Diverse Experimental Data Research Articles

A unified strain energy-based fatigue life prediction methodology for composite and metal adhesive joints: Effects of adhesive, geometry and, environment

Ensuring the efficiency and safety of bonded structures across diverse engineering sectors requires a robust fatigue model for adhesive joints in both composite and metal substrates. This study presents a unified fatigue life prediction methodology for similar and dissimilar composite and metal adhesive joints. The proposed model integrates hydrostatic and deviatoric strain energy density components with the theory of critical distances. The model has been validated against diverse experimental data from different joint geometries, loading conditions, and different environments. The linear-elastic assumption is sufficient for toughened epoxies, whereas an elastoplastic model is necessary for ductile methacrylate adhesives. Calibration based on a single fatigue test yields the highest accuracy, while quasi-static test calibration is also satisfactory. The methodology addresses diverse loading conditions and environmental factors, showing a strong correlation with experimental results. It advances the performance, design, and reliability of similar and dissimilar composite to metal adhesive joints.

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region.

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions. In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings. Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data

The distribution coefficient (Kd) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, Kd depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict Kd values for radionuclide adsorption in soils surrounding nuclear facilities in Republic of Korea. We integrated and trained three sub-networks reflecting different data domains: soil adsorption factors for physicochemical conditions, X-ray fluorescence (XRF) data, and X-ray diffraction (XRD) spectra for inherent soil properties. Our multimodal model achieved high performance, with a coefficient of determination (R2) of 0.84 and root mean squared error (RMSE) of 0.89 for natural log-transformed Kd. This is the first study to develop a multimodal model that simultaneously incorporates inherent soil properties and adsorption factors to predict Kd. We investigated influential peaks in XRD spectra and also revealed that pH and calcium oxide (CaO) were significant variables in soil adsorption factors and XRF data, respectively. These results promote the use of a multimodal model to predict Kd values by integrating data from different domains, providing a cost-effective and novel approach to elucidate the mechanisms of radionuclide adsorption in soil.

Transformation of significant wave height and set-up due to entrained air bubbles effect in breaking waves

The transformation of wave height is of paramount significance in coastal engineering and the design of coastal structures. Considering the influence of air bubbles, this study devised an optimal dissipation model for accurately calculating changes in significant wave height (Hm0) and wave set-up for irregular waves undergoing breaking. Existing regular wave breaking models, which consider the effects of air bubbles, were adapted for direct application to irregular waves by deriving novel formulations. The proposed models leverage the probability of the fraction of broken waves. Hm0 was computed using the energy balance equation, while the wave set-up was calculated based on the momentum balance equation. A wide range of test scenarios, incorporating diverse scales (small and large) and experimental field data, was considered for validation. One of the proposed models, namely model-I (M-I), particularly demonstrated superior performance, manifesting lower error indices (P20), root-mean-square relative error (RMSRE), and Brier skill score (BSS) values in computing both Hm0 and wave set-up. Therefore, utilising M-I is strongly recommended for the precise estimation of Hm0 and set-up transformation.

The HADDOCK2.4 web server for integrative modeling of biomolecular complexes.

Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server's various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody-antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types.

A generic approach for modelling hydrogen-methane-air detonation in hydrocode

Hydrogen is pivotal in the transition to a sustainable energy supply. The presence of hydrogen-methane mixtures is increasing as the feasibility of utilizing existing natural gas infrastructure for large-scale hydrogen transportation and distribution. However, recent explosion accidents underscore the need to understand the detonation hazards of hydrogen and methane. Current modelling techniques for hydrogen-methane-air detonations are computationally prohibitive due to extra-fine mesh and time step requirements to solve the chemical reaction coupling within detonation structure. This study presented a generic approach to address these challenges, enabling precise and rapid simulation of hydrogen-methane-air blast profiles within hydrocodes. Modified open-source codes helped bypass the time-consuming implicit coupling of detailed chemical reactions with detonation structure and thermodynamic properties based on Chapman-Jouguet (C-J) theory and equilibrium reactive flow assumption, yielding a 90% reduction in computational time. An empirical model, developed through theoretic calculations, provided C-J parameters for hydrocode input with high accuracy across typical industrial conditions. Then, these C-J parameters were employed in LS-DYNA hydrocode based on modified Jones-Wilkins-Lee (JWL) equation of state (EoS) to simulate the hydrogen-methane-air blast loading. This approach was validated against diverse experimental data, encompassing hydrogen-air, methane-air, and hydrogen-methane-air mixtures, and various fuel concentrations, experimental scales, confinement conditions, and fuel shapes. Although conservative results were observed in unstable detonations near the lower detonable limit, it outperformed traditional CFD methods in both accuracy and efficiency. Furthermore, the use of hydrocodes enables the analysis of blast loading as well as the dynamic behaviour of structures subjected to explosive forces. This approach is easily adaptable to other gaseous detonations beyond hydrogen-methane-air by simply replacing the chemical reaction models, making it a versatile tool across various fields.

A piecewise-linear backbone model for unbonded post-tensioned concrete masonry shear walls

Unbonded post-tensioned concrete masonry (UPCM) shear walls present an effective seismic force resisting system due to their ability to mitigate expected damage risks through their self-centering capabilities. A few design procedures were proposed to predict the in-plane flexural response of UPCM walls, albeit following only basic mechanics and/or extensive iterative methods. Such procedures, however, may not be capable of capturing the complex nonlinear relationships between different parameters that affect UPCM walls’ behavior or are rather tedious to be adopted for design practice. In addition, the restricted datasets used to validate these procedures may cast doubt on their accuracy and validity for new, unseen data, further hindering their adoption by practitioners and design standards. To address these issues, an experimentally-validated nonlinear numerical model was adopted in this study and subsequently employed to simulate 95 UPCM walls with a range of different design parameters to compensate for the lack of relevant diverse experimental data in the current literature. Guided by mechanics, and employing this database, an evolutionary algorithm—multigene genetic programming (MGGP), was adopted to uncover the relationships controlling the response of UPCM walls, and subsequently develop simplified closed-form wall behavior prediction expressions. Specifically, through integrating MGGP and basic mechanics, a piecewise-linear backbone model was developed to predict the load-displacement backbone for UPCM walls up to 20% strength degradation. Compared to existing predictive procedures, the prediction accuracy of the developed model and its closed-form nature are expected to facilitate better understanding of such wall behaviors and subsequently open the gate to further incorporate other experimental and numerical results to ultimately deploy UPCM in mainstream building designs in the near future.

Electrokinetic modeling of mixtures: Impact of size and surface electrical property of particles

This study investigates mixture electrokinetics and explores its dependencies on the properties of constituent particles and network characteristics. Different networks with particles varying in size and type are developed, and a simulation study is conducted on the networks in both pore and network scales. Various dependencies between network parameters, particle properties and zeta potential are observed in the networks. The results also show that introducing even slight heterogeneity to a homogeneous network significantly transforms the local electric currents within the networks. The findings are utilized to develop a correlation predicting the zeta potential of mixture based on particle and network properties. Both the model and introduced correlation are evaluated using diverse experimental data. The comparisons demonstrate robust predictive capabilities of the model and correlation.

Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration

Volumetric brain atlases are increasingly used to integrate and analyze diverse experimental neuroscience data acquired from animal models, but until recently a publicly available digital atlas with complete coverage of the rat brain has been missing. Here we present an update of the Waxholm Space rat brain atlas, a comprehensive open-access volumetric atlas resource. This brain atlas features annotations of 222 structures, of which 112 are new and 57 revised compared to previous versions. It provides a detailed map of the cerebral cortex, hippocampal region, striatopallidal areas, midbrain dopaminergic system, thalamic cell groups, the auditory system and main fiber tracts. We document the criteria underlying the annotations and demonstrate how the atlas with related tools and workflows can be used to support interpretation, integration, analysis and dissemination of experimental rat brain data.

A Multi-Indicator Fusion-Based Approach for Fault Feature Selection and Classification of Rolling Bearings

Concerning the problems of harrowing extraction and poor classification accuracy of fault features in rolling bearing vibration signals, a fault feature selection and classification method based on multi-indicator fusion is proposed. Firstly, the original signal is decomposed through the improved complementary ensemble local mean decomposition (CELMD) method into several physically meaningful product functions (PF) and single residual components; then, the three indicators of kurtosis, correlation coefficient, and K-L divergence are combined to extract the most suitable PF components for signal reconstruction. Ultimately, the reconstructed signal's multi-domain characteristics and entropy value features are retrieved and fed into the LightGBM classifier for classification in order to achieve an intelligent diagnosis of rolling bearing problems. The statistical results demonstrate that the proposed method can efficiently identify the functional PF components and has notable benefits in extracting features from diverse experimental data sets and detecting faults.

The role of electron extinction in the breakdown strength of nanocomposite capacitors

A first-principles theory of electrical breakdown in nanocomposite capacitors, which considers the trapping and scattering (extinction) of electrons originating from the presence of nanoinclusions in the polymer matrix, is developed. The breakdown strength relative to its value for a neat polymer is expressed in terms of two parameters, one of which is determined by the volume density of the nanoinclusion polarizability and the other one is proportional to the electron trap surface density around an inclusion, while the effect of electron scattering is shown to be insignificant. A comparison of the theoretical predictions with diverse experimental data demonstrates an excellent agreement and suggests an effective tool for the design of nanocomposite capacitors.

Scarce data driven deep learning of drones via generalized data distribution space

Increased drone proliferation in civilian and professional settings has created new threat vectors for airports and national infrastructures. The economic damage for a single major airport from drone incursions is estimated to be millions per day. Due to the lack of balanced representation in drone data, training accurate deep learning drone detection algorithms under scarce data is an open challenge. Existing methods largely rely on collecting diverse and comprehensive experimental drone footage data, artificially induced data augmentation, transfer and meta-learning, as well as physics-informed learning. However, these methods cannot guarantee capturing diverse drone designs and fully understanding the deep feature space of drones. Here, we show how understanding the general distribution of the drone data via a generative adversarial network (GAN), and explaining the under-learned data features using topological data analysis (TDA) can allow us to acquire under-represented data to achieve rapid and more accurate learning. We demonstrate our results on a drone image dataset, which contains both real drone images as well as simulated images from computer-aided design. When compared to random, tag-informed and expert-informed data collections (discriminator accuracy of 94.67%, 94.53% and 91.07%, respectively, after 200 epochs), our proposed GAN-TDA-informed data collection method offers a significant 4% improvement (99.42% after 200 epochs). We believe that this approach of exploiting general data distribution knowledge from neural networks can be applied to a wide range of scarce data open challenges.

Skeleton-creep based bubble growth model and multi-scale mechanical constitutive model for U-10Mo fuels under irradiation

The macroscale irradiation swelling deformations of nuclear fuels induced by the solid and gas fission products are one of the most concerned problems in nuclear reactor core design. Based on continuum mechanics, an innovative macroscale volume growth strain model is proposed for U-10Mo fuels, with the coupling contributions of irradiation creep, fission solid swelling and thermal expansion of fuel skeleton. Correspondingly, new three-dimensional mechanical constitutive models and stress update algorithms are also developed for the multi-scale irradiation-deformation and stress analysis. Good agreements of the predictions with diverse experimental data validate the effectiveness of newly developed models. It is obtained that the growth of fission gas bubbles is dominated by the creep deformations of the surrounding fuel skeleton, depending on the external hydrostatic pressure and the internal bubble pressure related to the intra-granular fission gas atom diffusion; the influences of temperature, external hydrostatic pressure and fuel-skeleton creep rate coefficient are found to become prominent at higher burnup levels, dominantly induced by the coupling of fuel-skeleton creep and local porosity. This study provides a new insight into the mechanism of bubble growth in the solid skeleton, revealing the coupling of irradiation swelling with fuel-skeleton creep.

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

A new set of software tools for the prediction of the higher heating values (HHV) of arbitrarily chosen biomass species is presented. A comparative qualitative and quantitative analysis of 12 algorithms for training artificial neural networks (ANN) which predict the HHV of biomass using the proximate analysis is given. Fixed carbon, volatile matter and ash percentage were utilized as inputs. Each ANN had the same structure but a different training algorithm (BFGS Quasi Newton, Bayesian Regularization, Conjugate Gradient—Powell/Beale Restarts, Fletcher–Powell Conjugate Gradient, Polak–Ribiére Conjugate Gradient, Gradient Descent, Gradient Descent Momentum, Variable Learning Rate Gradient Descent, Levenberg–Marquardt, One Step Secant, Resilient Backpropagation, Scaled Conjugate Gradient). To ensure an extended applicability of our results to a wide range of different biomass species, the data conditioning was based on diverse experimental data gathered from the literature, 447 samples overall. Out of these, 301 datasets were used for the training, validation and testing by MathWorks MATLAB Neural Network Fitting Application and by custom designed codes, and 146 remaining datasets were used for the independent evaluation of all training algorithms. The HHV predictions of the ANN-based fitting functions were thoroughly tested and intercompared, to which purpose we developed a test suite which applies mean squared error, coefficient of the determination, mean Poisson deviance, mean Gamma deviance and Friedman test. The comparative analysis showed that several algorithms resulted in ANN-based fitting functions whose outputs correlated well with measured values of the HHV. All programming codes are freely downloadable.

Minimizing calcium lactate precipitation via the addition of gluconate ions for matrix acidizing with lactic acid

Organic acids are commonly used to replace hydrochloric acid (HCl) in high reservoir temperature applications, as they are less corrosive and weaker than HCl. However, organic acids have shown some problems due to acid reaction product solubility. One such organic acid, lactic acid, produces calcium lactate when it reacts with calcite, which has a low solubility in water. However, reaction product solubility can be improved by up to five times when gluconate ions coexist with lactate and calcium ions. The objective of this research is to evaluate lactic and gluconic acid mixtures in term of dissolving calcite, reaction product, corrosion, wettability and generating dominant wormhole.Lactic and gluconic acids were mixed together using deionized water and seawater to conduct calcite solubility tests. Corrosion tests, between 4 and 12 h, were also run under reservoir conditions. A formation response test (FRT) apparatus was used to run different coreflood tests using different combinations of injection rates, total acid concentration, and temperatures. These tests were accompanied with analytical analysis utilizing ICP and IC to measure calcium, iron and sulfate ions in solution.The results showed that mixing lactic and gluconic acids at a 1:1 M ratio provided the optimal results as no precipitation occurred at total acid strengths of 10 wt% and up to 27 wt%. Seawater usage caused calcium sulfate precipitation; therefore, three scale inhibitors were evaluated to determine mitigation rates. Acid-calcite dissolving results were satisfactory when limestone was exposed to a 1:1 and 2:1 M ratio of crushed core-to-acid ratios as at least 50% of the crushed core was dissolved. However, the two-acid mixture showed a corrosion rate that was higher than the acceptable rates and a trace of iron lactate precipitation occurred at 200 and 300 °F. Five gpt from a sulfur-based corrosion inhibitor was enough to mitigate the corrosion rate to allow for 8 h of testing. Coreflood tests showed that the mixture penetrated limestone core with minimal acid pore volume without any face dissolution or salt precipitation on the core faces.This research presents a set of diverse experimental data to confirm that lactic acid accompanied by gluconic acid can penetrate carbonate formation without any by-product precipitation. The two organic acids are less corrosive and less hazardous which can provide a safe operation environment and can decrease replacement and maintenance costs.

Further development of the fission gas swelling model for U-10Mo fuels

The irradiation swelling of U-10Mo fuels is one of the critical factors to affect the in-pile structural integrity and dimensional stability of fuel plates. In this study, a mechanistic model of fission gas swelling for U-10Mo fuels is newly developed with a new evolution model of bubble density, considering the fission gas diffusion behavior and the grain recrystallization. The developed theoretical frame is validated by comparing the predictions in this study with diverse experimental data quantitatively and qualitatively, including the fission gas swelling, total irradiation swelling, the evolution rules of the bubble density and the bubble size, together with the thickness increments of fuel foil in a monolithic U-10Mo/Al fuel plates. The correlation of the irradiation creep with the fission-gas-swelling induced porosity for U-10Mo fuels is firstly considered in the simulation of irradiation-induced thermal-mechanical behaviors in Al-cladded monolithic fuel plates, and a new creep coefficient of dense U-10Mo fuels is identified as 180 × 10−22 mm3/MPa. The research results indicate that the developed fission gas swelling model has a satisfactory prediction ability, especially for higher burnup cases, and can describe the dominant dependences of external pressure and temperature found in the related experiments. In addition, parametric studies have been carried out to investigate the influences of temperature, external pressure and grain-boundary diffusion enhancement factor on the related fission gas behaviors.

Insulin Resistance and the Metabolic Syndrome severity – a mathematical model

Excessive fructose consumption was shown to have deleterious effects on the cardiovascular system, particularly as the metabolic syndrome. However, the degree by which alteration of each pathophysiological factor contributes to the morbidity associated with fructose consumption is not yet clear. We have developed a mathematical model to integrate and uniformly quantify pathophysiological features of the metabolic syndrome on a high fructose-fed dog model. A novel comprehensive measure for the syndrome severity (the “patholog”) and a more intuitive measure of the insulin resistance are introduced. Alteration of hemodynamics, echocardiography and blood chemistry were determined in adult male mongrel dogs fed with 60% isocaloric fructose or normal chow for 7 weeks. The diverse experimental data were transformed into comparable scores and a pre-Hilbert space of states constructed. In such a space one can quantify the severity of any combination of pathophysiological and genomic features and determine the global recovery resulting from a treatment. The model indicates increase of insulin resistance (new index proposed), systolic blood pressure, low-to-high density lipids ratio and angiotensin II as the major contributors to the excessive fructose morbidity. Our model provides the simplest, yet the most intuitive and comprehensive way to integrate data of a wide diversity in visualizing and quantifying a cardiovascular disease

Fault Development Characteristics and Their Effects on Current Gas Content and Productivity of No. 3 Coal Seam in the Zhengzhuang Field, Southern Qinshui Basin, North China

Faulting is one of the most common geological features that impacts current gas content (CGC) of coal seams, which is an important parameter for coalbed methane (CBM/gas) production. However, the detailed faulting effects on CGC and productivity have not been thoroughly investigated, because of inadequate methods for quantitative evaluation of the effects. In this study, we carry out comprehensive analysis of diverse geological coring data and experimental results and production data of exploration wells as well as the three-dimensional fault interpretation in the Zhengzhuang field. We introduce a parameter fault scale (F) to quantify the scale of faults, which is described by fault length, fault throw, and the investigated area. Then we propose a horizontal grid method to determine the faulting influence on CGC. The relationships between the CGC and the fracture density of coal core, the mechanical properties of the roof and floor of coal seam, the average gas production in a faulting system (including the upthrown side, downthrown side, and fault plain) are analyzed. In the study area, the well-developed normal faults have significant influence on CGC, but the locally developed thrust faults have negligible influence. The square grid with length of 1.5 km is probably the most effective grids for analyzing faulting effected distance on CGC. Thus, the normal faults can be divided into small- (F1.5 < 0.05), moderate- (0.05 < F1.5 < 0.15), and large-scale (F1.5 > 0.15). The evident effect of faulting on CGC can be observed up to ∼1.5 km away from the normal fault. When the coal seam is further away from the normal faults (i.e., >1.5 km), the CGC is higher or closer to 20.7 m3/t. In contrast, when the coal seam is <1.5 km from the normal faults, the CGC decreases with increasing value of F. In general, the CGC is higher on the downthrown side, with better gas preservation conditions than the upthrown side. Relatively high CGC (>20 m3/t) is necessary for good gas production of CBM wells. The CBM wells with low ADGP (<500 m3/d) and low CTGP (<105 m3) are commonly located on the fault plane and upthrown side of normal faults. In general, the places far away (>1.5 km) from the normal faults is the most favorable productivity area, and only the small-scale normal faults developed is the secondary favorable productivity area. These places have the average gas production as high as 1000 m3/d and cumulative total gas production as high as 4 × 105 m3.

Automated Experimentation Powers Data Science in Chemistry.

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.

Quantification of Ebola virus replication kinetics in vitro.

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.