Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
750
datasets available to search
ShareScore release 0.9.0
Dataset results
750 results for “heterogeneous data”
Data from: Associations between developmental stability, canalization and phenotypic plasticity in plants with temporally heterogeneous environmental experience
<p>We subjected eight plant species to a first round of alternating inundation and drought vs. constantly moderate water treatments and a second round of water conditions. Fluctuating asymmetry (FA), intra- and inter-individual variations (CV<sub>intra</sub> and CV<sub>inter</sub>), and plasticity in traits were measured and correlations between variables were calculated for each species. Early temporally heterogeneous experience decreased the leaf size of half of the species, but had complex effects on leaf fluctuating asymmetry (FA) and inter-individual variation (CV<sub>inter</sub>) in traits immediately or in late conditions, with little effects on intra-individual variation (CV<sub>intra</sub>). There were several positive correlations between FA and CV<sub>inter</sub>, while there were correlations between CV<sub>inter</sub> and plasticity in early treatments, but negative ones in late treatments.</p>
Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia
<p>This repository contains the Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia.</p> <p>Authors: M.W. Vaughan<sup>a</sup>, H. Lim<sup>a</sup>, B. Pham<sup>a</sup>, R. Seede<sup>a</sup>, A. T. Polonsky<sup> a</sup>, K. Johnson<sup> a</sup>, P. J. Noell<sup>a,*</sup></p> <p>a: Sandia National Laboratories, Albuquerque, NM 87123</p> <p>Each folder contains a ReadMe.txt describing the files and their organization within each folder. </p> <p><br>Acknowledgements:<br><span>This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the United States Government. </span></p>
Data from: The two environmental drivers of thermoregulatory costs: Interactions between thermal mean and heterogeneity influence thermoregulation
<p>Ectotherms often thermoregulate behaviourally within variable thermal environments in their attempts to acquire optimal body temperatures. Thermoregulation accrues benefits, but also incurs costs, which are crucial for understanding thermoregulatory behaviour. Costs of thermoregulating are influenced by two key attributes of the thermal environment – heterogeneity of thermal microhabitats and the deviation of the mean temperature from an animal's preferred temperature. However, empirical research has rarely distinguished between these two drivers of thermoregulatory costs or examined them concurrently.</p> <p>Our experiment used a novel thermoregulatory arena to examine the independent and interactive effects of the two environmental drivers of costs of thermoregulation on the thermoregulatory behaviour of a small ectotherm, the jacky dragon (<em>Amphibolurus muricatus</em>).</p> <p>Following previous theory we predicted that i) thermoregulation would be higher in environments with greater thermal heterogeneity (body temperatures closer to preferred temperatures, higher thermoregulatory accuracy, and higher effectiveness of thermoregulation); and ii) changes in thermoregulation as a function of mean environmental temperatures would differ depending on thermal heterogeneity.</p> <p>We found support for our prediction that these two environmental attributes had an interactive effect on thermoregulation, but not for our prediction that thermal heterogeneity would impact thermoregulation independently. Individuals in highly heterogeneous environments maintained greater thermoregulatory accuracy compared with those in less heterogeneous environments as the mean environment increasingly deviated from the preferred temperature range.</p> <p>The results emphasise the crucial conceptual distinction between thermal mean and heterogeneity as drivers of thermoregulatory costs and how this distinction underpins variation in the thermal behaviour of ectotherms.</p>
Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes
<p><strong>Supplemental files contain all necessary datasets to reproduce the analysis and figures in "Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes" Code and additional files can be found at https://github.com/FowlerLab/VEP-calibrations</strong></p>
Data from: Heterogeneous distribution of kinesin-streptavidin complexes revealed by mass photometry
<p>Kinesin-streptavidin complexes are widely used in microtubule-based active-matter studies. The stoichiometry of the complexes is empirically tuned but experimentally challenging to determine. Here, mass photometry measurements reveal heterogenous distributions of kinesin-streptavidin complexes. Our binding model indicates that heterogeneity arises from both the kinesin-streptavidin mixing ratio and the kinesin-biotinylation efficiency.</p>
Data for "effects of fault contact heterogeneity on laboratory earthquake initiation and dynamic rupture"
<p>The second column in the files named by "Time_and_dLP", "Time_and_Mu0", "Time_and_Sigma0", and "Time_and_Tau0" indicate the along-fault loading point displacement (dLP), macroscopic friction coefficient (Mu0), macroscopic normal stress (Sigma0), and macroscopic shear stress (Tau0), respectively, measured in the loading apparatus. The first column in these files are time.</p> <p>Local fault displacement data are named by the form of, for example, "Event101_FaultDisplacement_L1(x=270mm)", which means that the fault displacement measured by Sensor L1 located at x=270 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_FaultDisplacement_Time".</p> <p>Local shear stress data are named by the form of, for example, "Event101_ShearStress_S1(x=-323.95mm)", which means that the shear stress measured by Sensor S1 located at x=-323.95 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_ShearStress_Time".</p>
Data for Unifying thermochemistry concepts in computational heterogeneous catalysis
<p>Data and Jupyter notebooks for the preprint "<span>Unifying</span> <span>thermochemistry</span> <span>concepts</span> <span>in</span> <span>computational</span><br><span>heterogeneous catalysis</span>"</p>
Experimental data of Large Effects of Particle Size Heterogeneity and Measurement Method on Dynamic Saltation Threshold
<p>There are the experimental data we conducted in a wind tunnel in Lanzhou University. And we present the original data about wind velocitys <em><strong>U</strong></em> [m/s] and corresponding measuring locations <em><strong>H</strong></em> above the sand bed [cm] with different sand size distribution here.</p>
Supplementary Data for "Identifying Promising Metal−Organic Frameworks for Heterogeneous Catalysis via High-Throughput Periodic Density Functional Theory"
<p>Supplementary data to accompany:</p> <p>A.S. Rosen, J.M. Notestein, R.Q. Snurr. "Identifying Promising Metal-Organic Frameworks for Heterogeneous Catalysis via High-Throughput Periodic Density Functional Theory", J. Comput. Chem (2019). DOI: 10.1002/jcc.25787</p>
Data from: Heterogeneous zonal impacts of climate change on a wide hyperendemic area of human and animal fascioliasis assessed within a One Health action for prevention and control
Open the record for dataset details and reuse information.
Data from: Environmental heterogeneity and not vicariant biogeographic barriers generate community wide population structure in desert adapted snakes
Genetic structure can be influenced by local adaptation to environmental heterogeneity and biogeographic barriers, resulting in discrete population clusters. Geographic distance among populations, however, can result in continuous clines of genetic divergence that appear as structured populations. Here we evaluate the relevant importance of these three factors over a landscape characterized by environmental heterogeneity and the presence of a hypothesized biogeographic barrier in producing population genetic structure within 13 codistributed snake species using a genomic dataset. We demonstrate that geographic distance and environmental heterogeneity across western North America contribute to population genomic divergence. Surprisingly, landscape features long thought to contribute to biogeographic barriers play little role in divergence community wide. Our results suggest that isolation by environment is the most important contributor to genomic divergence. Furthermore, we show that models of population clustering that incorporate spatial information consistently outperform nonspatial models, demonstrating the importance of considering geographic distances in population clustering. We argue that environmental and geographic distances as drivers of community-wide divergence should be explored before assuming the role of biogeographic barriers.
Data from: Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilization
Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta-diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilization and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilization led to higher rates of local extinction whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.
Data and code - Disentangling dispersion from mean reveals true heterogeneity-diversity relationships
<p>Data and code for reproducing figures and results for the manuscript entitled "Disentangling dispersion from mean reveals true heterogeneity-diversity relationships". Published in <a href="https://doi.org/10.1038/s41467-025-64287-0">Nature Communications</a>. See references for data sources.</p> <p>Code tested with Julia version 1.11.1.</p> <p><strong>How to cite this repository</strong></p> <p>If using code or data from this repository, please cite the original publication (<a href="https://doi.org/10.1038/s41467-025-64287-0">Pellett and Valbuena, 2025</a>) and respective data source (see references and README.txt in respective data folder).</p> <p><strong>Update 2024-07-09</strong></p> <p>Minor changes to figure sizes and use of paired-sample t-tests when assessing empirical observations of heterogeneity measures.</p> <p><strong>Update 2024-08-04</strong></p> <p>Step by step instructions included in README</p> <p>Manifest.toml file included with julia and package version requirements.</p> <p><strong>Update 2024-11-18</strong></p> <p>Update following peer review feedback:</p> <p>Analysis of an additional dataset from MacArthurs' seminal paper on foliage height diversity.</p> <p>Hypothesis test of negligible trend for delta</p> <p>Modified extended data figures</p> <p><strong>Update 2025-05-16</strong></p> <p>Update following second round of peer review feedback:</p> <p>Change to equation notation for figure 2</p> <p>Additional script for simulation and review report figures </p> <p><strong>Update 2025-09-29</strong></p> <p>Figure text size adjustments after final round of peer review and editor requests.</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Burn severity data from: The heterogeneity of burn severity affects bird density in an abandoned mountain landscape of the Atlantic-Mediterranean transition
<p><strong>[Abstract]</strong></p> <p>Fire regimes in mountain landscapes of southern Europe have been shifting from their baselines due to the accumulation of fuel fostered by long-standing rural abandonment and fire exclusion policies. Understanding the role of fire on biodiversity is paramount to implement adequate management to mitigate the impacts of altered fire regimes and land abandonment on biodiversity. Here, we explored to what extent the spatiotemporal variation in burn severity has affected bird abundance of a mountain abandoned landscape located in the Atlantic-Mediterranean transition (NW Iberia). We took advantage of: (1) satellite images of Sentinel 2 and Landsat missions to compute burn severity indicators from 2010 to 2020, and (2) standardized bird surveys carried out over 206 point-counts along the breeding season of 2021. Bird abundance models were built from burn severity metrics together with well-known fire regime attributes (% of burnt area and time since fire). Our results showed that the spatiotemporal variation of burn severity significantly correlated with the abundance of the 39% of the modeled species, supporting the role of pyro-diversity in driving bird populations in our region. The burnt area also explained abundance patterns for 28% of species. Time since fire only correlated with the abundance of 3 species. Our findings confirm the importance of incorporating burn severity indicators into the toolkit of decision makers to anticipate the response of birds to fire management.</p> <p><strong>[Dataset Description]</strong></p> <p>For each year between 2010 and 2020, we used a pair of satellite images, one before (April - July) and one after (September - November) the fire season. In order to use the best available information, we selected different satellites along the study period: for years 2010 and 2011, we used Landsat 5 imagery, whereas for 2012 we used Landsat 7, since Landsat 5 imagery was not useful due to high cloudiness. Since its launch in 2013, we shifted to Landsat 8 data, and finally to Sentinel 2 data from 2015 onwards. For each of these images we calculated the Normalized Burn Ratio (NBR), which is the normalized ratio between near infrared (NIR) and short wave infrared (SWIR) radiation (Eq. 1). NIR and SWIR bands of satellite sensors respond in opposite ways to burned vegetation, allowing to identify burned areas.</p> <p>NBR =(NIR - SWIR) / (NIR +SWIR) (1)</p> <p>To obtain a quantitative measure of change for each year, we calculated the dNBR by subtracting the NBR of the post-fire season image from the NBR of the pre-fire season image (Eq. 2). Finally, dNBR values were used as an estimate for fire severity.</p> <p>dNBR = NBRprefire- NBRpostfire (2)</p>
Alpine climate and soils heterogeneity data and simulation results
<p>We developed metrics of climatic and edaphic heterogeneity, using principal components analyses and the shoelace algorithm, and added elevation range. We applied commonality analysis to partition the unique and shared explanation of the observed vascular plant species richness among selected metrics. A simulation was developed to separate the relative importance of area and heterogeneity at different extents and representations of spatial nestedness, and the heterogeneity – effective area tradeoff was evaluated by altering spatial discreteness.</p> <p>The simulations revealed that heterogeneity was consistently more important, but less so among smaller areas. This qualitative pattern was maintained regardless of whether and how nestedness was represented. The heterogeneity – effective area tradeoff occurred in a few simulations of more discrete habitats.</p>
Data from: Habitat heterogeneity determines species richness on small habitat islands in a fragmented landscape
<p><span><strong>Aim</strong>:</span><span> The small-island effect (SIE), as an exception to the species-area relationship, has received much attention in true island systems. However, the prevalence and related patterns of the SIE have not been well evaluated in habitat island systems. Here, we aimed to identify the existence of SIE for habitat islands in fragmented landscapes and determine the key factors influencing species richness on small habitat islands.</span></p> <p><span><strong>Location</strong>:</span><span> Inner Mongolia Autonomous Region, China.</span></p> <p><span><strong>Taxon</strong>:</span><span> Vascular plants.</span></p> <p><strong><span>Methods</span></strong><span>: Based on 78 grassland fragments in fragmented landscapes of the agro-pastoral ecotone of northern China, we used piecewise regression, path analysis, and null models to investigate the SIE of the species-area relationship. We then used a multi-model selection to evaluate the impacts of four influencing factors (instability, isolation, habitat heterogeneity, and surrounding productivity) on species richness (including habitat specialists and generalists) on small habitat islands within the range of SIE. </span></p> <p><span><strong>Results</strong>:</span><span> We found an obvious threshold of 5.1 ha in the species-area relationship,</span> <span>below which habitat island area had no direct and indirect effects on species richness.</span><span> Small habitat islands (< 5.1 ha) host a lower percentage of habitat specialists and a higher percentage of generalists. On small habitat islands, species richness was positively affected by habitat heterogeneity while negatively affected by instability and isolation. Habitat heterogeneity had the strongest effect on species richness, positively affecting specialist richness while negatively affecting generalist richness.</span></p> <p><strong><span>Main conclusions</span></strong><span>: There is a SIE in fragmented landscapes of the agro-pastoral ecotone of northern China, which should be considered in biodiversity conservation. Habitat heterogeneity had a key role in determining the pattern of species richness, especially for small islands. Habitat specialists and generalists had different SIE-related patterns. Our study highlights the importance of considering different ecological groups of species to improve our understanding of the SIE in fragmented habitats.</span></p>
Data for Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response
<p>Data from simulations used to generate the figures in the paper <em>Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response</em>.</p> <p>To reproduce analyses, extract <em>colony_data.zip</em> in the <em>data</em> folder after cloning the <em>vivarium-ecoli</em> repository.</p> <p>The extracted folder contains the following items:</p> <ul> <li><em>sim_dfs</em>: a folder containing the CSV files that represent a subset of the raw simulation data used for downstream analyses.</li> <li><em>glc_10000_fluxome.csv</em>: Each row represents a reaction in central carbon metabolism (in same order as listed in <em>validation/ecoli/flat/toya_2010_central_carbon_fluxes.tsv</em>). Each column represents a single time point for a single cell in a baseline glucose simulation (seed 10000). Each value is a flux (mmol/L/hr). Provided as input to <em>ecoli/analysis/centralCarbonMetabolism.py </em>script to reproduce fluxome validation plot.</li> <li><em>glc_10000_proteome_avgs.csv</em>: Each row represents a protein monomer (in same order as <em>sim_data.translation.monomer_data["id"]</em> where <em>sim_data</em> is <em>reconstruction/sim_data/kb/validationData.cPickle</em>). Each column represents a cell in a baseline glucose simulation (seed 10000). Each row represents a protein monomer. Each value represents the average count of a given protein monomer for a given cell. Provided as input to <em>ecoli/analysis/proteinCountsValidation.py</em> script to reproduce proteome validation plot.</li> <li><em>glc_10000_expressome.csv</em>: Each column represents a gene (with the exception of the final two metadata columns: "Time" and "Agent ID"). Each row represents a specific cell (agent) at a specific time in a baseline glucose simulation (seed 10000). Each value represents the number of new RNA transcripts for a given gene in a given cell at a given time. Provided as input to <em>ecoli/analysis/antibiotics_colony/subgen_gene_plots/count_subgen.py</em> script to calculate number of sub-generational genes among all genes and antibiotic response genes.</li> <li><em>glc_10000_total_mrna.json</em>: Mapping of agent IDs for all cells in a baseline glucose simulation (seed 10000) to their average total mRNA count. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. 2C,D.</li> <li><em>jenner_2013.csv</em>: Data extracted from Fig. 2C of <a href="https://doi.org/10.1073/pnas.1216691110">10.1073/pnas.1216691110</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. S6A.</li> <li><em>olson_2006.csv</em>: Data extracted from Fig. 2D of <a href="https://doi.org/10.1128%2FAAC.01499-05">10.1128/AAC.01499-05</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. S6A.</li> <li><em>lysis_ratios.csv</em>: Data extracted from Fig. 2 of <a href="https://doi.org/10.1099/00221287-31-3-339">10.1099/00221287-31-3-339</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. 4N.</li> </ul>
Data from: Biotic pressures and environmental heterogeneity shape beta-diversity of seedling communities in tropical montane forests
<p>Many theories have been proposed to explain the high diversity of plants in the tropics. However, we lack an understanding of the processes that drive plant diversity and community assembly at different spatial scales. Here, we applied beta-diversity partitioning to test how biotic and abiotic factors are associated with seedling beta-diversity in a tropical montane forest in Southern Ecuador. We recorded seedling communities on 81 subplots at nine plots located at three elevations along a 2000-m elevational gradient. We measured biotic pressures (i.e. herbivory and fungal pathogen attacks) and environmental conditions (i.e. soil moisture and canopy closure) at all subplots and related them to species turnover and richness differences in seedling communities within and between elevations. We found that species turnover increased with differences in biotic dissimilarity within elevations, while differences in species richness within elevations increased with increasing environmental dissimilarity. Between elevations, species turnover increased with increasing environmental dissimilarity. Our findings show that species turnover and changes in species richness are related differently to abiotic and biotic factors, and that the importance of these factors for shaping seedling diversity is scale-dependent. Our study contributes to better understand the processes driving seedling beta-diversity and the assembly of plant communities in highly diverse tropical montane forests.</p>
Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
<p>Python figures in pickle format</p> <p>matplotlib version 3.5.1 </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.