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This paper makes an important contribution to comparative research by examining data journalism in Spain and Austria. This paper examines the practice of data journalism from a triple perspective: (a) the common features of day-to-day work, (b) the organizational structure and the role of the teams in newsrooms, and (c) the obstacles to and the future of data-driven reporting. Results from content analysis of data-driven news stories in El país and Der standard (N = 136) show differences and similarities in the covered topics, sources, narrative style, visualizations, interactive functions, and levels of transparency. Interestingly, only 36.8% of the analyzed news stories correspond to the normative expectations of transparency by incorporating both sources and methodological details. While the Spanish newspaper shows significantly higher levels of transparency compared with the Austrian newspaper, both newspapers perform very similarly when it comes to providing access to raw data, which was the case in only every fifth news story analyzed. Findings from focused interviews with the heads of data journalism teams deliver interesting insights into specific challenges that each news outlet is facing when creating day-to-day data-driven news stories. This research confirms the relevance that data journalism has achieved in countries such as Spain and demonstrates the effort of journalists in countries without access to information and transparency laws to create data-driven stories. [ABSTRACT FROM AUTHOR]

Despite the increasing number of data visualization authoring systems in recent years, it remains a challenge to simultaneously achieve high expressive power and ease of use in a single tool. In this paper we present SCImago Graphica, a no-code tool which allows the creation of complex visualizations by simple drag-and-drop interactions. Users bind the data variables to the different encoding channels, and specify the settings of each binding, from which the tool generates the interactive graphical display. Due to its efficiency of use, SCImago Graphica is not only suitable for visually communicating data, but also for exploratory data analysis. We evaluate the expressiveness and ease of use of SCImago Graphica through various examples of chart construction and a catalog of visualizations. The results show that SCImago Graphica makes it possible to create a wide variety of data visualizations quickly and easily. [ABSTRACT FROM AUTHOR]

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Copyright of El Profesional de la Información is the property of EPI SCP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An updated metric developed to assess the degree of open data reusability, called MEtric for the evaLuation of Open DAta: Meloda 5 is presented. Previous version of the metric, Meloda 4, had six dimensions: the legal licensing of data, the mechanisms to access the data, the technical standards of the datasets, the data model, the geographic content of the data and the updating frequency. With all these dimensions, the metric provides a quantitative evaluation about how reusable the datasets released are. During the last five years, this metric has been cited and used by some other authors but the extensive use of the metric has brought to light some of its limitations. In order to get deeper insights into the topic, a panel of international experts has been surveyed about two aspects of the metric. First aspect was what other factors should be considered in order to qualify the reusability of a released dataset. And the second aspect was the internal structure, the levels for every dimension of the metric; if they should be increased, merged, removed or divided. Considering the results of the survey, first, we identify the factors / dimensions that should be kept: legal licensing, access to information, technical standard, standardization, geolocation content and updating frequency of data. Second, we consider the inclusion of two new dimensions: dissemination and reputation. Then, we present the new internal structure, the levels for each dimension, and the measures to evaluate the degree of reuse of each dataset. Finally, a standardization of the metric for other steps of the data impact process, data reuse analytics and data-driven services generation are presented together with future research lines. [ABSTRACT FROM AUTHOR]