Currently available surface seawater partial pressure carbon dioxide (pCO 2 ) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the pCO 2 measurements, we construct a neural network (NN) model based on satellite data to map pCO 2 for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where pCO 2 data are best available, to map and estimate the variability of pCO 2 based on in situ pCO 2 for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted pCO 2 values. The RMSE (root mean square error) of the NN model output was 19.2 µatm and much less than the variability of in situ pCO 2 . The variability of pCO 2 with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of pCO 2 increases. When SST is lower than 15 o C, pCO 2 variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than 15 o C, the variability of pCO 2 is less sensitive to changes in SST and chlorophyll. The mean rate of the annual pCO 2 increase estimated by the NN model output in the Ulleung Basin is 0.8 µatm yr -1 from 2003 to 2014. As NN model can successfully map pCO 2 data for the whole study area with a higher resolution

As the global open science movement has recently proven its effectiveness in responding to the corona pandemic, research on disciplinary or institutional data repositories and establishing service platforms for the open and sharing research data are also active in Korea. The purpose of the research data repository is not to manage data per se but to discover and innovate knowledge and to integrate and reuse subsequent data and knowledge. Therefore, recent repository-related studies emphasize implementing the FAIR principle in this collaborative process, from observation to data documentation, data combination, quality control, and data publication. In particular, high-level data interoperability through the FAIR implementation of the repository is essential for ocean observation that requires multidisciplinary collaborative research. In Korea, ocean observatory organizations have repositories, including the ocean science data repository, JOISS; however, no studies evaluate the establishment and operation of data repositories in the FAIR principle. Therefore, this study aims to examine the construction process and data management status of the JOISS repository and the main functions and services of the web platform in terms of the data lifecycle and evaluate The FAIR principle of Open Science works in such an operating system and its limitations. The study provides implications for the improvement direction of data management and services of domestic marine repositories, including the JOISS, in an environment where the diversity and volume of data are rapidly increasing along with the evolution of ocean observation.