Data Analysis Toolkit Research Articles

REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

Nonstationarity in Multifactor Models of Discrete Jump Processes, Memory, and Application to Cloud Modeling

Abstract Because of the mathematical and numerical limitations, standard statistical methods known from the literature are not applicable to inferring jump processes under exogenous influence. Such processes can be considered, for example, in the atmosphere (transitions between different cloud types) and in the ocean (phase transitions between water and ice). Reasons for these intrinsic limitations of standard methods are investigated and a method for the inference of discrete microscopic jump models based on macroscopic ensemble observations is presented. It significantly extends the recently developed methods of nonstationary Markov model parameterization (which are constrained to a single exogenous factor and to direct individual observations of the jump process realizations). The main advantage of the new method is the possibility of inference from indirect ensemble observations with multiple exogenous factors. Moreover, this method allows for a new possibility to test whether the available time series is best described via stationary or nonstationary and Markovian (with memory) or independent (without memory) processes. It also allows estimation of the relative significance of the exogenous factors and their impact on the jump probabilities. The new framework provides a unified toolkit for data analysis of jump processes with the same level of detail now possible for standard continuous state space tools. The resulting numerical algorithm is applied to analysis of the total relative cloud cover data in the midlatitudes and in the tropics under the influence of some meteorologically relevant local and global exogenous factors.

Open Plot Project: an open-source toolkit for 3-D structural data analysis

Abstract. In this work we present the Open Plot Project, an open-source software for structural data analysis, including a 3-D environment. The software includes many classical functionalities of structural data analysis tools, like stereoplot, contouring, tensorial regression, scatterplots, histograms and transect analysis. In addition, efficient filtering tools are present allowing the selection of data according to their attributes, including spatial distribution and orientation. This first alpha release represents a stand-alone toolkit for structural data analysis. The presence of a 3-D environment with digitalising tools allows the integration of structural data with information extracted from georeferenced images to produce structurally validated dip domains. This, coupled with many import/export facilities, allows easy incorporation of structural analyses in workflows for 3-D geological modelling. Accordingly, Open Plot Project also candidates as a structural add-on for 3-D geological modelling software. The software (for both Windows and Linux O.S.), the User Manual, a set of example movies (complementary to the User Manual), and the source code are provided as Supplement. We intend the publication of the source code to set the foundation for free, public software that, hopefully, the structural geologists' community will use, modify, and implement. The creation of additional public controls/tools is strongly encouraged.

Application of the parametric formulae for electromagnetic showers in unconverted γ/π0 discrimination

In the LHC experiment, the neutral pions produced during jet fragmentation are the background sources for all physics channels with high-energy photons in their final state. In this paper, the application of the three-dimensional parametric formula for electromagnetic (EM) showers, which we developed in the Alpha Magnetic Spectrometer II experiment, is presented to distinguish the unconverted photons from the neutral pions. With the constructed electromagnetic calorimeter (ECAL) in a GEANT4 simulation, the parametric formulae were validated and the unconverted γ/π0 discrimination was performed with the Toolkit for Multivariate Data Analysis (TMVA) package in ROOT for different transverse energies ranging from 15 GeV to 75 GeV, which is the most sensitive region for light Higgs (with mass ∼120 GeV) searches with the channel H → γγ. With this discrimination method and the selected transverse energy region, we can reject π0 with the efficiency from ∼40% (65–75 GeV) to ∼90% (15–25 GeV) when keeping 90% γ efficiency.

BAT - The Bayesian Analysis Toolkit

Bayesian Analysis Toolkit (BAT) is a new toolkit for data analysis based on Bayes' Theorem. BAT is realized with the use of Markov Chain Monte Carlo which gives access to the full posterior probability distribution. Parameter estimation, limit setting and uncertainty propagation are implemented in a straightforward manner. The tool allows to compare models and to estimate the goodness-of-fit using well-established methods.

DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for “pipeline” data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for “pipeline” data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression

Sliced inverse regression (SIR) is a renowned dimension reduction method for finding an effective low-dimensional linear subspace. Like many other linear methods, SIR can be extended to nonlinear setting via the ldquokernel trick.rdquo The main purpose of this paper is two-fold. We build kernel SIR in a reproducing kernel Hilbert space rigorously for a more intuitive model explanation and theoretical development. The second focus is on the implementation algorithm of kernel SIR for fast computation and numerical stability. We adopt a low-rank approximation to approximate the huge and dense full kernel covariance matrix and a reduced singular value decomposition technique for extracting kernel SIR directions. We also explore kernel SIR's ability to combine with other linear learning algorithms for classification and regression including multiresponse regression. Numerical experiments show that kernel SIR is an effective kernel tool for nonlinear dimension reduction and it can easily combine with other linear algorithms to form a powerful toolkit for nonlinear data analysis.

Connectivity in the regular polytope representation

In order to be able to draw inferences about real world phenomena from a representation expressed in a digital computer, it is essential that the representation should have a rigorously correct algebraic structure. It is also desirable that the underlying algebra be familiar, and provide a close modelling of those phenomena. The fundamental problem addressed in this paper is that, since computers do not support real-number arithmetic, the algebraic behaviour of the representation may not be correct, and cannot directly model a mathematical abstraction of space based on real numbers. This paper describes a basis for the robust geometrical construction of spatial objects in computer applications using a complex called the Regular Polytope. In contrast to most other spatial data types, this definition supports a rigorous logic within a finite digital arithmetic. The definition of connectivity proves to be non-trivial, and alternatives are investigated. It is shown that these alternatives satisfy the relations of a region connection calculus (RCC) as used for qualitative spatial reasoning, and thus introduce the rigor of that reasoning to geographical information systems. They also form what can reasonably be termed a Finite Boolean Connection Algebra. The rigorous and closed nature of the algebra ensures that these primitive functions and predicates can be combined to any desired level of complexity, and thus provide a useful toolkit for data retrieval and analysis. The paper argues for a model with two and three-dimensional objects that have been coded in Java and which implement a full set of topological and connectivity functions which is shown to be complete and rigorous.

BAT – The Bayesian analysis toolkit

We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes' Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution. Parameter estimation, limit setting and uncertainty propagation are implemented in a straightforward manner.

Testing TMVA software in b-tagging for the search of MSSM Higgs bosons at the LHC

We test the usage of a Toolkit for Multivariate Data Analysis (TMVA) in b tagging. Tagging b jets associated with heavy neutral MSSM Higgs bosons at the LHC can be used to extract the Higgs bosons from the Drell-Yan background, for which the associated jets are mainly light quark and gluon jets. Achievable b tagging efficiency is studied with more than ten MVA classifiers at 1% mistagging rate. Most classifiers were found to perform better than the simple track counting algorithm.

Introducing HYDRA: A Multispectral Data Analysis Toolkit

A freeware-based multispectral data analysis tool kit for satellite data has been developed to assist research and development of remote-sensing applications as well as education and training of remote-sensing scientists; it is called HYDRA—HYper-spectral data viewer for Development of Research Applications. HYDRA provides a fast and flexible interface that allows users to explore and visualize relationships between radiances (or reflectances and brightness temperatures) and wavelength (or wavenumber) using spectra diagrams, cross sections, scatter plots, multichannel combinations, and color enhancements on a pixel-by-pixel basis with full access to the underlying metadata of location and time. HYDRA enables interrogation of multispectral (and hyperspectral) fields of data so that a) pixel location and spectral measurement values can be easily displayed; b) spectral channels can be combined in linear functions and the resulting images displayed; c) false color images can be constructed from multiple channel combinations; d) scatter plots of spectral channel combinations can be viewed; e) pixels in images can be found in scatter plots, and vice versa; f) transects of measurements can be displayed; and g) soundings of temperature and moisture as well as spectra from selected pixels can be compared. The World Meteorological Organization has added HYDRA to its Virtual Laboratory for Satellite Training and Data Utilization to enable research with satellite data and to enhance training capabilities. The Virtual Laboratory is designed to provide the instructors and students with a set of easy-to-use tools for creating and conducting training sessions. HYDRA is now part of this international tool kit. This paper describes some of the procedures for displaying multispectral data using HYDRA and presents some examples with Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) data.

A Statistical Toolkit for Data Analysis

The present project aims to develop an open-source and object-oriented software Toolkit for statistical data analysis. Its statistical testing component contains a variety of Goodness-of-Fit tests, from Chi-squared to Kolmogorov-Smirnov, to less known, but generally much more powerful tests such as Anderson-Darling, Goodman, Fisz-Cramer-von Mises, Kuiper, Tiku. Thanks to the component-based design and the usage of the standard abstract interfaces for data analysis, this tool can be used by other data analysis systems or integrated in experimental software frameworks. This Toolkit has been released and is downloadable from the web. In this paper we describe the statistical details of the algorithms, the computational features of the Toolkit and describe the code validation.

A goodness-of-fit statistical toolkit

Statistical methods play a significant role throughout the life-cycle of physics experiments, being an essential component of physics analysis. The present project in progress aims to develop an object-oriented software Toolkit for statistical data analysis. The Toolkit contains a variety of Goodness-of-Fit (GoF) tests, from Chi-squared to Kolmogorov-Smirnov, to less known, but generally much more powerful tests such as Anderson-Darling, Goodman, Fisz-Cramer-von Mises, Kuiper. Thanks to the component-based design and the usage of the standard abstract interfaces for data analysis, this tool can be used by other data analysis systems or integrated in experimental software frameworks. In this paper we describe the statistical details of the algorithms and the computational features of the Toolkit. With the aim of showing the consistency between the code and the mathematical features of the algorithms, we describe the results we obtained reproducing by means of the Toolkit a couple of Goodness-of-Fit testing examples of relevance in statistics literature.

Echoview as a multibeam sonar data processing and analysis toolkit for fisheries research

Echoview is a hydroacoustic data analysis software package, widely used in the fisheries research and stock assessment communities. Originally developed to handle a variety of single-beam sonar data formats, Echoview has been extended to support multibeam data. Multibeam data logging, lossless compression, and real time beamforming and display are some of the software’s core features. Multibeam data has an additional dimension compared to single-beam data, and a 3D data viewer has been developed providing 3D visualizations of the seabed and fish schools detected by built-in algorithms. Since the multibeam module is just one of many software modules of the Echoview package, data from other sources such as single-beam sonar systems and current profilers can be combined and analyzed together with the multibeam data. The combination of coincident fish density estimates from calibrated single-beam backscatter data with school volume estimates from multibeam data will represent a significant improvement in stock assessment methods. Ongoing research and development will make it possible for Echoview to follow and even set new trends in multibeam water-column data analysis for fisheries research. Features under development include calibration, vessel motion compensation, improved feature detection, and enhanced and animated 3D displays.

Prediction of gas chromatographic retention indices of alkylbenzenes

The retention indices (RIs) of a set of alkylbenzenes on a polar gas chromatographic column are predicted directly from their molecular structures. Numerical descriptors are calculated based on the structure of a group of 150 alkylbenzenes. The descriptors are of three types: topological, geometric, and electronic. Statistical methods are employed to find an informative subset of these descriptors that can accurately predict the gas chromatographic RIs. The Automated Data Analysis and Pattern Recognition Toolkit (ADAPT) software system is used to construct a large pool of structurally derived numerical descriptors which are used to build quantitative structure-retention relationships (QSRRs). Multiple linear regression analysis and computational neural networks are used to map the descriptors to the RIs.

Chaos in ocean ambient ‘‘noise’’

Using a data analysis toolkit designed to explore data from nonlinear systems using time domain methods, it has been determined that the observed number of degrees of freedom in one pristine measurement of ocean ambient background pressure fluctuations or ‘‘noise’’ is nine. While the true number of degrees of freedom in the ocean is probably large, the observed measurements have nine degrees of freedom. There is a positive Lyapunov exponent, which identifies the system as being chaotic. The largest exponent is very large, indicating that the prediction horizon for these data is confined to a few samples. Determination of the degrees of freedom is important for the construction of physical models and nonlinear noise reduction filters, which may be based on characteristics of the observed few degrees of freedom from the background acoustic source. The magnitude of the largest Lyapunov exponent provides a measure of confidence for signal state prediction and mitigation.

Nonlinear analysis of environmental distortions of continuous wave signals in the ocean

Data from an underwater continuous wave signal are analyzed using methods derived from the study of chaotic systems. Distortions by the environment effectively add three degrees of freedom (dimensions) to the signal. The dimension required to reconstruct and analyze the received data is five in contrast to a pure tonal or sine wave that only requires two dimensions. The data are examined with a chaotic data analysis toolkit that includes determination of degrees of freedom using local and global false-nearest-neighbor statistics, average mutual information, correlation dimension, and local Lyapunov exponents. These results are important for the development of methods for correction of propagation distortions and nonlinear noise reduction algorithms. Application of this knowledge should lead to improvements in the detection and classification of underwater signals.

WATER QUALITY MODELLING WITHIN THE RAISON EXPERT SYSTEM

The RAISON (Regional Analysis by Intelligent Systems ON a microcomputer) system is a multimedia environmental data analysis tool-kit that contains a fully integrated database management system, spreadsheet, G.I.S., graphics, statistics, modelling and expert system modules as well as a programming language that allows the user to create specialized applications. This paper presents case studies of modelling applications which illustrate the utility of the system in assisting the users of water quality models to make the models more user friendly. This is accomplished through the use of added visualization of inputs and auxiliary information as well as on-line knowledge added. This system also enables the user to represent model results in numerous graphical forms as well as animated results presented on maps. In addition, it has the ability to interface models with expert systems to aid in the selection and use of models and in the interpretation of results.