Exploratory Data Analysis (
            <scp>EDA</scp>
            )

Camizuli, Estelle; Carranza, Emmanuel John M.

doi:10.1002/9781119188230.saseas0271

Cited by 21 publications

(10 citation statements)

References 4 publications

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“…Some elements better represent the rock types, such as Fe, whereas others are better appropriated to study the occurrence of mineral deposits (e.g., Pb, Zn; Monna et al, 2014;Kirkwood et al, 2016). The EDA method is well suited to track archaeological sites and plan field prospection (Camizuli and Carranza, 2018), as it focuses on a single element and as more anomalies are evidenced. This makes it easier to highlight contamination gradients downstream a hot spot, which could be related to an ancient mining site.…”

Section: Comparison Of the Statistical Methodsmentioning

confidence: 99%

“…Exploratory Data Analysis (EDA) is a robust method for the study of patterns and the identification of outliers (Reimann et al, 2008;Carranza, 2011), using descriptive statistics and graphical tools (Tukey, 1977). EDA-based mapping allows subdivision of the dataset into seven categories: (i) far low outliers, (ii) near low outliers, (iii) low background, (iv) background, (v) high background, (vi) near high outliers, and (vii) and far high outliers (see detailed methodology in Camizuli and Carranza, 2018).…”

Section: Exploratory Data Analysismentioning

confidence: 99%

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Trace metals dispersion from 1000 years of mining activity in the northern French Alps

Camizuli

Rossi

Gasquet

2021

The Extractive Industries and Society

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Section: Comparison Of the Statistical Methodsmentioning

confidence: 99%

Section: Exploratory Data Analysismentioning

confidence: 99%

Trace metals dispersion from 1000 years of mining activity in the northern French Alps

Camizuli

Rossi

Gasquet

2021

The Extractive Industries and Society

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“…This is a conventional method in statistics, and can still be very useful for data science applications of nowadays. Exploratory data analysis is used to get a better understanding of the data and draw plausible research questions or hypotheses (Turkey, 1977;Camizuli and Carranza, 2018). Confirmatory data analysis, in contrast, is where the complicated models and/or algorithms are applied to prove or disprove the hypotheses.…”

Section: Data Analysis and Results Interpretationmentioning

confidence: 99%

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Ma¹

2021

Preprint

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Data science receives increasing attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions have begun to increase in many geoscience conferences. Across academia, industry, and governmental sectors, there is a strong interest to know more about the current progress as well as the potential of data science for geoscience. To address that need, this article provides a review from the perspective of a data life cycle. The key steps in the data life cycle includes concept, collection, preprocessing, analysis, archive, distribution, discovery, and repurpose. Those subjects are intuitive and easy to follow even for geoscientists with very limited experience of cyberinfrastructure, statistics, and machine learning. The review includes two key parts. The first is about the fundamental concepts and theoretical foundation of data science, and the second is the summary of highlights and sharable experience from existing publications centered on each step in the data life cycle. At the end, a vision about the future trends of data science applications in geoscience is discussed, including topics on open science, smart data, and science of team science. We hope this review will be useful to data science practitioners in the geoscience community, and will lead to more discussions on the best practices and future trends of data science for geoscience.

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“…A scalable outlier detection technique [17,18] has been adopted to process large volumes of data and missing values [19]. EDA is an essential step and involves ascertaining classical statistics such as standard deviation, categorical variables, and confidence intervals, which provide insight into the dataset and can be used to guide subsequent approaches to analytics [20]. Additionally, guidance concerning thresholds for different extreme events at the regional scale can be applied [21,22].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

et al. 2022

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A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

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Exploratory Data Analysis ( EDA )

Cited by 21 publications

References 4 publications

Trace metals dispersion from 1000 years of mining activity in the northern French Alps

Trace metals dispersion from 1000 years of mining activity in the northern French Alps

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

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