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2,960 results for “elements”
Elemental Contrast in Secondary Electron Energy Spectrum
<p>The following data is collected from a secondary electron energy spectrometer. These data are a part of the project which intended to investigate the secondary electron energy spectrum of metal and semiconductor materials </p>
Dataset and code for A Multidimensional Error Verification Method for Weather Forecast Based on the Spatial Distribution Structural Similarity of Meteorological Elements
<p>The dataset includes rainfall observations and mesoscale numerical prediction data for 16 July 2019, 13 June 2020 and 2024 rainy season.<br>The code is the algorithm procedure of this paper. Among them, meiyutest.py is the rainfall scoring procedure for the Meiyu period, and 2019case is the forecast scoring and plotting procedure for two individual cases.</p>
The Data For Spatial Variations of Stellar Elemental Abundances in FIRE Simulations of Milky Way-Mass Galaxies: Patterns Today Mostly Reflect Those at Formation
<p>Spatial patterns of stellar elemental abundances encode rich information about a galaxy’s formation<br>history. We analyze the radial, vertical, and azimuthal variations of metals in stars, both today and at<br>formation, in the FIRE-2 cosmological simulations of Milky Way-mass galaxies, and we compare<br>with the Milky Way. Overall, spatial variations of stellar metallicities show only modest differences between formation and today; spatial variations today primarily reflect the conditions of stars at birth, with spatial redistribution of stars after birth contributing secondarily. </p> <p> </p> <p>This data abides by CC-BY.</p>
Supplementary initial and final MD configurations for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>Extended XYZ files for the initial and final configurations of the molecular dynamics simulations from the article 'General-purpose machine-learned potential for 16 elemental metals and their alloys' (https://arxiv.org/abs/2311.04732).</p> <p><span>The Supplementary Data is contained in the folder named:<br>1) Polycrystalline-MoTaVW: <span> </span>The plasticity MD simulations in multi-principal element alloys.</span></p> <p><span>2) MoTaVW-radiation: The primary radiation damage MD simulations in multi-principal element alloys.</span></p> <p><span>3) Goldene: Comparisons between UNEP-v1 and EAM models in MD simulations.</span></p> <p><span>4) NiAlMo: Comparisons between UNEP-v1 and EAM models in MCMD simulations.<br>5) AlCrCuNiV: Comparisons between UNEP-v1 and EAM models in MCMD simulations.</span></p>
Source Data for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>***Source Data***<br>This folder contains multiple .txt files that provide the source data for the figures and tables presented in the paper: "General-purpose machine-learned potential for 16 elemental metals and their alloys."</p> <p>The source data are organized in the following folders and files:</p> <p>1) Fig2<br>2) Fig3<br>3) Fig4<br>4) Fig5<br>5) Fig6<br>6) FigS1<br>7) FigS2<br>8) FigS3<br>9) FigS4-6-pure<br>10) FigS7-9-binary<br>11) FigS10-12-ternany<br>12) FigS13-15-quaternary<br>13) FigS16-17-quinary<br>14) FigS18-20<br>15) FigS21<br>16) FigS22<br>17) FigS23<br>18) FigS26<br>19) Table1-Element-atoms-GPU-Speed.txt<br>20) Table-S1-DFT-EAM-UNEP-Elastic.txt<br>21) Table-S2-DFT-EAM-UNEP-Mono-vacanc.txt<br>22) Table-s3-Surface-100-110-111-DFT-EAM-UNEP.txt<br>23) Table-S4-Melting-EAM-UNEP-Exp.txt</p>
IODP Expedition 356 Elemental analysis (CHNS)
Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.
BioAgora D3.1 Annex - BDS2020 actionable information elements
<p>Table from BioAgora D3.1. Annex 3 c. </p> <p>First attempt of extraction and classification of information elements (IE) from the <em>Support to the Evaluation of the EU biodiversity strategy to 2020, and follow-up: final evaluation report </em>(European Commission: Directorate-General for Environment, 2022)<em> </em>document, which helped to build the framework and identify actionable information types.</p> <p> </p>
Iron Bonding with Light Elements: Implications for Planetary Cores Beyond the Binary System
<p>X-ray Absorption Spectroscopy and DFT calculation dataset for Iron alloys at high pressures. <br><br></p> <p><strong>Abstract: </strong>Light element alloying in iron is required to explain density deficit and seismic wave velocities in Earth’s core. However, the light element composition of the Earth’s core seems hard to constrain as nearly all light element alloying would reduce the density and sound velocity (elastic moduli). The alloying light elements include oxidizing elements like oxygen and sulfur, and reducing elements like hydrogen and carbon, yet their chemical effects in alloy system are less discussed. Moreover, Fe-X-ray Absorption Near Edge Structure (Fe-XANES) fingerprints have been studied for silicate materials with ferrous and ferric ions, while not many X-ray absorption spectroscopy (XAS) studies have focused on iron alloys, especially at high pressures. To investigate the bonding nature of iron alloys in planetary interiors, we presented X-ray absorption spectroscopy of iron-nitrogen and iron-carbon alloys at high pressure up to 50 GPa. Together with existing literature on iron-carbon, -hydrogen alloys, we analyzed their edge positions and found no significant difference of degree of oxidation among these alloys. Pressure effects on edge positions were also found negligible. Our theoretical simulation of the valence state of iron, alloyed with S, C, O, N, P also showed nearly unchanged behavior under pressures up to 300 GPa. This indicates the high pressure bonding of iron alloyed with light elements closely resembles bonding at the ambient conditions. We suggest that the chemical properties of light elements constrain which ones can coexist within iron alloys.<span> </span></p>
Data from: Chironomus riparius (Diptera) genome sequencing reveals the impact of minisatellite transposable elements on population divergence
Active transposable elements (TEs) may result in divergent genomic insertion and abundance patterns among conspecific populations. Upon secondary contact, such divergent genetic backgrounds can theoretically give rise to classical Dobzhansky-Muller incompatibilities (DMI), thus contributing to the evolution of endogenous genetic barriers and eventually cause population divergence. We investigated differential TE abundance among conspecific populations of the non-biting midge Chironomus riparius and evaluated their potential role in causing endogenous genetic incompatibilities between these populations. We focussed on a Chironomus-specific TE, the minisatellite-like Cla-element, whose activity is associated with speciation in the genus. Using a newly generated and annotated draft genome for a genomic study with five natural C. riparius populations, we found highly population-specific TE insertion patterns with many private insertions. A significant correlation of the pairwise FST estimated from genome-wide single nucleotide polymorphisms (SNPs) and the FST estimated from TEs, is consistent with drift as the major force driving TE population differentiation. However, the significantly higher Cla-element FST level due to a high proportion of differentially fixed Cla-element insertions also indicates selection against segregating (i.e. heterozygous) insertions. With reciprocal crossing experiments and fluorescent in-situ hybridisation of Cla-elements to polytene chromosomes, we documented phenotypic effects on female fertility and chromosomal mispairings. We propose that the inferred negative selection on heterozygous Cla-element insertions may cause endogenous genetic barriers and therefore acts as DMI among C. riparius populations. The intrinsic genomic turnover exerted by TEs may thus have a direct impact on population divergence that is operationally different from drift and local adaptation.
Data from: High-throughput SNP genotyping of historical and modern samples of five bird species via sequence capture of ultraconserved elements
Sample availability limits population genetics research on many species, especially taxa from regions with high diversity. However, many such species are well represented in museum collections assembled before the molecular era. Development of techniques to recover genetic data from these invaluable specimens will benefit biodiversity science. Using a mixture of freshly preserved and historical tissue samples, and a sequence capture probe set targeting >5000 loci, we produced high-confidence genotype calls on thousands of single nucleotide polymorphisms (SNPs) in each of five South-East Asian bird species and their close relatives (N = 27–43). On average, 66.2% of the reads mapped to the pseudo-reference genome of each species. Of these mapped reads, an average of 52.7% was identified as PCR or optical duplicates. We achieved deeper effective sequencing for historical samples (122.7×) compared to modern samples (23.5×). The number of nucleotide sites with at least 8× sequencing depth was high, with averages ranging from 0.89 × 106 bp (Arachnothera, modern samples) to 1.98 × 106 bp (Stachyris, modern samples). Linear regression revealed that the amount of sequence data obtained from each historical sample (represented by per cent of the pseudo-reference genome recovered with ≥8× sequencing depth) was positively and significantly (P ≤ 0.013) related to how recently the sample was collected. We observed characteristic post-mortem damage in the DNA of historical samples. However, we were able to reduce the error rate significantly by truncating ends of reads during read mapping (local alignment) and conducting stringent SNP and genotype filtering.
Growth performance, bioavailability of toxic and essential elements and nutrients, and biofortification of iodine of rainbow trout (Onchorynchus mykiss) fed blends with sugar kelp (Saccharina latissima)
<p>Data from the publication Granby et al 2020 Growth performance, bioavailability of toxic and essential elements and nutrients, and biofortification of iodine of rainbow trout (Onchorynchus mykiss) fed blends with sugar kelp (Saccharina latissima). Food Chemical Toxicology 141, 111387, https://doi.org/10.1016/j.fct.2020.111387</p> <p> </p>
Transposable element annotation of Sitophilus oryzae
<p>Sitophilus oryzae TE annotation (GFF), and consensus sequences (fasta).</p>
FIGURE 5. Pluteus padanilus. a–b. Basidiomata. c. Basidiospores. d, e. Pleurocystidia. f. Cheilocystidia. g. Pileipellis elements. a, c, d, f, g. C.K. Pradeep 13844 in Taxonomy and phylogeny of Pluteus glaucotinctus sensu lato (Agaricales, Basidiomycota), a multicontinental species complex
FIGURE 5. Pluteus padanilus. a–b. Basidiomata. c. Basidiospores. d, e. Pleurocystidia. f. Cheilocystidia. g. Pileipellis elements. a, c, d, f, g. C.K. Pradeep 13844 (holotype) b, e. C.K. Pradeep 14418. Scale bars: a, b = 1 cm. c–g = 10 μm. Photos a–b by C.K. Pradeep, line drawings c–g by A. Justo.
Responses of marine diatom-dinoflagellate competition to multiple environmental drivers: abundance, elemental and biochemical aspects
<p>A key challenge in ecology and biogeochemistry is to quantitatively determine the effects of multiple environmental factors on the diatom-dinoflagellate community and the related changes in elemental and biochemical composition. Here, laboratory experiments were conducted to investigate the responses of a diatom-dinoflagellate community to the interactions between temperature, N and P concentrations and their ratios. In particular, we focus on quantitatively assessing the changes in elemental stoichiometry and lipid biomarkers associated with the shifts of community composition. The phytoplankton species used in the experiments were the diatom<em> Phaeodactylum tricornutum</em> and the dinoflagellate <em>Prorocentrum minimum</em>. Both monocultures and bicultures of the two species were exposed to a fully factorial combination of three temperatures (12, 18 and 24°C), three N:P supply ratios (molar ratios 10:1, 24:1 and 63:1) and three nutrient concentration levels in temperature-controlled cabinets. Algal cells were harvested at steady state in semi-continuous cultures. The following parameters were analyzed: Cell density, POC, PON, POP, FAs and sterols.</p>
Elements of fish metacommunity structure in Neotropical freshwater streams
<p>The identification of the mechanisms underlying co-occurrence patterns of species is a way to identify which processes (niche, neutral or both) structure metacommunities. In this paper, our goals are to identify patterns of co-occurrence in neotropical stream fish and determine which processes structure the metacommunity and the gradients that underlie this structure. Our results pointed out that the metacommunity formed by the total pool of species is structured by a nested pattern (Hyperdispersed Species Loss) of co-occurrence and the mass effect mechanism. On the other hand, a set of core species displays a Clementisian pattern and is structured by the species sorting mechanism. Both, hyperdispersed species loss and the Clementisian patterns point to a discrete set of communities in the metacommunity. These communities could be isolated by physicochemical conditions, or physical barriers, like dams or waterfalls.</p>
Figure 2. Measurements for each studied postcranial element. A, B in A non-aquatic otter (Mammalia, Carnivora, Mustelidae) from the Late Miocene (Vallesian, MN 10) of La Roma 2 (Alfambra, Teruel, Spain): systematics and functional anatomy
Figure 2. Measurements for each studied postcranial element. A, B, unciform in (A) dorsal and (B) lateral view. C, lateral view of the pyramidal. D, E, radius in (D) distal and (E) cranial views. F–H, tibia in (F) proximal, (G) cranial, and (H) distal views. I, caudal view of femur. J, K, idealized metapodial in (J) dorsal and (K) medial views. L–N, pisiform in (L) palmar, (M) lateral, and (N) medial views. O, lateral view of the hemimandible. P–R, cuboid in (P) proximal, (Q) plantar, and (R) distal views. S, T, calcaneus in (S) dorsal and (T) distal views. U, V, navicular in (U) proximal and (V) medial views. W, X, talus in (W) plantar and (X) distal views. Y, Z, ectocuneiform in (Y) distal and (Z) lateral and dorsal views. Abbreviations: dl, craniocaudal length of the distal epiphysis of the long bones and metapodials, and craniocaudal length of the distal facet in the pisiform, calcaneus, talus, cuboid, and ectocuneiform; dw, mediolateral width of the distal epiphysis of the long bones and metapodials, and mediolateral width of the distal facet in the pisiform, calcaneus, talus, cuboid, and ectocuneiform; h, dorsopalmar/plantar height of the unciform, navicular, cuboid, and cuneiforms; hac, height from the cranioventral edge of the angular process to the dorsal edge of the condylar process; hmr, height from the cranioventral edge of angular process to the dorsal edge of the coronoid process; mcl, mandibular length from the mesial border of the incisors to the caudal border of the coronoid process; ml, maximum mandibular length; mw, mediolateral width in the centre of the long bones and metapodials; pl, craniocaudal length of the proximal epiphysis of the long bones and metapodials, and craniocaudal length of the proximal facet in the cuboid; pw, mediolateral width of the proximal epiphysis of the long bones and metapodials, and mediolateral width of the proximal facet of the cuboid; tl, maximum length; tul, maximum length of tuber calcanei, tubercle of the pisiform, and talus head; tuw, width of the tuber calcanei, tubercle of the pisiform, and talus head; w, width of the unciform, pyramidal, navicular, and cuneiforms.
VCF files for D. serrata transposable elements
<p><span><span>Transposable elements are an important element of the complex genomic ecosystem. Transposable element insertion also appears to be bursty – either due to invasion of new transposable elements that are not yet repressed, de-repression due to instability of organismal defense systems, stress, or genetic variation in hosts. Here, we characterize the transposable element landscape in an important model <i>Drosophila</i>, <i>D. serrata</i>, and investigate variation in transposable element copy number between genotypes and in the population at large. We find that a subset of transposable elements are clearly related to elements annotated in <i>D. melanogaster</i> and <i>D. simulans</i>, suggesting they spread between species more recently than other transposable elements. We also find that some transposable elements proliferate in particular genotypes compared to population levels. In natural populations an active transposable element and a potentially permissive background would not be held in association as in inbred lines, thus this could be a product of inbreeding. Yet many of the inbred lines have actively proliferating transposable elements suggesting that active transposable elements are not uncommon and that genotypes vary in their permissiveness in populations. </span></span></p>
Figure 12. Postcranial elements associated with Allodaposuchus precedens Nopcsa, 1928 in New specimens of Allodaposuchus precedens from France: intraspecific variability and the diversity of European Late Cretaceous eusuchians
Figure 12. Postcranial elements associated with Allodaposuchus precedens Nopcsa, 1928. Cast of the left femur (MMS/ VBN-02-37) in A, ventral; B, lateral; C, medial; and D, dorsal views. E, isolated osteoderm (MMS/VBN-12-10F) in dorsal view. Abbreviations: 4t, fourth trochanter; af, anterior facet.
FIGURE 3. A. Basidia, B. Basidiospores, C. Cheilocystidia, D. Pileus covering elements, E in A new species of Lepiota (Agaricaceae) from Punjab, Pakistan
FIGURE 3. A. Basidia, B. Basidiospores, C. Cheilocystidia, D. Pileus covering elements, E. Stipe covering elements. Scale bars: A–C = 5 µm, D, E = 20 µm. Drawings by: Aiman Izhar & Muhammad Asif
Can volcanic trace elements facilitate Covid-19 diffusion? A hypothesis stemming from the Mount Etna area, Sicily
<p>In December 2019, severe cases of pneumonia of unknown aetiology were reported in Wuhan city, in China. Lately, the pneumonia was related to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and the diseases was termed coronavirus disease-2019 (COVID-19). At the end of January 2020, the infection spread all over Italy, but with high infection rates and mortality in the northern part, especially in Lombardy, the most industrialized and polluted region of the country. It is noteworthy that a strong association between severe viral respiratory disease and air pollution has been described. Air pollutant could be solid particles, liquid droplets, or gases and can be of natural origin (such as ash from a volcanic eruption) or released from motor vehicle depletes (carbon monoxide gas) or factories (sulfur dioxide). Volcanic eruptions release large amounts of sulphuric acid, hydrogen sulfide, and hydrochloric acid into the atmosphere. Pulmunary diseases spreadby means of small droplets in thebreath, also called aerosols, and air pollution may facilitate the outside survival of viruses. We suppose that ash and gases emitted from the Mount Etna contributed to air pollution, potentially favouring the major contagion of COVID-19 in the eastern flank of the mountain, as in Catania city. In fact, ash and gases (with regard to radon) are usually particularly intense in winter, with a reduction of emission of specific metals with warmer weather. This is the first paper that elaborates the hypothesis of a potential role of volcanic gases and heavy metals-related air pollution, combined to specific climatic conditions and regional topography, in favouring severe COVID-19 diffusion in Sicily. Clinical and epidemiological studies are needed to support the hypothesis and plan the due prevention and awareness-raising campaigns.</p>
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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.