Introduction With the penetration and suffusion of information and communication technology (ICT) in our lives, scientific research has evolved as well. As such, scientific research is more data intensive and derives information from massive volumes of digitized data. As of 2013, 2.5 quintillion bytes of data are being produced every day (https://www-01.ibm.com/software/data/bigdata/what-is-big-data.html), 90% of which was produced in the last two years (SINTEF, 2013). A correct assumption is that the amount of data being produced will continue to increase. For instance, Internet users numbered 2.8 billion in 2013, whereas today, they number more than 3.5 billion (http://www.internetlivestats.com/internet-users/). The use of social media has increased the amount of data being produced. The total amount of data in the world is expected to be 4.1 zetabytes in 2016 and is estimated to be 40 zetabytes in 2020. Therefore, data management has become an important issue. Likewise, in the scientific arena, data has become so prominent that it has been given a new name in "The Fourth Paradigm: Data-Intensive Scientific Discovery" in which "all of the science literature is online, all of the science data is online, and they interoperate with each other" (Hey et al., 2009). In previous paradigms scientific activities were driven by experimentation, theory, and computation (Hey et al., 2009). The traditional hypothesis-based scientific approach has been gradually replaced by the analyses of electronic databases that can hold large amounts of information. As papers, lab books, tapes, and photographic films have moved to digital archives, cloud storages, and data warehouses, science has gone beyond the boundaries of hypotheses. Analyses are built on the collections themselves, and patterns, anomalies, and diversities on which questions will be posed later are sought. Hence, the term "data-intensive science" has emerged, and this practice derives information from the datasets collected by various computerized modeling and simulation systems, imaging devices, sensors and sensor networks, and other data gathering and storage techniques (Hey et al., 2009; Knyazkov et al., 2012). The vision is to have "all of the science literature online, all of the science data online, and interoperate with each other" (Hey et al., 2009). These mega-scale databases consist of data captured by various novel scientific tools, sometimes on a realtime basis. With this continuous flow of electronic information, the need to collect, store, curate, integrate, and analyze data in a way that could help inter-institutional and interdisciplinary collaboration has gained importance for the advancement of science in the twenty-first century. According to Birnholtz and Bietz's study (2003, p. 339), data is an evidence for validation of scientific contribution and it makes a social contribution to the establishment of practice. Therefore, understanding the importance of the data is vital to design, sustain and curate well-structured research data management syst...

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An online survey instrument was developed in order to collect data from professionals regarding their familiarity with, knowledge and awareness of, and opinions on copyright-related issues. Findings-Findings of this study highlight gaps in existing knowledge and information about the level of copyright literacy competencies of LIS and cultural sector professionals and attitudes towards copyright learning content in academic education and continuing professional development training programs. Originality/value-This study aimed to address a gap in the literature by encompassing specialists from the cultural institutions in an international comparative context. The article furthers understanding of copyright in a wider framework of digital and information literacy; and offers guidance for the implementation of copyright policy, and the establishment of copyright advisor positions in cultural institutions. The recommendations support a revision of academic and continuing education programs learning curriculum and methods.

Purpose-This study aims to analyse the similarity of intra-indicators used in research-focused international university rankings (ARWU, NTU, URAP, QS, and RUR) over years, and show the effect of similar indicators on overall rankings for 2015. The research questions addressed in this study in accordance with these purposes are as follows: 1) At what level are the intra-indicators used in international university rankings similar? 2) Is it possible to group intra-indicators according to their similarities? 3) What is the effect of similar intra-indicators on overall rankings? Design/methodology/approach-Indicator-based scores of all universities in five research-focused international university rankings for all years they ranked, forms the dataset of this study for the first and second research questions. We used multidimensional scaling and cosine similarity measure to analyse similarity of indicators and to answer these two research questions. Indicator-based scores and overall ranking scores for 2015 are used as data and Spearman correlation test is applied to answer the third research question. Findings-Results of the analyses show that the intra-indicators used in ARWU, NTU, and URAP are highly similar and that they can be grouped according to their similarities. We also examined the effect of similar indicators on 2015 overall ranking lists for these three rankings. Using one of the indicators in the similar indicators group each time, we created new overall rankings and correlated these with the existing overall rankings for 2015. It was found that the new overall rankings created were highly correlated with the existing ones. NTU and URAP are affected least from the omitted similar indicators, which means it is possible for these two rankings to create very similar overall ranking lists to the existing overall ranking using fewer indicators. Research limitations/implications-This study covers the five research-focused international university rankings (ARWU, NTU, URAP, QS, and RUR) that create overall rankings annually and presents indicatorbased scores and overall ranking scores. CWTS, Mapping Scientific Excellence, Nature Index, and SIR (until 2015) are not included in the scope of this article, since they do not create overall ranking lists. Likewise, THE, CWUR, and US are not included because of not presenting indicator-based scores. Some difficulties were meet while obtaining the desired dataset from QS. Ranking lists (so the indicator-based scores) of 2010 and 2011 were not accessible for QS. Moreover, although QS ranks more than 700 universities, it reveals the scores of some indicators for only first 400 or 500 universities in the 2012-2015 rankings. Therefore, only first 400 universities in 2012-2015 rankings were analyzed for QS. Although, QS's (as far as possible) and RUR's data analysed in this study, it was statistically not possible to reach any conclusion from the results of multidimensional scaling conducted for QS and RUR because of the S-Stress values calculated over 0.200 that refers to ...