• 인터넷정보학회논문지
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• 제19권1호
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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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• 제9권2호
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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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• 제33권2호
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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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• 제10권spc호
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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.

• 천문학논총
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• 제28권3호
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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.

• 정보관리학회지
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• 제39권1호
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• pp.119-143
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• 2022

본 연구는 미국도서관협회(American Library Association, ALA)의 인가를 받은 문헌정보학 프로그램에서 제공하는 데이터사이언스와 관련된 수업의 내용을 조사했다. 연구의 목적은 강의 계획서의 내용 분석을 통해 해당 수업에서 다뤄지는 교과목 명, 교과 설명, 학습 목표, 주차 별 주제를 살펴보는 것이다. 문헌정보학 프로그램에서의 데이터사이언스와 관련된 필수 과목 및 선택 과목은, 데이터사이언스 개론, 데이터 마이닝, 데이터베이스, 데이터 분석, 데이터 시각화, 데이터 큐레이션 및 관리, 머신 러닝, 메타데이터, 컴퓨터 프로그래밍 등 데이터사이언스 전 분야에 걸쳐 다양하게 교과목이 개설되어 있었다. 본 연구의 결과는 문헌정보학 프로그램에서 데이터사이언스 교과 과정을 개설 및 개정할 때 논의의 시작점이 될 수 있는 기초 자료가 되어 운영 역량을 강화하는데 활용되기를 기대한다.

• 한국비블리아학회지
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• 제27권1호
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• pp.263-290
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• 2016

본 연구는 국내외 데이터 사이언티스트(Data Scientist) 양성을 위한 데이터 사이언스(Data Science) 프로그램의 교과과정을 분석하였다. 이를 위해 국내 7개 대학교와 미국의 10개 대학교를 분석하였다. 14개의 데이터 사이언스 과정이 대학원 중심으로 운영되고 있는 것으로 나타났다. Conway의 데이터 사이언스 3대 영역 중 수학 및 통계 지식 영역에 국내는 10% 그리고 미국은 26%가 치중되어 있는 것으로 분석되었다. 강의계획서 분석에서 수업내용 및 평가 방법은 국내외가 유사한 것으로 나타났다. 본 연구 결과는 국내 데이터 사이언스 교과과정 개발에 기초 자료로 활용될 수 있을 것이다.

• 인터넷정보학회논문지
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• 제21권6호
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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.

• 천문학회보
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• 제44권2호
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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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• 제14권1호
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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.