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 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 New approach for localization and smart data transmission inside underground mine environment

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ankita RayChowdhury ◽  
Ankita Pramanik ◽  
Gopal Chandra Roy
Keyword(s):  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Visible Light Communication ◽  
Underground Mine ◽  
Control Room ◽  
Time Data ◽  
New Approach ◽  
Mine Environment ◽  
Surface Control ◽  
Smart Data

AbstractThis paper presents an approach to access real time data from underground mine. Two advance technologies are presented that can improve the adverse environmental effect of underground mine. Visible light communication (VLC) technology is incorporated to estimate the location of miners inside the mine. The distribution of signal to noise ratio (SNR) for VLC system is also studied. In the second part of the paper, long range (LoRa) technology is introduced for transmitting underground information to above the surface control room. This paper also includes details of the LoRa technology, and presents comparison of ranges with existing above the surface technologies.

Child sexual abuse and the pathophysiology of suicide in adolescents and adults

2013 ◽  
Vol 25 (3) ◽  
pp. 201-205 ◽  
Author(s):  
Betsy S. O’Brien ◽  
Leo Sher
Keyword(s):  
Sexual Abuse ◽  
Child Sexual Abuse ◽  
Suicidal Behavior ◽  
Behavioral Science ◽  
Risky Behaviors ◽  
Conduct Disorders ◽  
Post Traumatic Stress ◽  
Adolescents And Adults ◽  
Axis I ◽  
Multiple Variables

Abstract Background: Child sexual abuse (CSA) is widespread and is associated with various psychopathologies, including Axis I and II disorders, maladaptive and impulsive behaviors, and suicidal behavior in adolescence and adults. The pathophysiology of this association is not well understood; however, it is clear that suicidal behavior in individuals with a history of CSA is a significant social and medical problem that warrants further investigation. Methods: An electronic search of the major behavioral science databases (limited to the most recent studies in the last 20 years) was conducted to retrieve studies detailing the social, epidemiological, and clinical characteristics of child sexual trauma and their relation to suicidal behavior in adolescents and adults. Results: Studies indicate that CSA is related to an increase in Axis I and II diagnoses, including depression, post-traumatic stress disorder, conduct disorders, eating disorders, alcohol and drug abuse, panic disorders, and borderline personality disorder. CSA not just related to an increase in impulsivity and risky behaviors, it has also been linked to an increase in suicidality as well. Conclusion: CSA makes both direct and indirect contributions to suicidal behavior. It is a complex process involving multiple variables, which include psychopathology, maladaptive personality features and the direct contribution of CSA itself. Psychopathologies, such as impulsivity and mood and personality disorders, may modulate the relationship between CSA and suicidal behavior. Some preventive measures for decreasing the prevalence of CSA and suicidality may include education as well as increased access to mental health services.

IPDfromKM: Reconstruct Individual Patient Data from Published Kaplan-Meier Survival Curves

2020 ◽  
Author(s):  
Na Liu ◽  
Yanhong Zhou ◽  
J. Jack Lee
Keyword(s):  
Survival Data ◽  
Individual Patient Data ◽  
Secondary Analysis ◽  
R Package ◽  
Patient Data ◽  
Survival Curves ◽  
Raw Data ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Shiny Application

Abstract BackgroundWhen applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to the IPD. We aim to propose a straightforward and robust approach to help researchers to obtain IPD from published survival curves with a friendly software platform. ResultsImproving upon the existing methods, we proposed an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we develop the R package IPDfromKM and an accompanied web-based Shiny application. Both the R package and Shiny application can be used to extract raw data coordinates from published K-M curves, reconstruct IPD from data coordinates extracted, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, as well as hazard ratios. ConclusionsIPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD data and advance the use of secondary data to make informed decision in medical research.

Outliers in L2 Research in Applied Linguistics: A Synthesis and Data Re-Analysis

2020 ◽  
Vol 40 ◽  
pp. 26-55 ◽  
Author(s):  
Christopher Nicklin ◽  
Luke Plonsky
Keyword(s):  
Phase I ◽  
Relative Effectiveness ◽  
Reaction Time Data ◽  
Future Research ◽  
Time Data ◽  
Two Phase ◽  
Raw Data ◽  
Study Results ◽  
Using Data ◽  
The Impact

AbstractData from self-paced reading (SPR) tasks are routinely checked for statistical outliers (Marsden, Thompson, & Plonsky, 2018). Such data points can be handled in a variety of ways (e.g., trimming, data transformation), each of which may influence study results in a different manner. This two-phase study sought, first, to systematically review outlier handling techniques found in studies that involve SPR and, second, to re-analyze raw data from SPR tasks to understand the impact of those techniques. Toward these ends, in Phase I, a sample of 104 studies that employed SPR tasks was collected and coded for different outlier treatments. As found in Marsden et al. (2018), wide variability was observed across the sample in terms of selection of time and standard deviation (SD)-based boundaries for determining what constitutes a legitimate reading time (RT). In Phase II, the raw data from the SPR studies in Phase I were requested from the authors. Nineteen usable datasets were obtained and re-analyzed using data transformations, SD boundaries, trimming, and winsorizing, in order to test their relative effectiveness for normalizing SPR reaction time data. The results suggested that, in the vast majority of cases, logarithmic transformation circumvented the need for SD boundaries, which blindly eliminate or alter potentially legitimate data. The results also indicated that choice of SD boundary had little influence on the data and revealed no meaningful difference between trimming and winsorizing, implying that blindly removing data from SPR analyses might be unnecessary. Suggestions are provided for future research involving SPR data and the handling of outliers in second language (L2) research more generally.

A method for analysis of pulmonary arterial and venous occlusion data

1992 ◽  
Vol 73 (3) ◽  
pp. 1190-1195 ◽  
Author(s):  
S. H. Audi ◽  
C. A. Dawson ◽  
J. H. Linehan
Keyword(s):  
Compartmental Model ◽  
Venous Occlusion ◽  
Time Data ◽  
Equilibrium Pressure ◽  
New Approach ◽  
Lung Lobe ◽  
C Distribution ◽  
Average Slope ◽  
Arterial Inflow ◽  
Pulmonary Arterial

Recently, we presented a compartmental model of the pulmonary vascular resistance (R) and compliance (C) distribution with the configuration C1R1C2R2C3 (J. Appl. Physiol. 70: 2126–2136, 1991). This model was used to interpret the pressure vs. time data obtained after the sudden occlusion of the arterial inflow (AO), venous outflow (VO), or both inflow and outflow (DO) from an isolated dog lung lobe. In the present study, we present a new approach to the data analysis in terms of this model that is relatively simple to carry out and more robust. The data used to estimate the R′s and C′s are the steady-state arterial [Pa(0)] and venous [Pv(0)] pressures, the flow rate (Q), the area (A2) encompassed by Pa(t) after AO and the equilibrium pressure (Pd) after DO, and the average slope (m) of the Pa(t) and Pv(t) curves after VO. The following formulas can then be used to calculate the 2 R′s and 3 C′s: [Pa(0) - Pv(0)]/Q = R1 + R2 = RT, R1C1 congruent to to A2/[Pa(0) - Pd], R1 congruent to [Pa(0) - Pd]/Q, Q/m = C1 + C2 + C3 = CT, and C2 = CT - (RTC1/R2).

scholarly journals AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

2019 ◽  
Author(s):  
Deepank R Korandla ◽  
Jacob M Wozniak ◽  
Anaamika Campeau ◽  
David J Gonzalez ◽  
Erik S Wright
Keyword(s):  
R Package ◽  
Evolutionary Conservation ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Gene Finding ◽  
Proteomics Data ◽  
Protein Coding ◽  
New Approach ◽  
Protein Coding Genes ◽  
Clear Winner

Abstract Motivation A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy. Results Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88–95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites. Availability and implementation AssessORF is available as an R package via the Bioconductor package repository. Supplementary information Supplementary data are available at Bioinformatics online.

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