Open AccessE-science: the fourth paradigm in science and research data management
Author(s) -
Ekawati Marlina
Publication year - 2019
Publication title -
proceeding of international conference on documentation and information
Language(s) - English
Resource type - Conference proceedings
ISSN - 2746-9654
DOI - 10.14203/icdi.v2i.47
Subject(s) - paradigm shift , data science , data sharing , computer science , data management , narrative , process (computing) , perspective (graphical) , big data , e science , normal science , engineering ethics , knowledge management , management science , epistemology , engineering , data mining , medicine , philosophy , linguistics , alternative medicine , geometry , mathematics , pathology , artificial intelligence , grid , operating system
Research is a process of systematic problem solving using scientific methods. One of the main outputs in research is data. The development of new analytical methods in research led to the emergence of the e-science era. In that era, there was a change in the type, volume, and diversity of research data. With the advent of the e-science era, libraries in universities and research institutes in addition to managing publications also manage research data. By using the narrative review method, an exploration is carried out to identify the paradigms in science and their relation to research data management. Identification is done by using the perspective of paradigm and epistemology. From the results of exploration, it is known that e-science is the fourth paradigm in science. The paradigm shift is due to the development of new analytical methods in scientific research. The shift in the research paradigm has changed the data life cycle. In the new life cycle, data becomes an important part of each stage of research activities. Research data management is important so that data can be accessed for a long period of time, shared and reused. Data sharing is a new stage that has emerged in the data lifecycle in the e-science era. From an epistemological perspective, reproduction and verification are problematic issues in data sharing