scholarly journals Phylogenetic analysis of large molecular data sets

2017 ◽  
pp. 99
Author(s):  
Pamela S. Soltis ◽  
Douglas E. Soltis
Keyword(s):  
Large Data ◽  
Molecular Data ◽  
Global Optimum ◽  
Large Data Sets ◽  
Phylogeny Reconstruction ◽  
Local Optimum ◽  
Data Sets ◽  
Additional Consideration ◽  
Technological Advances ◽  
The Difference

Technological advances in molecular biology have greatly increased the speed and efficiency of DNA sequencing, making it possible to construct large molecular data sets for phylogeny reconstruction relatively quickly. Despite their potential for improving our understanding of phylogeny, these large data sets also provide many challenges. In this paper, we discuss several of these challenges, including 1) the failure of a search to find the most parsimonious trees (the local optimum) in a reasonable amount of time, 2) the difference between a local optimum and the global optimum, and 3) the existence of multiple classes (islands) of most parsimonious trees. We also discuss possible strategies to improve the' likelihood of finding the most parsimonious tree(s) and present two examples from our work on angiosperm phylogeny. We conclude with a discussion of two alternatives to analyses of entire large data sets, the exemplar approach and compartmentalization, and suggest that additional consideration must be given to issues of data analysis for large data sets, whether morphological or molecular.

scholarly journals Big Qual: Defining and Debating Qualitative Inquiry for Large Data Sets

2019 ◽  
Vol 18 ◽  
pp. 160940691988069 ◽  
Author(s):  
Rebecca L. Brower ◽  
Tamara Bertrand Jones ◽  
La’Tara Osborne-Lampkin ◽  
Shouping Hu ◽  
Toby J. Park-Gaghan
Keyword(s):  
Qualitative Data ◽  
Large Data ◽  
Qualitative Inquiry ◽  
Large Data Sets ◽  
Computer Assisted ◽  
Data Sets ◽  
Sampling Strategies ◽  
Analysis Software ◽  
Technological Advances ◽  
Qualitative Data Analysis Software

Big qualitative data (Big Qual), or research involving large qualitative data sets, has introduced many newly evolving conventions that have begun to change the fundamental nature of some qualitative research. In this methodological essay, we first distinguish big data from big qual. We define big qual as data sets containing either primary or secondary qualitative data from at least 100 participants analyzed by teams of researchers, often funded by a government agency or private foundation, conducted either as a stand-alone project or in conjunction with a large quantitative study. We then present a broad debate about the extent to which big qual may be transforming some forms of qualitative inquiry. We present three questions, which examine the extent to which large qualitative data sets offer both constraints and opportunities for innovation related to funded research, sampling strategies, team-based analysis, and computer-assisted qualitative data analysis software (CAQDAS). The debate is framed by four related trends to which we attribute the rise of big qual: the rise of big quantitative data, the growing legitimacy of qualitative and mixed methods work in the research community, technological advances in CAQDAS, and the willingness of government and private foundations to fund large qualitative projects.

scholarly journals Exploring character conflict in molecular data

Zootaxa ◽  
2011 ◽  
Vol 2946 (1) ◽  
pp. 45 ◽  
Author(s):  
ROBERT H. CRUICKSHANK
Keyword(s):  
Phylogenetic Signal ◽  
Sliding Window ◽  
Large Data ◽  
Molecular Data ◽  
Large Data Sets ◽  
Phylogenetic Networks ◽  
Data Sets ◽  
Bayesian Analyses ◽  
Extra Step ◽  
Likelihood Mapping

Mooi & Gill (2010) have made a number of criticisms of statistical approaches to the phylogenetic analysis of molecular data as it is currently practiced. There are many different uses for molecular phylogenies, and for most of them statistical methods are entirely appropriate, but for taxonomic purposes the way that these methods have been used is questionable. In these cases it is necessary to introduce an extra step into the analysis – exploration of character conflict. Existing methods for exploring character conflict in molecular data such as spectral analysis, phylogenetic networks, likelihood mapping and sliding window analyses are briefly reviewed, but there is also a need for development of new tools to facilitate the analysis of large data sets. Incorporation of previous phylogenies as priors in Bayesian analyses could help to provide taxonomic stability, while still leaving room for new data to alter these conclusions if they contain sufficiently strong phylogenetic signal. Molecular phylogeneticists should make a clearer distinction between the different uses to which their phylogenies are put; methods suitable in one context may not be appropriate in others.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

2018 ◽  
Vol 2018 (6) ◽  
pp. 38-39
Author(s):  
Austa Parker ◽  
Yan Qu ◽  
David Hokanson ◽  
Jeff Soller ◽  
Eric Dickenson ◽  
...  
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Statistical Tools

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

Correlations of random classifiers on large data sets

2021 ◽  
Author(s):  
Věra Kůrková ◽  
Marcello Sanguineti
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets

scholarly journals Sparse regression for large data sets with outliers

2021 ◽  
Author(s):  
Lea Bottmer ◽  
Christophe Croux ◽  
Ines Wilms
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Sparse Regression
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