Dispersion parameters and statistical test of the items found at Ambrona.

We also report the 95% calibration interval (CI) for each point estimate.Calibrated σ˜ values for the five real datasets.

Over-dispersion parameters estimation of the Negative Binomial distribution (6).

The columns are: name of the dataset, the number of samples (control, treatment), the maximum likelihood estimate (MLE) σ^, and the standard error (SE) of σ^.Estimated level of residual dispersion variation in five real RNA-Seq datasets.

Frequency Dispersion Dataset 📡

Description
This dataset provides 100,000 synthetic rows to model frequency dispersion in photonic waveguides. It includes essential parameters such as group index, chromatic dispersion, and mode group delay across varying wavelength ranges, enabling simulation and optimization of waveguide designs for optical chips.

Key Highlights ✨

• Focus: Frequency-dependent behavior of waveguides, specifically for optical chips.… See the full description on the dataset page: https://huggingface.co/datasets/Taylor658/frequency-dispersion.

Supplemental material to the 2025 article "Demonstration of dispersion gas barometry" by Y. Yang, J.A. Stone, and P.F. Egan.The archive file contains two-color data for the gases helium, neon, argon, and nitrogen. Two-color data means measured quadruplets of pressure and temperature together with refractivity at two wavelengths 1542.3912 nm (194.368624 THz) and 632.9919 nm (473.611873 THz).Two analysis scripts are included:1. plotHelium.py: performs the helium analysis to deduce the cavity distortion coefficient kappa and the conversion factor epsilon_p needed to realize the optical pressure scale. The script reproduces Fig. 2 from the article.2. plotGases.py: analyzes the gases neon, argon, and nitrogen and deduces the two parameters describing dispersion polarizability A_epsilon and A_2. The script reproduces Fig 3 from the article.Additionally, the script "pgtProp.py" is a library function, which offers best knowledge (as of January 2025) of gas properties to be used in Polarizing Gas Thermometry. The library functions synthesize the optical and thermophysical properties of helium, neon, argon, and nitrogen. The synthesis combines literature sources plus the measurement results from the main text.

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.

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

Additional material for the article:S. Kaźmierczak, R. Kasztelanic, R. Buczyński, J. Mańdziuk, "Predicting optical parameters of nanostructured optical fibers using machine learning algorithms," Engineering Applications of Artificial Intelligence 132, 107921 (2024).Training data setTraining resultsPlease consult the Readme.txt file for additional information.

Access information to the datasets analyzed in this article (Table A), the mean-dispersion plots (Figs. A–D), and discussion of the relationship between σ ^ and d ^ 0 (Fig. E) are provided in the Supporting Information S1 File. (PDF)

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

This data set is associated with the results found in the journal article: Amini et al, 2018. Modeling Dispersion of Emissions from Depressed Roadways. Authors: Seyedmorteza Amini, Faraz Enayati Ahangar, David K. Heist, Steven G. Perry, Akula Venkatram. This paper presents an analysis of data from a wind tunnel study of dispersion of emissions from three depressed roadway configurations; a 6 m deep depressed roadway with vertical sidewalls, a 6 m deep depressed roadway with 30° sloping sidewalls, and a 9 m deep depressed roadway with vertical sidewalls. All these configurations induce complex flow fields, increase turbulence levels, and decrease surface concentrations downwind of the depressed road compared to those of the at-grade configuration. The parameters of flat terrain dispersion models are modified to describe concentrations measured downwind of the depressed roadways. In the first part of the paper, a flat terrain model proposed by van Ulden (1978) is adapted. It turns out that this model with increased initial vertical dispersion and friction velocity is able to explain the observed concentration field. The results also suggest that the vertical concentration profiles of all cases under neutral conditions are best explained by a vertical distribution function with an exponent of 1.3. In the second part of the paper, these modifications are incorporated into a model based on the RLINE line-source dispersion model. While this model can be adapted to yield acceptable estimates of near-surface concentrations (z< 6m) measured in the wind tunnel, the Gaussian vertical distribution in RLINE, with an exponent of 2, cannot describe the concentration at higher elevations. Our findings suggest a simple method to account for depressed highways in models such as RLINE and AERMOD through two parameters that modify vertical plume spread. This dataset is associated with the following publication: Amini, S., F. Ahangar, D. Heist, S. Perry, and A. Venkatram. Modeling Dispersion of Emissions from Depressed Roadways. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 186: 189-197, (2018).

Model archive for a numerical simulation of the one-dimensional advection-dispersion equation of a conservative tracer with oscillating tidal flows. The numerical simulation was used to examine how a conservative tracer evolves along a tidal channel. Scenarios were run to examine the effects increased dispersion, increased and decreased mixing at a channel junction, and increased advection. Code for the model was developed in Matlab (MathWorks® Inc., 2019). Model code, input and output files, and plotting scripts are available with this release. Details of the parameters used in the simulation and results are described in Stumpner et. al 2020. The README.doc file contains description of files provided and instructions to run the model.

Biological particles (e.g., bacteria, eggs, fruit/seeds, and larvae) of a wide range of sizes (i.e., 10-6 – 10-1 m) are transported over various distances (i.e., 100 - 104 m) downstream in rivers. We examined the effects of propagule size on downstream dispersal by releasing biodegradable microbeads (density ~ 1200 kg m-3) of three size classes (~150, 250, 350 µm) at the Speed River, Guelph, ON. Hitting distance estimates and longitudinal dispersion coefficients declined with particle size and were significantly different between 150 and 350 µm microbeads. The magnitude of these differences was relatively small (~ 5 m) because of the slow velocity (9.5 ± 0.01 cm s-1) and low turbulence (shear velocity = 1.9 ± 0.13 cm s-1) in the river. We examined the dispersion of larval and juvenile unionid mussels (size range = 56 – 415 µm, 247.54 ± 60.38 [mean ± SD] µm, N = 174) across a broader range of flow conditions by applying laminar and turbulent flow models in three river r..., , # Size matters: Effects of propagule size on dispersal in rivers

Dataset DOI: 10.5061/dryad.5tb2rbpgj

Description of the data and file structure

Particle release trials were undertaken at the Speed River reach on 29 September and 6, 13, and 24 October 2022. Alginate microbeads of three size classes (150, 250, and 350 µm) and types (calcium carbonate-, mica-, and riboflavin-loaded) were released into the stream and recaptured in drift nets (2.25 m length) positioned on the river bottom with their opening (0.45 m width × 0.30 m height) on the riverbed 2, 6, 10, 18, and 34-m downstream of the mid-stream release point.

Microbead capture data were used to calculate hitting distances and dispersion coefficients based on a 1D and 3D advection dispersion model. Parameters provided in the Data-from-Farrow-et-al-SizeMatters.xlsx file correspond to the raw microbead capture data, as well as the derived hitting distances and dispersion coefficients.

All data neede...,

Dataset description This dataset contains background data and supplementary material for Sönning (forthcoming), a study that looks at the behavior of dispersion measures when applied to text-level frequency data. For the literature survey reported in that study, which examines how dispersion measures are used in corpus-based work, it includes tabular files listing the 730 research articles that were examined as well as annotations for those studies that measured dispersion in the corpus-linguistic (and lexicographic) sense. As for the corpus data that were used to train the statistical model parameters underlying the simulation study reported in that paper, the dataset contains a term-document matrix for the 49,604 unique word forms (after conversion to lower-case) that occur in the Brown Corpus. Further, R scripts are included that document in detail how the Brown Corpus XML files, which are available from the Natural Language Toolkit (Bird et al. 2009; https://www.nltk.org/), were processed to produce this data arrangement. Abstract: Related publication This paper offers a survey of recent corpus-based work, which shows that dispersion is typically measured across the text files in a corpus. Systematic insights into the behavior of measures in such distributional settings are currently lacking, however. After a thorough discussion of six prominent indices, we investigate their behavior on relevant frequency distributions, which are designed to mimic actual corpus data. Our evaluation considers different distributional settings, i.e. various combinations of frequency and dispersion values. The primary focus is on the response of measures to relatively high and low sub-frequencies, i.e. texts in which the item or structure of interest is over- or underrepresented (if not absent). We develop a simple method for constructing sensitivity profiles, which allow us to draw instructive comparisons among measures. We observe that these profiles vary considerably across distributional settings. While D and DP appear to show the most balanced response contours, our findings suggest that much work remains to be done to understand the performance of measures on items with normalized frequencies below 100 per million words.

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 data set is associated with the results found in the journal article: Perry et al, 2016. Characterization of pollutant dispersion near elongated buildings based on wind tunnel simulations, Atmospheric Environment, 142, 286-295. The paper presents a wind tunnel study of the effects of elongated rectangular buildings on the dispersion of pollutants from nearby stacks. The study examines the influence of source location, building aspect ratio, and wind direction on pollutant dispersion with the goal of developing improved algorithms within dispersion models. The paper also examines the current AERMOD/PRIME modeling capabilities compared to wind tunnel observations. Differences in the amount of plume material entrained in the wake region downwind of a building for various source locations and source heights are illustrated with vertical and lateral concentration profiles. These profiles were parameterized using the Gaussian equation and show the influence of building/source configurations on those parameters. When the building is oriented at 45° to the approach flow, for example, the effective plume height descends more rapidly than it does for a perpendicular building, enhancing the resulting surface concentrations in the wake region. Buildings at angles to the wind cause a cross-wind shift in the location of the plume resulting from a lateral mean flow established in the building wake. These and other effects that are not well represented in many dispersion models are important considerations when developing improved algorithms to estimate the location and magnitude of concentrations downwind of elongated buildings. This dataset is associated with the following publication: Perry , S., D. Heist , L. Brouwer, E. Monbureau, and L. Brixey. Characterization of pollutant dispersion near elongated buildings based on wind tunnel simulations. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 142: 286-295, (2016).

Data from observations made at the Cape Verde Atmospheric Observatory (CVAO) which exists to advance understanding of climatically significant interactions between the atmosphere and ocean and to provide a regional focal point and long-term data. The observatory is based on Calhau Island of São Vicente, Cape Verde at 16.848N, 24.871W, in the tropical Eastern North Atlantic Ocean, a region which is data poor but plays a key role in atmosphere-ocean interactions of climate-related and biogeochemical parameters including greenhouse gases. It is an open-ocean site that is representative of a region likely to be sensitive to future climate change, and is minimally influenced by local effects and intermittent continental pollution. The dataset contains NAME dispersion model footprints images.

This dataset contains numerical simulation results for photonic crystal fibers (PCFs), generated using the Finite Element Method (FEM) and MATLAB-based dispersion analysis. The dataset is structured to support the development of AI models for predicting key optical parameters of solid-core PCFs, including refractive index, confinement loss, and dispersion.

Dataset Files Data generated using FEM method.xlsx

Contains computed optical properties of PCFs using COMSOL Multiphysics 6.1 with FEM simulations. Includes effective refractive index (neff), confinement loss, and other related parameters for different core diameters, pitches, and wavelengths. Dispersion Data.xlsx

Extends the FEM dataset by including dispersion values computed using MATLAB. Provides additional insights into wavelength-dependent dispersion effects in PCFs. Column Descriptions Column Name Description Diameter(μm) Core diameter of the PCF (0.9 to 1.5 µm). Pitch (μm) Lattice spacing or pitch of the PCF structure (2.7 to 3 µm). Wavelength(μm) Operating wavelength of the optical signal (0.7 to 1.6 µm). Y polar neff (real) Real part of the effective refractive index for Y-polarized light. Y polar neff (imag.) Imaginary part of the effective refractive index for Y-polarized light. X polar neff (real) Real part of the effective refractive index for X-polarized light. X polar neff (imag.) Imaginary part of the effective refractive index for X-polarized light. Confinement Loss (Y) Optical confinement loss for Y-polarized light (in dB/m). Confinement Loss (X) Optical confinement loss for X-polarized light (in dB/m). αC in log10 Logarithmic confinement loss coefficient. Dispersion (MATLAB) Chromatic dispersion computed using MATLAB (Only in Dispersion Data.xlsx). Use Cases AI-based Prediction Models – Training machine learning models like ElephantMPNet for predicting optical parameters. PCF Design Optimization – Helping researchers optimize PCF structures for specific optical applications. Comparison of Simulation Methods – Validating FEM results against MATLAB dispersion analysis.

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 "NG_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