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.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “hierarchical structure”
Unraveling hierarchical genetic structure of tea green leafhopper, Matsumurasca onukii, in East Asia based on SSRs and SNPs
<p><em>Matsumurasca onukii</em> (Matsuda, 1952), one of the dominant pests in major tea production areas in Asia, currently is known to occur in Japan, Vietnam, and China, and severely threatens tea production, quality, and international export trade. To elucidate the population genetic structure of this species, 1633 single nucleotide polymorphisms (SNPs) and 18 microsatellite markers (SSRs) were used to genotype samples from 27 sites representing 18 geographical populations distributed throughout the known range of the species in East Asia. Analyses of both SNPs and SSRs showed that <em>M</em>. <em>onukii</em> populations in Yunnan exhibit high genetic differentiation and structure compared to other populations. The Kagoshima (JJ) and Shizuoka (JS) populations from Japan were separated from populations from China by SNPs but clustered with Jinhua (JH), Yingde (YD), Guilin (GL), Fuzhou (FZ), Hainan (HQ), Leshan (CT), Chongqing (CY) and Zunyi (ZY) tea areas in China and the Vietnamese Vinh Phuc (VN) population based on SSR data. On the contrary, CT, CY, ZY, and Shaanxi (SX) populations clustered together based on SNPs, but were separated by SSRs. Both marker datasets identified significant geographic differentiation among the 18 populations. Various environmental and anthropogenic factors, including the geographical barriers to migration, human transport of hosts (<em>Camellia sinesis</em> (L.) O. Kuntze), and adaptability of <em>M. onukii</em> to various climatic zones possibly account for the rapid spread of this pest in Asia. The results demonstrate that SNPs from high-throughput genotyping data can be used to reveal subtle genetic substructure at broad scales in r-strategist insects.</p>
Statistical aspects of interface adhesion and detachment of hierarchically patterned structures: dataset
<p><strong>Data from:</strong></p> <p>N. Esfandiary, M. Zaiser, P. Moretti: Statistical aspects of interface adhesion and detachment of hierarchically patterned structures (2022) Journal of Statistical Mechanics: Theory and Experiment 023301,<br> https://doi.org/10.1088/1742-5468/ac52a4</p> <p> </p> <p><strong>Abstract:</strong></p> <p>We introduce a three dimensional model for interface failure of hierarchical materials adhering to heterogeneous substrates. We find that the hierarchical structure induces scale invariant detachment patterns, which in the limit of low interface disorder prevent interface failure by crack propagation ('detachment fronts'). In the opposite limit of high interface disorder, hierarchical patterns ensure enhanced work of failure as compared to reference non-hierarchical structures. While the study of hierarchical adhesion is motivated by examples of fibrous materials of biological interest, our results indicate that hierarchical patterns can be useful in engineering scenarios in view of tuning and optimizing adhesion properties.</p> <p> </p> <p><strong>Contact:</strong></p> <p>Paolo Moretti<br> Institute of Materials Simulation<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Dr.-Mack-Str. 77<br> 90762 Fürth<br> Germany</p> <p> </p> <p><strong>File naming scheme:</strong></p> <p>hfn: hierarchical fuse network<br> rrn: random reference network</p> <p>uni: uniform threshold distribution <br> wei15: Weibull threshold distribution (shape factor 1.5)<br> wei4: Weibull threshold distribution (shape factor 4)<br> wei9: Weibull threshold distribution (shape factor 9)</p> <p>s1: largest connected components<br> s2: 2nd largest connected components<br> iv: sample iv curve<br> envelope: envelope of iv (displacement control)</p>
Recipient and donor characteristics govern the hierarchical structure of heterospecific pollen competition networks
Open the record for dataset details and reuse information.
Data from: Determinants of hierarchical genetic structure in Atlantic salmon populations: environmental factors vs. anthropogenic influences
Open the record for dataset details and reuse information.
Data from: Detecting a hierarchical genetic population structure: the case study of the Fire Salamander (Salamandra salamandra) in Northern Italy
Open the record for dataset details and reuse information.
Data from: MHC structuring and divergent allele advantage in a urodele amphibian: a hierarchical multi-scale approach
Open the record for dataset details and reuse information.
Data from: Hierarchical structure of ecological and non-ecological processes of differentiation shaped ongoing gastropod radiation in the Malawi Basin
Open the record for dataset details and reuse information.
Data from: Fine scale hierarchical genetic structure and kinship analysis of the ascidian Pyura chilensis in the southeastern Pacific
Open the record for dataset details and reuse information.
Data from: Hierarchical population structure and habitat differences in a highly mobile marine species: the Atlantic spotted dolphin
Open the record for dataset details and reuse information.
Unraveling hierarchical genetic structure of tea green leafhopper, Matsumurasca onukii, in East Asia based on SSRs and SNPs
Open the record for dataset details and reuse information.
Data from: Hierarchical analysis of genetic structure in the habitat-specialist Eastern Sand Darter (Ammocrypta pellucida)
Open the record for dataset details and reuse information.
Data from: Metacommunity structure of stream insects across three hierarchical spatial scales
<p>A major challenge in community ecology is to understand the underlying factors driving metacommunity (i.e. a set of local communities connected through species dispersal) dynamics. However, little is known about the effects of varying spatial scale on the relative importance of environmental and spatial (i.e. dispersal related) factors in shaping metacommunities and on the relevance of different dispersal pathways. Using a hierarchy of insect metacommunities at three spatial scales (a small, within-stream scale, intermediate, among-stream scale, and large, among-sub-basin scale), we assessed whether the relative importance of environmental and spatial factors shaping metacommunity structure varies predictably across spatial scales, and tested how the importance of different dispersal routes vary across spatial scales. We also studied if different dispersal ability groups differ in the balance between environmental and spatial control. Variation partitioning showed that environmental factors relative to spatial factors were more important for community composition at the within-stream scale. In contrast, spatial factors (i.e. eigenvectors from Moran's eigenvector maps) relative to environmental factors were more important at the among-sub-basin scale. These results indicate that environmental filtering is likely to be more important at the smallest scale with highest connectivity, while dispersal limitation seems to be more important at the largest scale with lowest connectivity. Community variation at the among-stream and among-sub-basin scales were strongly explained by geographical and topographical distances, indicating that overland pathways might be the main dispersal route at the larger scales among more isolated sites. The relative effect of environmental and spatial factors on insect communities varied between low and high dispersal ability groups; this variation was inconsistent among three hierarchical scales. In sum, our study indicates that spatial scale, connectivity and dispersal ability jointly shape stream metacommunities.</p>
Supplementary Website, Data, and Scripts for the Paper "Hierarchical and Hybrid Organizational Structures in Open-Source Software Projects: A Longitudinal Study"
<p>Supplementary website containing result plots and data, anonymized raw data, and scripts used to produce the results of the paper "Hierarchical and Hybrid Organizational Structures in Open-Source Software Projects: A Longitudinal Study".</p>
Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
Hierarchical genetic structure and implications for conservation of the world's largest salmonid, Hucho taimen
<p>Population genetic analyses can evaluate how evolutionary processes shape diversity and inform conservation and management of imperiled species. Taimen (<i>Hucho taimen</i>), the world's largest freshwater salmonid, is threatened, endangered, or extirpated across much of its range due to anthropogenic activity including overfishing and habitat degradation. We generated genetic data using high throughput sequencing of reduced representation libraries for taimen from multiple drainages in Mongolia and Russia. Nucleotide diversity estimates were within the range documented in other salmonids, suggesting moderate diversity despite widespread population declines. Similar to other recent studies, our analyses revealed pronounced differentiation among the Arctic (Selenge) and Pacific (Amur and Tugur) drainages, suggesting historical isolation among these systems. However, we found evidence for finer-scale structure within the Pacific drainages, including unexpected differentiation between tributaries and the mainstem of the Tugur River. Differentiation across the Amur and Tugur basins together with coalescent-based demographic modeling suggests the ancestors of Tugur tributary taimen likely diverged in the eastern Amur basin, prior to eventual colonization of the Tugur basin. Our results suggest the potential for differentiation of<i> </i>taimen<i> </i>at different geographic scales, and suggest more thorough geographic and genomic sampling may be needed to inform conservation and management of this iconic salmonid.</p>
Data from: Damage shielding mechanisms in hierarchical composites in nature with potentials in design of tougher structural materials
Open the record for dataset details and reuse information.
Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Open the record for dataset details and reuse information.
Data from: Selective pressures on MHC class II genes in the guppy (Poecilia reticulata) as inferred by hierarchical analysis of population structure.
Open the record for dataset details and reuse information.
Data from: Metacommunity structure of stream insects across three hierarchical spatial scales
Open the record for dataset details and reuse information.
Hierarchical genetic structure and implications for conservation of the world’s largest salmonid, Hucho taimen
Open the record for dataset details and reuse information.
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.