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000049133 0247_ $$a10.1093/grurint/ikad098$$2doi
000049133 035__ $$a(OCoLC)1427545968
000049133 040__ $$aSzGeWIPO$$beng$$erda$$cSzGeWIPO$$dCaBNVSL
000049133 041__ $$aeng
000049133 24500 $$aOvercoming Barriers to Text and Data Mining in the Era of ChatGPT :$$bThe Proposed Data Act as a Game-Changer.
000049133 264_1 $$a[Oxford, England] :$$bOxford University Press (OUP),$$c2024.
000049133 300__ $$a1 online resource (pages  34–44)
000049133 336__ $$atext$$2rdacontent
000049133 337__ $$acomputer$$2rdamedia
000049133 338__ $$aonline resource$$bcr$$2rdacarrier
000049133 4901_ $$aGRUR International,$$x2632-8550 ;$$v73, 1, 2024
000049133 520__ $$aArticle 35 of the proposed Data Act intends to free databases comprised of machine-generated data from the constraints of sui generis database protection. The provision may provide some remedy for researchers facing challenges under the text and data mining (TDM) exceptions of the Directive on Copyright in the Digital Single Market. However, the wording of Art. 35 may also raise questions and create legal uncertainties for scholars, as scientific research is not the main aim of the proposed legislation. This article argues that, even after the proposed Art. 35 of the Data Act, some databases comprised of machine-generated data may still fall within the scope of the sui generis database protection under certain circumstances. The discussion revolves, inter alia, around the concept of recorded data in the context of the ‘obtaining-creating dichotomy’, the use of mixed databases and the notion of derived or inferred data, and the issue of researchers’ access to machine-generated data. The findings of this article are intended to offer guidance to researchers using Artificial Intelligence tools, such as ChatGPT, to mine databases on how they may effectively avoid a potential infringement of the sui generis database right. The paper hopes to encourage new changes in the EU regulation on TDM that could create a more balanced and research-friendly framework.
000049133 542__ $$fhttps://academic.oup.com/grurint/article/72/3/231/6998505
000049133 588__ $$aCrossref
000049133 590__ $$aPublished online: 29-Sept-23
000049133 650_0 $$aIntellectual property.
000049133 650_0 $$aData protection.
000049133 650_0 $$aArtificial intelligence.
000049133 650_0 $$aCopyright infringement.
000049133 650_0 $$aTrademark infringement.
000049133 650_0 $$aPatents.
000049133 7001_ $$aManteghi, Maryna,$$eauthor.
000049133 7731_ $$tGRUR International$$wGRUR
000049133 830_0 $$aGRUR International ;$$v73, 1, 2024,$$x2632-8550.
000049133 85641 $$uhttps://doi.org/10.1093/grurint/ikad098$$yonline version
000049133 904__ $$aArticle
000049133 980__ $$aGRUR