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Discusses how not only do students of technical writing courses need to learn how to prepare documents for translation properly, but students of translation need to learn technical and academic writing. Gives the example of a course taught at the Technical University of Budapest, Hungary. (SG)

Data science is an interdisciplinary field that draws on the ideas and tools of many disciplines and varied professions. Data scientists may work as data analysts, or data engineers. We also can speak about vertical and horizontal data scientists. Data science education requires a richly layered system of relationships that are not just useful mechanisms, but also tools for navigating data as social text. Library and information science (LIS) overlaps significantly with data science, but data science is (at least for the time being) not standardised, because it often demands personalised, exploratory solutions. At the same time, data science offers new methods and practices for data librarianship that draw on the core values, ethics, skills and professional knowledge of general librarianship. Computational thinking, a common way of human problem solving, is one of the increasingly prominent competencies of data literacy that is an important part of both disciplines. The ideas that characterise our thinking about data science are linked not only to the cultivation of science, or commercial goals, but also to active and informed citizenship and action, which is also one of the reasons for the emergence of critical data literacy. [ABSTRACT FROM AUTHOR]

Introduction Library literature seems to take criticisms of the Web 2.0 into consideration only to a small extent. This is understandable in the light of the sweeping popularity of Web [...]

In 2018, the authors conducted an online survey of Hungarian researchers' data literacy, as part of an international project. The basic questionnaire was compiled in 2017 by Professor Serap Kurbonoglu (University of Ankara) and Professor Joumana Boustany (Université Paris). Their findings have been reported in international publications. The Hungarian survey covered 108 researchers. The responses were compared with those from Croatia, Iceland, Spain and Slovenia within the international survey. Seven general questions (about demographics) were followed by 18 multiplechoice questions about how researchers manage their own research data. These questions focused on: access to data required for research, the forms of data used, the use of external data, knowledge of data storage solutions, issues of long-term data retention, use of supplementary information, issues of data sharing, knowledge of metadata, use of referencing systems, data storage requirements, open access, existence of institutional data management strategies, costs related to research data, etc. In their analysis the authors highlighted similarities to and differences from the responses of foreign researchers, and provided suggestions on steps to be taken to modernize research knowledge (e.g., participation in data management training, rethinking joint data use and data sharing under OA), further investigations, etc.). [ABSTRACT FROM AUTHOR]