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122 results for “nanoscale”
Dataset for Nanoscale Growth Mechanisms of Gypsum: Implications for Environmental Control of Crystal Habits
<p>This dataset collected here has been used in the paper "Nanoscale Growth Mechanisms of Gypsum: Implications for Environmental Control of Crystal Habits".</p> <p>The files are in .dat format and each one corresponds to the images indicated in the file name.</p>
Supplementary data: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping
<p>--- Data supplement---</p> <p>--- Title: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping</p> <p>The zip file contains the following directories and subdirectories. The files, recommended software, calling code and relevance to the main manuscript (preprint found at https://doi.org/10.1101/2023.02.20.529230) are included in the notes below:</p> <p>(i) “STL files” – Includes .stl file formats of the Expansion Microscopy microplate components. These files can be opened with any CAD software (e.g. Fusion 360) or any opensource 3D printer slicer programme (e.g. Chitubox v1.9.5)</p> <p>(ii) “Image alignment & distortion analysis code” – Directory containing the ImageJ macros and custom-written Python scripts for the analysis pipeline described in the main manuscript, from image scaling and alignment to for distortion plotting and RMS Error analysis and plotting. Subdirectories include:</p> <ul> <li>a.“Distortion analysis and RMSE plotting code” – Directory contains custom-written Python scripts for updated distortion analysis, plotting, RMS error calculation, and code for combining the plots. Each .py file can be run with any Python distribution. Dependency libraries and modules (all opensource) include numpy, os, cv2, skimage, and tifffile</li> <li>b.“Basic Align Macro.ijm” – ImageJ macro for basic image alignment of pre- and post-Expansion Microscopy images. Refer to <a href="https://imagej.nih.gov/ij/developer/macro/macros.html#tools">https://imagej.nih.gov/ij/developer/macro/macros.html#tools</a> on how to install and run ImageJ macros.</li> </ul> <p> </p> <p>(iii)“Example data and analysis scripts” – Directory containing example datasets and worked examples of analysis. Subdirectories include:</p> <ul> <li>a.“Single-channel_HeLa_cells”. Worked example of single-colour dataset of a cluster of HeLa cells stained with NHS-AZ488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format). This can be called in using a Python code similar to: numpy.load("file name")</li> <li>b.“Multiplexed_Drosophila_wing”. Worked example of two-colour dataset of Drosophila fly wing tissue images stained with NHS-Alexa647 and anti-E-cad-GFP/Alexa488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format).</li> <li>c.“Multiplexed_HeLa_cells”. Worked example of two-colour dataset of cultured HeLa cell images stained with NHS-AZ488 and antibodies. Each folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format): <ul> <li>I.“KDELAlexa594_NHSAZ488” – antibody staining against KDEL</li> <li>II.“NUP98Alexa594_NHSAZ488” – antibody staining against Nups98</li> </ul> </li> </ul> <p> </p> <ul> <li>d.“Distortion Correction”. Folder contains two subfolders of output images from distortion corrections using the Linear Stack Alignment with SIFT plugin in ImageJ v1.54f. Each example consists of a pre- and post-Expansion image file, and the ‘corrected’ image file generated with 5, 10, and 50 voxel B-spline sampling.</li> </ul> <p> </p> <p>These files are placed in public domain under Creative Commons license CC BY-ND 4.0</p>
Nanoscale Imaging of High-Field Magnetic Hysteresis in Meteoritic Metal Using X-Ray Holography
<p>Data of magnetisation (two datasets) of the cloudy zone of Tazewell IIICD iron meteorite. Data was obtained using X-ray holography. Magnetization data is a 3D matrix containing magnetisation data in form of data[x location][y location][applied field], applied field values is provided in a separate file.</p> <p>Further details about this dataset and conditions of measurements can be found in Blukis et al., 2020 submitted to Geochemistry, Geophysics, Geosystems</p>
Dataset used in manuscript Tailored Nanoscale Plasmon-Enhanced Vibrational Electron Spectroscopy
<p>This file contains the raw dataset used in the manuscript "Tailored Nanoscale Plasmon-Enhanced Vibrational Electron Spectroscopy" published in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659)</p> <p><br> Data has been acquired using Nion Swift (https://nionswift.readthedocs.io/en/stable/). Experimental details can be found in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659).<br> <br> The dataset has been analyzed using the following Python libraries:</p> <p>Numpy, Scipy, Hyperspy, Matplotlib</p> <p>EELS hyperspectral images have been aligned using the Hyperspy "align1D" method. Aligned EELS hyperspectral images are saved in files finished with "_Aligned.hspy":</p> <p>For the strong coupling experiments:<br> Tip 1 is on hBN<br> Tip 2 is on vacuum</p> <p>For each of the nanowires tips, a file with the fitted coefficients are available, as well as a plot of the data and the fitted curve.</p> <p>Datasets have been fitted with gaussian and/or lorentizan functions, as described in the published text.</p> <p>Any question can be forwarded to the corresponding authors of the published text.</p> <p> </p>
Picosecond time-resolved antibunching measures nanoscale exciton motion, annihilation, and true number of chromophores
<p>The particle-like nature of light becomes evident in the photon statistics of fluorescence of single quantum systems as photon antibunching. In multichromophoric systems, exciton diffusion and subsequent annihilation occur. These processes also yield photon antibunching but cannot be interpreted reliably. Here, we develop picosecond time-resolved antibunching (psTRAB) to identify and decode such processes. We use psTRAB to measure the true number of chromophores on well-defined multichromophoric DNA-origami structures, and precisely determine the distance-dependent rates of annihilation between excitons. Further, psTRAB allows us to measure exciton diffusion in mesoscopic H- and J-type conjugated-polymer aggregates. We distinguish between one-dimensional intra-chain and three-dimensional inter-chain exciton diffusion at different times after excitation and determine the disorder-dependent diffusion lengths. Our method provides a new lens through which excitons can be studied at the single-particle level, enabling the rational design of improved excitonic probes such as ultra-bright fluorescent nanoparticles, and materials for optoelectronic devices. Here we demonstrate the raw data of DNA Origami Microscopy on which our findings based on. </p>
Data set for Microscale and nanoscale strain mapping techniques applied to creep of rocks
<p>Data set (figures and data involved in their making) for Quintanilla-Terminel, A., M. E. Zimmerman, B. Evans, and D.L. Kohlstedt, Microscale and nanoscale strain mapping techniques applied to creep of rocks, Solid Earth Discuss., https://doi.org/10.5194/se-2017-27, in review, 2017.</p>
Efficient excitation transfer in an LH2-inspired nanoscale stacked ring geometry
<p>The data supporting the findings in the manuscript "Efficient excitation transfer in an LH2-inspired nanoscale stacked ring geometry" are available here.</p>
Tomography Data for: Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating
<p>This is data collected at the 3-ID Hard X-ray Nanoprobe beamline. This repository supports the following research article: </p><p>Data provided is the aligned dataset and reconstruction using a FISTA algorithm. Angles Collected -90 to +45 at 1 degree steps. </p><p><strong>Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating</strong></p><p>By Aaron Michelson. </p><p> </p>
Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid (data evaluation)
<p>This record contains a docker image for data evaluation of AFM-IR data for our publication 'Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid'. The evaluations can be accessed by running the container and accessing the contained Jupyter Lab via a browser. The calculations are contained in 'Bottom_illuminated_PTIR.ipynb'.</p> <p>To run the container (requires docker):</p> <p>1.download 'container.tar.gz'</p> <p>2. in the command line, execute 'docker load -i container.tar.gz'. This will return something like 'Loaded image: <image_name>'</p> <p>3. then 'docker run -p 8888:8888 <image_name>' replacing the brackets with the actual name of the image, such as paper_bottom_illuminated:submission</p> <p>4.In your command line a link starting in 'http://127.0.0.1:8888/lab?token=...' will appear. Open this link in your browser to access the evaluation.</p>
Data and code repository for «Magnetic Order in Nanoscale Gyroid Netwoks»
<p>Data and Code repository for </p> <p>«Magnetic Order in Nanoscale Gyroid Netwoks»</p> <p>Ami S. Koshikawa, Justin Llandro, Masayuki Ohzeki, Shunsuke Fukami, Hideo Ohno, and Naëmi Leo</p> <p>Physical Review B 108, 024414 (2023)</p> <p>https://doi.org/10.1103/PhysRevB.108.024414</p>
Measured and analyzed raw data for publication "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://doi.org/10.1038/s43246-024-00497-1)
<p>The data provided by this dataset are the raw data published in the paper "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://www.nature.com/articles/s43246-024-00497-1). </p> <p>It can be found:</p> <p>-In the folder figure2_IV, the current-voltage characteristics (IV) of standard Superconductor-Insulator-Superconductor Josephson Junctions (SIS JJ) and of Superconductor-Insulator-Ferromagnet-thin superconductor- Superconductor Josephson Junctions (SIsFS JJ) at 10 mK </p> <p>-In the folder figure2_IVH, the magnetic dependence of the critical current of the SIsS and SIsFS at 10 mK</p> <p>-In the folder figure3_IVHT, the magnetic dependence of the critical current of the SIsFS as a function of the temperature T</p> <p>-In the figure4_gamma, the experimental and theoretical dependence of \gamma, i.e., the magnetic moment of the S-layers normalized to the F-layer in absolute value, as a function of the characteristic energy of the inverse proximity effect</p>
Dataset of the publication: Unlocking room-temperature bistable spin transition at the nanoscale: the synthesis of core@shell [Fe(NH2trz)3(NO3)2]@SiO2 nanoparticles
<p>Dataset of the publication: Unlocking room-temperature bistable spin transition at the nanoscale: the synthesis of core@shell [Fe(NH2trz)3(NO3)2]@SiO2 nanoparticles</p> <p>DOI: 10.1039/d4dt00911h</p> <p>A. Regueiro, M. Marti-Carrascosa, R. Torres-Cavanillas, E. Coronado </p> <p>Dalton Trans., 53, 20, 8764-8771 (2024)</p>
Quantitative electrostatic force tomography for virus capsids in interaction with an approaching nanoscale probe
<p>This repository contains the simulated data of a simple electrostatic model, based on the Poisson-Boltzmann equation, that quantifies the subnanometric electrostatic interactions between an AFM tip and a proteinaceous capsid (Zika Virus) from molecular snapshots. This allows us to describe the contributions of specific amino acids and atoms to the interaction force.</p> <p>The contains of this repository can be easily visualized through Jupyter Notebooks contained here:</p> <p>https://github.com/pyF4all/eTipVirusForce</p>
Nanoscale Shape-Dependent Histone Modifications
<p>Recent observations suggest a role for complex nanoscale particulate shape in regulation of specific immune-related cellular and in vivo processes. We suspect that cellular recognition of nanostructure architecture could involve non-molecular inputs, including cellular transduction of nanoscale spatially resolved stresses induced by complex shape. Here we report nanoscale shape-dependent control of the cellular epigenome. ChIP-Seq sequencing datasets are provided.</p>
STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale (validation data)
<p>Raw and refined data for the validation tests performed in the paper "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale":<br> - rallpack 1, analytical, STEPS3, STEPS4, mesh scaling with STEPS4. It is so simple that I provide only the raw data. The space saving using refined data is minimal</p> <p>- rallpack 3, STEPS 3, STEPS 4. Both raw and refined data are provided.</p> <p>- caburst, STEPS 3, STEPS 4. Both raw and refined data are provided.</p>
Dataset for High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating
<p>This is a dataset for the manuscript High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating. This mansucript is currently under peer-review in ACS Nano. </p>
Research data supporting "Fractal-like hierarchical organisation of bone begins at the nanoscale"
<p>Raw research data supporting: N. Reznikov et al., Science 360, eaao2189 (2018). DOI: 10.1126/science.aao2189</p>
Replication Data for: Proton transport through nanoscale corrugations in two-dimensional crystals
<p>This dataset contains source data for Main Figures from "Proton transport through nanoscale corrugations in two-dimensional crystals, <i>Nature,</i> volume 620, pages 782–786" Data plotted as curves and histograms are provided in .xlsx files. AFM data are provided in both .txt and SPIP-compatible .asc file types. Filenames correspond to the figure labels and plot information as in the publication.</p>
Achieving Nanoscale Resolution with EBSD
<p>Struggling to map nanoscale features in your microstructures with EBSD? Perhaps this video can help... (Case study: indexing the β phase in Ti-6Al-4V).</p> <p>Original research paper: https://doi.org/10.1016/j.matchar.2022.112371</p> <p>Research credits: A. E. Davis, X. Zeng, R. Thomas, J. R. Kennedy, J. Donoghue, A. Gholinia, P. B. Prangnell.</p> <p>Video credits: Produced, written, and performed by Alec E. Davis. Recorded by Alec E. Davis, Jack Donoghue, and Vivek Sahu.</p> <p>This work was supported by grants: Lightform (EPSRC EP/R001715/1), NEWAM (EPSRC EP/R027218/1), and Henry Royce Institute for Advanced Materials (EPSRC EP/R00661X/1, EP/S019367/1, EP/P025021/1, and EP/P025498/1). Alec E. Davis is also appreciated for equipment loan from the @materialsavclub.</p> <p>Commercial EBSD indexing software links:</p> <p>Oxford Instruments: https://www.oxinst.com/blogs/pattern-matching-a-paradigm-shift-for-ebsd EDAX: https://www.edax.com/resources/application-notes/spherical-indexing</p>
After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics data
<p>The present folder contains the raw data as well as the R script used in the following publication : <em>After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics. </em>To extract the data:</p> <p> </p> <ol> <li>Download the .zip file and extract all the files it contains in a folder, without changing the hierarchy</li> <li>Open the Rproject by double-clicking on it</li> <li>Run the script and explore the outputs in the <em>Script_01_output </em>folder</li> </ol> <p>The data extracted from the HDF5 files before any processing are saved as .csv files in the <em>CSV </em>folder.</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.