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About 10,000 papers for “Binary data”

Binary Data

Encyclopedia of Statistical Sciences · 2005

Cited by 0

Binary data

Paul McCullagh, J. A. Nelder · 1989

Cited by 94

Up to this point, we have only studied applications that process textual data. Often, however, applications must work with binary data rather than text in order to save space and time. Additionally, some application data, such as images and audio, are inherently binary. As web applications increase in sophistication, the use of binary data is becoming more popular, even in the browser. Thus, this chapter’s focus shifts to applications that handle pure binary data. It examines what binary data is and how it is handled in the JavaScript standard, as well as features unique to Node.

Cited by 0

True binary data, observed binary data and binary mutation process example.

Yuan Gao (159945), Jeff Gaither (14210056), Julia Chifman (3532523) et al. · Figshare · 2022

(A) True binary mutation matrix of the sequenced tumor cells in the mutation tree in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010560#pcbi.1010560.g001" target="_blank">Fig 1E</a>. Each row represents true genotypes for one genomic site in all cells and each column represents the true genotypes of multiple genomic sites for one single cell. (B) Observed mutation matrix with missing and ambiguous values (red), as well as mutation states that are misrecorded with respect to the true mutation matrix (red numbers; these are either false positives or false negatives). The red dash indicates a missing value since the sequencing process does not return signal at this site of this cell, and the red question mark represents an ambiguous value. Each row represents observed states for one genomic site in all cells and each column represents the observed states of multiple genomic sites for one single cell. (C) Binary mutation process example. A mutation is acquired on branch e1 (highlighted in red). The cell descending from branch e8 (highlighted in black) does not carry the mutation, while the cells descending from the blue branches carry the mutation.

Cited by 0Open access

Binary Collisions Data

Kimberly Litwin Miller · Figshare · 2020

Data from experiments investigating attrition due to binary collision of two particles.

Cited by 0Open access

Data 2 Binary

Zaid Ali Khan · Figshare · 2022

Ton IoT dataset - binary

Cited by 0Open access

Modelling binary data /

Collett, D., 1952- 98036 author

Cited by 0

Data 2 Binary

Khan, Zaid A · Figshare · 2022

Ton IoT dataset - binary

Cited by 0Open access

Binary Data (NMC_90wt_2000bar)

Ebner, Martin · ETHZ Data Archive - Research Data · 2013

Cited by 0Open access

Binary Data, Multivariate

Wesley O. Johnson · Wiley StatsRef: Statistics Reference Online · 2014

Cited by 1

Continuous/binary data.

Kerry Dwan (62919), Douglas G. Altman (62920), Mike Clarke (9392) et al. · Figshare · 2015

aBased on summary assessments for three domains from <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001666#pmed-1001666-t004" target="_blank">Table 4</a>: H = high quality; X = methodological concerns; – = not applicable.

Cited by 0Open access

Binary Collisions Data

Miller, Kimberly Litwin · Figshare · 2020

Data from experiments investigating attrition due to binary collision of two particles.

Cited by 1Open access

Binary Mixtures: Data

Ch. Wohlfarth, B. Wohlfarth · 2005

Cited by 0

Binary Data, Multivariate

Wesley O. Johnson · Encyclopedia of Statistical Sciences · 2005

Cited by 1

Binary response data

Murray Aitkin, Brain Francis, John Hinde · 2005

Abstract Much social science data consist of categoricalvariables. Familiar examples are religion, nationality, residence (urban/rural), type of dwelling, level of education and social class. The categories may be unordered (religion, nationality) or ordered (degree of disablement, attitude to a social question).

Cited by 0

Binary Data Analysis

Michael W. Bruford, Mark Beaumont · 1998

Cited by 3

Correlated Binary Data

Jane Pendergast, Stephen J. Gange, Mary J. Lindstrom · Wiley StatsRef: Statistics Reference Online · 2015

Abstract Correlated binary data may arise in longitudinal or spatial studies or through some other form of clustering of sampling units. The simplest setting, with clusters of size two and no covariates, leads to familiar methods of analysis. Models with covariates include response feature models, conditionally specified models, transitional models, marginal models, and cluster‐specific models. These may lead to different interpretations of the regression parameters.

Cited by 0

Glass Binary Data

Yanbin Sun, H. Y. Bai, M. Z. Li et al. · Figshare · 2018

Metallic glass formation data for binary alloys, collected from various experimental techniques such as melt-spinning or mechanical alloying. This dataset covers all compositions with an interval of 5 at.% in 59 binary systems, containing a total of 5959 alloys in the dataset. The target property of this dataset is the glass forming ability (GFA), i.e. whether the composition can form monolithic glass or not, which is either 1 for glass forming or 0 for non-full glass forming. Data is available as Monty Encoder encoded JSON and as the source CSV file. Recommended access method is with the matminer Python package using the datasets module. Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page. Dataset discussed in: Machine Learning Approach for Prediction and Understanding of Glass-Forming AbilityY. T. Sun†§ , H. Y. Bai†§, M. Z. Li*‡, and W. H. Wang*†§† Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China‡ Department of Physics, Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-nano Devices, Renmin University of China, Beijing 100872, People’s Republic of China§ University of Chinese Academy of Science, Beijing 100049, People’s Republic of ChinaJ. Phys. Chem. Lett., 2017, 8 (14), pp 3434–3439DOI: 10.1021/acs.jpclett.7b01046Publication Date (Web): July 11, 2017

Cited by 0Open access

Binary response data

Murray Aitkin, Brian Francis, John Hinde et al. · 2009

Abstract Much social science data consist of categorical variables. Familiar examples are religion, nationality, residence (urban/rural), type of dwelling, level of education and social class. The categories may be unordered (religion, nationality) or ordered (degree of disablement, attitude to a social question).

Cited by 0