Enhancing Data Accessibility and Usability: Insights from Observation Data at the Ieodo Ocean Research Station (2005-2023)

Article information

J Coast Disaster Prev. 2024;11(3):59-70
Publication date (electronic) : 2024 September 30
doi : https://doi.org/10.20481/kscdp.2024.11.3.59
Coastal Disaster & Safety Research Department, Korea Institute of Ocean Science and Technology, Busan, Republic of Korea
Corresponding author: Hak-Soo Lim, hslim@kiost.ac.kr
Received 2024 August 25; Revised 2024 September 11; Accepted 2024 September 12.

Abstract

The Ieodo Ocean Research Station (IORS) has been a critical site for collecting oceanic and atmospheric data over 19 years (2005-2023). This study analyzes key parameters including tide, wave, water temperature, salinity, wind, air temperature, etc. While these datasets have provided valuable insights into regional climatic and oceanographic phenomena, significant incorrect data were identified, particularly in the records from 2015 to 2019. Issues such as overlapping data spanning multiple years, non-chronological ordering, and discrepancies in decimal precision were detected likely due to inadequate data handling during the adjustment process in programs like Excel. These errors, which persisted for years without detection or correction, highlight a lack of responsibility and verification among both government and private sector employees involved in data management. Given the substantial investment of government resources in the IORS, the continuous presence of incorrect data on official platforms raises concerns that extend beyond the responsible parties to the broader scientific and public communities. To prevent future occurrences, the study emphasized the need for stringent data management practices and ongoing accuracy in data handling, ensuring that the IORS continues to serve as a reliable resource for oceanographic research in Korea and globally.

1. Introduction

The Ieodo Ocean Research Station (IORS) is strategically situated at coordinates 125.182°E, 32.123°N, approximately 149 km southwest of Mara Island, the southernmost point of Korea, and to the southwest of Jeju Island (Fig. 1, Byun et al., 2021; IORS, 2024; Moon et al., 2010; Oh et al., 2014; Oh et al., 2006; Shim et al., 2004). Since its establishment in 2003, the IORS has served as a crucial monitoring site for oceanic and atmospheric parameters, providing valuable insights into various phenomena such as the Tsushima Warm Current pathway, freshwater discharge from the Changjiang River, and air-sea interactions in regions distant from land influences (e.g., Bae et al., 2022; Ha et al., 2019; Hwang and Jung, 2012; Kim et al., 2021; Lee et al., 2020; Lee et al., 2022; Yang et al., 2022; Yeo and Nam, 2020; You et al., 2011). This location is also particularly advantageous for observing precursors to the Changma (the Korean rainy season) and typhoons before they reach the Korean Peninsula, China, and Japan (e.g., Moon et al., 2010; Oh et al., 2014; Saranya et al., 2024; Woo et al., 2021; Yun et al., 2015).

Fig. 1.

Domain of the northwest Pacific Ocean and locations of the Ieodo Ocean Research Station (IORS, black square). KOR, CHN, JPN, and TWN are Korea, China, Japan, and Taiwan respectively. KS and TAS are Korea and Taiwan Straits, respectively.

Data collected at the IORS-including parameters such as aerosols, solar radiation, turbulent flux, wind, wave, current, tide, temperature, and salinity-have been extensively utilized in various oceanic and atmospheric research efforts (e.g., Hwang et al., 2008; Kang et al., 2017; Woo et al., 2018). These datasets have provided essential knowledge for understanding regional climatic and oceanographic phenomena, establishing the IORS as an indispensable resource for researchers (Kim et al., 2020; Park et al., 2014).

The primary objective of this study is to present a comprehensive overview of the observations and their maintenances at the IORS since 2005. While the consistent provision of data from the IORS since 2005 is commendable, the lack of rigorous quality control and continuous management of this data raises significant concerns that must be addressed. Ensuring the reliability and accessibility of this data is crucial for its effective use in both ongoing and future research. Therefore, this study not only highlights the urgent need for enhanced data management practices to maximize its utility in scientific research (Han, 2020).

2. Data and Methods

This study analyzes hourly, 10-minute, and 1-minute observational data collected at the IORS for parameters including tide, wave, current, water temperature, salinity, wind, air temperature and pressure, relative humidity, solar radiation, and precipitation. The data, covering the period from 2005 to 2023, were downloaded from the Korean Hydrographic and Oceanographic Agency (KHOA) website (KHOA, 2024).

The dataset was plotted to identify usable data periods and calculate other parameters of interest. Specifically, hourly data from 2005 to 2007, 10-minute data from 2008 to 2019, and 1-minute data from 2020 to 2023 (Table 1). Anomalies were observed in the dataset, such as 20-minute and 10-minute intervals before September 12, 2013, at 17:00 KST, followed by 1-minute intervals afterward. Additionally, from 2015 to 2019, the data included mixed and random entries that were overlapped and not in chronological order.

Data interval and correctness from 2005 to 2023 at the IORS

A comparison was made to ensure accuracy between the data downloaded from the KHOA website (KHOA, 2024) on July 8, 2020, and August 22, 2024.

3. Results

3.1 Observation Status over 19 years (2005-2023)

Over the 19-year period from 2005 to 2023, the IORS has collected extensive observational data encompassing more than ten parameters. These include tide, wave, water temperature, salinity, wind, air temperature, relative humidity, air pressure, visibility, solar radiation, sunshine hours, and precipitation. The data were recorded at varying intervals (1-minute, 10-minute, 20-minute, and hourly). The number in parenthesis for each parameter represents the percentage of the observation period within a year, indicating the proportion of data observed during that period (Figs. 2-10). Although it is difficult to distinguish erroneous data from the graph, certain gaps and peaks are easily identifiable, particularly in the data from 2005 to 2014 (not shown; Han, 2020) and 2020 to 2023 (Figs. 7-10).

Fig. 2.

Timeseries for observed parameters, showing (a) overlapped data from data_2015_IE_IE_286_2015_KR.txt, and (b) non-overlapping (corrected) data from 2015 only at the IORS

Fig. 3.

Timeseries for observed parameters, showing (a) overlapped data from data_2016_IE_IE_286_2016_KR.txt, and (b) non-overlapping (corrected) data from 2016 only at the IORS

Fig. 4.

Timeseries for observed parameters, showing (a) overlapped data from data_2017_IE_IE_286_2017_KR.txt, and (b) non-overlapping (corrected) data from 2017 only at the IORS

Fig. 5.

Timeseries for observed parameters, showing (a) overlapped data from data_2018_IE_IE_286_2018_KR.txt, and (b) non-overlapping (corrected) data from 2018 only at the IORS

Fig. 6.

Timeseries for observed parameters, showing (a) overlapped data from data_2019_IE_IE_286_2019_KR.txt, and (b) non-overlapping (corrected) data from 2019 only at the IORS

Fig. 7.

Timeseries for observed parameters from 2020 at the IORS

Fig. 8.

Timeseries for observed parameters from 2021 at the IORS

Fig. 9.

Timeseries for observed parameters from 2022 at the IORS

Fig. 10.

Timeseries for observed parameters from 2023 at the IORS

3.2 Data Anomalies between 2015 and 2019

Significant inconsistencies were identified in the dataset from 2015 to 2019 (Figs. 2-6). For instance, datasets labeled for a specific year contained overlapping data spanning multiple years, as seen in files like ‘data_2015_IE_IE_286_2015_KR.txt’, which included data from both 2015 and 2016 (Figs. 2a, 3a, 4a, 5a, and 6a). Additionally, these records were not in chronological order, leading to overlapping lines in plotted parameters such as water temperature and salinity (Fig. 3a).

To rectify these issues, we manually removed overlapping entries and reorganized the data chronologically, resulting in corrected plots that accurately represent single-year data (Figs. 2b, 3b, 4b, 5b, and 6b). These problematic datasets from 2015 to 2019 were initially downloaded from the KHOA website (KHOA, 2024) on August 22nd, 2024. In contrast, data downloaded on July 8, 2020, contained entries confined to their respective years without overlaps.

3.3 Discrepancies in Decimal Precision and Data Handling

A notable difference between the datasets downloaded on July 8, 2020, and August 22, 2024, is the variation in decimal precision. The 2020 data utilized zero decimal places, which cannot be used as scientific data, whereas the 2024 data employed two decimal places, which can be used as scientific data at a station. It is plausible that personnel at KHOA or junior employees from associated subcontracting companies attempted to standardize the data by adjusting the decimal precision using programs like Excel. During this process, incorrect data may have been included and saved.

The updated data containing these errors were uploaded without thorough verification, and neither responsible parties nor data users identified or reported the inaccuracies. This oversight underscores two critical issues:

1. Lack of Responsibility and Verification: Both government officials and private company employees did not exhibit adequate diligence in verifying and ensuring the accuracy of the newly updated data.

2. Insufficient Data Utilization: The persistence of erroneous data over several years suggests limited usage and scrutiny of the datasets by the user community.

3.4 Implications and Recommendations

Given that the IORS serves as a prominent ocean research station in the maritime regions surrounding Korea, China, and Japan, and operates with substantial funding from the Korean government, the continuous presence of incorrect data on official platforms is concerning. This situation not only reflects on the responsible authorities but also impacts the broader scientific and public communities reliant on accurate data for research and decision-makingg.

To prevent the recurrence of such issues, it is imperative that all involved parties, including government officials and private sector employees, uphold stringent standards of accuracy and responsibility throughout and beyond the project timelines. Implementing robust data management and quality control protocols will ensure the integrity of the datasets and enhance their utility for various applications in oceanographic research and environmental monitoring.

4. Summary and Discussion

The 19-year data (2005-2023) from the KHOA website (KHOA, 2024) consists of raw data, lacking sufficient metadata and data quality control flags. While the observation periods for parameters such as tide, wave, wind speed and direction, air temperature, and air pressure were extensive, spanning 19 years, making them potentially valuable for researchers, other datasets were less comprehensive. For instance, water speed and direction data are only less than four years, making them unsuitable for long-term trend analysis, such as 10 years. Additionally, there were no wave data available after 2011. However, there were sufficient water temperature and salinity data spanning 16 years, which could be used to study changes in temperature, salinity, and density.

The IORS plays a crucial role as an oceanic and atmospheric research station in the East China Sea (Shim et al., 2004). Since 2003, it has facilitated a variety of observations and research, including studies on aerosol, ozone, CO2, Changma, solar radiation, turbulent flux, wind, wave, fog, sea surface height, temperature, salinity, SST, underwater ambient noise, and typhoons. Previous studies utilizing data from the IORS have been conducted (Ha et al., 2019; Han, 2020; e.g., Moon et al., 2010; Oh et al., 2014; Yeo and Nam, 2020). Upon plotting the data downloaded from the KHOA website (Figs. 2-10), it was evident that the dataset lacked sufficient data across various fields. Notably, from 2015 to 2019, there were overlapped and non-chronological data (Figs. 2a, 3a, 4a, 5a, and 6a). To address this, we removed the overlapped data and corrected the chronological order, and re-plotted the data (Figs. 2b, 3b, 4b, 5b, and 6b).

We also identified issues related to data handling at the IORS. On July 8th, 2020, data was recorded using zero decimal places, likely using the Excel program, while on August 22nd, 2024, it was recorded with two decimal places, probably with the same program. It is suspected that KHOA personnel or subcontractors inadvertently introduced errors during the decimal adjustment process in Excel. This incorrect data was uploaded without proper verification, and neither those responsible nor the users identified or reported the errors. This situation highlights two primary issues: a lack of responsibility and verification among both government and private sector employees, and a possible underutilization of data, given that the errors persisted for years.

As a critical research facility supported by significant government funding, the IORS raises important concerns regarding the persistent presence of incorrect data on official platforms. To address these issues and prevent recurrence, it is essential that all stakeholders uphold accuracy and responsibility, even after project completion. Extending project timelines to 10 or 20 years could significantly improve the quality and maintenance of long-term data. Moreover, implementing incentives for identifying for errors, would enhance accountability and precision. A data real-name system, retaining records of responsible individuals for 100 years, would further reinforce responsible data management practices. Lastly, it is crucial to provide clear guidance to future users-including elementary, middle, and high school students, as well as undergraduate and graduate students, teachers, professors, and the general public-on how to effectively and confidently utilize IORS data for ocean science studies, both within Korea and globally.

Acknowledgements

Oceanic and atmospheric data at the IORS (http://www.khoa.go.kr/oceangrid/gis/category/reference/distribution.do#none) were used in this study. This research was funded by the Ministry of Trade, Industry, and Energy (MOTIE) of Korea under the “Regional Innovation Cluster Development Program (PN92300, P0025418)”, supervised by the Korea Institute for Advancement of Technology (KIAT). It was also supported by the Korea Institute of Ocean Science and Technology (PEA0231). Additionally, this study was supported by the project “Sustainable Research and Development of Dokdo (PG54141)” under the Ministry of Oceans and Fisheries, Korea.

References

Bae H.-J, Yang S, Jeong T.-B, Yang A.-R, Cha D.-H, Lee G, Lee H.-Y, Byun D.-S, Kim B.-M. 2022;An estimation of ocean surface heat fluxes during the passage of typhoon at the Ieodo ocean research station: Typhoon lingling case study 2019. Asia-Pacific Journal of Atmospheric Sciences 58:305–314.
Byun D.-S, Jeong J.-Y, Kim D.-J, Hong S, Lee K.-T, Lee K. 2021;Ocean and atmospheric observations at the remote Ieodo Ocean Research Station in the northern East China Sea. Frontiers in Marine Science 8:618500p.
Ha K.-J, Nam S, Jeong J.-Y, Moon I.-J, Lee M, Yun J, Jang C. J, Kim Y. S, Byun D.-S, Heo K.-Y. 2019;Observations utilizing Korea Ocean research stations and their applications for process studies. Bulletin of the American Meteorological Society 100:2061–2075.
Han M. 2020;Status of observation data at Ieodo ocean research station for sea level study. Journal of the Korean Earth Science Society 41(4):323–343.
Hwang G, Lee M, Shin B, Lee G, Lee J, Shim J. 2008;Mass concentration and ionic composition of PM 2.5 observed at Ieodo ocean research station. Journal of Korean Society for Atmospheric Environment 24:501–511.
Hwang K, Jung S. 2012;Decadal changes in fish assemblages in waters near the Ieodo ocean research station (East China Sea) in relation to climate change from 1984 to 2010. Ocean Science Journal 47:83–94.
IORS (n.d). Location of Ieodo ocean research station, http://www.khoa.go.kr/eng/kcom/cnt/selectContentsPage.do?cntId=31080200 (last date accessed: 22 August 2024).
Kang K.-M, Kim D.-J, Hwang J.-H, Choi C, Nam S, Kim S, Cho Y.-K, Byun D.-S, Lee J. 2017;Establishment of thermal infrared observation system on Ieodo ocean research station for time-series sea surface temperature extraction. The Sea 22(3):57–68.
KHOA (n.d). Yearly data at Ieodo ocean research station, http://www.khoa.go.kr/oceangrid/gis/category/reference/distribution.do#none (last date accessed: 22 August 2024).
Kim G.-I, Kug J.-S, Byun D.-S, Lee J. 2020;Impacts of SST pattern represented by ocean temperature near Ieodo ocean research station on winter climate variation over the Korean Peninsula. Asia-Pacific Journal of Atmospheric Sciences 56:429–438.
Kim J, Choi D.H, Lee H.E, Jeong J.-Y, Jeong J, Noh J. H. 2021;Phytoplankton diversity and community structure driven by the dynamics of the Changjiang diluted water plume extension around the Ieodo ocean research station in the summer of 2020. Journal of the Korean Society of Marine Environment & Safety 27(7):924–942.
Lee H, Lee K, Nam S, Lee J.-H. 2020;Observations of the warm-tongue circulation in the northern East China Sea. Scientific Reports 10:276.
Lee K, Kim J.-M, Lee G.-S, Lee E, Jeong J.-Y, Lee J, Han I.-S. 2022;Persistent continental shelf carbon sink at the ieodo ocean research station in the northern East China Sea. Frontiers in Marine Science 9:919249p.
Moon I.-J, Shim J.-S, Lee D.Y, Lee J.H, Min I.-K, Lim K.C. 2010;Typhoon researches using the Ieodo ocean research station: Part I. Importance and present status of typhoon observation. Atmosphere 20(3):247–260.
Oh H, Ha K.-J, Shim J.-S. 2014;Analysis for onset of Changma using Ieodo ocean research station data. Atmosphere 24(2):189–196.
Oh K.-H, Park Y.-G, Lim D.-I, Jung H.-S, Shim J.-S. 2006;Characteristics of temperature and salinity observed at the Ieodo ocean research station. Journal of the Korean Society for Marine Environment & Energy 9(4):225–234.
Park B, Musa T. A, Lee H, Choi Y, Yoon H, Cho C. 2014. The first results of analysing GPS observations at IEODO ocean research station in Korea. Proceedings of FIG Congress 2014, Kuala Lumpur, Malaysia, 16-21 June 2014
Saranya J, Dasgupta P, Nam S. 2024;Interaction between typhoon, marine heatwaves, and internal tides: Observational insights from Ieodo ocean research station in the northern East China Sea. Geophysical Research Letters 51(16):e2024GL109497p.
Shim J.-S, Chun I.-S, Min I.-K. 2004. Construction of Ieodo ocean research station and its operation. The Fourteenth International Offshore and Polar Engineering Conference: International Society of Offshore and Polar Engineers, Toulon, France, 23-28 May 2004
Woo H.-J, Park K.-A, Byun D.-S, Lee J, Lee E. 2018;Characteristics of the differences between significant wave height at Ieodo ocean research station and satellite altimeter-measured data over a decade (2004~ 2016). The Sea 23(1):1–19.
Woo H.-J, Park K, Byun D.-S, Jeong K.-Y, Lee E.-I. 2021;Comparison of methods for estimating extreme significant wave height using satellite altimeter and Ieodo ocean research station data. Journal of the Korean Earth Science Society 42(5):524–535.
Yang S, Moon I.-J, Bae H.-J, Kim B.-M, Byun D.-S, Lee H.-Y. 2022;Intense atmospheric frontogenesis by air-sea coupling processes during the passage of typhoon Lingling captured at Ieodo ocean research station. Scientific Reports 12(1):15513p.
Yeo D.-E, Nam S. 2020;Seasonal and spatial variations of air-sea heat exchange in the seas around the Korean Peninsula: Based on the observations and reanalysis products from 2011 to 2016. Progress in Oceanography 181:102239.
You S, Eom H, Ryoo S. 2011;Numerical study of typhoon effects on the Changjiang diluted water using an operational ocean forecasting system. Journal of Coastal Research 64:1927–1930.
Yun J, Oh H, Ha K.-J. 2015;Observation and analysis of turbulent fluxes observed at Ieodo ocean research station in autumn 2014. Atmosphere 25(4):707–718.

Article information Continued

Fig. 1.

Domain of the northwest Pacific Ocean and locations of the Ieodo Ocean Research Station (IORS, black square). KOR, CHN, JPN, and TWN are Korea, China, Japan, and Taiwan respectively. KS and TAS are Korea and Taiwan Straits, respectively.

Fig. 2.

Timeseries for observed parameters, showing (a) overlapped data from data_2015_IE_IE_286_2015_KR.txt, and (b) non-overlapping (corrected) data from 2015 only at the IORS

Fig. 3.

Timeseries for observed parameters, showing (a) overlapped data from data_2016_IE_IE_286_2016_KR.txt, and (b) non-overlapping (corrected) data from 2016 only at the IORS

Fig. 4.

Timeseries for observed parameters, showing (a) overlapped data from data_2017_IE_IE_286_2017_KR.txt, and (b) non-overlapping (corrected) data from 2017 only at the IORS

Fig. 5.

Timeseries for observed parameters, showing (a) overlapped data from data_2018_IE_IE_286_2018_KR.txt, and (b) non-overlapping (corrected) data from 2018 only at the IORS

Fig. 6.

Timeseries for observed parameters, showing (a) overlapped data from data_2019_IE_IE_286_2019_KR.txt, and (b) non-overlapping (corrected) data from 2019 only at the IORS

Fig. 7.

Timeseries for observed parameters from 2020 at the IORS

Fig. 8.

Timeseries for observed parameters from 2021 at the IORS

Fig. 9.

Timeseries for observed parameters from 2022 at the IORS

Fig. 10.

Timeseries for observed parameters from 2023 at the IORS

Table 1.

Data interval and correctness from 2005 to 2023 at the IORS

Year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Interval 60 60 60 10 10 10 10 10 20, 10, & 1 10
Correct O O O O O O O O O O
Year 2015 2016 2017 2018 2019 2020 2021 2022 2023
Interval 10 10 10 10 10 1 1 1 1
Correct X X X X X O O O O