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852 results for “grain”
Data for: Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy
<p><span>Understanding how species respond to human activities is paramount to ecology and conservation science, one outstanding question being how large-scale patterns in land use affect biodiversity. To facilitate answering this question, we propose a novel analytical framework that combines Environmental Niche Models, multi-grain analyses, and species traits. We illustrate the framework capitalizing on the most extensive dataset compiled to date for the butterflies of Italy (106,514 observations for 288 species), assessing how agriculture and urbanization have affected biodiversity of these taxa from landscape to regional scales (3–48 km grains) across the country while accounting for its steep climatic gradients.</span></p> <p><span>Multiple lines of evidence suggest pervasive and scale-dependent effects of land use on butterflies in Italy. While land use explained patterns in species richness primarily at grains ≤ 12 km, idiosyncratic responses in species highlighted "winners" and "losers" across human-dominated regions. Detrimental effects of agriculture and urbanization emerged from landscape (3-km grain) to regional (48-km grain) scales, disproportionally affecting small butterflies and butterflies with a short flight curve. Human activities have therefore reorganized the biogeography of Italian butterflies, filtering out species with poor dispersal capacity and narrow niche breadth not only from local assemblages but also from regional species pools. </span></p> <p><span>These results suggest that global conservation efforts neglecting large-scale patterns in land use risk falling short of their goals, even for taxa typically assumed to persist in small natural areas (e.g., invertebrates). Our study also confirms that consideration of spatial scales will be crucial to implementing effective conservation actions in the Post-2020 Global Biodiversity Framework. In this context, applications of the proposed analytical framework have broad potential to identify which mechanisms underlie biodiversity change at different spatial scales. </span></p> <p><span><em>Funding statement: </em>FR is supported by the PROBAE project "Protect butterflies across Europe through climate refugia" funded by the European Commission through Horizon 2020, Marie Skłodowska-Curie Actions (MSCA) individual fellowship, reintegration panel (Grant agreement ID: 101024579). Open Access Funding provided by Universita degli Studi di Torino within the CRUI-CARE Agreement.</span></p>
chen-echo/FGNER-corpus: Geological fine-grained corpus
<ul> <li>A corpus for the identification of CHINESE geologically named entities based on three-part geological reports.</li> <li>Contains twenty-one labels, the corresponding entities for the labels are listed in the below.</li> <li>ROC.SEDI 沉积岩 ROC.META 变质岩 ROC.IG 岩浆岩</li> <li>SMG.ROC 岩性地层 SMG.chrono 年代地层</li> <li>MIN.native 自然元素矿物 MIN.sulASIM 硫化物及其类似化合物 MIN.halide 卤化物矿物 MIN.oxihydro 氧化物及其氢氧化物矿物 MIN.oxis 含氧盐矿物 MIN.ROC 岩石类非金属矿物</li> <li>GCH.AR 太古宙 GCH.PT 元古宙 CGH.PH 显生宙</li> <li>GST.fold 褶皱 GST.fault 断裂 GST.joint 节理 GST.contact 接触关系</li> <li>GAC.ENDO 内力地质作用 GAC.EXO 外力地质作用</li> <li>OT 其他</li> </ul>
Dataset belonging to the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper"
<p>This repository contains the raw data of the experimental (S)TEM imaging and the data corresponding to the simulations and theoretical calculations of the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper",. A pre-print version of the paper is available on arXiv: <a href="http://doi.org/10.48550/arXiv.2212.01180">http://doi.org/10.48550/arXiv.2212.01180</a></p> <p>See the file README.md for a detailed description.</p>
Polarization dataset - Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies
<p>This data are relative to the polarization measurements of the paper </p> <p>"Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies"</p> <p>The dataset consists in 8 .txt files that represent the polarization measurements of mixtures of FeS and Olivine (ol) with different mass ratios. In the name of each file it is specified the mass percentage of the two components respect with the total mass of the sample (eg. data_ol_FeS_10-90_530.txt is the sample composed by 10% olivine and 90% FeS, measured at 530 nm). </p> <p>In each file, the data are organized in the following columns: </p> <p>#phase_angles[°] #Q/I #U/I #V/I #DOLP #DC #delta_Q/I #delta_U/I #delta_V/I #delta_DOLP #delta_dc</p> <p>"delta" refers to the standard deviation of the measurement upon rotation of the sample on the azimuthal axis. <br> </p>
Spatiotemporal dissolution of calcite grain column packs
<p>The binarized reference data from time-lapse high-resolution X-ray CT imaging of live flow experiments and effluent pH and TDS logs are stored here. Data post-processing and analysis were conducted using a commercial software Avizo - details if which are described in the methods and results sections of the companion manuscript.</p>
Rice Grain Development Database
<p>Being the staple food crop across the world, rice (<em>Oryza</em>) is commercially important. The grain/seed is the edible part. Owing to a steeply increasing population, and the limitations imposed by it, rice yield needs to be increased, and grain size is the best target. Rice also provides an array of nutrients, including carbohydrates, proteins, calcium, iron, folic acid and few B vitamins. This makes it an ideal crop to help alleviate malnutrition. Rice can be nutritionally enhanced and its size increased only when the process of its grain development has been elucidated in detail at the molecular level. Depending on the physiological and morphological changes occurring in the seed, over the period of its development, it has been divided into five stages. S1 (0-2 DAP, Days After Pollination), S2 (3-4 DAP), S3 (5-10 DAP), S4 (11-20 DAP) and S5 (21-29 DAP).</p> <p>In order to understand the genes/pathways regulating rice grain size, RNAseq has been performed on the above five stages and flag leaf (as a vegetative control, to remove genotypic effects), in two rice genotypes with contrasting grain size. SN (Sonasal) is small grained while LGR (Large grain rice) is as the name suggests, with a very large grain. The transcriptome data can be searched with the RGAP locus ID to obtain the expression graph (in terms of FPKM values) or differential expression data (in terms of fold change values) for all stages. For differentially expressed genes, only significant values will be displayed. Bulk locus IDs can be downloaded with transcription factor (TF), phytohormone, transposable element (TE), and putative function search. Seed-specific genes can also be searched for.</p> <p>miRNAs have emerged as important regulatory players in any process. In order to understand their role, miRNOme data of both SN and LGR from all the tissues mentioned above, can also be searched for. By using the miRNA ID, the expression data (in terms of TPM) or differential expression data (in terms of fold changes) can be downloaded. For differentially expressed miRNAs, only significant values will be displayed.</p>
Data for: Genetic control of grain amino acid composition in a UK soft wheat mapping population
<p>Wheat is a major source of nutrients for populations across the globe, but the amino acid composition of wheat grain does not provide optimal nutrition. The nutritional value of wheat grain is limited by low concentrations of lysine (the most limiting essential amino acid) and high concentrations of free asparagine (precursor to the processing contaminant acrylamide). There are currently few available solutions for asparagine reduction and lysine biofortification through breeding. In this study, we investigated the genetic architecture controlling grain-free amino acid composition and its relationship to other traits in a Robigus × Claire doubled haploid population. Multivariate analysis of amino acids and other traits showed that the two groups are largely independent of one another, with the largest effect on amino acids being from the environment. Linkage analysis of the population allowed the identification of QTL controlling free amino acids and other traits, and this was compared against genomic prediction methods. Following the identification of a QTL controlling free lysine content, wheat pangenome resources facilitated analysis of candidate genes in this region of the genome. These findings can be used to select appropriate strategies for lysine biofortification and free asparagine reduction in wheat breeding programmes.</p>
Short communication: Synchrotron-based elemental mapping of single grains to investigate variable infrared-radiofluorescence emissions [Data set]
<p>This dataset accompanies a research paper in the journal Geochronology (<span><a href="https://doi.org/10.5194/gchron-6-77-2024"><span>https://doi.org/10.5194/gchron-6-77-2024</span></a></span>). It contains the raw output of micro-XRF measurements carried out at the 5-ID SRX beamline at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory, USA, on coarse K-feldspar grains. Additionally, the XRF intensity attributed to each element after spectral fitting is provided for elemental mapping. The dataset also includes the output from scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) measurements on coarse K-feldspar grains of two samples taken at Archéosciences Bordeaux, France.</p>
Figure 2 in Multiple resistance to primary pests of grain sorghum hybrids: Spodoptera frugiperda (Lepidoptera: Noctuidae), Diatraea saccharalis (Lepidoptera: Crambidae), and Diceraeus melacanthus (Hemiptera: Pentatomidae)
Figure 2 Dendrogram of cluster analysis based on the Euclidean distance and grouping by UPGMA regarding scores of damage by Spodoptera frugiperda larvae on grain sorghum hybrids at 7 and 14 days after infestation.
Figure 5 in Multiple resistance to primary pests of grain sorghum hybrids: Spodoptera frugiperda (Lepidoptera: Noctuidae), Diatraea saccharalis (Lepidoptera: Crambidae), and Diceraeus melacanthus (Hemiptera: Pentatomidae)
Figure 5 Scores of injury on sorghum plants caused by S. frugiperda (a); D. saccharalis (b); and D. melacanthus (c).
Figure 1 in Multiple resistance to primary pests of grain sorghum hybrids: Spodoptera frugiperda (Lepidoptera: Noctuidae), Diatraea saccharalis (Lepidoptera: Crambidae), and Diceraeus melacanthus (Hemiptera: Pentatomidae)
Figure 1 Green-belly stink bug injury based on the damage rating scale adapted by Roza-Gomes et al. (2011) (0-4) to maize injury.
Sugar signal manipulation by T6P for yield on wheat grain - whole grain RNA-seq
<p>Trehalose 6-phosphate (T6P) is a powerful internal sugar signal in plants yet cannot be directly added nor<br>fully genetically controlled. A timed microdose of a plant-permeable T6P signalling precursor, DMNB-T6P, causes substantial yield improvements. This repository contains normalised counts table for a RNA-seq of wheat (Triticum aestivum) whole grains after spray with mock/DMNB-T6P.</p> <p>DESeq2 normalised count table for raw reads deposited in ncbi SRA under Bioproject PRJNA1007614. Related manuscript in press in Nature Biotechnology.</p> <p> </p> <p>Methods extract below:</p> <p>(...) Following RNA integrity analysis and quantitation (Bioanalyser; Agilent, USA), poly-A enriched cDNA libraries were generated and sequenced on an Illumina Novaseq 6000 sequencing platform generating 30–50 million 150 bp paired-end reads per sample. Low-quality reads and adaptor sequences were removed with Trimmomatic (trimmomatic-0.39.jar PE ILLUMINACLIP:TruSeq3-PE.fa:2:30:10:2:True TRAILING:30 MINLEN:40). The reads were aligned to the wheat reference genome (<em>Triticum aestivum</em> iwgsc_refseqv2.1 using HISAT2/2.2.1-foss-2019b with default parameters and converted to Bam format with SAMtools. Gene abundance was quantified using featureCounts with the High Confidence iwgsc_refseqv2.1 annotation (counting only primary alignments of read pairs with a quality cut-off of 10). Raw counts were normalized using the trimmed mean of M-values method by DESeq2.</p>
Optical constants of CO and CO2 in the IR ans THz ranges and code to calculate the opacity of icy interstellar grains
<p>Data set of optical constants of CO and CO2 in the IR and THz ranges (0.3–12.0 THz). These data are used to compute the opacity of coated materials in an astrophysical context in our accepted article for publication in A&A, 2022.</p> <p>The following link provides access to the "qabs" open access code and its source to calculate the opacity using the optical constants. The code computes Qabs, Qsca, and Qext from the refractive index or the dielectric constant using Mie's theory:</p> <p>https://bitbucket.org/tgrassi/compute_qabs/src/master/</p> <p>The link redirects you to an open-access environment where to execute the code properly.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages:<br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
AESOPUS 2.0: Low-Temperature Opacities with Solid Grains
<p>In this repository we include the optical constants of all dust species listed in Table 1, as well as Rosseland mean opacity tables for widely used scaled-solar chemical compositions (GS98, C11, AAG21, MBS22) with metallicities encompassing the range 0 <= Z <= 0.3, and several hydrogen abundance values.</p> <p>Additional opacity tables with different grain sizes, porosities, and shapes can be computed and uploaded in the same repository upon user request.</p> <p>All of the details can be found in a companion paper published in The Astrophysical Journal.</p>
Data for: Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy
Open the record for dataset details and reuse information.
Data for: Genetic control of grain amino acid composition in a UK soft wheat mapping population
Open the record for dataset details and reuse information.
Fine-grain predictions are key to accurately represent continental-scale biodiversity patterns
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Range expansion can promote the evolution of plastic generalism in coarse-grained landscapes
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Influence of sand supply and grain size on upper regime bedforms
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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.