cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11328605.v1
title: Development and validation of a prediction model for identifying men with intermediate- or high-risk prostate cancer for whom bone imaging is unnecessary: a nation-wide population-based study

description: <b>Objective:</b> To develop and validate a nomogram that identifies men for whom bone scan is unnecessary. <b>Material and methods:</b> The study datasets were derived from the National Prostate Cancer Register (NPCR) of Sweden. All men in the NPCR ≤80 years of age who were diagnosed with intermediate- or high-risk prostate cancer and who had pretreatment bone imaging (<sup>99m</sup>Tc MDP scintigraphy, plain x-ray, computed tomography, magnetic resonance imaging, and/or positron emission tomography fused with computed tomography) were included. Men diagnosed from 2015–2016 formed a development dataset and men diagnosed in 2017 formed a validation dataset. Outcome was metastasis on bone imaging as registered in NPCR. Multivariable logistic regression was used to develop a nomogram. <b>Results:</b> In the development dataset 482/5084 men (10%) had bone metastasis, the corresponding percentage in the validation dataset was 282/2554 (11%). Gleason grade group, clinical T stage, and prostate-specific antigen were included in the final model. Discrimination and calibration were satisfactory in both the development (AUC 0.80, 95% CI 0.78–0.82) and validation dataset (AUC 0.80, 95% CI, 0.77–0.82). Compared with using the EAU guidelines’ recommendation for selecting men for imaging, using the nomogram with a cut-off at 4% chance of bone metastasis, would have avoided imaging in 519/2068 men (25%) and miss bone metastasis in 10/519 (2%) men in the validation dataset. <b>Conclusion:</b> By use of our nomogram, bone scans of men with prostate cancer can be avoided in a large proportion of men.
DOI: 10.6084/m9.figshare.11328590
title: Design and evaluation of a dynamic job search website for non-literate users of Pakistan

description: Among the many available online job searching solutions, very limited are proposed for non-literate users. The available solutions have tested the possibilities using static web pages which are not applicable in the real world for non-literate users because of the dynamism of the real world. In this research, a dynamic job search solution is presented for non-literate users of Pakistan. User Interface (UI) requirements were identified through an extensive literature review. The culturally specific content in the form of hand-drawn sketches, voice feedback, semi-illustrations was identified and incorporated in the job seekers (non-literate) module. The same module was evaluated by the non-literate and semi-literate participants in a field study as per the usability specification defined by ISO-9241-11. The results have shown that non-literate users could search for a job efficiently in the minimum task completion time as compared to the time reported in the previous literature. They were satisfied and motivated to use the website again to search for jobs. The results indicate a potential for designing ICT-based solutions to provide services to the non-literate users in developing countries.
DOI: 10.6084/m9.figshare.11328596
title: Penalized variable selection in copula survival models for clustered time-to-event data

description: A dependence among individual event times within a cluster can be modelled using a copula. Variable selection methods using a penalized likelihood allowing for several penalty functions have been widely studied in various statistical models. To the best of our knowledge, however, there is no literature on variable selection methods for the copula survival models. In this paper, we propose a variable selection procedure in the copula survival models with a parametric (e.g. Weibull) marginal using a one-stage estimation method based on a penalized likelihood. Here, we consider four penalty functions, i.e. LASSO, adaptive LASSO, SCAD and HL (h-likelihood). The performance of the proposed method is demonstrated via simulation study. The usefulness of the new method is illustrated using two well-known clinical data sets.
DOI: 10.6084/m9.figshare.11328584
title: Data and codes for ST-ISE.zip

description: The data and codes for ST-ISE model. <br>
DOI: 10.6084/m9.figshare.11328590.v1
title: Design and evaluation of a dynamic job search website for non-literate users of Pakistan

description: Among the many available online job searching solutions, very limited are proposed for non-literate users. The available solutions have tested the possibilities using static web pages which are not applicable in the real world for non-literate users because of the dynamism of the real world. In this research, a dynamic job search solution is presented for non-literate users of Pakistan. User Interface (UI) requirements were identified through an extensive literature review. The culturally specific content in the form of hand-drawn sketches, voice feedback, semi-illustrations was identified and incorporated in the job seekers (non-literate) module. The same module was evaluated by the non-literate and semi-literate participants in a field study as per the usability specification defined by ISO-9241-11. The results have shown that non-literate users could search for a job efficiently in the minimum task completion time as compared to the time reported in the previous literature. They were satisfied and motivated to use the website again to search for jobs. The results indicate a potential for designing ICT-based solutions to provide services to the non-literate users in developing countries.
DOI: 10.6084/m9.figshare.11328596.v1
title: Penalized variable selection in copula survival models for clustered time-to-event data

description: A dependence among individual event times within a cluster can be modelled using a copula. Variable selection methods using a penalized likelihood allowing for several penalty functions have been widely studied in various statistical models. To the best of our knowledge, however, there is no literature on variable selection methods for the copula survival models. In this paper, we propose a variable selection procedure in the copula survival models with a parametric (e.g. Weibull) marginal using a one-stage estimation method based on a penalized likelihood. Here, we consider four penalty functions, i.e. LASSO, adaptive LASSO, SCAD and HL (h-likelihood). The performance of the proposed method is demonstrated via simulation study. The usefulness of the new method is illustrated using two well-known clinical data sets.
DOI: 10.6084/m9.figshare.11328584.v1
title: Data and codes for ST-ISE.zip

description: The data and codes for ST-ISE model.
DOI: 10.6084/m9.figshare.11328530
title: Data on Thermal Conductivity and Dynamic Mechanical Properties of Graphene Quantum dots in epoxy

description: Graphene Quantum Dots (GQDs) and epoxy have been combined into a nanocomposite and evaluated for their thermal and dynamic mechanical properties. Samples of varying GQD mass loading were first examined with SEM in several images. Thermal conductivity was estimated using Differential Scanning Calorimetry (DSC) with a step analysis technique and analysis program. Several dynamic mechanical properties were recorded using Dynamic Mechanical Analysis (DMA) and displayed in their raw and analyzed formats. For more insight please see Infusion of graphene quantum dots to modulate thermal conductivity and dynamic mechanical properties of polymers[1]
DOI: 10.6084/m9.figshare.11328530.v1
title: Data on Thermal Conductivity and Dynamic Mechanical Properties of Graphene Quantum dots in epoxy

description: Graphene Quantum Dots (GQDs) and epoxy have been combined into a nanocomposite and evaluated for their thermal and dynamic mechanical properties. Samples of varying GQD mass loading were first examined with SEM in several images. Thermal conductivity was estimated using Differential Scanning Calorimetry (DSC) with a step analysis technique and analysis program. Several dynamic mechanical properties were recorded using Dynamic Mechanical Analysis (DMA) and displayed in their raw and analyzed formats. For more insight please see Infusion of graphene quantum dots to modulate thermal conductivity and dynamic mechanical properties of polymers[1]
DOI: 10.6084/m9.figshare.11328521
title: EAP19-0321_R1_Table_S3

description: <b>Actual and potential yield losses caused by soil insect pests in four consecutive years (2014-2017)</b>
DOI: 10.6084/m9.figshare.11328521.v1
title: EAP19-0321_R1_Table_S3

description: <b>Actual and potential yield losses caused by soil insect pests in four consecutive years (2014-2017)</b>
DOI: 10.6084/m9.figshare.11328479
title: EAP19-0321.R1_Table_S1

description: <b>Summary of soil moisture contents and corresponding egg numbers in each field-cage replicate</b>
DOI: 10.6084/m9.figshare.11328518
title: EAP19-0321_R1_Table_S2

description: <b>Summary of soil moisture content and corresponding egg number in each Plexiglas experiment replicate</b>
DOI: 10.6084/m9.figshare.11328518.v1
title: EAP19-0321_R1_Table_S2

description: <b>Summary of soil moisture content and corresponding egg number in each Plexiglas experiment replicate</b>
DOI: 10.6084/m9.figshare.11328479.v1
title: EAP19-0321.R1_Table_S1

description: <b>Summary of soil moisture contents and corresponding egg numbers in each field-cage replicate</b>
DOI: 10.6084/m9.figshare.11328419
title: Zebrafish patterns simulated with stochastic length scales

description: We provide model simulations of wild-type, shady, nacre, and pfeffer zebrafish patterns generated using a modified version of the the agent-based model from "Volkening A and Sandstede B (2018) Iridophores as a source of robustness in zebrafish stripes and variability in Danio patterns. Nat. Commun. 9(3231)." These simulations were generated by choosing each length scale parameter in the model randomly per cell and per day from a normal distribution. The mean of the normal distributions was set to the default parameter value (presented in the aforementioned paper) for each parameter. We varied the standard deviation of the noise across the simulations and this information is stored in the file names. For example, WT_sigma_1 corresponds to stochastic wild type simulations with a standard deviation set to 1% the parameter means. <br>The shady patterns were generated by turning off cell birth for iridiopores; the nacre patterns were generated by turning off cell birth for melanophores; and, the pfeffer patterns were generated by turning off cell birth for xanthophores (see reference above for details). All simulations were run in MATLAB.<br>Each simulation output contains data in the form of .mat files. Within each .mat file are the cell locations of all cells across the simulation, an array that tracks the distances that the cells moved throughout the simulation, vectors containing the number of cells born/dead/present at each day of the simulation, and the x- and y-boundaries of the domain for each day of the simulations. The cell locations are stored in N X 2 X T arrays, where N is an upper bound on the total number of cells in the simulation, and T is the number of days that the simulation was run for. For example, cellsM(1:N_M,:,T) contains the x- and y-coordinates of the melanophore cells on the last day of the simulation. Similarly, cellsIl contains the cell coordinates of the loose iridophores, cellsId contains the cell coordinates of the dense iridophores, cellsXc contains the cell coordinates of the dense xanthophores, and cellsXsn contains the cell coordinates of the loose xanthophores.
DOI: 10.6084/m9.figshare.11328419.v1
title: zebrafish patterns simulated with stochastic length scales

description: We provide model simulations of wild-type, shady, nacre, and pfeffer zebrafish patterns generated using a modified version of the the agent-based model from "Volkening A and Sandstede B (2018) Iridophores as a source of robustness in zebrafish stripes and variability in Danio patterns. Nat. Commun. 9(3231)." These simulations were generated by choosing each length scale parameter in the model randomly per cell and per day from a normal distribution. The mean of the normal distributions was set to the default parameter value (presented in the aforementioned paper) for each parameter. We varied the standard deviation of the noise across the simulations and this information is stored in the file names. For example, WT_sigma_1 corresponds to stochastic wild type simulations with a standard deviation set to 1% the parameter means. <br>The shady patterns were generated by turning off cell birth for iridiopores; the nacre patterns were generated by turning off cell birth for melanophores; and, the pfeffer patterns were generated by turning off cell birth for xanthophores (see reference above for details). All simulations were run in MATLAB.<br>Each simulation output contains data in the form of .mat files. Within each .mat file are the cell locations of all cells across the simulation, an array that tracks the distances that the cells moved throughout the simulation, vectors containing the number of cells born/dead/present at each day of the simulation, and the x- and y-boundaries of the domain for each day of the simulations. The cell locations are stored in N X 2 X T arrays, where N is an upper bound on the total number of cells in the simulation, and T is the number of days that the simulation was run for. For example, cellsM(1:N_M,:,T) contains the x- and y-coordinates of the melanophore cells on the last day of the simulation. Similarly, cellsIl contains the cell coordinates of the loose iridophores, cellsId contains the cell coordinates of the dense iridophores, cellsXc contains the cell coordinates of the dense xanthophores, and cellsXsn contains the cell coordinates of the loose xanthophores.
DOI: 10.6084/m9.figshare.11328419.v2
title: zebrafish patterns simulated with stochastic length scales

description: We provide model simulations of wild-type, shady, nacre, and pfeffer zebrafish patterns generated using a modified version of the the agent-based model from "Volkening A and Sandstede B (2018) Iridophores as a source of robustness in zebrafish stripes and variability in Danio patterns. Nat. Commun. 9(3231)." These simulations were generated by choosing each length scale parameter in the model randomly per cell and per day from a normal distribution. The mean of the normal distributions was set to the default parameter value (presented in the aforementioned paper) for each parameter. We varied the standard deviation of the noise across the simulations and this information is stored in the file names. For example, WT_sigma_1 corresponds to stochastic wild type simulations with a standard deviation set to 1% the parameter means. <br>The shady patterns were generated by turning off cell birth for iridiopores; the nacre patterns were generated by turning off cell birth for melanophores; and, the pfeffer patterns were generated by turning off cell birth for xanthophores (see reference above for details). All simulations were run in MATLAB.<br>Each simulation output contains data in the form of .mat files. Within each .mat file are the cell locations of all cells across the simulation, an array that tracks the distances that the cells moved throughout the simulation, vectors containing the number of cells born/dead/present at each day of the simulation, and the x- and y-boundaries of the domain for each day of the simulations. The cell locations are stored in N X 2 X T arrays, where N is an upper bound on the total number of cells in the simulation, and T is the number of days that the simulation was run for. For example, cellsM(1:N_M,:,T) contains the x- and y-coordinates of the melanophore cells on the last day of the simulation. Similarly, cellsIl contains the cell coordinates of the loose iridophores, cellsId contains the cell coordinates of the dense iridophores, cellsXc contains the cell coordinates of the dense xanthophores, and cellsXsn contains the cell coordinates of the loose xanthophores.
DOI: 10.6084/m9.figshare.11328380
title: Research computing project data

description: datasets for three dates
DOI: 10.6084/m9.figshare.11328380.v1
title: Research computing project data

description: datasets for three dates
DOI: 10.6084/m9.figshare.11328419.v3
title: zebrafish patterns simulated with stochastic length scales

description: We provide model simulations of wild-type, shady, nacre, and pfeffer zebrafish patterns generated using a modified version of the the agent-based model from "Volkening A and Sandstede B (2018) Iridophores as a source of robustness in zebrafish stripes and variability in Danio patterns. Nat. Commun. 9(3231)." These simulations were generated by choosing each length scale parameter in the model randomly per cell and per day from a normal distribution. The mean of the normal distributions was set to the default parameter value (presented in the aforementioned paper) for each parameter. We varied the standard deviation of the noise across the simulations and this information is stored in the file names. For example, WT_sigma_1 corresponds to stochastic wild type simulations with a standard deviation set to 1% the parameter means. <br>The shady patterns were generated by turning off cell birth for iridiopores; the nacre patterns were generated by turning off cell birth for melanophores; and, the pfeffer patterns were generated by turning off cell birth for xanthophores (see reference above for details). All simulations were run in MATLAB.<br>Each simulation output contains data in the form of .mat files. Within each .mat file are the cell locations of all cells across the simulation, an array that tracks the distances that the cells moved throughout the simulation, vectors containing the number of cells born/dead/present at each day of the simulation, and the x- and y-boundaries of the domain for each day of the simulations. The cell locations are stored in N X 2 X T arrays, where N is an upper bound on the total number of cells in the simulation, and T is the number of days that the simulation was run for. For example, cellsM(1:N_M,:,T) contains the x- and y-coordinates of the melanophore cells on the last day of the simulation. Similarly, cellsIl contains the cell coordinates of the loose iridophores, cellsId contains the cell coordinates of the dense iridophores, cellsXc contains the cell coordinates of the dense xanthophores, and cellsXsn contains the cell coordinates of the loose xanthophores.
DOI: 10.6084/m9.figshare.11328419.v4
title: zebrafish patterns simulated with stochastic length scales

description: We provide model simulations of wild-type, shady, nacre, and pfeffer zebrafish patterns generated using a modified version of the the agent-based model from "Volkening A and Sandstede B (2018) Iridophores as a source of robustness in zebrafish stripes and variability in Danio patterns. Nat. Commun. 9(3231)." These simulations were generated by choosing each length scale parameter in the model randomly per cell and per day from a normal distribution. The mean of the normal distributions was set to the default parameter value (presented in the aforementioned paper) for each parameter. We varied the standard deviation of the noise across the simulations and this information is stored in the file names. For example, WT_sigma_1 corresponds to stochastic wild type simulations with a standard deviation set to 1% the parameter means. <br>The shady patterns were generated by turning off cell birth for iridiopores; the nacre patterns were generated by turning off cell birth for melanophores; and, the pfeffer patterns were generated by turning off cell birth for xanthophores (see reference above for details). All simulations were run in MATLAB.<br>Each simulation output contains data in the form of .mat files. Within each .mat file are the cell locations of all cells across the simulation, an array that tracks the distances that the cells moved throughout the simulation, vectors containing the number of cells born/dead/present at each day of the simulation, and the x- and y-boundaries of the domain for each day of the simulations. The cell locations are stored in N X 2 X T arrays, where N is an upper bound on the total number of cells in the simulation, and T is the number of days that the simulation was run for. For example, cellsM(1:N_M,:,T) contains the x- and y-coordinates of the melanophore cells on the last day of the simulation. Similarly, cellsIl contains the cell coordinates of the loose iridophores, cellsId contains the cell coordinates of the dense iridophores, cellsXc contains the cell coordinates of the dense xanthophores, and cellsXsn contains the cell coordinates of the loose xanthophores.
DOI: 10.6084/m9.figshare.11328341
title: Diversidad de insectos en cadaveres de cerdo.xlsx

description: Datos de prueba<br>
DOI: 10.6084/m9.figshare.11328419.v6
title: Zebrafish patterns simulated with stochastic length scales

description: We provide model simulations of wild-type, shady, nacre, and pfeffer zebrafish patterns generated using a modified version of the the agent-based model from "Volkening A and Sandstede B (2018) Iridophores as a source of robustness in zebrafish stripes and variability in Danio patterns. Nat. Commun. 9(3231)." These simulations were generated by choosing each length scale parameter in the model randomly per cell and per day from a normal distribution. The mean of the normal distributions was set to the default parameter value (presented in the aforementioned paper) for each parameter. We varied the standard deviation of the noise across the simulations and this information is stored in the file names. For example, WT_sigma_1 corresponds to stochastic wild type simulations with a standard deviation set to 1% the parameter means. <br>The shady patterns were generated by turning off cell birth for iridiopores; the nacre patterns were generated by turning off cell birth for melanophores; and, the pfeffer patterns were generated by turning off cell birth for xanthophores (see reference above for details). All simulations were run in MATLAB.<br>Each simulation output contains data in the form of .mat files. Within each .mat file are the cell locations of all cells across the simulation, an array that tracks the distances that the cells moved throughout the simulation, vectors containing the number of cells born/dead/present at each day of the simulation, and the x- and y-boundaries of the domain for each day of the simulations. The cell locations are stored in N X 2 X T arrays, where N is an upper bound on the total number of cells in the simulation, and T is the number of days that the simulation was run for. For example, cellsM(1:N_M,:,T) contains the x- and y-coordinates of the melanophore cells on the last day of the simulation. Similarly, cellsIl contains the cell coordinates of the loose iridophores, cellsId contains the cell coordinates of the dense iridophores, cellsXc contains the cell coordinates of the dense xanthophores, and cellsXsn contains the cell coordinates of the loose xanthophores.