• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.123-130
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• 2018

With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.171-176
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• 2022

Today, it is imperative to educate students on how to best prepare themselves for the new data driven era of the future. Undergraduate education plays an important role in providing students with more Data Science opportunities and expanding the supply of Data Science talent. This paper surveys and analyzes the curricula of Data Science-related bachelor's degree programs in the United States. The 'required' and 'elective' courses in a curriculum for obtaining a B.S. degree were evaluated by course weight to indicate its necessity. As a result, it was possible to find out which courses were important in Data Science programs and which areas were emphasized for B.S. degrees in Data Science. We found that courses belong to the Data Science area, such as data management, data visualization, and data modeling, were more required for Data Science B.S. degrees in the United States.

• Journal of Astronomy and Space Sciences
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• v.33 no.2
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• pp.137-146
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• 2016

The correlation between meteorological data collected at the optical wide-field patrol network (OWL-Net) Station No. 1 and the seeing of satellite optical observation data was analyzed. Meteorological data and satellite optical observation data from June 2014 to November 2015 were analyzed. The analyzed meteorological data were the outdoor air temperature, relative humidity, wind speed, and cloud index data, and the analyzed satellite optical observation data were the seeing full-width at half-maximum (FWHM) data. The annual meteorological pattern for Mongolia was analyzed by collecting meteorological data over four seasons, with data collection beginning after the installation and initial set-up of the OWL-Net Station No. 1 in Mongolia. A comparison of the meteorological data and the seeing of the satellite optical observation data showed that the seeing degrades as the wind strength increases and as the cloud cover decreases. This finding is explained by the bias effect, which is caused by the fact that the number of images taken on the less cloudy days was relatively small. The seeing FWHM showed no clear correlation with either temperature or relative humidity.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.25-38
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• 2022

Research data means data used or created in the course of research or experiments. Research data is very important for validation of research conducted and for use in future research and projects. Recently, convergence research between various fields and international cooperation has been continuously done due to the explosive increase of research data and the increase in the complexity of science and technology. Developed countries are actively promoting open science policies that share research results and processes to create new knowledge and values through convergence research. Communities to promote the sharing and utilization of research data such as RDA (Research Data Alliance) and COAR (Confederation of Open Access Repositories) are active, and various platforms for managing and sharing research data are being developed and used. OpenAIRE (Open Access Infrastructure for Research In Europe), a research data platform in Europe, ARDC (Australian Research Data Commons) in Australia, and IRDB (Institutional Repositories DataBase) in Japan provide research data or research data related services. Korea has been establishing and implementing a research data sharing and utilization strategy to promote the sharing and utilization of research data at the national level, led by the central government. Based on this strategy, KISTI has been building a Korean research data platform (DataON) since 2018, and has been providing research data sharing and utilization services to users since January 2020. This paper reviews the characteristics of DataON and how it is used for research by showing its applications.

• Publications of The Korean Astronomical Society
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• v.28 no.3
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• pp.65-73
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• 2013

We have developed a data integration system for ground-based space weather facilities in Korea Astronomy and Space Science Institute (KASI). The data integration system is necessary to analyze and use ground-based space weather data efficiently, and consists of a server system and data monitoring systems. The server system consists of servers such as data acquisition server or web server, and storage. The data monitoring systems include data collecting and processing applications and data display monitors. With the data integration system we operate the Space Weather Monitoring Lab (SWML) where real-time space weather data are displayed and our ground-based observing facilities are monitored. We expect that this data integration system will be used for the highly efficient processing and analysis of the current and future space weather data at KASI.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.119-143
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• 2022

This preliminary study examined the status of data science-related course syllabi in the American Library Association (ALA) accredited Library and Information Science (LIS) programs. The purpose of this study was to explore LIS course syllabi related to data science, such as course title, course description, learning outcomes, and weekly topics. LIS programs offer various topics in data science such as the introduction to data science, data mining, database, data analysis, data visualization, data curation and management, machine learning, metadata, and computer programming. This study contributes to helping instructors develop or revise course materials to improve course competencies related to data science in the ALA-accredited LIS programs.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.27 no.1
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• pp.263-290
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• 2016

The purpose of this study is to compare seven data science programs in Korea and ten data science programs in the US. Results show that 14 data science programs are housed in graduate schools. 10% of data science courses in Korea and 26% in the US fall under the Math and Statistics Knowledge area, one of the three areas defined by Conway. The syllabus analysis does not show much differences in terms of class contents and grading. The results of this study can be used to design data science programs that are more effective and well-grounded.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.58.1-58.1
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• 2019

We have developed Ground Software (GSW) of BITSE. The ground software includes mission operation software, data visualization software and data processing software. Mission operation software is implemented using COSMOS. COSMOS is a command and control system providing commanding, scripting and data visualization capabilities for embedded systems. Mission operation software send commands to flight software and control coronagraph. It displays every telemetry packets and provides realtime graphing of telemetry data. Data visualization software is used to display and analyze science image data in real time. It is graphical user interface (GUI) and has various functions such as directory listing, image display, and intensity profile. The data visualization software shows also image information which is FITS header, pixel resolution, and histogram. It helps users to confirm alignment and exposure time during the mission. Data processing software creates 4-channel polarization data from raw data.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.37-44
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• 2003

One of the characteristics of both on-line analytical processing(OLAP) applications and decision support systems is to provide aggregated source data. The purpose of this study is to discuss on the aggregation of multi-dimensional data. In this paper, we (1) examine the SQL aggregate functions and the GROUP BY operator, (2) introduce the Data Cube and MDX, (3) present an example for the practical usage of the Data Cube and MDX using sample data.