who own the data. Thus, the open access to empirical data is of extreme value for researchers of several fields (Piwowar &amp; Vision, 2013; Van Raaij, 2018; Wallis, Rolando, &amp; Borgman, 2013; Wegener &amp; R&uuml;ping, 2011; Zimmerman, 2008). In the past few years, the worldwide community (Wilkinson, Dumontier, Aalbersberg, Appleton, Axton, Baak, et al., 2016) initiated a process to make research data FAIR (findable, accessible, interoperable, and reusable) throughout Open Science practices. RAC supports this movement and is committed to boosting open science practices (including open data) in the local community.</p>\n\n<p>One of the cornerstones of Open Data is to make it accessible for reuse. To foster reuse of data, RAC is inviting the submission of articles that build upon reused data. We encourage authors to access data used in previously <strong>published works</strong> to investigate new or current issues that are of interest to the management community. We are looking for articles that, building upon open data, take a different look than the original article either by developing further the original research or providing new insights to it. Articles also may try to validate the original results using different estimation techniques or different theories.</p>", 
  "author": [
      "family": "Henrique Castro Martins"
      "family": "Marcelo S. Perlin"
  "type": "article", 
  "id": "3858031"
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