Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
647
datasets available to search
ShareScore release 0.7.1
Dataset results
647 results for “Silicon”
The response of silicon carbide composites to He ion implantation and ramifications for use as a fusion reactor structural material
<p>Raw datasets for the publication "The response of silicon carbide composites to He ion implantation and ramifications for use as a fusion reactor structural material".</p>
NFFA-Europe|Pilot proporsal "Production and characterization of highly controlled silicon oxide nanoparticles for solid polymer electrolytes" (PID: 444).
<p>XPS, IR, and QMS data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal PID 444</p>
Quasi-free-standing AA-stacked bilayer graphene induced by calcium intercalation of the graphene-silicon carbide interface
<p>APRES datasets and LEED images for "Quasi-free-standing AA-stacked bilayer graphene induced by calcium intercalation of<br>the graphene-silicon carbide interface" publication.</p>
Heterotelechelic silicones: Facile synthesis and functionalization using silane-based initiators
<p><span>The synthetic utility of heterotelechelic polydimethylsiloxane (PDMS) derivatives is limited due to challenges in preparing materials with high chain-end fidelity. In this study, anionic ring-opening polymerization (AROP) of hexamethylcyclotrisiloxane (D<sub>3</sub>) monomer using a specifically designed silyl hydride (Si–H)-based initiator provides a versatile approach towards a library of heterotelechelic PDMS polymers. A novel initiator, where the Si–H terminal group is connected to a C atom (H</span><span>–</span><span>Si–C) and not an O atom (H–Si–O) as in traditional systems, suppresses intermolecular transfer of the Si–H group, leading to heterotelechelic PDMS derivatives with a high degree of control over chain-ends. <em>In-situ</em> termination of the D<sub>3</sub> propagating chain end with commercially available chlorosilanes (alkyl chlorides, methacrylates, and norbornenes) yields an array of chain-end functionalized PDMS derivatives. This diversity can be further increased by hydrosilylation with functionalized alkenes </span><span>(alcohols, esters, and epoxides) to </span><span>generate a library of heterotelechelic PDMS polymers. Due to the living nature of ring opening polymerization and efficient initiation, narrow-dispersity (<em>Đ</em> < 1.2) polymers spanning a wide range of molar masses (2 – 11 kg mol<sup>−1</sup>) were synthesized. With facile access to <em>α</em>-Si–H and <em>ω</em>-norbornene functionalized PDMS macromonomers (H–PDMS–Nb), the synthesis of well-defined super-soft (<em>G</em>ʹ = 30 kPa) PDMS bottlebrush networks, which are difficult to prepare using established strategies, was demonstrated</span><span>.</span></p>
Effects of leaf silicon on drought performance of tropical tree seedlings
<p>Elevated leaf silicon (Si) concentrations improve drought resistance in cultivated plants, suggesting Si might also improve the drought performance of wild species. Tropical tree species, for instance, take up substantial amounts of Si, and leaf Si varies markedly at local- and regional-scales, suggesting consequences for seedling drought resistance. Yet, whether elevated leaf Si improves seedling drought performance in tropical forests is unknown. To manipulate leaf Si concentrations, seedlings of seven tropical tree species were grown in Si-rich and -poor soil, before exposing them to drought in the forest understory. Survival, growth, and wilting were monitored. Elevated leaf Si did not improve drought survival and growth in any of the species. In one species, drought survival was reduced in seedlings previously grown in Si-rich soil, contrary to our expectations. Our results suggest that elevated leaf Si does not improve the drought resistance of wild tropical tree species. Elevated leaf Si may even reduce drought performance, suggesting differences in soil conditions influencing leaf Si may contribute to soil-related variation of tropical seedling performance. Furthermore, our results are at odds with most studies on cultivated species and show that alleviative effects of Si in crops cannot be generalized to wild plants in natural systems.</p>
Silicon photomultiplier (SiPM) dynamic response and dark count vs temperature correlation
<p>Project FleX-RAY employed photon counting capable sensors in order to capture light that was generated by the scintillation phenomenon, in plastic scintillating fibres. These special photodiodes are called Silicon Photomultipliers, or SiPMs. In order to determine the suitability and performance of different SiPM models available in the market, we carried out a series of tests to : </p> <ol> <li>Evaluate the correlation of dark count rate against temperature and</li> <li>Compare the amplitude response of the actual devices when excited by a 50 picosecond light pulse.</li> </ol> <ul> <li>The data of series 1 are tabulated in the spreadsheet titled "darkCountVsTemp.xlsx".</li> <li>The data of series 2 are arranged in separate "zip" archives, each titled by the device model under test. These zip archives are within a master zip file titled "SiPM_laser_pulse_response.zip".</li> </ul> <p>Test equipment and set-up:</p> <p>Devices under test: Hamamatsu models S13360-1375, S13360-3075 and ON-Semi models microFC-10020, microFC-10035 </p> <p>Measurement device(s)/board(s): To excite SiPMs, we used picosecond pulse laser type PLDD-100k-OEM from Alphalas. To record raw pulse output of SiPMs we used oscilloscope Tektronix DPO7104. To record the darkcount rate of SiPMs we used oscillsocope Keysight DSOX1204G. A custom, 3D-printed light-tight box was used to shield the SiPMs from ambient light.</p> <p> </p> <p>This work was financed by the European Union's Horizon 2020 program under grant agreement No. 899634.</p>
AFM interlaboratory comparison for nanodimensional metrology on silicon nanowires [Dataset]
<p>Dataset related to "AFM interlaboratory comparison for nanodimensional metrology on silicon nanowires" paper</p>
GAP interatomic potential for silicon
<p><strong>Gaussian approximation potential</strong> (GAP) for <strong>silicon</strong> [1]. It has been fitted with <strong>QUIP/GAP </strong>[1,2] by recomputing the <strong>Si database of Bartók et al.</strong> [3] at the <strong>PW91</strong> level of theory [4] using the VASP code [5,6,7]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>3-body</strong> (angle_3b) descriptors [5] plus <strong>SOAP-type descriptors</strong> (soap_turbo) [9,10], as implemented in the <strong>TurboGAP</strong> code [11]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and TurboGAP. More details will follow in a scientific<br> publication in due course (bibligraphical data will be added as it becomes available).</p> <p><strong>References</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).</li> <li>LibAtoms: <a href="https://libatoms.github.io">https://libatoms.github.io</a></li> <li>A.P. Bartók, J. Kermode, N. Bernstein, and G. Csányi. Phys. Rev. X 8, 041048 (2018).</li> <li>J.P. Perdew and Y. Wang. Phys. Rev. B 45, 13244 (1992).</li> <li>V.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).</li> <li>VASP: <a href="http://vasp.at">http://vasp.at</a></li> <li>G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).</li> <li>T. Bucko, S. Lebègue, T. Gould, and J.G. Ángyán, J. Phys.: Condens. Matter 28, 045201 (2016).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).</li> <li>M.A. Caro. Phys. Rev. B 100, 024112 (2019).</li> <li>TurboGAP: <a href="http://turbogap.fi">http://turbogap.fi</a></li> </ol>
Figures and datasets of paper titled "Bulk carrier lifetime surpassing 600 us in Upgraded Metallurgical-grade Silicon multicrystalline wafers after Phosphorus Diffusion Gettering"
<p>Datasets and figures of paper titled "Figures and datasets of paper titled "Bulk carrier lifetime surpassing 600 us in Upgraded Metallurgical-grade Silicon multicrystalline wafers after Phosphorus Diffusion Gettering" published in arXiv (<a href="https://arxiv.org/abs/2111.13522">https://arxiv.org/abs/2111.13522</a>).</p>
Dataset: Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching
<p>The following files provide the dataset of the work "Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching". The description and organization of the files are explained in the file README.txt.</p>
Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations"
<p>This is the data set for the preprint <a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a> [cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> 1). NEMD<br> 2). NEPpotential<br> 3). PDOS<br> 4). kappa-quenchRate<br> 5). kappa-size<br> 6). kappa-temperature<br> <br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.</p> <p> </p>
Supporting data for 'A shuttling-based two-qubit logic gate for linking distant silicon quantum processors'
<p>Data supporting for paper 'A shuttling-based two-qubit logic gate for linking distant silicon quantum processors'.</p> <p>All the data are stored in the HDF5 format that can be conveniently loaded by the xarray Python package.</p>
Process simulation-based inventory data for the perovskite single-junction, Silicon (PERC) and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles
<p>Process simulation-based inventory data (mass and energy balances) for the perovskite single-junction, silicon (PERC architecture), and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles. The file "0 Overview of simulation flowsheets.xlsx" contains images of the 11 flowsheets that constitute the perovskite/silicon tandem simulation model, which encompasses the perovskite single-junction and silicon (PERC) simulation models. For each unit process shown in each of the flowsheet images, the corresponding Excel file in this repository (with the same name) contains the detailed mass and energy balances, as well as full compositions and thermochemical properties of all streams and the compounds in them. That is, streams are not assumed to consist of pure elements simply moving through the system together, but rather taking into account that streams consist of compounds in solution, which have different thermochemical properties than simple mixtures of the elements involved.</p> <p>Nine additional data files, the names of which start with "Inventory - " contain summarized inventory data for the production of 1000 perovskite single-junction, silicon (PERC), and silicon/perovskite tandem PV modules, each with no Si recycling (i.e. zero circularity), 50% Si recycling, and 100% Si recycling (i.e. full Si circularity).</p>
Glass electrode and silicon probe recordings from THY-Tau22 mice
<p>This repository contains code used to analyse electrophysiological data obtained from THY-Tau22 mice. Example datasets are included.</p>
Thermal conductivity of silicon dioxide at various temperatures
<p><strong>Thermal conductivity of silicon dioxide at various temperatures</strong></p> <p>Junjie Chen</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p> </p> <p>Silicon dioxide, also known as silica, is an oxide of silicon. In many parts of the world, silica is the major constituent of sand. Silica is one of the most complex and most abundant families of materials, existing as a compound of several minerals and as a synthetic product. Notable examples include fused quartz, fumed silica, silica gel, opal and aerogels. It is used in structural materials, and microelectronics. Because silicon dioxide is a native oxide of silicon it is more widely used compared to other semiconductors like Gallium arsenide or Indium phosphide. Silicon dioxide could be grown on a silicon semiconductor surface. Silicon oxide layers could protect silicon surfaces during diffusion processes, and could be used for diffusion masking. The process of silicon surface passivation by thermal oxidation is critical to the semiconductor industry. It is commonly used to manufacture metal-oxide-semiconductor field-effect transistors and silicon integrated circuit chips.</p> <p> </p> <p>Thermodynamic temperature (degrees kelvin), Thermal conductivity (watts per meter-kelvin)</p> <p>300 12, 6.80</p> <p>311 11.1, 5.88</p> <p>366 9.34, 5.19</p> <p>422 8.68, 4.50</p> <p>500 6.00, 3.90</p> <p>600 5.00, 3.41</p> <p>700 4.47, 3.12</p> <p>800 4.19, 3.04</p>
Strong silicon oxide inclusions in iron
<p>Dataset corresponding to:</p> <p>Alejandra Slagter, Joris Everaerts, Léa Deillon, Andreas Mortensen,<br> Strong silicon oxide inclusions in iron,<br> Acta Materialia,<br> Volume 242,<br> 2023,<br> 118437,<br> ISSN 1359-6454,<br> https://doi.org/10.1016/j.actamat.2022.118437.<br> (https://www.sciencedirect.com/science/article/pii/S135964542200814X)</p>
Dataset for Generation of multiple user-defined dispersive waves in a silicon nitride waveguide
<p>This is the data set for paper: Generation of multiple user-defined dispersive waves in a silicon nitride waveguide published in Optica. DOI: https://doi.org/10.1364/OPTICA.521625</p>
Laser-driven acceleration by microstructured silicon targets
<p>Diagnostic data from a high power laser plasma experiment realised at the Central Laser Facility, UK. Target Area Petawatt with the VULCAN OPCPA laser system was used, to compare microstructured silicon targets (produced at the Detektor- & Targetlabor at the Institut für Kernphysik, Technische Universität Darmstadt, Germany) with flat silicon foils. This spreadsheet shows the laser parameters together with the diagnostic data for 1053nm reflection from target, 527nm emission from target, characteristic X-ray, electron and ion spectra.</p>
High-Speed Serializer for a 64 GS/s Digital-to-Analog Converter in a 28 nm Fully-Depleted Silicon-on-Insulator CMOS Technology
<p>This data set contains simulation results of a high-speed serializer for a 64 GS s<sup>-1</sup> digital-to-analog converter. The circuit is presented in the paper "High-Speed Serializer for a 64 GS s<sup>-1</sup> Digital-to-Analog Converter in a 28 nm Fully-Depleted Silicon-on-Insulator CMOS Technology" in the open access journal "Advances in Radio Science".</p>
Raw data for "Radii of Rydberg states of isolated silicon donors" by Juerong Li et al, Phys Rev B 2018
<p>This upload contains raw data for the field dependent spectra, as in the example of Figure 1a of the manuscript. </p> <p>The data consists of interferograms in columns. Each interferogram has been averaged 30 times. The first row of each data matrix refers to the magnetic field value in the units of Tesla, while the first column indicates the step number in units of wavenumber, which is in power-of-two multiples of the HeNe laser wavelength i.e. 16x632.8 nm. A tab is used to separate data within the rows. The length of the interferogram determines the resolution, which is different for each figure/file.</p> <p>The samples (and field resolution) used for each figure are different. All samples are FZ grown with the growth direction of <100>, all were polished to 1 degree wedge. The sample details are as follows:</p> <p>Name, Doping species, Doping concentration (10^14 cm-3), data file name. <br> Corretto (reference sample), P, 8, Supplementary Data 1.txt<br> Black, Bi, 1.28, Supplementary Data 2.txt<br> 85-3, Li and Mg, 2.3 for Li and 5 x for Mag, Supplementary Data 3.txt<br> V496, P and Sb, 18 for P and 12 for Sb, Supplementary Data 4.txt<br> 72-8a, Se, 26, Supplementary Data 5.txt<br> 66.7, S, 13, Supplementary Data 6.txt </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.