This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
This repository contains the raw photon-by-photon single-molecule FRET (smFRET) trajectories, SAXS data, and MD simulation trajectories, multi-sequence alignment, and gel images for the paper titled "Sub-Domain Dynamics Enables Chemical Chain Reactions in Nonribosomal Peptide Synthetases."
Large-eddy simulations were employed over five different sea ice patterns, with a constant ice fraction, to test if the overlying atmospheric boundary layer (ABL) dynamics and thermodynamics differs. The results of these simulations were used to determine that there were differences in vertical heat flux, momentum flux, and horizontal wind speed, and that more surface information is needed to predict the ABL over the sea ice surface. To see what other surface information is needed, twenty-two landscape metrics were calculated over forty-four different maps at differing resolutions, using the FRAGSTATs program. The results of that analysis are available in a .csv file in this dataset.
O'Neill, Eric; Lark, Tyler; Xie, Yanhua; Basso, Bruno
Abstract:
Collection of the underlying spatially explicit data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison. Includes raw biomass yield data and soil carbon sequestration potential data for three types of marginal land for the USA midwest at the field level including field areas. Collection also includes raw land rasters for the three types of marginal land, model parameters for the MILP model used in the study, and results used to generate the figures in the paper.
Numerical data is tabulated for all plots (Figures 2, 3a-b, 4-89, S1, S4a-b,d, S5a-b,d, S6-S156) and included as separate spreadsheets categorized by figure in a .zip file in the Supplementary Material. Error bars in Figure 4 show the spread of data observed for 4 and 5 trials on independent samples for MIL-101 and MOF-235, respectively. Figure 6a shows the average of triplicate filtrate test conversions with error propagated based on this spread. Figures 6b and S165 error bars on rate constants are determined based on propagated conversion uncertainty for independent trials and extracted standard deviations of pseudo-first order rate constants from linearized plots. Error bars on other plots represent propagation of experimental uncertainty on single trials.
