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38 results for “functional distance”
The data for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant"
<p>Data used for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant" by Siyang Li, Adam G. Riess, Stefano Casertano, Gagandeep S. Anand, Daniel M. Scolnic, Wenlong Yuan, Louise Breuval, and Caroline D. Huang. Magnitudes provided are after correcting for foreground extinction and crowding bias.</p>
Neural Joint Space Implicit Signed Distance Functions [Data & Code]
<p>These data files containg code sources for dataset creation & model learning (neural-jsdf.zip) and collected synthetic dataset of free & collided postures for robotic arm Franka (sdf_3m_full_mesh.mat). Follow the Readme.MD files to launch the code if needed.</p> <p>Corresponding Git repo: https://github.com/epfl-lasa/Neural-JSDF</p>
Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis
<p>Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1,204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show for the first time that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both predators and granivores and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.</p>
Supplementary Data to *Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data*
<p>Supplementary code and data to <strong>Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_rad, of which this archive is a snapshot.</p> <p> </p>
Figure 2 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy
Figure 2. - Juvenile Trachipterus arcticus, 129 mm SL, collected at Faial Island, Azores, 14 May 2014, on the surface. A: Portrait with anterior black facet visible; B: Oblique lateral view with first spines erected; note orange bulbous outgrowths on the prolonged spine; C: Lateral view showing proportions, markings and orientation of fins. Scale bars: A = 1 cm; B, C = 5 cm.
Figure 1 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy
Figure 1. - Adult Trachipterus arcticus, about 1.8 m long, observed south of Pico Island, Azores, 18 Aug. 2013, 950 m deep.
A near-field Head-Related Transfer Function (HRTF) data set of KEMAR with high distance resolution
<p>A near-field Head-Related Transfer Function (HRTF) data set measured on a KEMAR head and torso simulator with high distance resolution and multiple elevations is presented ('KEMAR_NFHRIRmea_1cm.sofa'). HRTFs are measured at 83448 spatial points at distances ranging from 20 to 110 cm, elevations from -25° to 35°, and azimuths from 0° to 355°. The distance resolution of the HRTF data is 1 cm, higher than that of any existing public near-field HRTF databases. Therefore, the dataset enables further exploration of the distance dependence of near-field HRTFs, and is beneficial for applications of realistic and dynamic binaural rendering of nearby sound sources. An additional data set of simulated HRTFs with 1.5 cm distance resolution is also provided ('KEMAR_NFHRIRsim_1.5cm.sofa') for a direct comparison with the measured HRTFs or other purposes.</p>
Beyond trait distances: Functional distinctiveness captures the outcome of plant competition
<p>Functional trait distances between coexisting organisms reflect not only complementarity in the way they use resources, but also differences in their competitive abilities. Accordingly, absolute and relative trait distances have been widely used to capture the effects of niche dissimilarity and competitive hierarchies, respectively, on the performance of plants in competition. However, multiple dimensions of the plant phenotype are involved in these plant-plant interactions (PPI), challenging the use of relative trait distances to predict their outcomes. Furthermore, estimating the effects of competitive hierarchy on the performance of a group of coexisting plants remains particularly difficult since relative trait distances relate to the effects of a focal plant on another. We argue that trait distinctiveness, an emerging facet of functional diversity that characterizes the eccentric position of a species (or genotype) in a phenotypic space, can reveal the unique role played by a given individual plant in a group of competing plants . We used the model crop species <em>Oryza sativa</em> spp. japonica to evaluate the ability of trait distances and trait distinctiveness to predict the outcome of intraspecific PPI on the performance of single genotype and genotype mixtures. We performed a screening experiment to characterize the phenotypic space of 49 rice genotypes based on 11 aboveground and root traits. We selected nine genotypes with contrasting positions in the phenotypic space and grew them in pots following a complete pairwise interaction design. Relative distances and distinctiveness based on traits associated with light competition were by far the best predictors of the performance of single genotypes - taller genotypes that acquired resource faster being the best competitors - while absolute trait distances had no effect. These results indicate that competitive hierarchy for light dominates PPI in this experiment. Consistently, trait distinctiveness in plant height and age at flowering had the strongest, positive effects on mixture performance, confirming that functional distinctiveness captures the effects of trait hierarchies and asymmetric PPI at this scale. Our findings shed new light on the role of trait diversity in regulating PPI and ecosystem processes and call for a greater consideration of functional distinctiveness in studies of coexistence mechanisms.</p>
Beyond trait distances: Functional distinctiveness captures the outcome of plant competition
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Spatial, environmental, and functional distances among temporal ponds attenuate synchronization, stabilizing plant richness and biomass dynamics
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Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis
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Insect pollinator behavior as a function of distance to the nearest turbine
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Distance decay 2.0 – a global synthesis of taxonomic and functional decay in ecological communities
<p>Datasets used in the analysis of the manuscript by Graco-Roza, C., Aarnio, S., Abrego, N., Acosta, A. T., Alahuhta, J., Altman, J., ... & Soininen, J. (2022). Distance decay 2.0–a global synthesis of taxonomic and functional turnover in ecological communities.<em> Global Ecology and Biogeography.</em></p> <p> </p> <p><strong>raw_data.zip - </strong>Includes the raw datasets used in the analysis. </p> <p><strong>processed_data.xlsx - </strong>Includes the results from the distance decay analysis, specifically: </p> <p>- Dataset : dataset code (same as in raw_data)</p> <p>- Beta_type: The component of beta diversity (i.e., total similarity, replacement, richness differences)</p> <p>- Level : Taxonomic (TAX) or functional (FUN)</p> <p>- Based: Occurrence (occ) or Abundance (abund)</p> <p>- Organism: Code used to describe organisms (see Appendix S1 of the paper)</p> <p>- Realm: Aquatic, Terrestrial, or Freshwaters</p> <p>- Body_size </p> <p>- Dispersal_mode: Seeds, Passive or Active</p> <p>- Latitude: Mean latitude of the dataset (average of all data points)</p> <p>- Latitude_range Distance in kilometres between the two vertically most distant points.</p> <p>- Longitude_range: Distance in kilometres between the two horizontally most distant points.</p> <p>- spa_min: minimum distance between sites (in kilometres) </p> <p>- spa_mean: average distance between sites (in kilometres) </p> <p>- spa_max: maximum distance between sites (in kilometres)</p> <p>- ext: area in kilometres covered by all sites in the dataset</p> <p>- n_sites: Number of sites in each dataset</p> <p>- n_var: Number of environmental variables in each dataset</p> <p>- gamma_spe: Number of species observed in each dataset</p> <p>- gamma_trait: Volume of the hypervolume constructed using the traits in each dataset</p> <p>- n_traits: Number of traits in each dataset</p> <p>- Intercept_spa: Intercept of GLM including community similarity and spatial distances</p> <p>- Slope_Spa: Slope of GLM including community similarity and spatial distances</p> <p>- R2_spa: R² of GLM including community similarity and spatial distances</p> <p>- Intercept_env: Intercept of GLM including community similarity and environmental distances</p> <p>- Slope_env: Slope of GLM including community similarity and environmental distances</p> <p>- R2_env: R² of GLM including community similarity and environmental distances</p> <p>- Mantel_spa: Mantel statistics of community similarity and spatial distances</p> <p>- spa_signif: Significance of Mantel statistics considering community similarity and spatial distances</p> <p>- Mantel_env: Mantel statistics considering community similarity and environmental distances</p> <p>- env_signif: Significance of Mantel statistics considering community similarity and environmental distances</p> <p><strong>Null_models.zip - </strong>Includes the results from the null models for each dataset.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for Functional Group Pair Distance Based Descriptor for Isomerisation in Porous Molecular Framework Materials
<p>This is a dataset of isomer structure files for pore topology: Tri2Di3, Tri4Di6, Tri4-2Di6,Tri6Di9, Tet2Di4, Tet3Di3, Tet4-4Di8, Tet5Di10, and Tet6Di12. </p> <p>All.tar.bz2 contains all pore topologies, the total disk space after unzipping the bundle is 1.8 Gb. The total disk space for pore Tet6Di12 alone is 1.5Gb.</p> <p>The base structure of all pore topologies are constructed using a metal node of radius ~5 (represented by a Zirconium atom) and a benzene linker, while the functional group is represented by a Nitrogen atom.</p>
Data and Code for "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application" authored by Juan José Price and Arne Henningsen and published in the Journal of Productivity Analysis (DOI: <a href="https://doi.org/10.1007/s11123-023-00684-1">10.1007/s11123-023-00684-1</a>).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.0) using the add-on packages "combinat" (version 0.0.8), "miscTools" (version 0.6.28), "quadprog" (version 1.5.8), sfaR (version 1.0.0), stargazer (version 5.2.3), and "xtable" (version 1.8.4) that are available at CRAN. We created the R package "micEconDistRay" that provides the functions for empirical analyses with ray-based input distance functions that we developed for the above-mentioned paper. Also this R package is available at CRAN (https://cran.r-project.org/package=micEconDistRay).</p> <p>This replication package contains the following files and folders:</p> <ul> <li><strong>README</strong><br> This file</li> <li><strong>MuseumsDk.csv</strong><br> The original data obtained from the Danish Ministry of Culture and from Statistics Denmark. It includes the following variables: <ul> <li><em>museum</em>: Name of the museum. </li> <li><em>type</em>: Type of museum (Kulturhistorisk museum = cultural history museum; Kunstmuseer = arts museum; Naturhistorisk museum = natural history museum; Blandet museum = mixed museum).</li> <li><em>munic</em>: Municipality, in which the museum is located.</li> <li><em>yr</em>: Year of the observation.</li> <li><em>units</em>: Number of visit sites.</li> <li><em>resp</em>: Whether or not the museum has special responsibilities (0 = no special responsibilities; 1 = at least one special responsibility).</li> <li><em>vis</em>: Number of (physical) visitors.</li> <li><em>aarc</em>: Number of articles published (archeology).</li> <li><em>ach</em>: Number of articles published (cultural history).</li> <li><em>aah</em>: Number of articles published (art history).</li> <li><em>anh</em>: Number of articles published (natural history).</li> <li><em>exh</em>: Number of temporary exhibitions.</li> <li><em>edu</em>: Number of primary school classes on educational visits to the museum.</li> <li><em>ev</em>: Number of events other than exhibitions.</li> <li><em>ftesc</em>: Scientific labor (full-time equivalents).</li> <li><em>ftensc</em>: Non-scientific labor (full-time equivalents).</li> <li><em>expProperty</em>: Running and maintenance costs [1,000 DKK].</li> <li><em>expCons</em>: Conservation expenditure [1,000 DKK]. </li> <li><em>ipc</em>: Consumer Price Index in Denmark (the value for year 2014 is set to 1).</li> </ul> </li> <li><strong>prepare_data.R</strong><br> This R script imports the data set MuseumsDk.csv, prepares it for the empirical analysis (e.g., removing unsuitable observations, preparing variables), and saves the resulting data set as DataPrepared.csv.</li> <li><strong>DataPrepared.csv</strong><br> This data set is prepared and saved by the R script prepare_data.R. It is used for the empirical analysis.</li> <li><strong>make_table_descriptive.R</strong><br> This R script imports the data set DataPrepared.csv and creates the LaTeX table /tables/table_descriptive.tex, which provides summary statistics of the variables that are used in the empirical analysis.</li> <li><strong>IO_Ray.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions with the 'optimal' ordering of outputs, imposes monotonicity on this distance function, creates the LaTeX table /tables/idfRes.tex that presents the estimated parameters of this function, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>IO_Ray_ordering_outputs.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions, imposes monotonicity for each of the 720 possible orderings of the outputs, and saves all the estimation results as (a huge) R object allOrderings.rds.</li> <li><strong>allOrderings.rds</strong> (not included in the ZIP file, uploaded separately)<br> This is a saved R object created by the R script IO_Ray_ordering_outputs.R that contains the estimated ray-based Translog input distance functions (with and without monotonicity imposed) for each of the 720 possible orderings.</li> <li><strong>IO_Ray_model_averaging.R</strong><br> This R script loads the R object allOrderings.rds that contains the estimated ray-based Translog input distance functions for each of the 720 possible orderings, does model averaging, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>/tables/</strong><br> This folder contains the two LaTeX tables table_descriptive.tex and idfRes.tex (created by R scripts make_table_descriptive.R and IO_Ray.R, respectively) that provide summary statistics of the data set and the estimated parameters (without and with monotonicity imposed) for the 'optimal' ordering of outputs.</li> <li><strong>/figures/</strong><br> This folder contains 48 figures (created by the R scripts IO_Ray.R and IO_Ray_model_averaging.R) that illustrate the results obtained with the 'optimal' ordering of outputs and the model-averaged results and that compare these two sets of results.</li> </ul>
Building Envelopes in New York's CBD: Normalized Signed Distance Function Representation
<p>This dataset, developed to substantiate the findings of the study by Zhuang et al. [1], comprises 1,521 building envelopes from the Central Business District (CBD) of New York City (NYC), represented in three formats: the wavefront (obj) format, the Signed Distance Function (SDF) format and 3D binary-volume format. </p> <p>The original mesh data is procured from the NYC Open Data Portal [2]. The process of data manipulation is thoroughly delineated in the referenced research paper [1].</p> <p>[1] Zhuang, X., Ju, Y., Yang, A., & Caldas, L. (2023). Synthesis and Generation for 3D Architecture Volume with Generative Modeling. International Journal of Architecture Computing, AI, Architecture, Accessibility, & Data Justice. DOI: 10.1177/14780771231168233.</p> <p>[2] New York city department of city planning. NYC 3D model by community district, manhattan district, MN05. 2018. Available at: https://www.nyc.gov/site/planning/data-maps/open-data/dwn-nyc-3d-model-download.page. Accessed 15 July 2023.</p> <p> </p>
Taxonomic and functional biogeographies of soil bacterial communities across the Tibet plateau are better explained by abiotic conditions than distance and plant community composition
<p><span>The processes governing soil bacteria biogeography are still not fully understood. It remains unknown how the importance of environmental filtering and dispersal differs between bacterial taxonomic and functional biogeography, and whether their importance is scale-dependent. We sampled soils across the Tibet plateau, with distances among plots ranging from 20 m to 1,550 km. Taxonomic composition of bacterial community was characterized by 16S amplicon sequencing and functional community composition by qPCR targeting 9 functional groups involved in N dynamics. Factors representing climate, soil, and plant community were measured to assess different facets of environmental dissimilarity. Both bacterial taxonomic and functional dissimilarities were more related to abiotic dissimilarity than biotic (vegetation) dissimilarity or distance. Taxonomic dissimilarity was mostly explained by differences in soil pH and mean annual temperature (MAT), while functional dissimilarity was linked to differences in soil N and P availabilities and N:P ratio. Soil pH and MAT remained the main determinants of taxonomic dissimilarity across spatial scales. In contrast, the explanatory variables of N-related functional dissimilarity varied across the scales, with soil moisture and organic matter having the highest role across short distances (<~330 km), and available P, N:P ratio and distance being important over long distances (>~660 km). Our results demonstrate how biodiversity dimension (taxonomic versus functional aspects) and spatial scale influence the factors driving soil bacterial biogeography.</span></p>
Data from: Global variation in the relationship between avian phylogenetic diversity and functional distance is driven by environmental context and constraints
<p>Aim: If evolutionary distance is akin to evolutionary chance, then it follows that species assemblages that are distantly related will also be more disparate in terms of their traits, features and the niches they occupy. Yet, studies have found that the total phylogenetic distance of an assemblages, known as phylogenetic diversity, is an unreliable surrogate for functional diversity. We investigate global variation in the relationship between Faith's Phylogenetic Diversity (PD) and Mean Pairwise Functional Distance (MPFD) across latitude and the influence of migratory species on both these aspects of diversity.</p> <p>Location: Global.</p> <p>Time period: Present day.</p> <p>Major taxa studied: Birds.</p> <p>Methods: We measure PD and MPFD for over 9,000 species of bird across more than 17,000 globally distributed assemblages. We obtain standardised effect sizes for both indices by simulating assemblage composition under an ecologically informed null model. We employ path analysis to characterise variation in the relationship between PD's and MPFD across latitude, elevation and with proportion of migratory species.</p> <p>Results: Globally, assemblages that were phylogenetically diverse tended to be less functionally dispersed than expected; however this relationship showed considerable variation across latitude decreasing with distance from the equator. The proportion of migratory species in an assemblage was found to be an important predictor of functional diversity, with migrant rich assemblages generally showing less functional diversity than expected. We identify the Andes and Hengduan Mountains as regions of exceptional bird functional diversity.</p> <p>Main conclusions: The relationship between phylogenetic diversity and function diversity is context specific, varying across environmental gradients such as latitude, and influenced by ecological phenomena such a migration. Thus, care should be taken using phylogenetic diversity as a proxy for functional diversity, particularly in clades with sparse functional data. Instead we recommend that studies consider how phylogenetic diversity's surrogacy for functional diversity may be impacted by environmental context and evaluate empirical observations against biogeographically constrained and ecological informed null models.</p>
Data from: Global variation in the relationship between avian phylogenetic diversity and functional distance is driven by environmental context and constraints
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Taxonomic and functional biogeographies of soil bacterial communities across the Tibet plateau are better explained by abiotic conditions than distance and plant community composition
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