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31 results for “product distribution”
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
Spatially Coherent 3D Distributions of HI and CO in the Milky Way - Data Products
<p>Data products from the joint reconstruction of Galactic HI and H2 (via CO).</p> <h3>Primary data products:</h3> <p>These are the posterior samples of the <strong>"densities"</strong> (HI and H2) in cm^-3 and <strong>"auxiliary"</strong> fields (i.e. the three components of the Galactic velocity field and the two spatially resolved line-widths) in km/s on our Sun-centered HEALPix-times-radius grid. These files also contain two tables with the centres and edges of the pixelisation in radial direction. The nearest (farthest) bin is at approximately 50 pc (28 kpc). The HEALPix dimension is ordered using the "nested" scheme.</p> <ul> <li><em>samples_densities_hpixr.fits </em></li> <li><em>samples_auxiliary_hpixr.fits</em></li> </ul> <h3>Interpolated to a regular grid:</h3> <p>For convenience, we also provide versions linearly interpolated to regular, Cartesian grids. Due to the strongly inhomogeneous original numerical grid, these interpolated versions contain regions of significant over/undersampling. To mitigate this a little, we provide a <strong>"local"</strong> (800 x 800 x 320 grid points with -1.25 kpc < x < 1.25 kpc, -1.25 kpc < y < 1.25 kpc and -0.5 kpc < z < 0.5 kpc) and a <strong>"global"</strong> (1250 x 1250 x 125 grid points with -12 kpc < x < 28 kpc, -20 kpc < y < 20 kpc, -2 kpc < z < 2 kpc) version. The origin (0,0,0) is defined by the position of the Sun and positive x points towards the Galactic centre.</p> <p>In an attempt to keep the file sizes reasonable, we provide the mean and standard deviation of each field instead of all eight individual samples.</p> <ul> <li><em>mean_std_densities_xyz_global.fits</em></li> <li><em>mean_std_densities_xyz_local.fits</em></li> <li><em>mean_std_auxiliary_xyz_global.fits</em></li> <li><em>mean_std_auxiliary_xyz_local.fits</em></li> </ul>
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton. in Spesovicornea Pacltovae Gen. Nov. Et Sp. Nov., A New Elateroid Sporomorph From The Bohemian Cenomanian (Czech Republic)
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton.
Fig. 3 in Spatial distribution and egg production in squat lobsters (Decapoda: Munididae, Munidopsidae) from the collection of the Museu de Zoologia of the Universidade de São Paulo, Brazil
Fig. 3. Fecundity relationships involving number of eggs (EN) vs. carapace length (CL) in six squat lobsters of Munididae from the coast of Brazil.
Fig. 2 in Spatial distribution and egg production in squat lobsters (Decapoda: Munididae, Munidopsidae) from the collection of the Museu de Zoologia of the Universidade de São Paulo, Brazil
Fig. 2. BathYmetric distribution of ovigerous females in eleven squat lobster species (Munididae and Munidopsidae) from the Brazilian coast. Depth range for each species is shown within brackets.
Fig. 1 in Spatial distribution and egg production in squat lobsters (Decapoda: Munididae, Munidopsidae) from the collection of the Museu de Zoologia of the Universidade de São Paulo, Brazil
Fig. 1. Latitudinal distribution in squat lobsters (Munididae and Munidopsidae) along the Brazilian coast. Spatial distribution in Paramunida scabra from the Pacific Ocean waters is also shown. In legend box, sYmbol '?' indicates absence of information on specific spatial distribution in archives of the Museu de Zoologia of the Universidade de São Paulo (MZUSP), Brazil. ES, RJ, SP, PR, SC, RS, represent the Brazilian states of Espirito Santo, Rio de Janeiro, São Paulo, Paraná and Rio Grande do Sul, respectively.
Kinetic Insights into Glycerol Electrooxidation on Nickel: Current-Dependent Product Distribution and Reaction Mechanism
<p>## FILE DESCRIPTION<br>--------------<br>### Figure 1<br>- Fig1a.txt : Cyclic voltammetry of a Ni-based electrode in 0.1 M LiOH and 50 mM glycerol, measured at 5 mV s^-1. <br>- Fig1b.txt : IS spectra obtained at 1.50 V vs RHE in 0.1 M LiOH and 0.1 M LiOH + 50 mM glycerol.<br>- Fig1c.txt : Chronopotentiometric curves at 1 mA cm^-2, 3 mA cm^-2, 5 mA cm^-2 during 2 hours in 0.1 M LiOH + 50 mM glycerol.</p> <p>### Figure 2<br>- Fig2a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1 mA cm^-2.<br>- Fig2b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 3 mA cm^-2.<br>- Fig2c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 5 mA cm^-2.<br>- Fig2d.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 10 mA cm^-2.<br>- Fig2e.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 1 mA cm^-2.<br>- Fig2f.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 3 mA cm^-2.<br>- Fig2g.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 5 mA cm^-2.<br>- Fig2h.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 10 mA cm^-2.</p> <p>### Figure 3<br>- Fig3a.txt : Rate constant comparison at varying current densities applied for the formation of formate, glycolate, glycerate, tartronate and oxalate with its error bar.</p> <p>### Figure 4<br>- Fig4a.txt : Differential optical density (m∆O.D) taken at the maximum absorption peak as a function of potential applied in a solution of 0.1 M LiOH and LiOH 0.1 M + 50 mM glycerol in different regions (capacitive, NiOOH formation and GEOR and OER).<br>- Fig4b.txt : Rate law plot for glycerol and LiOH considering current density as a function of the normalized differential absorption (m∆O.D).</p> <p>### Figure S3<br>- FigS3.txt : X-ray diffraction (XRD) analysis </p> <p><br>### Figure S4<br>- FigS4a.txt : Cyclic Voltammetries in different electrolytes.</p> <p>### Figure S5<br>- FigS5a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.53 V vs RHE.<br>- FigS5b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.62 V vs RHE.<br>- FigS5c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.78 V vs RHE.</p> <p>### Figure S6<br>- FigS6a.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 1 mA cm^-2.<br>- FigS6b.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 3 mA cm^-2.<br>- FigS6c.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 5 mA cm^-2.</p> <p>### Figure S7<br>- FigS7a.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH.<br>- FigS7b.txt : Chronoamperometry measurement without glycerol.<br>- FigS7c.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8d.txt : Chronoamperometry measurement with glycerol.</p> <p>### Figure S8<br>- FigS8a.txt : Differential UV-Vis spectra of pre-catalytic (species formed in capacitive and NiOOH formation region) in 0.1 M LiOH.<br>- FigS8b.txt : Differential UV-Vis spectra of catalytic species (formed in OER and GEOR region) in 0.1 M LiOH.<br>- FigS8c.txt : Differential UV-Vis spectra of pre-catalytic species in 0.1 M LiOH + 50 mM glycerol. <br>- FigS8d.txt : Differential UV-Vis spectra of catalytic species in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8e.txt : Steady state J-V curve with the onset for OER (Oxygen Evolution Reaction) and GEOR (Glycerol Electrooxidation Reaction) indicated.</p> <p>### Figure S9<br>- FigS9.txt : Rate law plot for glycerol and LiOH considering current density (j) as a function of the normalized differential absorption (m∆O.D).</p>
Multisource product distribution dataset
<p>This dataset is generated for testing and comparing algorithms used for product distribution from production centers to demand centers with a possibility of non-connectivity constraints. These datasets are put together for this purpose. In the datasets x refers to the production centers and X for the demand centers.</p>
A meta-analysis of butterfly structural colors: their color range, distribution, and biological production
<p><span>Butterfly scales are among the richest natural sources of optical nanostructures, which produce structural color and iridescence. Several recurring nanostructure types have been described, such as ridge multilayers, gyroids, and lower lamina thin films. While the optical mechanisms of these nanostructure classes are known, their phylogenetic distributions and functional ranges have not been described in detail. In this Review, we examine a century of research on the biological production of structural colors, including their evolution, development, and genetic regulation. We also create a database of more than 300 optical nanostructures in butterflies and conduct a meta-analysis of the color range, abundance, and phylogenetic distribution of each nanostructure class. Butterfly structural colors are ubiquitous in short wavelengths but extremely rare in long wavelengths, especially red. In particular, blue wavelengths (around 450 nm) occur in more clades and are produced by more kinds of nanostructures than other hues. Nanostructure categories differ in prevalence, phylogenetic distribution, color range, and brightness. For example, lamina thin films are the least bright; perforated lumen multilayers occur most often but are almost entirely restricted to the family Lycaenidae; and 3D photonic crystals, including gyroids, have the narrowest wavelength range (from about 450 to 550 nm). We discuss the implications of these patterns in terms of nanostructure evolution, physical constraint, and relationships to pigmentary color. Finally, we highlight opportunities for future research, such as analyses of subadult and Hesperid structural colors and the identification of genes that directly build the nanostructures, with relevance for biomimetic engineering.</span></p>
A meta-analysis of butterfly structural colors: their color range, distribution, and biological production
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Data and R code used in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment
<p>This release contains the raw data, R code, and RootPainter model supporting the results described in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment.</p>
Derivative products: "IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux" by Swaczyna et al.
<p>Derivative products from: Swaczyna et al. 2022, "IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux", ApJS, 258:6</p>
Moving bedforms control CO2 production and distribution in sandy river sediments
<p>This dataset includes raw and processed data, as well as modeling codes that were used to produce the results presented in Schulz et al. (submitted) "Moving bedforms control CO<sub>2</sub> production and distribution in sandy river sediments" submitted to Journal of Geophysical Research: Biogeosciences.</p> <p> </p> <p>The dataset contains:</p> <p>1) Data for sediment characterization: Grain size distribution, hydraulic conductivity, loss on ignition, and porosity data.</p> <p>2) Data on hydrology and morphodynamics: Bedform celerity, height, and length, Damkoehler number, shear velocities, and Python codes for modeling the hyporheic exchange flux.</p> <p>3) Data compilation: Data of online sensors that continuously measured various parameters of the streamwater, atmospheric pressure, and CO<sub>2 </sub>air concentrations.</p> <p>4) Digital raw images of dye tracer experiments.</p> <p>5-10) Image data: Raw and processed planar optode and IR images, spreadsheets containing data analysis results.</p>
Natural product biosynthetic potential reflects macroevolutionary diversification within a widely distributed bacterial taxon
<p>Code and data for the article "Natural product biosynthetic potential reflects macroevolutionary diversification within a widely distributed bacterial taxon".</p>
Supplementary data: Optimising centralisation and decentralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning
<p><span>The success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e., stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. In this dataset, a comparison has been made between the cost performance of centralized, decentralized, and hybrid stock allocation policies in distribution networks for perishable products. The dataset comprises 100,000 realistic case studies generated through a Sobol quasi-random low discrepancy series.</span></p>
Upscaling Tower-Based Net Ecosystem Productivity to global 250m using the Data Augmentation Method by Considering their Spatial Distribution
<p>Terrestrial ecosystems have emerged as critical carbon sinks, holding a crucial role in the carbon cycle. Net ecosystem productivity (NEP) is a highly significant parameter in terrestrial ecosystems, representing the net ecosystem exchange (NEE) between ecosystems and the atmosphere, without considering other carbon fluxes from disturbances. In this NEP product, we harmonized various sets of tower-based NEP from flux sites as target variable, remote sensing product and meteorological data as traning variables. We further optimizied these smaple sets to address the problems in spatial distribution, culminating in a global NEP product spanning the years 2001-2022, achieved through the application of the random forest method. This dataset contains NEP data for global terrestrial ecosystems for the period 2001-2022 in MgC with a temporal resolution of 1 year. The spatial resolution of the product is 250m and the data format is TIFF.</p> <p><strong>For detailed instructions on how to use the dataset, see User Guides.doc!</strong></p>
Subspecies and Distribution. V. i. indica Geoffroy Saint-Hilaire, 1803 — S peninsular India. V. i. atchinensis Sody, 1931 — Sumatra. V. i. baliensis Sody, 1931 — Bali. V. i. baptistae Pocock, 1933 — Bangladesh to NE India (Assam), and Bhutan. V. i. deserti Bonhote, 1898 — Pakistan through C India to Nepal. V. i. klossi Pocock, 1933 — Malaysia. V. i. mayori Pocock, 1933 — Sri Lanka. V. i. muriavensis Sody, 1931 — Java. V.i. taivana Schwarz, 1911 — Taiwan. V.i. thai Kloss, 1919 — Myanmar and Thailand through Indochina to China. V. i. wellsi Pocock, 1933 — NW India. It has been introduced to Madagascar, Zanzibar Island, the Comoro Islands, and Socotra Island for the production of civet or to be used as rat catchers; its presence on some of the Indonesian islands (Bali, Bawean, Kangean, Lombok, and Sumbawa) could also have resulted from introductions. in Viverridae
Subspecies and Distribution. V. i. indica Geoffroy Saint-Hilaire, 1803 — S peninsular India. V. i. atchinensis Sody, 1931 — Sumatra. V. i. baliensis Sody, 1931 — Bali. V. i. baptistae Pocock, 1933 — Bangladesh to NE India (Assam), and Bhutan. V. i. deserti Bonhote, 1898 — Pakistan through C India to Nepal. V. i. klossi Pocock, 1933 — Malaysia. V. i. mayori Pocock, 1933 — Sri Lanka. V. i. muriavensis Sody, 1931 — Java. V.i. taivana Schwarz, 1911 — Taiwan. V.i. thai Kloss, 1919 — Myanmar and Thailand through Indochina to China. V. i. wellsi Pocock, 1933 — NW India. It has been introduced to Madagascar, Zanzibar Island, the Comoro Islands, and Socotra Island for the production of civet or to be used as rat catchers; its presence on some of the Indonesian islands (Bali, Bawean, Kangean, Lombok, and Sumbawa) could also have resulted from introductions.
Size spectrum, time series and atom number distribution of macroalgal emission vapors and oxidation products
<p>We conducted oxidation and NPF experiments with vapor emissions from real-world coastal macroalgae in a bag reactor. The dataset showed the measurement results of volatile precursors and their oxidation products. The dataset was organized by the order of figures in our submitted manuscript.</p>
The isomer distribution of C6H6 products from the propargyl gas-phase recombination investigated by threshold-photoelectron spectroscopy - Datasets
<p>This dataset is a collection of the threshold photoelectron spectra (TPES) published in the article entitled: "The isomer distribution of C6H6 products from the propargyl gas-phase recombination investigated by threshold-photoelectron spectroscopy" published in Comm. Chem. 2024. </p>
Supplemental Dataset for Article: Non-invasive measurements on production, release, and vertical distribution of biogenic gas content in large peat soil columns
<p>Supplemental dataset for a journal article in preparation for publication:</p> <p><strong>Structure controls gas migration: Non-invasive measurements on production, release, and vertical distribution of biogenic gas content in large peat soil columns</strong></p> <p>William Wright<sup>1*</sup> and Xavier Comas<sup>1</sup></p> <p><sup>1</sup>Department of Geosciences, Florida Atlantic University, Davie, Florida, 33314, USA</p> <p>*Corresponding author (wwrigh19@gmail.com)</p>
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