scholarly journals ViSiElse: An innovative R-package to visualize raw behavioral data over time

2019 ◽  
Author(s):  
Elodie M Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals ViSiElse: an innovative R-package to visualize raw behavioral data over time

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8341
Author(s):  
Elodie M. Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this article, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals ViSiElse: An innovative R-package to visualize raw behavioral data over time

2019 ◽  
Author(s):  
Elodie M Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency

2019 ◽  
Author(s):  
Elodie Marie Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
Behavioral Science ◽  
R Package ◽  
Group Behavior ◽  
Small Samples ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Multiple Variables ◽  
Statistical Indicators ◽  
Single Graph

Background. In recent years, the scientific community encouraged the use of raw data graphs to improve the reliability and transparency of the results presented in papers. However, methods to visualize raw data are limited to one variable per graph and/or only small samples. In behavioral science as in many other fields, multiple variables need to be plotted together to allow insights of the behavior or the process observations. In this paper, we present ViSiElse, an R-package offering a new approach in raw data visualization. Methods. ViSiElse was developed with the open-source software R to provide a solution for the complete visualization of the raw time data. Results. ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding statistical indicators and/or time constraints. ViSiElse provides a global visualization of actions acquired from timestamps in any quantifiable observations.

scholarly journals ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency

2019 ◽  
Author(s):  
Elodie Marie Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
Behavioral Science ◽  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Raw Data ◽  
New Approach ◽  
Multiple Variables ◽  
Data Transparency ◽  
Sample Representation ◽  
Statistical Indicators

Background. In recent years, the scientific community encouraged the use of raw data graphs to improve the reliability and transparency of the results presented in papers. However, methods to visualize raw data are limited to one variable per graph and/or only small sample representation. In behavioral science as in many other fields, multiple variables need to be plotted together to allow insights of a behavior and/or process observations. In this paper, we present ViSiElse, a R-package that offers a new approach in raw data visualization. Methods. This visualization tool was developed as a package of the open-source software R to provide a solution to both the lack of tools allowing visual insights of a whole dataset and the lack of innovative tools for raw data transparency. Results. ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps and all participants in a single graph. Individuals and/or group behavior can easily be assessed and supplementary features allow users to further inspect their data by adding statistical indicators and/or time constraints. ViSiElse allows a global visualization of actions, acquired from timestamps in any quantifiable observations.

scholarly journals Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 505
Author(s):  
Jorge D. Machicado ◽  
Eugene J. Koay ◽  
Somashekar G. Krishna
Keyword(s):  
Predictive Accuracy ◽  
Quantitative Imaging ◽  
Standard Of Care ◽  
Small Sample ◽  
Cystic Lesions ◽  
Current Standard ◽  
Cross Sectional ◽  
Small Sample Sizes ◽  
Insight Into

Radiomics, also known as quantitative imaging or texture analysis, involves extracting a large number of features traditionally unmeasured in conventional radiological cross-sectional images and converting them into mathematical models. This review describes this approach and its use in the evaluation of pancreatic cystic lesions (PCLs). This discipline has the potential of more accurately assessing, classifying, risk stratifying, and guiding the management of PCLs. Existing studies have provided important insight into the role of radiomics in managing PCLs. Although these studies are limited by the use of retrospective design, single center data, and small sample sizes, radiomic features in combination with clinical data appear to be superior to the current standard of care in differentiating cyst type and in identifying mucinous PCLs with high-grade dysplasia. Combining radiomic features with other novel endoscopic diagnostics, including cyst fluid molecular analysis and confocal endomicroscopy, can potentially optimize the predictive accuracy of these models. There is a need for multicenter prospective studies to elucidate the role of radiomics in the management of PCLs.

scholarly journals Double reduction estimation and equilibrium tests in natural autopolyploid populations

2021 ◽  
Author(s):  
David Gerard
Keyword(s):  
Random Mating ◽  
R Package ◽  
Small Sample ◽  
Double Reduction ◽  
Bootstrap Procedure ◽  
Likelihood Approach ◽  
Small Sample Sizes ◽  
Minimization Approach ◽  
Quantities Of Interest ◽  
Do So

AbstractMany bioinformatics pipelines include tests for equilibrium. Tests for diploids are well studied and widely available, but extending these approaches to autopolyploids is hampered by the presence of double reduction, the co-migration of sister chromatid segments into the same gamete during meiosis. Though a hindrance for equilibrium tests, double reduction rates are quantities of interest in their own right, as they provide insights about the meiotic behavior of autopolyploid organisms. Here, we develop procedures to (i) test for equilibrium while accounting for double reduction, and (ii) estimate double reduction given equilibrium. To do so, we take two approaches: a likelihood approach, and a novel U-statistic minimization approach that we show generalizes the classical equilibrium χ2 test in diploids. For small sample sizes and uncertain genotypes, we further develop a bootstrap procedure based on our U-statistic to test for equilibrium. Finally, we highlight the difficulty in distinguishing between random mating and equilibrium in tetraploids at biallelic loci. Our methods are implemented in the hwep R package on GitHub https://github.com/dcgerard/hwep.

scholarly journals Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies

2015 ◽  
Author(s):  
Julia A Palacios ◽  
John Wakeley ◽  
Sohini Ramachandran
Keyword(s):  
Population Size ◽  
Broad Class ◽  
Likelihood Estimation ◽  
Small Sample ◽  
Bayesian Nonparametric ◽  
Coalescent Model ◽  
Credible Intervals ◽  
Population Sizes ◽  
Small Sample Sizes ◽  
Over Time

Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past population sizes from genomic data. Accurate methods are available for data from a single locus or from independent loci. Recent methods that model recombination require small sample sizes, make constraining assumptions about population size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model which allows accurate inference of population sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method's credible intervals for population size as a function of time cover 90 percent of true values across multiple demographic scenarios, enabling formal hypothesis testing about population size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of population size history over the past 150,000 years.

scholarly journals COVID-19 Infection-Related Coagulopathy and Viscoelastic Methods: A Paradigm for Their Clinical Utility in Critical Illness

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 817 ◽  
Author(s):  
Argirios E. Tsantes ◽  
Andreas G. Tsantes ◽  
Styliani I. Kokoris ◽  
Stefanos Bonovas ◽  
Frantzeska Frantzeskaki ◽  
...  
Keyword(s):  
Clinical Utility ◽  
Small Sample ◽  
Close Monitoring ◽  
Rotational Thromboelastometry ◽  
Coagulation Tests ◽  
Attractive Option ◽  
Small Sample Sizes ◽  
Fibrinolysis Shutdown ◽  
Insight Into

Hypercoagulability and thrombosis remain a challenge to diagnose and treat in severe COVID-19 infection. The ability of conventional global coagulation tests to accurately reflect in vivo hypo- or hypercoagulability is questioned. The currently available evidence suggests that markedly increased D-dimers can be used in identifying COVID-19 patients who may need intensive care unit (ICU) admission and close monitoring or not. Viscoelastic methods (VMs), like thromboelastography (TEG) and rotational thromboelastometry (ROTEM), estimate the dynamics of blood coagulation. The evaluation of coagulopathy by VMs in severe COVID-19 infection seems an increasingly attractive option. Available evidence supports that COVID-19 patients with acute respiratory failure suffer from severe hypercoagulability rather than consumptive coagulopathy often associated with fibrinolysis shutdown. However, the variability in definitions of both the procoagulant profile and the clinical outcome assessment, in parallel with the small sample sizes in most of these studies, do not allow the establishment of a clear association between the hypercoagulable state and thrombotic events. VMs can effectively provide insight into the pathophysiology of coagulopathy, detecting the presence of hypercoagulability in critically ill COVID-19 patients. However, it remains unknown whether the degree of coagulopathy can be used in order to predict the outcome, establish a diagnosis or guide anticoagulant therapy.

scholarly journals Peruvian Small and Medium-Sized Enterprises in Times of Crisis—Or What Is Happening over Time?

2021 ◽  
Vol 13 (24) ◽  
pp. 13560
Author(s):  
Susanne Durst ◽  
Ann Svensson ◽  
Mariano Martin Genaro Palacios Acuache
Keyword(s):  
Latin America ◽  
Crisis Management ◽  
Structured Interviews ◽  
Time Data ◽  
New Situation ◽  
Further Development ◽  
Insight Into ◽  
Over Time ◽  
Peruvian Firms

Crises means a particular threat to small and medium-sized enterprises (SMEs). The pandemic is no exception; on the contrary, it reinforces this threat. This study provides insight into crisis management in SMEs over a period of time. Data were collected through semi-structured interviews in Peruvian SMEs at two points in time. The findings provide insight into how the Peruvian firms studied adapted to the new situation, and initiated responses to cope with the crisis covering the period April–December 2020. By having studied the phenomenon of crisis management in SMEs at different stages, the study contributes to the further development of still underdeveloped fields of research, namely, crisis management in Latin America in general, and crisis management in SMEs in particular.

scholarly journals Second Order Bayes Prediction of Functionals of Exponential Dispersion Distributions and an Application to the Prediction of the Tails

2004 ◽  
Vol 34 (02) ◽  
pp. 285-298 ◽  
Author(s):  
Zinoviy Landsman
Keyword(s):  
Statistical Theory ◽  
Main Tool ◽  
Small Sample ◽  
Second Order ◽  
Bayes Estimators ◽  
Natural Basis ◽  
New Approach ◽  
High Effectiveness ◽  
Bayes Prediction ◽  
Small Sample Sizes

Second order Bayes estimators, being the main tool in second order optimal statistical theory, provide a natural basis for a new approach to the problem of the prediction of functions of expectation functional for members of an exponential dispersion family. A general formula, providing such prediction up to the term of the order 1/n, is suggested and the application to the problem of the prediction of the tail of distributions is demonstrated. The results are illustrated with normal and gamma claim sizes. The numerical experiment demonstrates the high effectiveness of the approach even for small sample sizes.

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