cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11931795.v1
title: Fig 2 from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931798
title: fig 7 from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931789
title: fig_6 from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931792
title: Malariah_data from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931798.v1
title: fig 7 from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931792.v1
title: Malariah_data from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931789.v1
title: fig_6 from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931786
title: Run_file_Malariah from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931786.v1
title: Run_file_Malariah from A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

description: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generates the data, the observed measurements invariably include errors. In this paper, we argue that several popular ABC approaches fail to adequately model these errors because the acceptance probability depends on the choice of the discrepancy function and the tolerance without any consideration of the error term. We observe that the so-called posterior distributions derived from such methods do not accurately reflect the epistemic uncertainties in parameter values. Moreover, we demonstrate that these methods provide minimal computational advantages over exact Bayesian methods when applied to one ODE epidemiological models with simulated data and one with real data concerning malaria transmission in Afghanistan.
DOI: 10.6084/m9.figshare.11931774
title: N-mixture model data creation and model fitting script from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: R script used to load, modify, clean, and create specialized dataframes for N-mixture models. Additionally, this script includes code to generate and fit N-mixture models in parallel.
DOI: 10.6084/m9.figshare.11931774.v1
title: N-mixture model data creation and model fitting script from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: R script used to load, modify, clean, and create specialized dataframes for N-mixture models. Additionally, this script includes code to generate and fit N-mixture models in parallel.
DOI: 10.6084/m9.figshare.11931768
title: Nmix model Workflow from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: Unsustainable hunting is emptying forests of large animals around the world, but current understanding of how human foraging spreads across landscapes has been stymied by data deficiencies and cryptic hunter behaviour. Unlike other global threats to biodiversity like deforestation, climate change and overfishing, maps of wild meat hunters' movements—often based on forest accessibility—typically cover small scales and are rarely validated with real-world observations. Using camera trapping data from rainforests across Malaysian Borneo, we show that while hunter movements are strongly correlated with the accessibility of different parts of the landscape, accessibility measures are most informative when they integrate fine-scale habitat features like topography and land cover. Measures of accessibility naive to fine-scale habitat complexity like distance to the nearest road or settlement, generate poor approximations of hunters’ movements. In comparison, accessibility as measured by high-resolution movement models based on circuit theory provide vastly better reflections of real-world foraging movements. Our results highlight that simple models incorporating fine-scale landscape heterogeneity can be powerful tools for understanding and predicting widespread threats to biodiversity.
DOI: 10.6084/m9.figshare.11931771
title: Circuit Theoretic Map Creation GFLOW from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: Unsustainable hunting is emptying forests of large animals around the world, but current understanding of how human foraging spreads across landscapes has been stymied by data deficiencies and cryptic hunter behaviour. Unlike other global threats to biodiversity like deforestation, climate change and overfishing, maps of wild meat hunters' movements—often based on forest accessibility—typically cover small scales and are rarely validated with real-world observations. Using camera trapping data from rainforests across Malaysian Borneo, we show that while hunter movements are strongly correlated with the accessibility of different parts of the landscape, accessibility measures are most informative when they integrate fine-scale habitat features like topography and land cover. Measures of accessibility naive to fine-scale habitat complexity like distance to the nearest road or settlement, generate poor approximations of hunters’ movements. In comparison, accessibility as measured by high-resolution movement models based on circuit theory provide vastly better reflections of real-world foraging movements. Our results highlight that simple models incorporating fine-scale landscape heterogeneity can be powerful tools for understanding and predicting widespread threats to biodiversity.
DOI: 10.6084/m9.figshare.11931768.v1
title: Nmix model Workflow from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: Unsustainable hunting is emptying forests of large animals around the world, but current understanding of how human foraging spreads across landscapes has been stymied by data deficiencies and cryptic hunter behaviour. Unlike other global threats to biodiversity like deforestation, climate change and overfishing, maps of wild meat hunters' movements—often based on forest accessibility—typically cover small scales and are rarely validated with real-world observations. Using camera trapping data from rainforests across Malaysian Borneo, we show that while hunter movements are strongly correlated with the accessibility of different parts of the landscape, accessibility measures are most informative when they integrate fine-scale habitat features like topography and land cover. Measures of accessibility naive to fine-scale habitat complexity like distance to the nearest road or settlement, generate poor approximations of hunters’ movements. In comparison, accessibility as measured by high-resolution movement models based on circuit theory provide vastly better reflections of real-world foraging movements. Our results highlight that simple models incorporating fine-scale landscape heterogeneity can be powerful tools for understanding and predicting widespread threats to biodiversity.
DOI: 10.6084/m9.figshare.11931759
title: Data used in N-mixture model formulation and model fitting results of all 139 candidate models from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: Hunter observations, site-, and observation-level covariates used to fit N-mixture models of hunter site usage, and fit model rankings of all 139 models.
DOI: 10.6084/m9.figshare.11931771.v1
title: Circuit Theoretic Map Creation GFLOW from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: Unsustainable hunting is emptying forests of large animals around the world, but current understanding of how human foraging spreads across landscapes has been stymied by data deficiencies and cryptic hunter behaviour. Unlike other global threats to biodiversity like deforestation, climate change and overfishing, maps of wild meat hunters' movements—often based on forest accessibility—typically cover small scales and are rarely validated with real-world observations. Using camera trapping data from rainforests across Malaysian Borneo, we show that while hunter movements are strongly correlated with the accessibility of different parts of the landscape, accessibility measures are most informative when they integrate fine-scale habitat features like topography and land cover. Measures of accessibility naive to fine-scale habitat complexity like distance to the nearest road or settlement, generate poor approximations of hunters’ movements. In comparison, accessibility as measured by high-resolution movement models based on circuit theory provide vastly better reflections of real-world foraging movements. Our results highlight that simple models incorporating fine-scale landscape heterogeneity can be powerful tools for understanding and predicting widespread threats to biodiversity.
DOI: 10.6084/m9.figshare.11931756
title: GFLOW Iteration script from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: Bash script to iteratively run GFLOW simulations over a generated resistance map using .txt and .tsv node files stored in input folders. This script runs over multiple cores and iterates through the node files until no nodes have gone unsimulated.
DOI: 10.6084/m9.figshare.11931759.v1
title: Data used in N-mixture model formulation and model fitting results of all 139 candidate models from Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas

description: Hunter observations, site-, and observation-level covariates used to fit N-mixture models of hunter site usage, and fit model rankings of all 139 models.
DOI: 10.6084/m9.figshare.11931750
title: File S2: raw data from Are humans constantly but subconsciously smelling themselves?

description: All the raw data provided by 400 questionnaire respondents
DOI: 10.6084/m9.figshare.11931750.v1
title: Online questionnaire raw data from Are humans constantly but subconsciously smelling themselves?

description: All the raw data provided by 400 questionnaire respondents
DOI: 10.6084/m9.figshare.11931750.v2
title: File S2: raw data from Are humans constantly but subconsciously smelling themselves?

description: All the raw data provided by 400 questionnaire respondents
DOI: 10.6084/m9.figshare.11931726
title: Multi-Energy X-ray analysis data

description: CT and radiographic measurements with results of a novel multi energy reconstruction method<br>