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395 results for “Homogenization”
Raw experiment data from: Homogeneity of agriculture landscape promotes insecticide resistance in the ground beetle Poecilus cupreus
<p>This dataset is raw research data related to the paper published in PLOS ONE: Sowa et al. 2022, Homogeneity of agriculture landscape promotes insecticide resistance in the ground beetle <em>Poecilus cupreus</em>; <a href="https://doi.org/10.1371/journal.pone.0266453">https://doi.org/10.1371/journal.pone.0266453</a></p>
Urban biotic homogenization: approaches and knowledge gaps
<p>Urbanization is restructuring ecosystems at an unprecedented pace, with complex and profound consequences for life on earth. One of the hypothesized trajectories of urban ecosystems and species communities is biotic homogenization, possibly leading to very similar species assemblages in cities across the globe. Urbanization can, however, also have the opposite effect: biotic diversification, with cities – at least on the local scale – becoming biologically more diverse, mainly as a consequence of high species introduction rates and habitat diversification. Applying the hierarchy-of-hypotheses approach, we systematically map and structure the comprehensive body of literature on the urban biotic homogenization (UBH) hypothesis, comprising 225 individual studies (i.e. tests of the hypothesis) retrieved from 145 publications. The UBH hypothesis is studied at multiple levels with a multitude of approaches and underlying assumptions. We show that UBH is generally used with two very different connotations: about half of the studies investigated a potential increase in community similarity across cities, whereas the other half investigated biotic homogenization within cities, the latter being supported more frequently. We also found strong research biases: (1) a taxonomic bias towards birds and plants; (2) a bias towards small and medium distances (<5000 km) in comparisons across cities; (3) a dominance of studies substituting space for time vs. true temporal studies; (4) a strong focus on terrestrial vs. aquatic systems; (5) more extraurban (including periurban) areas than natural or rural ecosystems for comparison to urban systems; (6) a bias towards taxonomic vs. functional, phylogenetic and temporal homogenization; and (7) more studies undertaken in Europe and North America than in other continents. The overall level of empirical support for the UBH hypothesis was mixed, with 55% of the studies reporting supporting evidence. Results significantly differed when a natural/nature reserve, an extraurban or rural/agricultural area served as reference to infer biotic homogenization, with homogenization being detected least frequently when urban systems were compared to agricultural, i.e. other anthropogenically influenced, study sites. We provide an evidence map and a bibliographic network, and identify key references on UBH with the goal to enhance accessibility and orientation for future research on this topic.</p>
Mass spectrometric investigation of pharmaceuticals in environmental matrices - homogenate analysis: Thesis Data 2
<p>RT Thesis - Mass spectrometric investigation of pharmaceuticals in environmental matrices - homogenate analysis_data set 4 contains Mass Lynx data sets part 2</p> <p> </p> <p>RT Thesis - Mass spectrometric investigation of pharmaceuticals in environmental matrices - homogenate analysis_data set 5 contains Mass Lynx data sets part 3</p>
Data Shared: Human land-uses homogenize stream assemblages and reduce animal biomass production
<p>This is a dataset compiled from 117 sampling stream sites distributed across two neotropical biomes (rainforest and grassland). Dataset included single scaled vales of human land-use (agriculture, pasture, urbanization, and afforestation), scaled values of multifaceted biodiversity of fish, arthropods, and macrophytes (taxonomic richness, functional diversity[FDis], CWV and CWM of trait categories [recruitment and life-history, resource and habitat-use, and body size]), scaled values of local environmental predictors (stream site depth, water quality deterioration index, and sediment heterogeneity index), scaled values of climate predictors (temperature and precipitation), and log of animal biomass (fish and arthropod). </p>
Database for climate time series homogenization with metadata
<p>Usefulness of metadata in the automatic version of ACMANTv5 was tested.<br> A benchmark database has been developed, which consists of 41 datasets<br> of 20,500 networks of 170,000 synthetic monthly temperature time series<br> and the relating metadata dates. The research was supported by the<br> Catalan Meteorological Service. The research results will be published<br> in the open access MDPI journal Atmosphere. </p> <p>See more in the "Readme.txt" file of the dataset.</p>
Simulation data : Triggering of whistler-mode rising and falling tone emissions in a homogeneous magnetic field
<p>This dataset is obtained from <a href="http://space.rish.kyoto-u.ac.jp/software/">KEMPO1</a> code with minor modifications. This dataset contains simulation data and Python3 code examples. For more details, please refer to README.md.</p>
DNS dataset for modelling homogeneous ignition processes of clustering solid particle clouds in isotropic turbulence
<h2>Abstract</h2> <p>This dataset is being published to enable the development of models for igniting and combusting solid particles in isotropic turbulence using flamelet tabulated chemistry. This dataset is generated using the forced homogeneous isotropic turbulence in order to investigate the effect of active turbulent forces on the ignition of particles, and it is used as the supplementary material for the manuscript "<em>Modeling homogeneous ignition processes of clustering </em><em>solid particle clouds in isotropic turbulence</em>", which was accepted for publication in the special issue of Fuel Journal for the proceeding of the 4th international Oxyflame workshop. The particles are chosen to have near unity Stokes numbers, which promote particle clustering to study the ignition phenomenon for particle clusters, which is common during ignition and combustion of particle clouds in large industrial solid fuel-powered burners. Since the ignition process of clustering solid particle clouds is transient, different time instances during the ignition process are presented to facilitate the modelling effort for the transient ignition behaviour of the particles. The dataset consists of gas-phase data, particle data and the most important routines required to process the dataset. </p> <p>This database is a valuable resource for users developing solid fuel ignition and combustion in turbulent conditions. </p> <p>It should be noted that this dataset is a reduced version of the full dataset in order to size limitations in the sharing platforms. More information and full dataset can be provided upon request. For more information, contact: p.farmand@itv.rwth-aachen.de</p> <h2>Technical details</h2> <p>The data provided in this dataset contains gas phase data, particle data, and some post-processing scripts for visualization of the data. The data is generated in forced homogeneous isotropic turbulence (HIT) with an initial preferential concentration of the particles in a hot atmosphere to study the impact of particle clustering on ignition. Simulations were performed within a region with the physical size of 12.8mm * 12.8mm * 12.8mm with periodic boundary conditions in all directions. The domain size is discretized with a three-dimensional cartesian mesh with a resolution of Δx = 50 μm. A forced isotropic turbulent field with Re_λ=30 and the Kolmogorov length scale η =100 microns has been chosen. The dispersed phase consists of 10,000 particles of Colombian coal with D_p= 20 microns and T0=300K and with an apparent density of 700kg/m3. Non-reactive particles are first randomly distributed in the box filled with air with 20% oxygen and an initial gas temperature of T = 1500K, which is relevant to practical PCC applications. These conditions lead to an initial Stokes number of around 5, for which a clustering behaviour in particle cloud motion is expected. The employed forced isotropic turbulence ensures maintaining the same turbulence statistics during non-reactive and reactive simulations, as summarised in the following table:</p> <table> <tbody> <tr> <td> <p><em>time [ms]</em></p> </td> <td> <p><em>Re_</em><em>λ</em></p> </td> <td> <p><em>Re_</em><em>Turb</em></p> </td> <td> <p><em>η[m]</em></p> </td> <td> <p><em>l_</em><em>t</em><em>[m]</em></p> </td> <td> <p><em>t_</em><em>η</em><em>[ms]</em></p> </td> <td> <p><em>t_</em><em>l</em><em>[ms]</em></p> </td> <td> <p><em>St</em></p> </td> </tr> <tr> <td>0</td> <td>30.7</td> <td>141.8</td> <td>1.04e-4</td> <td>4.27e-3</td> <td>4.47e-2</td> <td>5.33e-1</td> <td>6.199</td> </tr> <tr> <td>0.46</td> <td>30.9</td> <td>143.9</td> <td>9.94e-5</td> <td>4.13e-3</td> <td>4.16e-2</td> <td>4.99e-1</td> <td>6.207</td> </tr> <tr> <td>0.5</td> <td>30.5</td> <td>140.1</td> <td>9.96e-5</td> <td>4.06e-3</td> <td>4.27e-2</td> <td>5.05e-1</td> <td>6.333</td> </tr> <tr> <td>0.55</td> <td>29.4</td> <td>129.8</td> <td>1.01e-4</td> <td>3.88e-3</td> <td>4.34e-2</td> <td>4.94e-1</td> <td>6.032</td> </tr> <tr> <td>0.6</td> <td>28.5</td> <td>122.1</td> <td>1.02e-4</td> <td>3.76e-3</td> <td>4.46e-2</td> <td>4.92e-1</td> <td>5.775</td> </tr> <tr> <td>0.65</td> <td>27.7</td> <td>115.2</td> <td>1.04e-4</td> <td>3.66e-3</td> <td>4.48e-2</td> <td>4.81e-1</td> <td>5.365</td> </tr> </tbody> </table> <p><em>η and t_η are the respective Kolmogorov length and time scales, and l_t and t_l correspond to the integral length and time scales.</em></p> <p> </p> <h3>Gas phase and particle data:</h3> <p>The gas phase data has HDF5 formation, which contains selected scalars relevant to model developments. Since the ignition process is a transient process, two different time instances, t=0.5ms with the maximum number of ignited regions during the ignition process and t=0.65ms at the end of the ignition process, are chosen. Before starting the reactive simulations, a non-reactive simulation for 20.5ms was performed to form the particle clusters, and then the reactive simulation was started. Therefore, the data.out_2.100E-02.h5 corresponds to t = 0.5ms and data.out_2.115E-02.h5 corresponds to t = 0.65ms. Each HDF5 dataset has the following structure:</p> <p>Group Flow:</p> <ul> <li>U, V, W</li> </ul> <p>Group scalar: </p> <ul> <li>CO, CO2, H2O, C2H2, O2, N2, OH, progress variable (PV), temperature(T), enthalpy(h), heat capacity (Cp), density(RHO), pressure(P), mixture fraction(Z), dissipation rate, heat conductivity</li> </ul> <p>Group C_dot:</p> <ul> <li>Molar production rate of CO, CO2, O2, OH</li> </ul> <p>Group ST:</p> <ul> <li>Mass fraction rate of OH</li> </ul> <p>Group var (data required for subfilter analysis and PDF modelling):</p> <ul> <li>RHO * h</li> <li>RHO * h * h</li> <li>RHO *<em> </em>PV</li> <li>RHO *<em> </em>PV <em>* </em>PV</li> <li>RHO *<em> </em>RHO</li> <li>RHO *<em> </em>RHO <em>* </em>RHO</li> <li>RHO * T</li> <li>RHO *<em> </em>T <em>* </em>T</li> <li>RHO * Z</li> <li>RHO *<em> </em>Z <em>* </em>Z</li> </ul> <p>These gas phase data can be used to study the Eulerian field, which is impacted by the particles through the source terms, which are obtained from the Lagrangian framework. For the particle data, two CSV files containing information about each particle's position and temperature are provided. </p> <p>In each CSV file, which corresponds to the same time instance as the gas phase data, this structure can be found:</p> <ul> <li>Points_0, Points_1, Points_2: particle position x,y,z</li> <li>coal_defaultT: particle temperature</li> </ul> <h3>Scripts:</h3> <p>Different scripts are provided for a reader to enable the first-time use of the data, such as visualising the gas phase quantities, calculating the conditional mean and statistical analysis, and clustering analysis for the particles. Here is a brief information regarding the scripts:</p> <ul> <li><strong>hdf5_2D_Con_Mean_Plots.m</strong>: This script can be used for loading the HDF5 file and visualising the field, joint PDF, and joint correlation of different quantities with respect to different parameters. This script requires another module to calculate the conditional mean of the data.</li> <li><strong>Compute_ConditionalMean_histograms_2D.m: </strong>This module calculates the conditional average of a 2D array.</li> <li><strong>clustering_voronoi_3D.m</strong>: This script can analyse the particle data and calculate the clustering limit based on the Voronoi algorithm. It can also filter the clustered particles and separate different clusters using the nearest neighbour and DB-SCAN methods.</li> </ul> <p> </p> <p> </p>
Data from: Natural selection drives emergent genetic homogeneity in a century-scale experiment with barley
<p>Direct observation is central to our understanding of the process of adaptation, but evolution is rarely documented in a large, multicellular organism for more than a few generations. Here, we observe genetic and phenotypic evolution across a century-scale competition experiment, barley composite cross II (CCII). CCII was founded in 1929 with tens of thousands of unique genotypes and has been adapted to local conditions in Davis, CA, USA for 58 generations. We find that natural selection has massively reduced genetic diversity leading to a single clonal lineage constituting most of the population by generation F50. Selection favored alleles originating from similar climates to that of Davis, and targeted genes regulating reproductive development, including some of the most well-characterized barley diversification loci, Vrs1, HvCEN, and Ppd-H1. We chronicle the dynamic evolution of reproductive timing in the population and uncover how parallel molecular pathways are targeted by stabilizing selection to optimize this trait. Our findings point to selection as the predominant force shaping genomic variation in one of the world's oldest ongoing biological experiments.</p>
Observed changes in significant wave heights derived from long-term homogenized measurements offshore mainland Portugal
<p>The data set contains statistical estimators of significant height acquired over the last 40 years by wave stations operating on the Portuguese coast; Monthly Mean, Monthly Maximum and the monthly 99, 95, 90, 50 , 10 percentiles</p> <p>The data was calculated based on long-term homogenized 3h series of in-situ significant wave height</p> <p>These buoys have been maintained by the Instituto Hidrografico and constitute one of the longest time series of wave data.</p> <p>Each file correspond to a particular wave station. All files are in ASCII and have the same 9 column format. The heading identify the variables</p> <p>Leixoes_corr_monthly_percentiles.txt - Data acquired by Leixoes buoy: Location 41º 19.00' N 8º 59.00' W, depth app 100 m</p> <p>Sines_corr_monthly_percentiles.txt - Data acquired by Sines buoy: Location 37º 55.27' N 8º 55.73 ' W, depth app 100 m</p> <p>Faro_coor_monthly_percentiles.txt - Data acquired by Faro buoy: Location 36º 54.28' N 7º 53.90' W, depth app 100 m</p> <p> </p>
FIGURE S3 in Accounting for differences in element size and homogeneity when comparing Finite Element Models: Armadillos as a case study
FIGURE S3. Map of Von Mises stress distribution in the 20 FEA models of Cingulata mandibles.
1.Figure cut off value of NLRP3 calculation process by R 2.Normality check results and Variance homogeneity.
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A new parameterisation for homogeneous ice nucleation driven by highly variable dynamical forcings
<p>This archive contains the data associated with the preprint of the article <em>"A New Parameterization for Homogeneous Ice Nucleation Driven by Highly Variable Dynamical Forcings."</em> The collection includes datasets used to construct the initial conditions for forcing an air parcel model with ice physics, outputs produced by the parcel model, and data necessary for generating the plots presented in the paper. A README file is provided, offering a detailed explanation of the contents and structure of the archive,</p>
Data from: Biotic homogenization and differentiation of plant communities in tropical and subtropical forests
<h4><strong>The article is published in Conservation Biology and available at: <a href="https://doi.org/10.1111/cobi.14025">https://doi.org/10.1111/cobi.14025</a></strong></h4> <h4>Here we developed a framework to assess the current 'state of the art' of biotic homogenization and differentiation processes in tropical and subtropical plant communities globally, using a systematic literature review.</h4> <h4>Please find below the analysis code and data used in the study.</h4> <h2>Descriptions for the files and scripts</h2> <h3>The `R` folder contains.</h3> <p><strong>1. *analysis_homogenization-differentiation.R*</strong> - script to generate the frequency and explanatory plots used in the manuscript.</p> <h3><br>The `output` folder contains:</h3> <p><strong>1. figures</strong><br><strong> 1. *Fig.1.tiff*</strong> - Figure 1 main text.<br><strong> 2. *Fig.2.tiff*</strong> - Figure 2 main text.<br><strong> 3. *Fig.3.tiff*</strong> - Figure 3 main text.<br><strong> 4. *Fig.4.tiff*</strong> - Figure 4 main text.<br><strong> 5. *Fig.5.tiff*</strong> - Figure 5 main text.<br><strong> 6. *Fig.6.tiff*</strong> - Figure 6 main text.<br><strong> 7. *Fig.7.tiff*</strong> - Figure 7 main text.</p> <p><strong>2. supp</strong><br><strong> 1. *appendix_S1-homogenization-differentiation.xlsx*</strong> - Supplementary material of the article. The file contains 4 sheets (*data*, *legend*, *scales_size* and *search_keywords*)</p> <h3><br>The `data` folder contains:</h3> <p><strong>1. processed</strong><br><strong> 1. *data_homogenization_refined.xlsx*</strong> - the data used in the analysis. Each analyzed variable was separated by a different sheet.</p> <p><br><strong>1. raw</strong><br><strong> 1. *data_homogenization.xlsx*</strong> - the raw data used in the analysis.</p> <p> </p> <h2>Acknowledgments</h2> <p>This work was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Finance code 001), through Portal de Periódicos to articles access and scholarship granted to J.M.F.K.</p>
Data of homogeneity of the photocathode in the Hamamatsu R15458-02 PMT under different magnetic field orientations
<p>The Hamamatsu R15458-02 PMT's photocathode was scanned with a collimated beam at 777 different positions to determine the relative transit time, TTS and gain. This measurement was performed at 192 different magnetic field orientations with a strength of 54.7 microtesla. The analyzed data is available in a JSON file.<br> To access the results for example in Python, you can import the data to a dictionary, and retrieve the values as follows:</p> <ul> <li> <p>The relative transit time is stored in the "RelTransitTime" key, TTS in "TTS," and gain in "Gain." Each of these keys contains a list of 192 lists, with each list containing 777 values.</p> </li> <li> <p>The 192 lists for each key correspond to scans measured at different magnetic fields. The "B_field" key retrieves the corresponding normalized magnetic field direction, which is a list of 192x3 elements.</p> </li> <li> <p>The 777 values in each list correspond to the value at different positions on the photocathode. You can access these positions using the "x" and "y" keys.</p> </li> </ul> <p>For further details, you can refer to Chapter 7 of M. Unland thesis "Development, simulation, and characterisation of a novel multi-PMT optical module for IceCube Upgrade with emphasis on detailed understanding of photomultiplier performance parameters" published in 2023.</p>
SNDlib-MIPs: A new set of homogeneous MILP instances
<p>We constructed 289 MILP instances based on the Survivable Network Design Library (SNDLib) <a href="http://sndlib.zib.de/home.action">http://sndlib.zib.de/home.action</a> and the models presented within. Our models differ from the formulations provided in that SOS constraints are used in place of big-M constraints, a set of edge disjoint paths are used in place of all possible paths, pre-installed capacity is only available if links are included, and in the bidirectional case capacities are the sum of both capacities instead of the maximum. The models were constructed using SCIP 8.0.3, with the code available at <a href="https://github.com/Opt-Mucca/branching-via-cut-selection">https://github.com/Opt-Mucca/branching-via-cut-selection</a>. Please see the attached PDF for a complete model description.</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Data from: Mother knows best: Nest-site choice homogenizes embryo thermal environments among populations in a widespread ectotherm
<p><span>Species with large geographic ranges provide an excellent model of how different populations respond to dissimilar local conditions, particularly variation in climate. Maternal effects, such as oviposition-site choice greatly affect offspring phenotypes and survival. Thus, maternal behavior has the potential to mitigate the effects of divergent climatic conditions across a species' range. We delineated natural nesting areas of six populations of painted turtles (<em>Chrysemys picta</em>) that span a broad latitudinal range and quantified spatial and temporal variation in nest characteristics. To quantify microhabitats available for females to choose, we also identified sites within the nesting area of each location that were representative of available thermal microhabitats. Across the range females nested non-randomly and targeted microhabitats that generally had less canopy cover and thus higher nest temperatures. Nest microhabitats differed among locations but did not predictably vary with latitude or historic mean air temperature during embryonic development. In conjunction with other studies of these populations, nest-site choice appears to be homogenizing nest environments, buffering embryos from thermally-induced selection, which could stymie evolution of embryonic traits. Thus, although effective at a macroclimatic scale, nest-site choice may be unable to compensate for novel stressors that rapidly increase local temperatures.</span></p>
Tackling local ecological homogeneity: Finding intraspecific trait variability in local populations of Mediterranean plants
<p>Local homogeneity, in ecology, is the often undisclosed assumption that variability within populations is negligible or mostly distributed evenly. In large areas, this can lead to the aggregation of different populations without regard for their unique needs and characteristics, such as drought sensitivity and functional traits distributions. Here we discuss whether this assumption can be justified, and we hypothesize that discerning the source of variation between plasticity and adaptation could be a feasible approach to formulate an informed decision. We test this hypothesis on plants, resorting to a common garden experiment to determine the source of variation of several plant functional traits at a local scale (~60 Km) of three wild species: <em>Quercus ilex</em>, <em>Pistacia lentiscus</em> and <em>Cistus salviifolius</em>. Individuals of each species were sourced from three key sites chosen along a local aridity gradient. Our approach led to the rejection of the local homogeneity assumption for <em>Q. ilex</em> and <em>C. salviifolius</em> at this scale due to the adaptive divergence observed among neighbouring populations. This case study provides evidence that addressing local homogeneity can highlight diverging populations in a relatively simple way. We conclude that gathering empirical evidence on intraspecific variability is a feasible approach that can provide researchers with solid bases to decide whether to adopt the local homogeneity assumption or not.</p>
Data for: Cessation of grazing causes biodiversity loss and homogenization of soil food webs
<p><span>There is widespread concern that cessation of grazing in historically grazed ecosystems is causing biotic homogenization and biodiversity loss. We used 12 montane grassland sites along an 800-km north-south gradient across the United Kingdom, to test whether cessation of grazing affects local ɑ- and β-diversity of belowground food webs. We show cessation of grazing leads to strongly decreased ɑ-diversity of most groups of soil microbes and fauna, particularly of relatively rare taxa. In contrast, the β-diversity varied between groups of soil organisms. While most soil microbial communities exhibited increased homogenization after cessation of grazing, we observed </span><span>decreased homogenization for soil fauna after cessation of grazing. Overall, our results indicate that exclusion of domesticated herbivores from historically grazed montane grasslands has far-ranging negative consequences for diversity of belowground food webs. This underscores the importance of grazers for maintaining the diversity of belowground communities, which play a central role in ecosystem functioning. </span></p>
Dataset_Multiscale Fast Fourier Transform homogenization of additively manufactured fiber reinforced composites from component-wise description of morphology
<p>Original micro-CT imaging data and output of processing via the OpenFiberSeg software of additively manufactured carbon fiber reinforced composites. </p> <p>This dataset accompanies the publication in Composite Science and Technology available at https://doi.org/10.1016/j.compscitech.2023.110261</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.