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Volume 3(3); September 2021
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Digital Coal Geological Map
Saro Lee
GEO DATA. 2021;3(3):1-7.   Published online September 30, 2021
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  • 53 Download
AbstractAbstract PDF
From 1974 to 1994, the Korea Institute of Geoscience and Mineral Resources (KIGAM) systematically prepared and published relatively precise coal cell geology maps of 1:10,000 or 1:25,000 scale for major coal fields across the country. Such a coal cell geology map includes information about the coal seams as well as the geology of the coal field area, so it can be used as an important basic data for coal development. In this paper, the current state of the coal geology map, which was digitized into a spatial DB using GIS, was introduced. This digital coal geological map can be downloaded free of charge from the Geo-Big Data Open Platform ( and the Environmental Big Data Platform (
Atmospheric and Surface Seawater CO2 Measurements on R/V ISABU in the Western North Pacific in the Summer of 2018
Seon-Eun Lee, Sosul Cho
GEO DATA. 2021;3(3):8-15.   Published online September 30, 2021
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  • 1 Citations
AbstractAbstract PDF
The ocean takes up approximately 24% of anthropogenic carbon dioxide (CO2) emitted into the atmosphere in a year. The oceanic CO2 uptake shows regional and seasonal differences depending on physical and chemical characteristics of seawater and biological activities (such as CO2 fixation). In the tropical Western North Pacific, the surface water temperature is high, the supply of deep water is limited, and tropical cyclones usually pass in summer. We investigated atmospheric and sea surface CO2 concentrations in this area using the continuous underway pCO2 measuring system equipped on the Research Vessel ISABU of Korea Institute of Ocean Science and Technology for about 21 days from August 29 to September 19, 2018. During the cruise, 9,367 CO2 data were obtained from this measuring system with temperature, salinity, and GPS information. Higher CO2 concentrations of the surface seawater than those of the atmosphere were observed in the areas of 22°N-23.5°N and 29°N-35°N where CO2 was emitted into the atmosphere, while most of the areas between 17.5°N and 20.5°N were sinks for the atmospheric CO2. This dataset can be used for future research on the distribution of partial pressure of carbon dioxide over the global ocean surface.


Citations to this article as recorded by  
  • Sea Surface CO2 Measurements on the R/V ISABU in the Northwestern Pacific in October 2019
    Nayeon Kang, Sosul Cho, Seon-Eun Lee
    GEO DATA.2022; 4(3): 8.     CrossRef
Statistical Data of Mineral Commodity
Sunjin Lee, Yujeong Kim, Saro Lee
GEO DATA. 2021;3(3):16-34.   Published online September 30, 2021
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AbstractAbstract PDF
The advanced countries, including the United States of America, Japan, the United Kingdom, etc., struggle to secure mineral resources for their development of economy and industry themselves. We, the Republic of Korea need to respond to come up with an effective counterplan for the resource supply and demand accordingly. The Korea Institute of Geological Resources (KIGAM) is a state-designated approval statistic institution and has been building the mineral commodity statistic data of the minerals, officially defined by the mining act of the Korean government since 1986. The statistical data of mineral commodity consist of the sales volume, distribution, and production of mines, and the labor conditions for operation, etc., based on the type of ore and the production of the region, month and mine. KIGAM published the various statistical data containing the production, import, export of the mineral commodity in various forms, including annual and monthly reports and by English. This mineral commodity/mining statistics data can be viewed and downloaded from Korean Statistical Information Services(, ( by Statistic Korea, Mineral Resources Statistics Portal and also bigdata environment-platform(
Quality Control and Verification of Artificial Intelligence Data
Myeung Un Kim, Hyojung Ahn
GEO DATA. 2021;3(3):35-40.   Published online September 30, 2021
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AbstractAbstract PDF
High-quality artificial intelligence (AI) data provides accurate information for developing AI models. These results in increasing the efficiency of the model. On the other hand, if low-quality data is used, it may adversely affect the development of AI models. To improve the quality of our research, we need to increase the quality of AI data. This is possible through systematic quality control and verification of the data. Currently, there are various guidelines such as the data quality act of the US, the ISO 8000 series of the International Organization for Standardization, and the Big Data quality verification standard of the United Nations, as well as Korea's database quality certification. In this study, the current status of data quality management is identified and its implications are considered.