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3,655 results for “Structural data”
Crystal Structure and Energetics of Arsenic(III)-oxide Intercalates with Rubidium Chloride and Their Comparison with Isostructural Intercalates of Potassium Halides. Raw diffraction data
<p>Raw diffraction data for:</p> <ul> <li>intercalate <strong>P<sub>RbCl</sub></strong> - pg152</li> <li>intercalate <strong>Y'<sub>RbCl</sub></strong> <ul> <li>measurement of a small crystal - pg149</li> <li>measurement of a large crystal - pg153</li> </ul> </li> </ul> <p>CSD 2073008-2073009.</p> <p>Crystal structures published in a Crystal Growth & Design article DOI: 10.1021/acs.cgd.1c01220</p>
Data from: Grazing and nitrogen addition restructure the spatial heterogeneity of soil microbial community structure and enzymatic activities
<p>1. In grassland ecosystems, large herbivorous animal grazing activity and increasing nitrogen deposition strongly alters microbial community structure and function. Understanding the effects of grazing and nitrogen addition on the spatial heterogeneity in soil microbial community structure, enzymatic activities and the underlying mechanisms are crucial for making better predictions of soil organic matter dynamics and nutrient cycling. </p> <p>2. We examined the spatial heterogeneity of soil microbial community structure and enzymatic activity associated with changes in soil microclimate, soil characteristics, plant biomass and soil nutrient responses to grazing and nitrogen addition using a manipulative experiment with control (CK), grazing (G), nitrogen addition (N) and grazing plus nitrogen addition (NG) treatments in a <i>Leymus chinensis </i>meadow steppe, in northeastern China. </p> <p>3. The results demonstrated that soil microbial community structure and enzymatic activities showed a high level of spatial dependence [C/(C + C0)≥0.9] in the CK plot. G, N and NG treatments not only reduced the spatial variability ofsoil microbial community structure and enzymatic activities, but also reshaped the spatial links between enzymes activities and microbial community structure. Litter biomass, soil temperature and soil nutrients (soil dissolved inorganic nitrogen or soil dissolved organic carbon) explained 21-27% of the spatial variability of soil microbial community structure in the CK treatment and pH was the strongest driver for the spatial variability of soil enzymatic activities. Meanwhile, the homogenization in soil water content induced by the N addition treatment was a determinant of the reduction in spatial heterogeneity of the microbial community structure. The combination of soil physicochemical properties (bulk density, soil pH and soil dissolved inorganic nitrogen), soil temperature and root biomass explained 32-43% of the spatial variability of the microbial community structure in the G treatment, and N and G treatments had additive effects on the spatial heterogeneity of total PLFAs by homogenizing root biomass. Plant biomass and microbial community structure were the major drivers for the spatial heterogeneity of enzymatic activities under G, N and NG. In NG, the change in spatial variability of enzymatic activities was dominated by N addition. Regardless of grazing, N addition facilitated the spatial correlation between microbial community structure and enzyme activities. </p> <p>4. Overall, our results revealed the drivers of soil microbial community structure and enzymatic activities spatial pattern shift due to grazing and N addition, highlighting the role that spatial variability in soil microbial community structure and enzymatic activities has on the <i>L. chinensis</i> meadow steppe.</p>
Raw data repository for the article: Influence of Contacts and Applied Voltage on a Structure of a Single GaN Nanowire
<p>The archive NWrawdata.zip contains information about the raw data collected at P10 beamline at PETRA III during this experiment, which is shown in Figs. 4, 5, and 6 of the main text.</p>
Data for: Perceived risk structures the space use of competing carnivores
<p>Competition structures ecological communities. In carnivorans, competitive interactions are disproportionately costly to subordinate carnivores who must account for risk of interspecific killing when foraging. Accordingly, missed opportunity costs for meso-carnivores imposed by risk can benefit the smallest-bodied competitors. However, the extent to which the risk perpetuates into spatial partitioning in hierarchically structured communities remains unknown. To determine how risk-avoidance behaviors shape the space-use of carnivore communities, we studied a simple community of carnivores in northern Patagonia, Argentina: pumas (<i>Puma concolor</i>; an apex carnivore), culpeo foxes (<i>Lycalopex culpaeus</i>; a meso-carnivore), and chilla foxes (<i>Lycalopex griseus</i>; a small carnivore). We used multi-species occupancy models to quantify the space use within the carnivore community and giving-up densities to understand the behaviors that structure space use. Notably, we applied an analytical framework that tests whether actual or perceived risk of predation most strongly influences the space use of subordinate carnivores while accounting for their foraging and vigilance behaviors. We found that there was a dominance hierarchy from the apex carnivore through the meso-carnivore to the subordinate small carnivore, which was reflected in space. Although both meso- and small carnivores exhibited similar predator avoidance behavioral responses to apex carnivores, the habitat associations of apex carnivores only altered meso-carnivore space use. The biases in risk management we observed for meso-carnivores likely translates into stable co-existence of this community of competing carnivores. We believe our analytical framework can be extended to other communities to quantify the spatial-behavioral tradeoffs of risk.</p>
Data from: Elements of metacommunity structure of diatoms and macroinvertebrates within stream networks differing in environmental heterogeneity
<p><strong>Aim:</strong> Idealized metacommunity structures (i.e. checkerboard, random, quasi-structures, nested, Clementsian, Gleasonian, and evenly spaced) have recently gained increasing attention, but their relationships with environmental heterogeneity and how they vary with organism groups remain poorly understood. Here we tested two main hypotheses: (1) gradient-driven patterns (Clementsian and Gleasonian) occur frequently in heterogeneous environments, and (2) small organisms (here, diatoms) are more likely to exhibit gradient-driven patterns than large organisms (here, macroinvertebrates).</p> <p><strong>Location:</strong> Streams in three regions in China.</p> <p><strong>Taxon:</strong> Diatoms and macroinvertebrates.</p> <p><strong>Methods:</strong> The stream diatom and macroinvertebrate data, as well as the environmental data collected from the same set of sites were used to examine the idealized metacommunity structures via the elements of the metacommunity structure (EMS; coherence, turnover, and boundary clumping) analysis in three regions. We extended the traditional EMS approach by ordering sites along known environmental gradients.</p> <p><strong>Results: </strong>We found that Clementsian structure with high degrees of coherence and turnover, and significantly positive clumping was typically observed in the high-heterogeneity regions, whereas randomness was prevalent in the low-heterogeneity region. Macroinvertebrates exhibited clearer Clementsian structures compared with diatoms, while diatoms showed more randomness compared with macroinvertebrates, indicating a stronger role of environmental filtering for macroinvertebrates than diatoms. In most cases, the results of the more novel EMS approach differed from the results of the traditional EMS technique.</p> <p><strong>Main Conclusions:</strong> Our results suggested that the occurrence of different metacommunity structures may be related with the degree of regional environmental heterogeneity. However, diatom metacommunities were more random than those of macroinvertebrate, and such an unexpected result may result from different dispersal abilities between the two organism groups. In addition, we found that the novel EMS approach increased power in discerning metacommunity structure in comparison to the traditional EMS technique.</p>
Rotating shallow water flow under location uncertainty with a structure-preserving discretization -- Data set
<p>With this data set one can reproduce the figures in the manuscript "Rotating shallow water flow under location uncertainty with a structure-preserving discretization".</p> <p>The figures can be created with the following MATLAB scripts:</p> <p>Figure 3 - plot_contour_plane.m</p> <p>Figure 4 and 5 - plot_Energy_convergence.m</p> <p>Figure 6 - plot_sphere_snapshot.m</p> <p>Figure 7 - plot_contour_sphere.m</p> <p>Figure 8 - plot_specs.m</p> <p>Figure 9 - plot_spread.m</p> <p>Figure 10 - createhist.m</p> <p>Figure 11 - plot_MSB_MEV.m</p> <p> </p> <p> </p>
Figure 3 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 3 - An updated database with final column titles and unique identifier added for each record.
Figure 5 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 5 - Simplified network of Bactrocera carambolae and Bactrocera dorsalis groups, and the sequential disconnection of the network. The network was constructed using eight SSRs. Scanning was done for decreasing thresholds A is the fully connected network B is the percolation threshold (Dp = 0.20, with all links corresponding to distances superior to Dp excluded). DP, JK, and NT are connecting between Bactrocera carambolae and Bactrocera dorsalis groups. Red dashed lines with number are corresponded to the threshold values, revealing serial disconnection of the network C is the lowest threshold (thr = 0.15).
Figure 4 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 4 - Simplified network of seven Bactrocera carambolae populations, and the sequential forms of cluster. The network was constructed using eight SSRs. Scanning was done for decreasing thresholds A is the fully connected network B is the percolation threshold (Dp = 0.52, with all links corresponding to distances superior to Dp excluded). JK plays an important role connecting between native and introduced populations C–D are the lower thresholds chosen (thr = 0.40 and 0.15, respectively) to reveal sub-structured network.
Figure 3 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 3 - The individual admixture plot for K = 3. Each bar reveals a single individual. Each color of bars represents each genetic cluster. Samples of Bactrocera carambolae belong to clusters 2 and 3 (green and blue, respectively) while samples of Bactrocera dorsalis belong to cluster 1 (red). Potential hybrids have a proportion of genetic cluster (Q) between 0.100 to 0.900 (0.100 ≤ Q ≤ 0.900) as identified with asterisk (*).
Figure 1 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 1 - Sampling collections of Bactrocera carambolae and Bactrocera dorsalis in this study. Seven populations of Bactrocera carambolae (blue dots) were collected from Southeast Asia and Suriname. Three populations of Bactrocera dorsalis (red dots) were sampled from East and Southeast Asia. Two other unidentified populations (purple dots) were included. Information for each population is described in Table 1.
Figure 6 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 6 - Simplified network of the SY5 strain and wild populations, and the sequential disconnection of the network. The network was constructed using seven SSRs. Scanning was done for decreasing thresholds A is the fully connected network B is the percolation threshold (Dp = 0.23, with all links corresponding to distances superior to Dp excluded). DP, JK, and NT are connecting between Bactrocera carambolae and Bactrocera dorsalis groups C is the lowest threshold (thr = 0.15). Red dashed lines with number are corresponded to the threshold values, revealing serial disconnection of the network.
Figure 2 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Figure 2 - Three-dimensional plot of Principal Coordinate Analysis (PCoA) and STRUCTURE analysis. A the planes of the first three principal coordinates explain 43.65%, 20.13%, and 16.91% of total genetic variation, respectively, for seven Bactrocera carambolae populations using eight SSRs B the planes of the first three principal coordinates explain 33.05%, 23.17%, and 15.87%, respectively, for Bactrocera carambolae and Bactrocera dorsalis groups using eight SSRs C the planes of the first three principal coordinates explain 30.50%, 22.14%, and 18.53%, respectively, for the SY5 strain and wild populations using seven SSRs. Pie graphs, consisting of different colored sections, represent co-ancestor distribution of 185, 289, and 321 individuals in A two, B three, and C two hypothetical clusters, respectively.
Supporting data and files for PNAS paper entitled Ice sheet contributions to future sea level rise from structured expert judgement.
<p>This repository contains supporting documentation with background information about the structured expert judgement described in the paper entitled: Ice sheet contributions to future sea level rise from structured expert judgement.. It also includes the files containing the anonymous expert judgements for the target questions elicited.</p>
Open data for assessing habitats degree of conservation at plot level. An example dataset of forest structural attributes in Val d'Agri (Basilicata, Southern Italy)
<p>We provide a georeferenced dataset of vertical and horizontal structure of forest types belonging to 4 habitat types, sensu Council Directive 92/43/EEC. The dataset includes structural indicators commonly linked to old-growth forests in Europe, in particular the amount of standing and lying deadwood. We collected data on 32 plots (24 of 225 m<sup>2</sup>, and 8 of 100 m<sup>2</sup>, according to different forests type) during spring and summer of 2022, in Val d’Agri (Basilicata, Southern Italy). The dataset we provide follows the common national standard for field data collection in forest habitat types, published by ISPRA in 2016 (https://www.isprambiente.gov.it/public_files/direttiva-habitat/Manuale-142-2016.pdf) with the aim to promote a greater homogeneity in assessment of habitat conservation status at Country and biogeographical level, as requested by the Habitats Directive.</p> <p>Data were acquired by using a free Android application (VegApp, (https://vegapp.de/), and standardized field survey forms.</p> <p>The selection of the location of the sample sites were spatially balanced by using the sofware QGis vers. 3.22 (http://qgis.osgeo.org)</p> <p>plots_Table1 shows the characteristics of the sample sites, and information on type of management, when available.</p> <p>plots_Table2 provides the description and the measuring unit of all the attributes included in the dataset at plot level. It includes parameters used for assessing old-growth forests, linking them to the main type forests management, in order to both select indicators useful for assessing the conservation degree of forest habitat types, and identify good practices for nature conservation in these ecosystems.</p> <p>plots_Table3 contains information on living trees and deadwood at plot level.</p> <p>Trees describes the dataset related to single-tree data, and it contains the following fields for all living trees, exceeding 10 cm DBH, occurring in the plots. The nomenclature follows the Italian checklist available at https://dryades.units.it/floritaly/</p> <p>The shapefile "plots" contains the same information included in plots_Table1.</p>
"Onset of post-cratering melting of target rocks at the impact melt contact: observations from the Vredefort impact structure, South Africa" µCT data
<p>µCT Dataset. </p>
Gravity data of the Vargeão impact structure
<p>This is a gravity dataset that was collected during one field campaign in the Vargeão impact structure, southern Brazil. Vargeão is a complex impact structure that has an overall diameter of approximately 12.4 km. The available file provides Free-air and Bouguer anomalies.</p>
MD data for "Structural Basis of Efficacy-Driven Ligand Selectivity at GPCRs"
<p>Molecular dynamics (MD) data for "Structural Basis of Efficacy-Driven Ligand Selectivity at GPCRs" published Nature Chemical Biology 2023. See the included readme.txt for more details. Please cite the paper if you use these data.</p>
VCF of structural variant calls of Nanopore data aligned to dm6 reference genome
<p>Heterozygous chromosome inversions suppress meiotic crossover (CO) formation within an inversion, potentially because they lead to gross chromosome rearrangements that produce inviable gametes. Interestingly, COs are also severely reduced in regions nearby but outside of inversion breakpoints even though COs in these regions do not result in rearrangements. Our mechanistic understanding of why COs are suppressed outside of inversion breakpoints is limited by a lack of data on the frequency of noncrossover gene conversions (NCOGCs) in these regions. To address this critical gap, we mapped the location and frequency of rare CO and NCOGC events that occurred outside of the <em>dl</em>-<em>49</em> <em>chrX</em> inversion in <em>D</em>. <em>melanogaster</em>. We created full-sibling wildtype and inversion stocks and recovered COs and NCOGCs in the syntenic regions of both stocks, allowing us to directly compare rates and distributions of recombination events. We show that COs are completely suppressed within 500 kb of inversion breakpoints, are severely reduced within 2 Mb of an inversion breakpoint, and increase above wildtype levels 2–4 Mb from the breakpoint. We find that NCOGCs occur evenly throughout the chromosome and, importantly, occur at wild-type levels near inversion breakpoints. We propose a model in which COs are suppressed by inversion breakpoints in a distance-dependent manner through mechanisms that influence DNA double-strand break repair outcome but not double-strand break location or frequency. We suggest that subtle changes in the synaptonemal complex and chromosome pairing might lead to unstable interhomolog interactions during recombination that permits NCOGC formation but not CO formation.</p>
Data set and analytic codes supporting "The impacts of within-stream physical structure and riparian buffer strips on semi-aquatic bugs in Southeast Asian oil palm"
<p>This deposit contains data set and analytic codes (accompanied with a meta data) supporting "The impacts of within-stream physical structure and riparian buffer strips on semi-aquatic bugs in Southeast Asian oil palm". We assessed the impacts of within-stream physical structure and riparian buffer strips on semi-aquatic bug (Gerromorpha, Hemiptera) communities in oil palm streams in Sabah, Malaysia. Collections of semi-aquatic bugs were conducted from oil palm with and without riparian buffer strips.</p> <p>Several environmental parameters were collected to represent within-stream physical structure. We investigated the impacts on the abundance, biomass, species richness, and community composition of semi-aquatic bugs. Additionally, we studied the effects on the proportion of juveniles as well as female <em>Ptilomera</em> sp. (a morphospecies with clear sexual dimorphism in this study).</p> <p>This research was funded by the Jardine Foundation, the Cambridge Trust, the Natural Environment Research Council (NERC) (studentship 1122589), Proforest, the Varley Gradwell Travelling Fellowship, the Tim Whitmore Fund, the Panton Trust, the Cambridge University Commonwealth Fund, the Hanne and Torkel Weis-Fogh Fund, and the S.T. Lee Fund.</p>
ScienceDex guides
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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.