R2 values are also included for the quality of the corresponding fit to the raw data.

R code used to generate the pseudo-datasets and conduct the analyses. The software requirements can be found in Table 1. (R)

The file named as “S1 Link Speed Data” records the average speed for all links every 20-second time interval, and the other file named as “S1 Link Volume data” records volume for all links every 20-second time interval. (RAR)

R Packages Required for the Simulation Study’s Implementation.

Dataset description

This repository contains the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which corresponds to the main outputs of 200 large-eddy simulations (LES) of microscale pollutant dispersion that replicate the MUST field experiment [Biltoft. 2001, Yee and Biltoft. 2004] for varying meteorological forcing parameters.

The goal of the PPMLES dataset is to provide a comprehensive dataset to better understand the complex interactions between the atmospheric boundary layer (ABL), the urban environment, and pollutant dispersion. It was originally used to assess the impact of the meteorological uncertainty on microscale pollutant prediction and to build a surrogate model that can replace the costly LES model [Lumet et al. 2025]. The total computational cost of the PPMLES dataset is estimated to be about 6 million core hours.

For each sample of meteorological forcing parameters (inlet wind direction and friction velocity), the AVBP solver code [Schonfeld and Rudgyard. 1999, Gicquel et al. 2011] was used to perform LES at very high spatio-temporal resolution (1e-3s time step, 30cm discretization length) to provide a fine representation of the pollutant concentration and wind velocity statistics within the urban-like canopy. The total computational cost of the PPMLES dataset is estimated to be about 6 million core hours.

File list

The data is stored in HDF5 files, which can be efficiently processed in Python using the h5py module.

input_parameters.h5: list of the 200 input parameter samples (alpha_inlet, ustar) obtained using the Halton sequence that defines the PPMLES ensemble.

ave_fields.h5: lists of the main field statistics predicted by each of the 200 LES samples over the 200-s reference window [Yee and Biltoft. 2004], including:

c: the time-averaged pollutant concentration in ppmv (dim = (n_samples, n_nodes) = (200, 1878585)),

(u, v, w): the time-averaged wind velocity components in m/s,

crms: the root mean square concentration fluctuations in ppmv,

tke: the turbulent kinetic energy in m^2/s^2,

(uprim_cprim, vprim_cprim, wprim_cprim): the pollutant turbulent transport components

uncertainty.h5: lists of the estimated aleatory uncertainty induced by the internal variability of the LES (variability_#) [Lumet et al. 2024] for each of the fields in ave_fields.h5. Also includes the stationary bootstrap [Politis and Romano. 1994] parameters (n_replicates, block_length) used to estimate the uncertainty for each field and each sample.

mesh.h5: the tetrahedral mesh on which the fields are discretized, composed of about 1.8 millions of nodes.

time_series.h5: HDF5 file consisting of 200 groups (Sample_NNN) each containing the time series of the pollutant concentration (c) and wind velocity components (u, v, w) predicted by the LES sample #NNN at 93 locations.

probe_network.dat: provides the location of each of the 93 probes corresponding to the positions of the experimental campaign sensors [Biltoft. 2001].

Code examples

A) Dataset reading

Imports

import h5py import numpy as np

Load the input parameters list into a numpy array (shape = (200, 2))

inputf = h5py.File('PPMLES/input_parameters.h5', 'r') input_parameters = np.array((inputf['alpha_inlet'], inputf['friction_velocity'])).T### Load the domain mesh node coordinatesmeshf = h5py.File('../PPMLES/mesh.h5', 'r')mesh_nodes = np.array((meshf['Nodes']['x'], meshf['Nodes']['y'], meshf['Nodes']['z'])).T

Load the set of time-averaged LES fields and their associated uncertainty

var = 'c' # Can be: 'c', 'u', 'v', 'w', 'crms', 'tke', 'uprim_cprim', 'vprim_cprim', or 'wprim_cprim' fieldsf = h5py.File('PPMLES/ave_fields.h5', 'r') fields_list = fieldsf[var] uncertaintyf = h5py.File('PPMLES/uncertainty_ave_fields.h5', 'r') uncertainty_list = uncertaintyf[var]

Time series reading example

timeseriesf = h5py.File('PPMLES/time_series.h5', 'r') var = 'c' # Can be: 'c', 'u', 'v', or 'w' probe = 32 # Integer between 0 and 92, see probe_network.csv time_list = [] time_series_list = [] for i in range(200): time_list.append(np.array(timeseriesf[f'Sample_{i+1:03}']['time'])) time_series_list.append(np.array(timeseriesf[f'Sample_{i+1:03}'][var][probe]))

B) Interpolation of one-field from the unstructured grid to a new structured grid

Imports

import h5py import numpy as np from scipy.interpolate import griddata

Load the mean concentration field sample #028

fieldsf = h5py.File('PPMLES/ave_fields.h5', 'r') c = fieldsf['c'][27]

Load the unstructured grid

meshf = h5py.File('PPMLES/mesh.h5', 'r') unstructured_nodes = np.array((meshf['Nodes']['x'], meshf['Nodes']['y'], meshf['Nodes']['z'])).T

Structured grid definition

x0, y0, z0 = -16.9, -115.7, 0. lx, ly, lz = 205.5, 232.1, 20. resolution = 0.75 x_grid, y_grid, z_grid = np.meshgrid(np.linspace(x0, x0 + lx, int(lx/resolution)), np.linspace(y0, y0 + ly, int(ly/resolution)), np.linspace(z0, z0 + lz, int(lz/resolution)), indexing='ij')

Interpolation of the field on the new grid

c_interpolated = griddata(unstructured_nodes, c, (x_grid.flatten(), y_grid.flatten(), z_grid.flatten()), method='nearest')

C) Expression of all time series over the same time window with the same time discretization

Imports

import h5py import numpy as np from scipy.interpolate import griddata

Define a common time discretization over the 200-s analysis period

common_time = np.arange(0., 200., 0.05) u_series_list = np.zeros((200, np.shape(common_time)[0]))

Interpolate the u-compnent velocity time series at probe DPID10 over this time discretization

timeseriesf = h5py.File('PPMLES/time_series.h5', 'r') for i in range(200): sample_time = np.array(timeseriesf[f'Sample_{i+1:03}']['time']) -
np.array(timeseriesf[f'Sample_{i+1:03}']['Parameters']['t_spinup']) # Offset the spinup time u_series_list[i] = griddata(sample_time, timeseriesf[f'Sample_{i+1:03}']['u'][9], common_time, method='linear')

D) Surrogate model construction example

The training and validation of a POD-GPR surrogate model [Marrel et al. 2015] learning from the PPMLES dataset is given in the following GitHub repository. This surrogate model was successfully used by Lumet et al. 2025 to emulate the LES mean concentration prediction for varying meteorological forcing parameters.

Acknowledgments

This work was granted access to the HPC resources from GENCI-TGCC/CINES (A0062A10822, project 2020-2022). The authors would like to thank Olivier Vermorel for the preliminary development of the LES model, and Simon Lacroix for his proofreading.

Electrochemical impedance spectroscopy (EIS) is the widely used technique to monitor the electrical properties of a catalyst under electrocatalytic conditions. Although it is extensively used for research in electrocatalysis, its effectiveness and power have not been fully harnessed to elucidate complex interfacial processes. Herein, we use the frequency dispersion parameter, n, which is extracted from EIS measurements, to describe the dispersion characteristics of capacitance and interfacial properties of Co3O4 under alkaline oxygen evolution reaction (OER) conditions. We first prove the n-value is sensitive to the interfacial electronic changes associated with Co redox processes and surface reconstruction. The n-value decreases by increasing the specific/active surface area of the catalysts. We further modify the interfacial properties by changing different components, i.e., replacing the proton with deuterium, adding ethanol as a new oxidant, and changing the cation in the electrolyte. Intriguingly, the n-value can identify different influences on the interfacial process from proton transfer, the decrease and carbon-poisoning of oxidized Co species, and the interfacial water structure. These findings convey that the frequency dispersion of capacitance is a convenient and useful method to uncover the interfacial properties under electrocatalytic conditions, which helps to advance the understanding of the interface-activity relationship.

Abstract: This data was generated in association with the publication: "Hamilton, L.D., Zetzener, H. and Kwade, A., 2024, June. The effect of process parameters on the formulation of a dry water-in-air dispersion. Advanced Powder Technology (Volume 35, Issue 7). https://doi.org/10.1016/j.apt.2024.104553." Within the publication, we investigated the influence of process parameters on the formulation of a dry water-in-air dispersion - commonly known as dry water - on the product particle size within an intensive mixer (Eirich EL1). In addition, we proposed a stress model, characterising the process in terms of a stress number and stress intensity. The data provided here coincides with all figures and further findings described in the publication. Thus, a transparent overview and recreation of the complete publication is ensured. The first data sheet "2024_Hamilton_Dry_Water_Eirich_Calculations" includes measured particle sizes as well as the correlating energy consumption during mixing. Furthermore, it contains all calculation steps for the stress number, stress intensity as well as other factors such as Reynolds numbers. The second data sheet "2024_Hamilton_Dry_Water_Eirich_Power_Draw" is comprised of power draw data resulting directly from the mixing device.

Successful predictions of the fate and transport of solutes in the subsurface hinges on the availability of accurate transport parameters. We modified and updated the CXTFIT (version 1.0) code of Parker and van Genuchten [1984] for estimating solute transport parameters using a nonlinear least-squares parameter optimization method. The program may be used to solve the inverse problem by fitting mathematical solutions of theoretical transport models, based upon the convection-dispersion equation (CDE), to experimental results. This approach allows parameters in the transport models to be quantified. The program may also be used to solve the direct or forward problem to determine the concentration as a function of time and/or position. Three different one-dimensional transport models are included: the conventional CDE; the chemical and physical nonequilibrium CDE; and a stochastic stream tube model based upon the local-scale CDE with equilibrium or nonequilibrium adsorption. The two independent stochastic parameters in the stream-tube model are the pore-water velocity, v, and either the dispersion coefficient, D, the distribution coefficient, Kd, or the nonequilibrium rate parameter, alpha. These pairs of stochastic parameters were described with a bivariate lognormal probability density function (pdf). Examples are given on how transport parameters may be determined from laboratory or field tracer experiments for several types of initial and boundary conditions, as well as different zero-order production profiles. The program comes with a user manual giving a detailed description of the computer program, including the subroutines used to evaluate the analytical solutions for optimizing model parameters. Input and output files for all major problems are also included in the manual. Resources in this dataset:Resource Title: CXTFIT download page. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=92&modecode=20-36-15-00

Magnetotactic bacteria intracellularly biomineralize magnetite of an ideal grain size for recording palaeomagnetic signals. However, bacterial magnetite has only been reported in a few pre-Quaternary records because progressive burial into anoxic diagenetic environments causes its dissolution. Deep-sea carbonate sequences provide optimal environments for preserving bacterial magnetite due to low rates of organic carbon burial and expanded pore-water redox zonations. Such sequences often do not become anoxic for tens to hundreds of metres below the seafloor. Nevertheless, the biogeochemical factors that control magnetotactic bacterial populations in such settings are not well known. We document the preservation of bacterial magnetite, which dominates the palaeomagnetic signal throughout Eocene pelagic carbonates from the southern Kerguelen Plateau, Southern Ocean. We provide evidence that iron fertilization, associated with increased aeolian dust flux, resulted in surface water eutrophication in the late Eocene that controlled bacterial magnetite abundance via export of organic carbon to the seafloor. Increased flux of aeolian iron-bearing phases also delivered iron to the seafloor, some of which became bioavailable through iron reduction. Our results suggest that magnetotactic bacterial populations in pelagic settings depend crucially on particulate iron and organic carbon delivery to the seafloor.

We present revised magnetostratigraphic interpretations for Ocean Drilling Program Sites 1095, 1096, and 1101, cored in sediment drifts located off the Pacific margin of the Antarctic Peninsula. The revised interpretations incorporate a variety of observations and results obtained since the end of Leg 178, of which the most significant are new paleomagnetic measurements from U-channel samples, composite depth scales that allow stratigraphic correlation between multiple holes cored at a site, and revised biostratigraphic interpretations. The U-channel data, which include more than 102,000 paleomagnetic observations from more than 13,400 intervals along U-channel samples, are included as electronic files. The magnetostratigraphic records at all three sites are consistent with sedimentation being continuous over the intervals cored, although the data resolution does not preclude short hiatuses less than a few hundred thousand years in duration. The magnetostratigraphic records start at the termination of Subchron C4Ar.2n (9.580 Ma) at ~515 meters composite depth (mcd) for Site 1095, at the onset of Subchron C3n.2n (4.620 Ma) at ~489.68 mcd for Site 1096, and at the onset of Subchron C2An.1n (3.040 Ma) at 209.38 meters below seafloor for Site 1101. All three sites provide paleomagnetic records that extend upward through the Brunhes Chron.

The datasets support the figures in the paper Refining pulsar radio emission due to streaming instabilities: Linear theory and PIC simulations (figures (2) to (7)) and provide additional information discussed in Section (5) of the paper. They contain parametric studies investigating a relativistic dispersion relation for an electron-positron pair plasma near a pulsar. The investigated quantities are the maximum growth rates (max_growth_rate) in units of (10^{-3}\omega_\mathrm{p}), an integrated value over the entire range of positive wave growth (int_growth_rate) in units of (10^{-3}\omega_\mathrm{p}), the fractional bandwidth (bandwidth) in units of (\omega_\mathrm{p}), the minimum and maximum values where the growth rate exceeds a threshold value of (2.8\times10^{-4}/\omega_\mathrm{p}) - which can be associated to effective wave growth - in units of (1 / d_\mathrm{e}) (k_thr_min and k_thr_max) as well as a relative deviation of the range between these two values and the entire range of positive wave growth (rel_dev). All of these parameters are investigated while changing a specific parameter in the dispersion relation ((\rho_0), (\rho_1), simultaneously (\rho_0) and (\rho_1), (\epsilon_\mathrm{n}) and (\gamma_\mathrm{b}), the latter once with (\epsilon_\mathrm{n}=10^{-3}) and once with fixed (n_0 = n_1 =1)and variable (\epsilon_\mathrm{n}=10^{-3})). These parameters appear in the titles of the text files which contain the respective dataset.

The files contain the data for the effective diffusivity D_e obtained by numerical integration of the equations (5), (8) and (9).

The file « data_assym_channel.xlsx » contains data for the asymmetric channel [Fig2.(a),(b)]

The file « data_sine_channel.xlsx » contains data for the sinusoidal channel [Fig2.(c)]

Note that, in these files, L represents L/a, while the parameter b is b=H/a. For the asymmetric channel, the parameter c=1.1 is such that the profile of the channel is given by the function h(X) written in the caption of Fig. 2(a). The Peclet Pe is defined in the caption of Fig.2.

This data release documents seven tables that contain environmental tracer data and lumped parameter model (LPM) results that are used for assessing the distribution of groundwater age in 23 Principal Aquifers of the continental United States. Groundwater samples were collected from 1,279 sites and analyzed for environmental tracers: tritium, tritiogenic helium-3, sulfur hexafluoride, carbon-14, and radiogenic helium-4. For each sample, groundwater age distributions were estimated by fitting LPMs to the environmental tracers using the computer program TracerLPM (Jurgens and others, 2012). Calibrated lumped parameter models provide the optimal distribution of ages in a sample that explain the measured concentrations of tracers. Information about each site, including well construction and _location information, is presented in table 1. The mean of each sample’s age distribution, or mean age, as well as the fractions of Anthropocene (since 1953), Holocene, and Pleistocene water, are presented in table 2. Concentrations of the environmental tracers for each sample is presented in table 3. Detailed information on calibrated age distributions is presented in table 4. Table 4 contains all the information necessary to reproduce the model fit of each sample using TracerLPM. Table 4 includes the lumped parameter model name, the fitted parameters, the measured and modeled tracer concentrations, and the regional tracer histories in recharge used in the calibration process. Tracer concentrations, except for tritium, require corrections to their analytical measurement before they can be used to calibrate age distributions. For sulfur hexafluoride, chlorofluorocarbons, tritiogenic helium-3 and radiogenic helium-4, corrections for contributions from solubility equilibrium and excess air components were made using the computer program DGMETA (Jurgens and others, 2020). Groundwater samples were also analyzed for dissolved gases (helium, neon, argon, krypton, xenon, and nitrogen), which provide an independent set of data to compute solubility and excess air component concentrations. Concentrations of these dissolved gases in water were fit to air-water equilibrium models using DGMETA. Calibrated air-water equilibrium models provide the optimal solubility equilibrium and excess air component concentrations that explain the measured dissolved gases in a sample. Table 5 contains the details of the air-water equilibrium model results for each sample, including the concentrations of dissolved gases. Table 5 includes information necessary to reproduce the modeling results using DGMETA. Table 6 contains the computations of environmental tracer concentrations using the air-water equilibrium model results. Tracer concentrations of samples listed in Table 3 can correspond to the average computed concentration in Table 6. Carbon-14 (14C) concentrations were corrected for 14C-free sources using three methods: analytical models (Tamers, 1975; Han and Plummer, 2013), inverse geochemical models (Plummer and others, 1994; Parkhurst and Charlton, 2008), and scaling of the carbon-14 record. The first two are common carbon-14 correction methods whereas the scaling method is new. Table 7 presents calculated 14C corrections as well as an adjusted measured 14C concentration for all three methods to provide comparison of the methods. This dataset is composed of new and previously compiled data. In some cases, the previous data were revised for consistency among modeled age distributions. For example, in past compilations a dispersion parameter of 0.1 was used as a fixed value in models. In the revised models, the dispersion parameter was set to 0.01. This value of the dispersion parameter is more consistent with the range of dispersion parameters obtained from model fits to the tracer data. The dispersion parameter was a fitted parameter in models of Anthropocene water when multiple tracers were available. The change in dispersion parameter results in a younger mean age than originally reported but is usually within 10%. Previously calculated tracer data, from which the models rely, were not revised. As such, this dataset presents a complete and consistent set of results for evaluating age distributions in groundwater nationally. References to the original source of tracer data and dissolved gas modeling results are listed in tables 3. In addition to these seven tables, three ancillary tables that provide references to original datasets (table 8), descriptions of the fields in each table (table 9) and abbreviations used throughout the tables (table 10). Please see processing steps in the general metadata file for more detailed information about the methods used to create the tables. References: Jurgens, B.C., Böhlke, J.K., and Eberts, S.M., 2012, TracerLPM (Version 1): An Excel® workbook for interpreting groundwater age distributions from environmental tracer data: U.S. Geological Survey Techniques and Methods Report 4-F3, 60 p., https://pubs.usgs.gov/tm/4-f3 Jurgens, B.C., Böhlke, J., Haase, K., Busenberg, E., Hunt, A.G., and Hansen, J.A., 2020, DGMETA (version 1)—Dissolved gas modeling and environmental tracer analysis computer program: U.S. Geological Survey Techniques and Methods 4-F5, 50 p., https://doi.org/10.3133/tm4F5.

This dataset contains results from low energy dispersion tests (LEDTs) on dispersants containing different solvents. The data were analyzed using the Hansen Solubility Parameter (HSP) plots. This dataset supports the publication: Fernandes, J. C., Agrawal, N. R., Aljirafi, F. O., Bothun, G. D., McCormick, A. V., John, V. T., & Raghavan, S. R. (2019). Does the Solvent in a Dispersant Impact the Efficiency of Crude-Oil Dispersion? Langmuir, 35(50), 16630–16639. doi:10.1021/acs.langmuir.9b02184

The eruption source parameter database contains information on those parameters most frequently used in the initiation of ash-dispersion models. The database was originally published as a special USGS report in 2009 [Mastin et al., 2009a], and has since been converted to a searchable database.

1、 Carry out a parametric scan with COMSOL to calculate the effective refractive index of a photonic crystal fiber.The distance between air holes (Λ) should be between 2-5μm with a 0.1μm interval.The diameter (d) of the air hole divided by the spacing (Λ) is between 0.25 and 0.45 with an interval of 0.05.The wavelength range of operation is from 0.5 to 5 micrometers with an interval of 50 nanometers.2、 Inputting all the simulated data into the fitting, using conjugate gradient descent and global optimization algorithms to calculate the fitting parameters of the empirical formula for the dispersion of photonic crystal fibers.There are a total of 56 parameters.3、 Input the fitted empirical formula into Matlab to write a four-wave mixing phase matching program and calculate the phase-matching curves for 0.8-1.5 microns under different photonic crystal fiber structures; five sets of data (0.1, 1, 10, 100, 1000 kW) based on different pumping powers were calculated here.The air gap (Λ) between the air holes should be 2-5μm with an interval of 0.005μm (601).The diameter (d) to spacing (Λ) ratio of the air hole is between 0.25 to 0.45 with an interval of 0.01 (21).The operating wavelength range is 0.8-1.6μm, with a 1nm interval between each data point, totaling 801 data points.There are 5,801,126,210 (21 x 601) data points.4、 A neural network architecture was constructed with five sets of training data, resulting in five distinct DNN programs training.5、 A program was written on the basis of DNN to reverse engineer the overall architecture, seeking the curve closest to the pump and signal wavelength, shifting to generate a new curve, and inputting the vertical coordinates into the neural network.6、 A table of discrepancies for simulated data.

Overview of model parameters.

This Zenodo dataset contains the data processing pipeline, as well as the data products, corresponding to the scientific journal paper "NANOGrav 12.5-Year Data Set: Dispersion Measure Mis-Estimation with Varying Bandwidths". The processing pipeline is structured as follows:

1. 1_fit_dispersion_3terms.py reads the .tim files (containing the times-of-arrival), creates the broadband and narrowband datasets, and fits a dispersion model to both datasets using three parameters.
2. 2_plot_fits_differences.py creates the residual plots showing the differences in the fitted values of the parameters.
3. 3_autocovariance.py calculates the autocovariance function of the differences in the fitted values, and creates the corresponding plots.
4. All the files starting with "plot" are convenience scripts for creating the plots presented in the paper.
5. All the files starting with "sophia" are utility functions created by the authors that are used in the main pipeline.
6. The folder "NANOGrav_12yv4" contains the dataset analyzed in this work.
7. The folder "timing_analysis" contains utility functions created by the NANOGrav collaboration that are used in the main pipeline.

Please do not hesitate to send all your questions, concerns, or commentaries to sophia.sosa@nanograv.org

Particulate matter concentration and water temperature at 5 m depth level are compared in the Canary upwelling region to the east of the Cape Blanc. It was found that accumulation of particulate matter was timed to hydrofrontal zones. Particle size distributions for particulate matter obtained using the Coulter counter agree with the hyperbolic law (of the Junge type) with double values for the size parameter, which changes for particle diameters of 5-6 microns. Average values for the size parameter in the region of the upwelling are significantly lower than in the open ocean. Specific surface of particulate matter associated with reactivity differs significantly on different sides of the upwelling front and increases beyond the upwelling.

Values of mean multiplicity dispersion and k parameter from fits of Inclusive data with negative binomial distribution.