Dataset Information

On data benchmarking and verification of discrete granular simulations.

ABSTRACT: Since the seminal work of Cundall and Strack (1979), the Discrete Element Method (DEM) has now become accepted as a key tool amongst researchers exploring the fundamental behavior of granular materials. Along with a sustained increase in the number of publications documenting use of DEM in research, intensive development of new open-source and commercial DEM codes has taken place in the last decades. The credibility of these software packages depends on their capacity to replicate physical observations and to reproduce theoretical expressions. Researchers often calibrate DEM codes against laboratory data to gain confidence about their predictions, however, theoretical verifications at the macro and particle levels are often omitted or not explicitly documented or acknowledged. The validation of DEM codes against theoretical expressions is fundamental to guarantee reproducibility and generality of the software, and to avoid bias in more complex simulations. In this article, a dataset providing numerical simulation data along with input files is presented. The dataset relates to a series of theoretical validation approaches, previously documented in the literature, that were here applied to verify the open-source DEM code LAMMPS. The ability of LAMMPS to capture the macroscopic behaviour of granular packages is evaluated by shearing a face-center-cubic (FCC) array of monosized spheres. The calculation of particle translational/rotational motions and forces/torques is checked by considering a clump rolling down an inclined plane. Additionally, the stress-strain behavior of Toyoura sand under "drained" and "undrained" shearing is characterized by a series of LAMMPS outputs. The dataset collected from these simulations can be employed by users to benchmark new or existing DEM codes. Both the LAMMPS input scripts and the simulation results for all the cases are available in a public repository.

SUBMITTER: Salomon J

PROVIDER: S-EPMC10964069 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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On data benchmarking and verification of discrete granular simulations.

Salomon Jose J O'Sullivan Catherine C Patino-Ramirez Fernando F

Data in brief 20240227

Since the seminal work of Cundall and Strack (1979), the Discrete Element Method (DEM) has now become accepted as a key tool amongst researchers exploring the fundamental behavior of granular materials. Along with a sustained increase in the number of publications documenting use of DEM in research, intensive development of new open-source and commercial DEM codes has taken place in the last decades. The credibility of these software packages depends on their capacity to replicate physical obser ...[more]

PMID: 38533119

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Project description:ImportanceCommercial healthcare claims datasets represent a sample of the US population that is biased along socioeconomic/demographic lines; depending on the target population of interest, results derived from these datasets may not generalize. Rigorous comparisons of claims-derived results to ground-truth data that quantify this bias are lacking.Objectives(1) To quantify the extent and variation of the bias associated with commercial healthcare claims data with respect to different target populations; (2) To evaluate how socioeconomic/demographic factors may explain the magnitude of the bias.DesignThis is a retrospective observational study. Healthcare claims data come from the Merative™ MarketScan® Commercial Database; reference data for comparison come from the State Inpatient Databases (SID) and the US Census. We considered three target populations, aged 18-64 years: (1) all Americans; (2) Americans with health insurance; (3) Americans with commercial health insurance.ParticipantsWe analyzed inpatient discharge records of patients aged 18-64 years, occurring between 01/01/2019 to 12/31/2019 in five states: California, Iowa, Maryland, Massachusetts, and New Jersey.OutcomesWe estimated rates of the 250 most common inpatient procedures, using claims data and using reference data for each target population, and we compared the two estimates.ResultsThe average rate of inpatient discharges per 100 person-years was 5.39 in the claims data (95% CI: [5.37, 5.40]) and 7.003 (95% CI: [7.002, 7.004]) in the reference data for all Americans, corresponding to a 23.1% underestimate from claims. We found large variation in the extent of relative bias across inpatient procedures, including 22.8% of procedures that were underestimated by more than a factor of 2. There was a significant relationship between socioeconomic/demographic factors and the magnitude of bias: procedures that disproportionately occur in disadvantaged neighborhoods were more underestimated in claims data (R 2 51.6%, p < 0.001). When the target population was restricted to commercially insured Americans, the bias decreased substantially (3.2% of procedures were biased by more than factor of 2), but some variation across procedures remained.Conclusions and relevanceNaïve use of healthcare claims data to derive estimates for the underlying US population can be severely biased. The extent of bias is at least partially explained by neighborhood-level socioeconomic factors.

Dataset Information

On data benchmarking and verification of discrete granular simulations.

Publications

On data benchmarking and verification of discrete granular simulations.

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