• Title/Summary/Keyword: Data Scientists

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PLS Path Modeling to Investigate the Relations between Competencies of Data Scientist and Big Data Analysis Performance : Focused on Kaggle Platform (데이터 사이언티스트의 역량과 빅데이터 분석성과의 PLS 경로모형분석 : Kaggle 플랫폼을 중심으로)

  • Han, Gyeong Jin;Cho, Keuntae
    • Journal of Korean Institute of Industrial Engineers
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    • v.42 no.2
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    • pp.112-121
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    • 2016

  • This paper focuses on competencies of data scientists and behavioral intention that affect big data analysis performance. This experiment examined nine core factors required by data scientists. In order to investigate this, we conducted a survey to gather data from 103 data scientists who participated in big data competition at Kaggle platform and used factor analysis and PLS-SEM for the analysis methods. The results show that some key competency factors have influential effect on the big data analysis performance. This study is to provide a new theoretical basis needed for relevant research by analyzing the structural relationship between the individual competencies and performance, and practically to identify the priorities of the core competencies that data scientists must have.

  • https://doi.org/10.7232/JKIIE.2016.42.2.112 인용 PDF KSCI

Monitoring, collecting, and validating data of inland wetland survey based on citizen science methodology

  • Inae Yeo;Kwangjin Cho;Yeonsu Chu;Pyoungbeom Kim;Sangwook Han
    • Journal of Ecology and Environment
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    • v.48 no.3
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    • pp.395-404
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    • 2024

  • Background: In this study, citizen scientists gathered survey data by monitoring inland wetlands, recognized as carbon sinks, and verified the accuracy of the data for incorporation into ecosystem management policies. Results: In October 2022, citizen scientists conducted surveys on three taxonomical groups (plants, mammals, terrestrial insects) in three wetland protection areas. After capturing photographs with location information, these images were uploaded to a national ecological information bank (EcoBank) managed in Korea. The information collected by citizen scientists underwent cross-validation through two expert methods, involving ecology field experts. First, experts conducted a survey of invasive alien plants in the designated areas and compared their findings with those of citizen scientists. The choice of survey locations by citizen scientists was influenced by their proximity to their residences. Second, an expert scrutinized the accuracy of species names collected and uploaded to EcoBank by citizen scientists, presenting their findings. The classification accuracy for species names was 98.8% for vegetation (n = 83), 21.6% for terrestrial insects (n = 21), and 66.7% for mammals (n = 8). These results indicate that citizen scientists may lack detailed classification ability at the species level. Conclusions: Moving forward, it will be imperative to offer diverse forms of education to strengthen the capabilities of the citizen scientists, including sharing wetland survey results to enhance expertise in species identification, creating and distributing educational materials, and providing on-site education through professional surveyors.

  • https://doi.org/10.5141/jee.23.092 인용 PDF