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750 results for “heterogeneous data”
Data from: Environmental heterogeneity leads to higher plasticity in dry-edge populations of a semiarid Chilean shrub: insights into climate change responses
1.Interannual variability in climatic conditions should be taken into account in climate change studies in semiarid ecosystems. It may determine differentiation in phenotypic plasticity among populations, with populations experiencing higher environmental heterogeneity showing higher levels of plasticity. 2.The ability of populations to evolve key functional traits and plasticity may determine the survival of plant populations under the drier and more variable climate expected for semiarid ecosystems. 3.Working with populations of the semiarid Chilean shrub Senna candolleana along its entire distribution range, we assessed inter- and intra-population variation in functional traits as well as in their plasticity in response to water availability. We measured morphological and physiological traits related to drought resistance in both field conditions and in a greenhouse experiment, where drought response was evaluated under two water availability treatments. 4.All populations responded plastically, but higher precipitation heterogeneity in dry-edge populations seemed to have selected for more plastic genotypes compared to populations growing at mesic sites and with more homogeneous environmental conditions. 5.Synthesis: Our results suggest adaptive plasticity since higher levels of phenotypic plasticity were positively associated with plant performance. However, we did not find evidence for genetic variation for plasticity within populations. To the extent that phenotypic plasticity may play a key role in future persistence, populations at mesic sites may be more vulnerable to climate change due to their lower plasticity and their current limitations to evolve novel norms of reaction. Conversely, although Senna candolleana populations at the dry-edge are exposed to higher levels of stress, they may be less susceptible to climate change in view of their greater plasticity. We highlight the need to consider population differentiation in both mean traits and their plasticity to model realistic scenarios of species distribution under climate change.
Data from: Spatial heterogeneity in landscape structure influences dispersal and genetic structure: empirical evidence from a grasshopper in an agricultural landscape
Dispersal may be strongly influenced by landscape and habitat characteristics that could either enhance or restrict movements of organisms. Therefore, spatial heterogeneity in landscape structure could influence gene flow and the spatial structure of populations. In the past decades, agricultural intensification has led to the reduction in grassland surfaces, their fragmentation and intensification. As these changes are not homogeneously distributed in landscapes, they have resulted in spatial heterogeneity with generally less intensified hedged farmland areas remaining alongside streams and rivers. In this study, we assessed spatial pattern of abundance and population genetic structure of a flightless grasshopper species, Pezotettix giornae, based on the surveys of 363 grasslands in a 430-km² agricultural landscape of western France. Data were analysed using geostatistics and landscape genetics based on microsatellites markers and computer simulations. Results suggested that small-scale intense dispersal allows this species to survive in intensive agricultural landscapes. A complex spatial genetic structure related to landscape and habitat characteristics was also detected. Two P. giornae genetic clusters bisected by a linear hedged farmland were inferred from clustering analyses. This linear hedged farmland was characterized by high hedgerow and grassland density as well as higher grassland temporal stability that were suspected to slow down dispersal. Computer simulations demonstrated that a linear-shaped landscape feature limiting dispersal could be detected as a barrier to gene flow and generate the observed genetic pattern. This study illustrates the relevance of using computer simulations to test hypotheses in landscape genetics studies.
Data from: Designed habitat heterogeneity on green roofs increases seedling survival but not plant species diversity
Urban areas benefit from the ecosystem services provided by low input green roofs. However, limited substrate depth on these green roofs creates challenging conditions for plant establishment and survival, leading to industry reliance on non-native succulents. Through a green roof and glasshouse study, we assessed the impact of simple design modifications to the green roof surface, including redistribution of substrate and addition of logs and pebble piles, on both substrate temperature and moisture content. We added seeds of 26 native species and quantified seedling density, species richness and composition over a single growing season. Overall effects of microsite heterogeneity on species diversity were assessed using species accumulation curves. The modifications altered substrate temperature and moisture. Deep substrate (10-12 cm) and the presence of surface features reduced temperature by 14.6°C and, while surface features had mixed effects on substrate moisture on the green roof, pebble piles slowed moisture loss during a six-week drought in the glasshouse. Following drought conditions, seedling density and species richness was greatest, relative to seeded controls, where substrate was deep on the green roof and where pebbles were present in glasshouse modules, despite high mortality overall. Design modifications did not result in differentiation of seedling communities among different microsite types. Species accumulation curves showed no difference in species richness between aggregates of modified vs. unaltered microsites. Synthesis and applications. Redistribution of green roof substrate and the addition of logs and pebble piles altered microsite conditions and created habitat heterogeneity on a green roof. These design modifications represent a minimalist strategy to ameliorate growing conditions, improve seedling survival and decrease species loss on shallow substrate green roofs.
Data from: Functional heterogeneity facilitates effectual collective task performance in a worker-polymorphic ant
<p class="MsoNormal"><span>Effective coordination of group actions underlies the success of group-living organisms. Recent studies of animal personality have shown that groups composed of individuals with different behavioral propensities can outperform uniform groups in a range of different tasks, but we have only a rudimentary understanding of how differences in individual behavior influence the behavior of the group as a whole. In this study, we use natural variation in behavioral propensity among morphologically distinct worker castes of the small carpenter ant <em>Camponotus yamaokai</em> to shed new light on this. Iterative testing indicated that ants displayed consistent behavioral differences among individuals and between castes, with major workers exhibiting a lower exploratory tendency than minors. By constructing groups of different caste composition and quantifying their performance in the task of colony emigration, we show that group performance is an asymmetric humped function of caste ratio, with optimal performance achieved by groups with natural caste ratios. Using a simulation model based on our empirical data, we demonstrate that inter-individual differences in social attraction and exploratory tendency are sufficient to explain the observed patterns. Our results provide new insights into how group performance in collective tasks can vary with group composition. </span></p>
Data from: geo-genomic predictors of genetree heterogeneity explain phylogeographic and introgression history: a case study in an Amazonian bird (Thamnophilus aethiops)
<p>Can knowledge about genome architecture inform biogeographic and phylogenetic inference? Selection, drift, recombination, and gene flow interact to produce a genomic landscape of divergence wherein patterns of differentiation and genealogy vary nonrandomly across the genomes of diverging populations. For instance, genealogical patterns that arise due to gene flow should be more likely to occur on smaller chromosomes, which experience high recombination, whereas those tracking histories of geographic isolation (reduced gene flow caused by a barrier) and divergence should be more likely to occur on larger and sex chromosomes. In Amazonia, populations of many bird species diverge and introgress across rivers, resulting in reticulated genomic signals. Herein, we used reduced representation genomic data to disentangle the biogeographic history of four populations of an Amazonian antbird, Thamnophilus aethiops, whose biogeographic history was associated with the dynamic evolution of the Madeira River Basin. Specifically, we evaluate whether a large river capture event ca. 200 kya, gave rise to reticulated genealogies in the genome by making spatially explicit predictions about isolation and gene flow based on knowledge about genomic processes. We first estimated chromosome-level phylogenies and recovered two primary topologies across the genome. The first topology (T1) was most consistent with predictions about population divergence, and was recovered for the Z chromosome. The second (T2), was consistent with predictions about gene flow upon secondary contact. To evaluate support for these topologies, we trained a convolutional neural network to classify our data into alternative diversification models and estimate demographic parameters. The best-fit model was concordant with T1 and included gene flow between non-sister taxa. Finally, we modeled levels of divergence and introgression as functions of chromosome length, and found that smaller chromosomes experienced higher gene flow. Given that (1) gene-trees supporting T2 were more likely to occur on smaller chromosomes and (2) we found lower levels of introgression on larger chromosomes (and especially the Z-chromosome), we argue that T1 represents the history of population divergence across rivers and T2 the history of secondary contact due to barrier loss. Our results suggest that a significant portion of genomic heterogeneity arises due to extrinsic biogeographic processes such as river capture interacting with intrinsic processes associated with genome architecture. Future biogeographic studies would benefit from accounting for genomic processes, as different parts of the genome reveal contrasting, albeit complementary histories, all of which are relevant for disentangling the intricate biogeographic mechanisms of biotic diversification.</p>
Raw Data and Scripts used in Regulation of single-cell heterogeneity of capsular polysaccharide synthesis in a human gut symbiont
<p>Raw data used in this publication. Single-cell analysis of promoter inversions reveals differential inversion rates as a determinant of bacterial population heterogeneity.</p> <p> </p> <p>Libx.zip contains raw sequencing reads</p> <p>scripts.zip contains code for analysis of reads and growth curve data</p> <p>SequencingRawDataFilesIndex.xls contains a description of all the raw data in each libx.zip.</p>
Data from: Environmental heterogeneity influences liana community differentiation across a Neotropical rainforest landscape
<p>We examined the variation in the liana community composition and structure across geopedological land units to test the hypothesis that environmental heterogeneity is a driving force in the liana community assembly. At the Los Tuxtlas Tropical Biology Station, SE Mexico (640 ha of tropical rain forest), we sampled all lianas with basal diameter ≥ 1 cm in three 0.5-ha plots established in each of five land units (totaling 15 plots and 7.5 ha). We censused 6055 individuals and 110 species. Overall, the most speciose families were also the most abundant ones. The density and basal area (m<sup>2</sup>) of some dominant liana species differed among land units, and a permutational multivariate analysis of variance (PERMANOVA) and a non-metric multidimensional scaling ordination (NMDS) revealed differences in the presence, density, and basal area of liana species across the landscape. Liana composition and structure were highly heterogeneous among land units, suggesting that variations in soil water availability and relief are key drivers of liana community spatial differentiation. By showing that soil and topography play an important role at the landscape scale, we underscore the ecological relevance of environmental heterogeneity for liana community assembly. In the future, as our ability to assess the local environmental complexity increases, we will gain a better understanding of the liana community assembly process and its heterogeneous distribution in tropical forests.</p>
Mass cytometry data for "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma"
<p>Raw suspension mass cytometry data supporting the publication "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma".</p>
Data and Codes of A Deep Learning-Based Consistency Test for Earth System Models on Heterogeneous Many-Core Systems
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Data Sets For: Heterogeneous Structure, Mechanisms of Counterion Exchange, and the Spacer Salt Effect in Complex Molten Salt Mixtures Including LaCl3
<p>Data Sets For: Heterogeneous Structure, Mechanisms of Counterion Exchange, and the Spacer Salt Effect in Complex Molten Salt Mixtures Including LaCl3</p>
Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data
<p>This is the data repository for Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data.</p> <p>Catalog:</p> <ol> <li>Intermediate data used in plotting: CANVAS_source_data.zip <ol> <li>Single cell monocyte data: monocyte.h5ad</li> <li>qPCR table: qPCR_1013.csv</li> <li>NanoString GeoMx cell composition: fig6b.csv</li> </ol> </li> <li>Pretrained CANVAS model: checkpoint-1999.pth</li> </ol> <p>The source IMC data is avaiable at: https://zenodo.org/records/7760826</p>
Crustal heterogeneity onshore central Spitsbergen: insights from new gravity and vintage geophysical data (digital appendix)
<p>This is a digital appendix with data sets related to a scientific paper in G-cubed. It contains gravity, GPR and positioning data from Svalbard.</p> <p> </p> <p><span>Crustal heterogeneity onshore central Spitsbergen: insights from new gravity and vintage geophysical data </span></p> <p><span>Kim Senger<sup>1,2*, </sup>Fenna Ammerlaan<sup>1,3</sup>, Peter Betlem<sup>1,</sup> <sup>†</sup>, Marco Brönner<sup>4</sup>, Marie-Andrée Dumais<sup>4</sup>, Jomar Gellein<sup>4</sup>, Tormod Henningsen<sup>5</sup>, Julian Janocha<sup>1,6</sup>, Erik P. Johannessen<sup>7</sup>, Jonas Liebsch<sup>1,8</sup>, Jakob Machleidt<sup>9,1</sup>, Tereza Mosočiová<sup>1,10,11</sup>, Snorre Olaussen<sup>1</sup>, Bo Olofsson<sup>12</sup>, Nil Rodes<sup>1</sup>, Sofia Rylander<sup>12,1</sup>,<span> </span>Grace E. Shephard<sup>13,14</sup>, Aleksandra Smyrak-Sikora<sup>15</sup>, Juan D. Solano-Acosta<sup>16,1 </sup>and Anna Sterley<sup>11,1</sup></span></p>
Example data for "Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data"
<p>This repository includes two example dataset and configurations for running CANVAS (https://github.com/tanjimin/CANVAS).</p> <p>The repostory is structured as follows:</p> <p>├── Kim_2022<br>│ ├── configs<br>│ │ ├── config.yaml<br>│ │ └── preprocess<br>│ │ ├── channels_vis_strength.yaml<br>│ │ └── selected_channels_w_color.yaml<br>│ └── data<br>│ └── raw_data<br>│ ├── common_channels.txt<br>│ └── image_files<br>└── Sorin_2023<br> ├── configs<br> │ ├── config.yaml<br> │ └── preprocess<br> │ ├── channels_vis_strength.yaml<br> │ └── selected_channels_w_color.yaml<br> └── data<br> └── raw_data<br> ├── common_channels.txt<br> └── image_files</p> <p> </p> <p>The source IMC data from this repository are from Kim et al. 2022 (https://www.nature.com/articles/s41592-022-01657-2) and Sorin et al. 2023 (https://www.nature.com/articles/s41586-022-05672-3). They are avaiable at: https://zenodo.org/records/4110560 and https://zenodo.org/records/7760826.</p>
Including population and environmental dynamic heterogeneities in continuum models of collective behaviour with applications to locust foraging and group structure Data and Code
<p>This dataset includes all data used for the creation of "Including dynamic population and environmental heterogeneity in continuum models of collective behaviour with applications to locust foraging and group structure" as well as a snapshot of the code used.<br><br>Each zip should be unzippable and the code should operate with only the contents of the zip file.</p>
data for modeling scale of representation of heterogeneity on simulated salinity and saltwater circulation in coastal aquifers
<p>This is the modeling data for modeling scale of representation of heterogeneity on simulated salinity and saltwater circulation in coastal aquifers. We setup a series of SEAWAT models to see the scale-dependent heterogeneity in simulations of saltwater circulation and cautions</p>
Data from: Resource heterogeneity but not inbreeding affects growth and grouping behaviour in socially foraging juvenile cichlid fish
<p>1. Spatial food distribution determines resource profitability, defensibility and encounter rate of foragers. Clumped food distribution can promote aggressiveness and resource monopolisation, in turn increasing within-group variation in food intake and growth. However, the effects of food distribution may depend on foraging strategies. Little is known about the impact of spatial food heterogeneity on growth and grouping behaviour in social foragers in the absence of monopolisation.</p> <p>2. Social foraging is present in many fishes, particularly at early juvenile life stages when fish are especially sensitive to environmental variation. Here, a heterogeneous food distribution may impair foraging success and growth and juveniles may increase sociability to attain social information about food resources.</p> <p>3. We examined the impact of the spatial distribution of food as well as inbreeding on growth and social behaviour in juveniles of the cichlid fish <i>Pelvicachromis pulcher. </i>Inbred individuals often show poorer performance than outbred individuals (inbreeding depression), but inbreeding effects can be environment-dependent. In the experiment, in- and outbred fish were reared in a split-clutch design either under homogeneously distributed or spatially clumped food conditions for eight weeks starting one week after juveniles could actively feed. We documented growth and performed a shoaling assay and a sociality test (choice between a large vs. a small shoal) after six weeks.</p> <p>4. Spatial food distribution did not affect within-group body length variation, but individuals reared under clumped food conditions were smaller. Shoals of the different feeding conditions differed in social behaviour. Shoals of the clumped treatment group showed higher variation in inter-individual distances compared to shoals of the homogeneous treatment group. Furthermore, focal fish of the clumped treatment adjusted their association preference to the position of the groups' largest individual. We did not find significant inbreeding or environment-dependent inbreeding effects regarding growth or social behaviour.</p> <p>5. Our study suggests that a clumped food distribution can impede localisation of food resources and thus growth in juvenile social foragers. Accordingly, in heterogeneous environments, the use of social information may be highly relevant to increase individuals' foraging success potentially explaining orientation on successful foragers, i.e. large individuals. Inter-individual variation in juvenile social behaviour may precede variation in food monopolisation capability and in growth emerging at later life stages.</p>
Combining molecular data sets with strongly heterogeneous taxon coverage enlightens the peculiar biogeographic history of stoneflies (Insecta: Plecoptera)
<p class="Standard1">Extant members of the ancient insect order of stoneflies exhibit a disjunct, antitropical distribution, with one major lineage exclusively occurring in the Southern Hemisphere and the other, with few exceptions, on the Northern continents. Here, we address the biogeographic distribution and phylogenetic relationships of stoneflies using a phylogenetic workflow that combines both transcriptomic and Sanger sequence datasets with heterogeneous taxon coverage. We used a dataset comprising 2997 genes derived from the transcriptomes of 30 species and Sanger sequences of seven genes for 498 species. The backbone phylogeny was mainly inferred from the transcriptomic data, whereas the Sanger nucleotide sequence data provided high species density for divergence time estimation and diversification analyses. Our results show that the biogeographic pattern we observe today is primarily more likely shaped by long-distance over-land dispersal than by vicariance. We inferred that the ancestors of extant stoneflies originated in the Northern Hemisphere approximately 265 Ma and were presumably restricted to this area due to climatic and geographic boundaries. Our analyses suggest that with the break-up of Pangaea around 200 Ma and the associated climatic and geographical changes, two groups of stoneflies, the Anarctoperlaria and the Notonemouridae, dispersed to Gondwana and subsequently went extinct on the northern continents. Both groups likely dispersed across Gondwana before its break-up into the modern continents. At least one member of another group of 'northern' stoneflies, the Acroneuriinae, seems to have migrated from North America to South America around 67 Ma. We found four major net diversification rate shifts, indicating rapid radiation patterns that hampered a robust phylogenetic placement of these stonefly groups. Our study provides the first conclusive evolutionary explanation for the unique distribution pattern of stoneflies.</p>
Data from: Deep learning unlocks X‐ray microtomography segmentation of multiclass microdamage in heterogeneous materials
<p>Four-dimensional quantitative characterization of heterogeneous materials using <i>in situ</i> synchrotron radiation computed tomography can reveal 3D sub-micron features, particularly damage, evolving under load, leading to improved materials. However, dataset size and complexity increasingly require time-intensive and subjective semi-automatic segmentations. Here, we present the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale damage in heterogeneous bulk materials, teaching on advanced aerospace-grade composite damage using ≈65,000 (trained) human-segmented tomograms. The trained CNN machine segments complex and sparse (<<1% of volume) composite damage classes to ≈99.99% agreement, unlocking both objectivity and efficiency, with nearly 100% of the human time eliminated, which traditional rule-based algorithms do not approach. The trained machine is found to perform as well or better than the human due to 'machine-discovered' human segmentation error, with machine improvements manifesting primarily as new damage discovery and segmentation augmentation/extension in artifact-rich tomograms. Interrogating a high-level network hyperparametric space on two material configurations, we find DL to be a disruptive approach to quantitative structure-property characterization, enabling high-throughput knowledge creation (accelerated by two orders of magnitude) via generalizable, ultra-high-resolution feature segmentation.</p>
Data for the manuscript "Direct visualization of colloid transport over natural heterogeneous and artificial smooth rock surfaces"
<p>The files contain the data used to produce the figures in the manuscript "<strong>Direct visualization of colloid transport over natural heterogeneous and artificial smooth rock surfaces</strong>" by Borgman, Be'er, and Weisbrod.</p> <p>Included in the data set:</p> <ol> <li>ImageAnalysesAndPlots.m: A MATLAB script to generate the figures from the included images and data files.</li> <li>myCmap.mat: A custom set of colors for the images.</li> <li>LH_btc.csv, LH_b_btc.csv, HH_btc.csv, HH_b_btc.csv: Data for the breakthrough curves.</li> <li>LH_t=360min.czi, HH_t=300min.czi: Final images from the experiments, from which the residual surface fluorescence is calculated.</li> <li>LHSurfTopo.csv, HHSurfTopo.csv: Tables containing the profilometer scan data.</li> <li>LH, HH: Folders containing the images for the colloid displacement front</li> <li>Exp01-Exp04: Images for calculating the dispersion coefficient </li> <li>PlotVelocities2.m: A script for plotting the calculated velocity fields</li> <li>velocity_magnitude_HH_flux_boundary.txt/velocity_magnitude_LH_flux_boundary.txt: The data files for the velocity fields.</li> </ol> <p>For the .czi files, it's necessary to use the Bio-Formats for MATLAB <a href="https://docs.openmicroscopy.org/bio-formats/6.1.0/users/matlab/index.html">package</a>.</p>
Data and Codes of Characterizing Uncertainties of Earth System Modeling with Heterogeneous Many-core Architecture Computing
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</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.