کتابداری و اطلاعرسانی (Oct 2019)
Identification and Data Mining of the Publishing Elements in Relation to the Use of Creative Commons Licenses in Open-Access Publications on the Directory of Open Access Journals (DOAJ) for Supporting Intellectual Property Rights
Abstract
Objective: The use of the Creative Commons (CC) licenses in the cyberspace is a significant outcome of the revolution in the information exchange from traditional media to modern digital media and the evolution of the intellectual property rights, which has been achieved through the open access to scientific information movement while respecting the intellectual copyright regarding the content of the human knowledge. The present paper aims at identifying and data mining the publishing elements in relation to the use of CC licenses in open-access publications on the Directory of Open Access Journals (DOAJ) for supporting the intellectual property rights. Methodology: This research was performed via descriptive data mining. The statistical population was made up of all publications indexed on the DOAJ by early 2016 (9389 titles). The required data waer retrieved from the DOAJ database in the form of metadata in CVS format, including the list of publishers and the 128 member countries. The collected data was analyzed in RapidMiner© Ver. 7 by using a new model based on the cross-industry standard process for data mining (CRISP-DM) and the C5.0 decision tree algorithm. Findings: Findings of the present research showed that, out of the 9389 titles indexed on the DOAJ, 4361 titles (46.44%) used no CC license while 5028 publications (53.55%) had applied 6 CC licenses. A total of 4565 publishers had used these licenses. Among these, 2711 publications had taken the most out of the attribution license, including three groups of publishers (academic, institutional-association, and commercial). Focusing on the studied database, the attribution license was the most frequently used CC license. In total, the largest fraction of publishers had used the attribution license (711 titles, 28.88%) while the attribution-no derivative license (37 titles, 0.4%). The academic publishers composed the largest group of the CC license users (35.07%). Considering the pool of countries, Egypt (11.39%), Britain (8.64%), and Brazil (8.33%) were the three largest users of the CC license users. At the level of continents, Europe was the largest user of the licenses. The C5.0 decision tree algorithm could predict the use of the attribution license by the publishers in different countries including USA, Brazil, Egypt, Britain, and Poland at an accuracy of 69.20%, precision of 64.90%, and percent error of 35.10% (error = 1 - precision), confirming the mentioned findings. Conclusion: The use of different CC licenses, especially the attribution license, by various countries and publishers indicate the acceptance of these licenses by the community and the continuation and promotion of the knowledge development process at an increasing rate toward the science production, because the CC licenses are the open-most of their kind. These licenses provide the users with a wider spectrum of authorities including not only the attribution authority but also the permission to distribute, reproduce, and download their own works for commercial and non-commercial uses.
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