• 대기
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• 제21권1호
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• pp.85-93
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• 2011

UK Met Office Unified Model (UM) is a grid model applicable for both global and regional model configurations. The Met Office has developed a 4D-Var data assimilation system, which was implemented in the global forecast system on 5 October 2004. In an effort to improve its Numerical Weather Prediction (NWP) system, Korea Meteorological Administration (KMA) has adopted the UM system since 2008. The aim of this study is to provide the basic information on the effects of satellite data assimilation on UM performance by conducting global satellite data denial experiments. Advanced Tiros Operational Vertical Sounder (ATOVS), Infrared Atmospheric Sounding Interferometer (IASI), Special Sensor Microwave Imager Sounder (SSMIS) data, Global Positioning System Radio Occultation (GPSRO) data, Air Craft (CRAFT) data, Atmospheric Infrared Sounder (AIRS) data were assimilated in the UM global system. The contributions of assimilation of each kind of satellite data to improvements in UM performance were evaluated using analysis data of basic variables; geopotential height at 500 hPa, wind speed and temperature at 850 hPa and mean sea level pressure. The statistical verification using Root Mean Square Error (RMSE) showed that most of the satellite data have positive impacts on UM global analysis and forecasts.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2005년도 춘계학술발표회
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• pp.119-120
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• 2005

• 한국대기환경학회지
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• 제29권6호
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• pp.838-848
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• 2013

The Community Multiscale Air Quality (CMAQ) model is capable of providing high quality atmospheric chemistry profiles through the utilization of high-resolution meteorology and emissions data. However, it cannot simulate air quality accurately if input data are not appropriate and reliable. One of the most important inputs required by CMAQ is the air pollutants emissions, which determines air pollutants concentrations during the simulation. For the CMAQ simulation of Korean peninsula, we, in general, use the Korean National Emission Inventory data which are estimated by Clean Air Policy Support System (CAPSS). However, since they are not provided by model-ready emission data, we should convert CAPSS emissions into model-ready data. The SMOKE is the emission model we used in this study to generate CMAQ-ready emissions. Because processing the emissions data is very monotonous and tedious work, we have developed CAPSS2SMOKE program to convert CAPSS emissions into SMOKE-ready data with ease and effective. CAPSS2SMOKE program consists of many codes and routines such as source classification code, $PM_{10}$ to $PM_{2.5}$ ratio code, map projection conversion routine, spatial allocation routine, and so on. To verify the CAPSS2SMOKE program, we have run SMOKE using the CAPSS 2009 emissions and found that the SMOKE results inherits CAPSS emissions quite well.

• 한국환경과학회지
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• 제17권12호
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• pp.1353-1361
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• 2008

Quality control of Argo(Array for Real-time Geostrophic Oceanography) data is crucial by reason that salinity measurements are liable to experience some drift and offset due to biofouling, contamination of sensor and wash-out of biocide. The automated Argo real-time quality control has a limit of sorting data quality, so that WJO program is adopted as standardized method of Argo delayed mode quality control (DMQc) in the world that is a precise quality control method. We conducted DMQC on pressure, temperature and salinity measured by Argo floats in the Pacific Ocean including expert evaluation. Particularly, salinity data were corrected using WJO program. 4 salinity profiles of Argo delayed mode were compared with nearby in situ CTD data and other Argo data in deep layer where oceanographic conditions are stable in time and space. The differences of both salinities were lower than target accuracy of Argo. As compared with the difference of salinities before DMQC, those after DMQC decreased by 60-80 percent. Quality of delayed mode salinity data seemed to be improved correcting salinity data suggested by WJO program.

• 한국문헌정보학회지
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• 제53권3호
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• pp.101-120
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• 2019

클라우드 형태의 학술연구데이터 인프라에서는 데이터 분석과 융합의 작업환경에 연구자가 직접 개입할 수 있게 된다. 때문에 연구기록 등의 학술연구데이터에 대한 명확한 연구자 인식은 보다 중요하다. 본 연구는 기록관리 분야 연구자들의 인식에 초점을 두고 학술연구데이터의 하나인 연구기록이 갖는 구조적 논리성을 차세대 연구정보 인프라 요건으로 강조한다. 최근 발행된 논문들의 연구프레임 분석을 통하여 연구의 구조적 요건에 대한 저자들의 인식을 진단하고 인식교정이 필요한 취약 부분들을 지적하고 개선 방안을 제시했다.

• 한국비블리아학회지
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• 제29권4호
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• pp.319-344
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• 2018

본 연구에서는 오픈사이언스 정책의 일환으로 학문분야별 연구데이터 공유 활용에 관한 인식을 비교분석하였다. 이를 기반으로 과학기술분야 정부출연연구기관 27개 기관 데이터 업무담당자를 대상으로 반구조화된 질문을 통하여 심층인터뷰를 실시하였다. 그 중 데이터 관리 면에서 우수기관 9개 기관을 선정하여 연구데이터 수집 및 관리현황 사례를 구체적으로 제시하였다. 결과 현재 출연연구기관의 연구데이터 수집 관리는 전반적으로 시범사업 단계이며, 데이터의 수집 구축 수준 또한 기관별로 상이하다. 기관별로 살펴보면 수집을 시작하는 단계(KIOM), 수집 관리가 고도화되고 있는 단계(KIST), 공유 활용을 시작하는 단계(KRIBB, KRICT) 등으로 구분된다.

• ETRI Journal
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• 제36권6호
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• pp.988-998
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• 2014

For memory-based big data storage, using hybrid memories consisting of both dynamic random-access memory (DRAM) and non-volatile random-access memories (NVRAMs) is a promising approach. DRAM supports low access time but consumes much energy, whereas NVRAMs have high access time but do not need energy to retain data. In this paper, we propose a new data migration method that can dynamically move data pages into the most appropriate memories to exploit their strengths and alleviate their weaknesses. We predict the access frequency values of the data pages and then measure comprehensively the gains and costs of each placement choice based on these predicted values. Next, we compute the potential benefits of all choices for each candidate page to make page migration decisions. Extensive experiments show that our method improves over the existing ones the access response time by as much as a factor of four, with similar rates of energy consumption.

• Journal of Contemporary Eastern Asia
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• 제17권2호
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• pp.200-221
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• 2018

Open Science is an umbrella term that involves various movements aiming to remove the barriers to sharing any kind of output, resources, methods or tools at any stage of the research process. While the study of open science is relatively advanced in Western countries, we know of no scholarship that attempts to understand open science in Hong Kong. This paper provides a broad-based background on the major research data management organisations, policies and institutions with the intention of laying a foundation for more rigorous future research that quantifies the benefits of open access and open data policies. We explore the status and prospects for open science (open access and open data) in the context of Hong Kong and how open science can contribute to innovation in Hong Kong. Surveying Hong Kong's policies and players, we identify both lost research potential and provide positive examples of Hong Kong's contribution to scientific research. Finally, we offer suggestions regarding what changes can be made to address the gaps we identify.

• Genomics & Informatics
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• 제1권2호
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• pp.65-74
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• 2003

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

• Ocean and Polar Research
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• 제41권2호
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• pp.107-120
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• 2019

The frequency analysis of scientific terms using bibliographic information is a simple concept, but as relevant data become more widespread, manual analysis of all data is practically impossible or only possible to a very limited extent. In addition, as the scale of oceanographic research has expanded to become much more comprehensive and widespread, the allocation of research resources on various topics has become an important issue. In this study, the frequency analysis of scientific terms was performed using text mining. The data used in the analysis is a general-purpose scholarship database, totaling 2,878 articles. Hypoxia, which is an important issue in the marine environment, was selected as a research field and the frequencies of related words were analyzed. The most frequently used words were 'Organic matter', 'Bottom water', and 'Dead zone' and specific areas showed high frequency. The results of this research can be used as a basis for the allocation of research resources to the frequency of use of related terms in specific fields when planning a large research project represented by single word.