Dam monitoring data analysis methods: A literature review

2019 ◽  
Vol 27 (3) ◽  
Author(s):  
Bin Li ◽  
Jie Yang ◽  
Dexiu Hu
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Monitoring Data ◽  
Analysis Methods ◽  
Dam Monitoring ◽  
Data Analysis Methods

Quantitative Data Analysis for Information Science Professionals

2020 ◽  
pp. 349-372
Author(s):  
Regis Chireshe
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Quantitative Data ◽  
Information Science ◽  
Analysis Software ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Quantitative Data Analysis ◽  
General Aspects ◽  
Data Analysis Methods

The chapter presents general aspects of quantitative data analysis as they relate to information sciences. The chapter is based on a literature review. It begins with explaining the meaning of data and quantitative data. Kinds of quantitative data are presented. The meaning of data analysis and the reasons for data analysis are also discussed. Reasons for quantitative data analysis are also discussed. The ‘what' and ‘why' of statistics in general and for information science researchers in particular is also presented. The chapter also presents the main issues of quantitative data analysis. Steps in quantitative data analysis are also presented. Preparation of quantitative data analysis is followed by a presentation on quantitative data analysis methods. The chapter highlights the popular quantitative data analysis software. A brief presentation on how quantitative data are presented and interpreted is given. The chapter ends with a discussion on the advantages and disadvantages of quantitative data analysis.

scholarly journals A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

2020 ◽  
Vol 10 (9) ◽  
pp. 3013
Author(s):  
Alen Rajšp ◽  
Iztok Fister
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Intelligent Data Analysis ◽  
Way Of Life ◽  
Sport Training ◽  
Analysis Methods ◽  
The Internet Of Things ◽  
Data Analysis Methods ◽  
The Way

The rapid transformation of our communities and our way of life due to modern technologies has impacted sports as well. Artificial intelligence, computational intelligence, data mining, the Internet of Things (IoT), and machine learning have had a profound effect on the way we do things. These technologies have brought changes to the way we watch, play, compete, and also train sports. What was once simply training is now a combination of smart IoT sensors, cameras, algorithms, and systems just to achieve a new peak: The optimum one. This paper provides a systematic literature review of smart sport training, presenting 109 identified studies. Intelligent data analysis methods are presented, which are currently used in the field of Smart Sport Training (SST). Sport domains in which SST is already used are presented, and phases of training are identified, together with the maturity of SST methods. Finally, future directions of research are proposed in the emerging field of SST.

Assessment of data analysis methods to identify the heat loss coefficient from on-board monitoring data

2020 ◽  
Vol 209 ◽  
pp. 109706 ◽  
Author(s):  
Marieline Senave ◽  
Staf Roels ◽  
Glenn Reynders ◽  
Stijn Verbeke ◽  
Dirk Saelens
Keyword(s):  
Data Analysis ◽  
Heat Loss ◽  
Loss Coefficient ◽  
Monitoring Data ◽  
Analysis Methods ◽  
Board Monitoring ◽  
Data Analysis Methods

scholarly journals Objective assessment of SERS thin films: comparison of silver on copper via galvanic displacement with commercially available fabricated substrates

2017 ◽  
Vol 9 (33) ◽  
pp. 4783-4789 ◽  
Author(s):  
Samuel Mabbott ◽  
Yun Xu ◽  
Royston Goodacre
Keyword(s):  
Thin Films ◽  
Data Analysis ◽  
Objective Assessment ◽  
Galvanic Displacement ◽  
Sers Signal ◽  
Analysis Methods ◽  
Data Analysis Methods

Reproducibility of SERS signal acquired from thin films developed in-house and commercially has been assessed using seven data analysis methods.

Omaha System Public Health Data: Analysis Methods

2010 ◽  
Vol 58 (2) ◽  
pp. e22-e23
Author(s):  
Karen A. Monsen ◽  
Karen S. Martin ◽  
Bonnie L Westra
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Health Data ◽  
Public Health Data ◽  
Analysis Methods ◽  
Omaha System ◽  
Data Analysis Methods

Peak detection in sediment - charcoal records: impacts of alternative data analysis methods on fire-history interpretations

2010 ◽  
Vol 19 (8) ◽  
pp. 996 ◽  
Author(s):  
Philip E. Higuera ◽  
Daniel G. Gavin ◽  
Patrick J. Bartlein ◽  
Douglas J. Hallett
Keyword(s):  
Data Analysis ◽  
Fire History ◽  
Threshold Value ◽  
Decomposition Methods ◽  
Peak Detection ◽  
Detection Methods ◽  
Statistical Framework ◽  
Analysis Methods ◽  
Decomposition Models ◽  
Data Analysis Methods

Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight alternative decomposition models (four detrending methods used with two threshold-determination methods) and evaluate their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the combination of a globally defined threshold with specific detrending methods can produce strongly biased results, depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the alternative decomposition methods on three previously published charcoal records largely supports our conclusions from simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are warranted with a given sediment–charcoal record.

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