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E-book Altmetrics for Digital Libraries : Concepts, Applications, Evaluation, and Recommendations

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Penilaian

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The continuing adoption of technology (i.e., computers, cell phones,and information systems) and the associated large-scale growth of information haveled to the “big data” movement (Diebold, 2012; Mayer-Schönberger & Cukier, 2013), where “big data” refers to the large volume of information that no longer fits in the memory that modern computers use for processing (Mayer-Schönberger & Cukier, 2013). According to Boyd andCrawford (2012), the definition of “big data” is composed of technologythat includes maximum computation power and accurate algorithms. This technology can help to analyze different large datasetsto figure out patterns for better social, technological, and legal statements. And finally, there is a belief thatwith the large datasets,new knowledge and insights will be generated. However, according to literature, there are different definitions and interpretations of the term “big data”because big data is not only about the size of the data but alsoaboutthe change that is present with the digital reality (Kaufmann, 2019). Bigdata,first,has been characterized based on the three Vs dimension model: “Volume”, which depicts the size of the data;“Velocity”, which considers the speed of the data;and “Variety”, which includes various data types. Nevertheless, with the continuousdevelopment and the change of digital information, other dimensions were added to the big data movement:first, the “Value”, which is related to the process of pulling out valuable information, or also known as “Big Data Analytics”;and second, “Veracity”, which considers data governance and privacy concerns (Blazquez & Domenech, 2018).Theinformation growthcomes from the utilization of digital devices,networks, web, social media platforms,and more(Blazquez & Domenech, 2018),for example, the use of digital cameras that provide high-resolution photos and require more storage capacity. This digital information is stored, shared,and replicated. Additionally, people place online orders, share their opinions about the products, make contacts,and more. These actions leave traces online, which,first,can lead to a massive growth of data and,second,can be further analyzed to help trackeconomic, industrial,and social behaviors (Blazquez & Domenech, 2018).As reported by the International Data Corporation (IDC)1, which is owned by the world’s leading company for technology data—International Data Group (IDG)2, the size of digital information has grown faster than expected; IDC research expectsthe global amount of data, which in 2018 was 33 zettabytes, to grow by an average of 61% each year, resulting in 175 zettabytes of data by For the first time, astronomy and genomics experienced digital data explosion,and then afterward, big data affected businesses, education, health, science, government, and every other field in the general public (Mayer-Schönberger & Cukier, 2013). Generally, the stored information increases four times faster than the world economy,and this information overload causes people to feel overwhelmed and causes them difficulties, for instance, in how to narrow a massive amount of digital information to use for a specific purpose.This issue affected academics (researchers) because online publishing and dissemination became easier with the use of digital archives and the volume of scientific output exploded, meaning that researchers are no longer able to read all content that is relevant for them (Mayer-Schönberger & Cukier, 2013). Scientific output is shared by researchers in different formats (e.g., journal articlesandconference proceedings) with the intention to communicate scientific knowledge (Borgman, 1989). Several studies analyzed the growth of scientific output over the last centuries (Price, 1963; Bornmann & Mutz, 2015; White, 2019). The first study that made an alarming prediction about the increase in scientific output is from Price (1963) with the well-known figure of journal distribution year-wise (Figure 1.1). Price observed that the number of journals increased exponentially from the year 1665 and predicted a 10-fold increase in journals every 50 years (~4.7% per year). His prediction suggested that in the year 2000, the number ofjournals will reach 1 million.Figure 1.1: Graphic illustration by Price (1963) of the exponential growth of journals over the years. Source: The figure is a remake taken from Leydesdorff (2008) for better illustrative reasons.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

004.071 NUR a

Penerbit

Berlin : Logos Verlag Berlin., 2021

Deskripsi Fisik

292 hlm

Bahasa

English

ISBN/ISSN

9783832553098

Klasifikasi

004.071

Tipe Isi

text

Tipe Media

computer

Tipe Pembawa

online resource

Edisi

-

Subjek

Computer Science Education

Info Detail Spesifik

-

Pernyataan Tanggungjawab

-

Versi lain/terkait

Lampiran Berkas

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