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48 results for “hierarchical structure”
Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
Simulated genetic data in a hierarchical metapopulation structure
<p>The data are linked to a research article entitled: “<em>Interactions between microenvironment, selection and genetic architecture drive multiscale adaptation in a simulation experiment” </em>in<em> Journal of Evolutionary Biology</em> (see References).</p> <p>In this research on multiscale adaptation, we simulated a hierarchical metapopulation structure with four populations, two environments per population and three patches per environment, in a two-step procedure:</p> <ul> <li>an initialization step without selection, with eight combinations of mutation type, selfing rate and QTL number parameters (2 modes each); out of 200,000 simulated generations in each case, we chose one with appropriate characteristics as a starting point for the next step;</li> <li>a selection step with all possible combinations of the following parameters: environmental pattern (4 modes), environmental range (5 modes), selection intensity (4 modes), fecundity (3 modes).</li> </ul> <p>This resulted in 240 scenarios for each initialized metapopulation, i.e. 1,920 scenarios in total. Each scenario was replicated 10 times, i.e. 19,200 simulation runs.</p> <p>The archive includes all data needed to reproduce the simulations and analyses, or to re-use the simulated metapopulations for other analyses. It has the following structure (further detailed below):</p> <ol> <li><strong>NemoScripts directory </strong>contains the <em>Nemo </em>input files used to perform simulations for the initialization step and the selection step;</li> <li><strong>RScripts directory </strong>contains the <em>R</em> scripts to read the <em>Nemo </em>output files, compute synthetic variables(*), and produce the figures as they appear in the publication and supplementary material (*: long computations, therefore we also directly provide those synthetic variables in the Data directory);</li> <li><strong>Data directory </strong>contains the <em>Nemo </em>output files, the synthetic variables, and other data needed to reproduce the figures; this directory can be used as a working directory for the <em>R</em> scripts (recommended).</li> </ol> <p>Running the following command in a terminal <strong><em>tar –xzvf Archive_PC_SOM_IS_FL.tar</em></strong> will create a directory named <strong><em>Archive_PC_SOM_IS_FL</em></strong>, which detailed content is described in the <strong><em>README.pdf</em></strong> file.<br> Warning: the extracted archive is large (460Go, >40,000 files) and extraction may take some time.</p>
Data for the publication: High performance of porous, hierarchically structured P2- Na0.6Al0.11 – xNi0.22 – yFex+yMn0.66O2 cathode materials
<p>Data sets: SEM-images, EIS, ex situ XRD, operando XRD, electrochemical cycling.</p> <p>Abstract: Sodium-ion-batteries (SIB) are a low-cost alternative to currently used lithium-ion batteries (LIB) but suffer from poor cycling stability. Spray drying provides porous, hierarchically structured particles of cathode active material (CAM) in large amounts, suitable for up-scaling. Changing the chemical composition of the Na0.6Al0.11–xNi0.22–yFex+yMn0.66O2 layered oxides under identical synthesis conditions leads to differences in particle morphology, conductivities, sodium vacancy ordering and phase transition, therefore influencing the electrochemical performance via several mechanisms. Here, a broad overview on these changes for samples with variable nickel and iron content is presented. With increasing iron content, the particle porosity is reduced and lower of initial capacity is received for most cycling windows. Substituting half of the original Ni amount with Fe still leads to high capacities and improved cycling stability. The influence of Al as electrochemical inactive element becomes visible in stabilised cycling stability as well.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 2. An example of smart power grid with hierarchical structure
<p> Figure 2 shows an example of smart power grid with hierarchical structure, where every segment equals a room or office. This approach is similar to the idea presented in Alboteanu et al. (2015), where the connecting / disconnecting of renewable energy sources and consumers are made via the appropriate contactors, automatically (or manually) controlled according to the energy consumption/generation. However, the management of micro smart grid is discussed in Alboteanu et al. (2015) only</p>
Experimental and Simulation Data for "Hierarchical structure formation by crystal growth-front instabilities during ice templating" (2023) PNAS
<pre>Experimental and Simulation Data for: "Hierarchical structure formation by crystal growth-front instabilities during ice templating" by Kaiyang Yin, Kaihua Ji, Louise Strutzenberg Littles, Rohit Trivedi, Alain Karma, Ulrike G.K. Wegst (2023) PNAS, DOI: 10.1073/pnas.2210242120. </pre>
The heritability of size in a wild annual plant population with hierarchical size structure
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Data from: Unraveling hierarchical genetic structure in a marine metapopulation: a comparison of three high-throughput genotyping approaches
<p>Marine metapopulations often exhibit subtle population structure that can be difficult to detect. Given recent advances in high-throughput sequencing, an emerging question is whether various genetic approaches, in concert with improved sampling designs, will substantially improve our understanding of genetic structure in the sea. To address this question, we explored hierarchical patterns of structure in the coral reef fish <i>Elacatinus lori</i> using a high-resolution approach with respect to both genetic and geographic sampling. Previously, we identified three putative <i>E. lori</i> populations within Belize using traditional genetic markers and sparse geographic sampling: barrier reef and Turneffe Atoll; Glover's Atoll; and Lighthouse Atoll. Here, we systematically sampled individuals at ~10 km intervals throughout these reefs (1,129 individuals from 35 sites) and sequenced all individuals at three sets of markers: 2,418 SNPs; 89 microsatellites; and 57 non-repetitive nuclear loci. At broad spatial scales, the markers were consistent with each other and with previous findings. At finer spatial scales, there was new evidence of genetic substructure, but our three marker sets differed slightly in their ability to detect these patterns. Specifically, we found subtle structure between the barrier reef and Turneffe Atoll, with SNPs resolving this pattern most effectively. We also documented isolation by distance within the barrier reef. Sensitivity analyses revealed that the number of loci (and alleles) had a strong effect on the detection of structure for all three marker sets, particularly at small spatial scales. Taken together, these results illustrate empirically that high-throughput genotyping data can elucidate subtle genetic structure at previously-undetected scales in a dispersive marine fish.</p>
Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
<p class="BodyText1">Population dynamics can be highly variable in the face of environmental heterogeneity, and understanding this variation is central in the study of ecology. Robust management decisions require that we understand how populations respond to management at a range of scales, and under a broad suite of conditions. Population models are potentially valuable tools in addressing this challenge. However, without adequate data, models can fail to produce useful results. Populations of arable weeds are particularly problematic in this respect, as they are widespread and their dynamics are extremely variable. Owing to the inherent cost of collecting data, most studies of weed population dynamics are derived from localized experiments under a small range of environmental conditions, limiting the extent to which variance in population dynamics can be measured. Density-structured models provide a route to rapid, large-scale analysis of population dynamics, and can expand the scale of ecological models that are directly tied to data. Here we extend previous density-structured models to include environmental heterogeneity, variation in management, and to account for inter-population variation. We develop, parameterize and test hierarchical density-structured models for a common agricultural weed, black-grass (<i>Alopecurus myosuroides</i>). We model the dynamics of this species in response to crop management, using survey data gathered over 4 years from 364 fields across a network of 45 UK farms. We show that hierarchical density-structured models provide a substantial improvement over their non-hierarchical counterparts. Using these models, we demonstrate that several alternative crop-rotations are effective in reducing weed densities. Rotations with high wheat prevalence exhibit the most severe infestations, and diverse rotations generally have lower weed densities. However, a key outcome is that in many cases the effect of crop rotation is small compared to the high variability arising from spatio-temporal heterogeneity. This result highlights the need to monitor and model population dynamics across large spatial and temporal scales in order to account for variation in the drivers of plant dynamics. Our framework for data collection and modelling provides a means to achieve this.</p>
Data and code for the article "Hierarchical tensile structures with ultralow mechanical dissipation"
<p>Data and code for the article "Hierarchical tensile structures with ultralow mechanical dissipation", consisting of all the ringdown measurements, spectra and codes for data analysis and generation of the fabrication GDS masks.</p>
Mielke & Carvalho 2022 Chimpanzee play sequences are structured hierarchically as games - Data
<p>Data and scripts for the 2022 manuscript 'Chimpanzee play sequences are structured hierarchically as games' - preprint here: </p> <p>https://doi.org/10.1101/2022.06.14.496075</p> <p>Dataset and scripts generated on 20/09/2022. For potential changes and all information see:</p> <p>https://github.com/AlexMielke1988/Mielke-Carvalho_Chimpanzee-Play</p>
Research data supporting "Rolling Circle Transcription-Amplified Hierarchically Structured Organic-Inorganic Hybrid RNA Flowers for Enzyme Immobilization""
<p>Raw research data supporting the publication:</p> <p>Wang Y. et al., 2019, ACS Applied Materials and Interfaces, DOI: 10.1021/acsami.9b04663</p>
Data and Code For : "The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."
<p>The repository contains the principal 4D-STEM datasets and code used in the paper:</p> <p>"The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."</p> <p> </p> <p>* The measured and analyzed material is p(g3T2).</p> <p>* The code can be adjusted and used for the analysis of other conjugated polymers.</p> <p>* It should be noted that newer py4DSTEM versions with additional capabilities were released since the paper was </p> <p>submitted. </p> <p> </p> <p><strong>Contents:</strong></p> <p><strong>1. 4D-STEM_DATA_OMIECs.zip : </strong></p> <p><strong>4D-STEM data : </strong></p> <ul> <li>Dry_CL_2p1.dm4. </li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li> Calibrant_Dry_CL_2p1.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 15 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 25, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 46, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 15, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 80, E(extraction voltage): 300 kV, temprature: LN.</p> <p> </p> <p><strong>2. Notebooks.zip : </strong></p> <p><strong>Jupyter Lab Notebooks : </strong></p> <ul> <li>1A_pg3T2_dry_LN_CL2p1.ipynb. </li> </ul> <p>Analysis of dry film using CL 2.1.</p> <p>Goes with datasets: Dry_CL_2p1.dm4 and Calibrant_Dry_CL_2p1.dm4. </p> <ul> <li>1A_pg3T2_dry_LN_CL2p7.ipynb</li> </ul> <p>Analysis of dry film using CL 2.7.</p> <p>Goes with datasets: Dry_CL_2p7.dm4 and Calibrant_Dry_CL_2p7.dm4. </p> <ul> <li>1B_pg3T2_water_LN_CL2p7.ipynb</li> </ul> <p>Analysis of hydrated in water film using CL 2.7.</p> <p>Goes with datasets: Hydrated_Water_CL_2p7.dm4 and Calibrant_Hydrated_Water_CL_2p7.dm4. </p> <ul> <li>1C_pg3T2_NaCl_LN_CL2p7. </li> </ul> <p>Analysis of hydrated in 0.1 M NaCl(aq) film using CL 2.7.</p> <p>Goes with datasets: Hydrated_NaCl_CL_2p7.dm4 and Calibrant_Hydrated_NaCl_CL_2p7.dm4.</p> <ul> <li>aux_func.py: </li> </ul> <p>Contains auxilary functions and required for running the other notebooks.</p>
STL files: Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing
<p>STL files for paper titled modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing</p>
Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
Open the record for dataset details and reuse information.
Data from: Unraveling hierarchical genetic structure in a marine metapopulation: a comparison of three high-throughput genotyping approaches
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A multiscale optimization framework for bone remodeling: Integrating material and structural adaptations across hierarchical scales
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Recipient and donor characteristics govern the hierarchical structure of heterospecific pollen competition networks
<ul> <li>Pollinator sharing can have negative consequences for plant fitness with the arrival of foreign (i.e. heterospecific) pollen, yet responses are often variable among species. Plant traits and relatedness of donor and recipient species have been suggested to drive the variations in plant fitness, but how they shape the structure of pollen competition networks has been overlooked at the community level.</li> <li>To understand the importance of reproductive traits and relatedness on the impacts of heterospecific pollen we conducted a controlled glasshouse experiment with an artificial co-flowering community. We performed 1800 reciprocal crosses by experimentally transferring 50% and 100% foreign pollen among 10 species belonging to three different plant families.</li> <li>We found a significant reduction in seed set with 50% foreign pollen for 67% of the crosses driven largely by recipient traits and the interaction between recipient-donor traits under specific circumstances of trait-matching. In general, species with shorter styles, smaller stigmas and lower pollen:ovule ratios were more impacted by foreign pollen. These traits and their differences among species led to a hierarchical (or transitive) structure of pollen competition with clear winners and losers. However, phylogenetic distance among recipient and donor species did not explain the effects.</li> <li> <i>Synthesis</i>: Our study shows that specific traits and trait combinations between donor and recipient species are important in determining fitness outcomes with heterospecific pollen deposition. Moreover, the differences in traits between species lead to a competitive structure with clear "winners" or "losers" species. The results of this study indicate the need to shift from pairwise to community level interactions to elucidate the mechanisms underlying foreign pollen impacts upon plant reproductive fitness.</li> </ul>
Data from: Hierarchical structure of ecological and non-ecological processes of differentiation shaped ongoing gastropod radiation in the Malawi Basin
Ecological processes, non-ecological processes or a combination of both may cause reproductive isolation and speciation, but their specific roles and potentially complex interactions in evolutionary radiations remain poorly understood, which defines a central knowledge gap at the interface of microevolution and macroevolution. Here I examine genome scans in combination with phenotypic and environmental data to disentangle how ecological and non-ecological processes contributed to population differentiation and speciation in an ongoing radiation of Lanistes gastropods from the Malawi Basin. I found a remarkable hierarchical structure of differentiation mechanisms in space and time: neutral and mutation-order processes are older and occur mainly between regions, whereas more recent adaptive processes are the main driver of genetic differentiation and reproductive isolation within regions. The strongest differentiation occurs between habitats and between regions, i.e. when ecological and non-ecological processes act synergistically. The structured occurrence of these processes based on the specific geographic setting and ecological opportunities strongly influenced the potential for evolutionary radiation. The results highlight the importance of interactions between various mechanisms of differentiation in evolutionary radiations, and suggest that non-ecological processes are important in adaptive radiations, including those of cichlids. Insight into such interactions is critical to understanding large-scale patterns of organismal diversity.
Data from: Hierarchical analysis of genetic structure in the habitat-specialist Eastern Sand Darter (Ammocrypta pellucida)
Quantifying spatial genetic structure can reveal the relative influences of contemporary and historic factors underlying localized and regional patterns of genetic diversity and gene flow – important considerations for the development of effective conservation efforts. Using 10 polymorphic microsatellite loci, we characterize genetic variation among populations across the range of the Eastern Sand Darter (Ammocrypta pellucida), a small riverine percid that is highly dependent on sandy substrate microhabitats. We tested for fine scale, regional, and historic patterns of genetic structure. As expected, significant differentiation was detected among rivers within drainages and among drainages. At finer scales, an unexpected lack of within-river genetic structure among fragmented sandy microhabitats suggests that stratified dispersal resulting from unstable sand bar habitat degradation (natural and anthropogenic) may preclude substantial genetic differentiation within rivers. Among-drainage genetic structure indicates that postglacial (14 kya) drainage connectivity continues to influence contemporary genetic structure among Eastern Sand Darter populations in southern Ontario. These results provide an unexpected contrast to other benthic riverine fish in the Great Lakes drainage and suggest that habitat-specific fishes, such as the Eastern Sand Darter, can evolve dispersal strategies that overcome fragmented and temporally unstable habitats.
Wing with Root Holes - Hierarchical, random and bifurcation tiling with heterogeneity in micro-structures construction via functional composition.
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific wing is a functional composition of trivariate spline tiles inside a macro trivariate shape of a wing. The root tiles have through vertical holes in them.</p> <p>Model is provided in STL format.</p>
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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.