Review Of Data Analysis Methods Research Articles

Gamma-Ray Dark Matter Searches in Milky Way Satellites—A Comparative Review of Data Analysis Methods and Current Results

If dark matter is composed of weakly interacting particles with mass in the GeV-TeV range, their annihilation or decay may produce gamma rays that could be detected by gamma-ray telescopes. Observations of dwarf spheroidal satellite galaxies of the Milky Way (dSphs) benefit from the relatively accurate predictions of dSph dark matter content to produce robust constraints to the dark matter properties. The sensitivity of these observations for the search for dark matter signals can be optimized thanks to the use of advanced statistical techniques able to exploit the spectral and morphological peculiarities of the expected signal. In this paper, I review the status of the dark matter searches from observations of dSphs with the current generation of gamma-ray telescopes: Fermi-LAT, H.E.S.S, MAGIC, VERITAS and HAWC. I will describe in detail the general statistical analysis framework used by these instruments, putting in context the most recent experimental results and pointing out the most relevant differences among the different particular implementations. This will facilitate the comparison of the current and future results, as well as their eventual integration in a multi-instrument and multi-target dark matter search.

X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods

The main advantage of X-ray microcomputed tomography (µCT) as a non-destructive imaging tool lies in its ability to analyze the three-dimensional (3D) interior of a sample, therefore eliminating the stereological error exhibited in conventional two-dimensional (2D) image analysis. Coupled with the correct data analysis methods, µCT allows extraction of textural and mineralogical information from ore samples. This study provides a comprehensive overview on the available and potentially useful data analysis methods for processing 3D datasets acquired with laboratory µCT systems. Our study indicates that there is a rapid development of new techniques and algorithms capable of processing µCT datasets, but application of such techniques is often sample-specific. Several methods that have been successfully implemented for other similar materials (soils, aggregates, rocks) were also found to have the potential to be applied in mineral characterization. The main challenge in establishing a µCT system as a mineral characterization tool lies in the computational expenses of processing the large 3D dataset. Additionally, since most of the µCT dataset is based on the attenuation of the minerals, the presence of minerals with similar attenuations limits the capability of µCT in mineral segmentation. Further development on the data processing workflow is needed to accelerate the breakthrough of µCT as an analytical tool in mineral characterization.

EEG-Informed fMRI: A Review of Data Analysis Methods.

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

Review of Data Analysis Methods in Measuring Technology Fusion and Trend

Review of Data Analysis Methods in Measuring Technology Fusion and Trend

Methods for Estimating and Mitigating Errors in Spotted, Dual-color DNA Microarrays

The conceptual simplicity of DNA microarray technology often belies the complex nature of the measurement errors inherent in the methodology. As the technology has developed, the importance of understanding the sources of uncertainty in the measurements and developing ways to control their influence on the conclusions drawn has become apparent. In this review, strategies for modeling measurement errors and minimizing their effect on the outcome of experiments using a variety of techniques are discussed in the context of spotted, dual-color microarrays. First, methods designed to reduce the influence of random variability through data filtering, replication, and experimental design are introduced. This is followed by a review of data analysis methods that partition the variance into random effects and one or more systematic effects, specifically two-sample significance testing and analysis of variance (ANOVA) methods. Finally, the current state of measurement error models for spotted microarrays and their role in variance stabilizing transformations are discussed.

EXAFS as a tool for catalyst characterization: a review of the data analysis methods

A review of the EXAFS data analysis methods is presented. A detailed description of the EXAFS signal extraction and the Fourier transform of the data are discussed. The procedure for determining interatomic distances, coordination numbers and disorder effects from EXAFS data is described. This paper also discusses the data analysis statistics. Finally, one example of catalyst characterization by the EXAFS technique is reported.

Review of Data Analysis Methods for Seed Germination1

Numerous procedures for data analysis of seed germination responses are scattered throughout the literature. Here an attempt has been made to summarize the existing methods, to compare the information they provide and to examine their strengths and weaknesses. The methods reviewed include the percent germination, germination index, coefficient of velocity, median response time, probit analysis, curve‐fitting of cumulative germination, heat sums, survival analysis with life tables, logistic regression, proportional hazards regression and accelerated failure time analysis. Comparisons among these method are discussed and illustrated with data from germination responses in tomato (Lycopersicon eseulentum Mill.). Seed germination involves not only qualitative responses of individuals, but also population responses which are distributed over time. Standard analysis of variance or regression methods are appropriate for a data analysis where germination of all viable seed is observed, but they are inappropriate when some viable seeds fail to germinate. Such missing (censored) data complicates statistical analysis and subsequent interpretation. Germinations tests should be designed to determine the nature of censored responses, which can be subsequently accommodated by several statistical procedures referred to as survival analysis.