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115 results for “AFM”
DNA Origami Raw AFM Data - NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM
<p>The data file is in the original ARIS data format as captured on a Cypher VRS1250 AFM (Oxford Instruments)<br><br><br></p>
AFM Surface Coating
<p><strong>AFM Surface Coating - AFMBioMed Summer School 2020</strong></p> <p>This 7 minutes video tutorial shows how to properly label, handle, incubate (coat), rinse and store AFM surfaces (cantilevers and coverslips) during a multi-stage coating process.</p>
MADIA_732678_SCRIBA_AFM images_01
<p>ONLY METADATA<br> Collection of AFM Images of magnetic nanoparticles in microfluidic channels. Data produced between June 2017 to July 2017</p>
Application of contact-resonance AFM methods to polymer samples (Raw Data)
<p>Raw data and figures of the article "Application of contact-resonance AFM methods to polymer samples", published in Beilstein Journal of Nanotechnology on 12 Nov 2020</p> <p> </p> <p>The raw data can be opened with the software "Igor Pro"</p>
Reconstruction Algorithms in Undersampled AFM Imaging - results
<p>This data set contains numerical simulation results from experiments for the paper "Review of compressed sensing reconstruction algorithms in AFM cell imaging", submitted to IEEE Journal of Selected Topics in Signal Processing.</p> <p>The data set consists of an HDF5 file containing the simulation results as well as MD5 and SHA checksums of the HDF5 database for validating the integrity of the data after download.</p> <p>The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/).</p> <p>Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://doi.org/10.5281/zenodo.18745.</p> <p>The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.</p>
Reconstruction Algorithms in Undersampled AFM Imaging - final results
<p>This data set contains numerical simulation results from experiments for the paper "Reconstruction Algorithms in Undersampled AFM<br /> Imaging", published in IEEE Journal of Selected Topics in Signal Processing.</p> <p>The data set consists of a set of HDF5 files containing the simulation results as well as MD5 and SHA checksums of the HDF5 databases for validating the integrity of the data after download.</p> <p>The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/).</p> <p>Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://dx.doi.org/10.5281/zenodo.32959.</p> <p>The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.</p>
METADATA for results of irradiation-induced complex DNA damage measurements using plasmid pBR322 along a typical Proton Treatment Plan at the MedAustron proton and carbon beam therapy facility (energy 137–198 MeV and Linear Energy Transfer (LET) range 1–9 keV/μm), by means of Agarose Gel Electrophoresis and DNA fragmentation using Atomic Force Microscopy (AFM)
Open the record for dataset details and reuse information.
A dataset on "Coating of self-sensing AFM cantilevers with boron-doped nanocrystalline diamond films at low temperatures"
<p>The data set to paper: </p> <p>Coating of self-sensing AFM cantilevers with boron-doped nanocrystalline diamond at low temperatures</p> <p>Štěpán Potocký1*, Jaroslav Kuliče1k, Egor Ukraintsev1, Ondřej Novotný2, Alexander Kromka3, and Bohuslav Rezek1</p> <p>1 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 16627 Prague, Czech Republic<br>2 NenoVision s.r.o., Purkyňova 649, 61200 Brno, Czech Republic <br>3 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>*corresponding author: potocky@fel.cvut.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 10. 2023 - 31. 3. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: pdf<br>Figure 2: pdf<br>Figure 3: pdf, csv<br>Figure 4: pdf, csv, gwy<br>Figure 5: pdf, gwy<br>Figure S1: pdf<br>Figure S2: pdf</p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. Gwy correspond to free Gwyddion SPM data analysis software (gwyddion.net). In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.1002/pssa.202400553.</p>
Linked collectors and determiners for: All herbarium specimens of grasses from Madagascar and the surrounding islands: family Poaceae, sector AFM.
Natural history specimen data linked to collectors and determiners held within, "All herbarium specimens of grasses from Madagascar and the surrounding islands: family Poaceae, sector AFM". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ac8e5183-e233-4168-96a5-67541049aa67">https://bionomia.net/dataset/ac8e5183-e233-4168-96a5-67541049aa67</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ac8e5183-e233-4168-96a5-67541049aa67">https://gbif.org/dataset/ac8e5183-e233-4168-96a5-67541049aa67</a>. Formatted as a Frictionless Data package.
AFM movie of DHR10-mica18 on m-mica (001) in 100 mM KCl solution
<p>AFM movie of DHR10-mica18 on m-mica (001) in 100 mM KCl solution. The experimental AFM dataset was collected by Shuai Zhang and converted to Python-compatible format by Maxim Ziatdinov.</p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset 2
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into ten parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-04</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-05</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-06</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-07</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-08</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-09</li> </ol> <p>These tem parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>$ cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>$ tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 114 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into four parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> </ol> <p>These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 70 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Automated bio-AFM generation of large mechanome data set and their analysis by machine learning to classify prostatic cell lines_Training base 100 PC3-GFP
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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>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh outward facing state (OFS) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state closed (IFSclosed) conformation at apo condition, imaged from the cytoplasmic side, in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
Nanoindentation and AFM Dataset for the Microscale Mechanical Property of Granites
<p>This dataset contains the raw results of nanoindentation testing and Atomic Force Microscopy (AFM) testing, which were used to determine the elastic property of rock-forming minerals and interphases in granites. The dataset is presented and utilized in the paper 'Determining Young's Modulus of Arbitrarily-shaped Granite Samples using Accurate Grain-based Modelling with Micro-RME'. Full details of the experimental setup, procedure, and imaging analysis can be found in the paper and supporting information.</p> <p>The results of nanoindentation testing for different rock-forming minerals in granites are contained within three files: nanoindentation_quartz, nanoindentation_feldspar and nanoindentation_biotite. In these files, the raw data during nanoindentation testing is shown with achieved parameters, including elastic modulus, hardness, maximum load fore, maximum contact area, and contact depth. These results are then used to determine the elastic modulus of rock-forming minerals. The achieved Young's modulus for quartz, feldspar and biotite are 96.81 GPa, 69.50 GPa and 46.41 GPa, respectively, for present granitic samples.</p> <p>The results of AFM testing for interphases between different rock-forming minerals in granites are contained within three files: AFM_quartz_feldspar_interphase, AFM_feldspar_biotite_interphase and AFM_quartz_biotite_interphase. In these files, the raw data of 196,608 indents during AFM testing is shown with obtained parameters, including elastic modulus and surface roughness. These results are then used to determine the geometry and elastic modulus of interphases. The achieved Young’s modulus of interphase is 24.48 GPa for present granitic samples. Additionally, the geometry of striped interphases between different rock-forming minerals is shown, which is complex with varying widths.</p>
AFM-IR images and spectra of commercial polymers
<p>This data set contains photothermal IR images (AFM-IR) and spectra of a commercial polymers taken with a Bruker nanoIR3. A Daylight solutions MIRcat-QT external cavity quantum cascade laser. Fourier transform infrared (FTIR) spectra were recorded with Bruker Tensor 35 spectrometer using a "Platinum ATR" (Bruker) attenuated total reflection sampling accessory with a diamond ATR element.</p> <p> </p> <p>The AFM-IR raw data files (".axz") are gzipped XML files that can be opened with the <a href="https://github.com/GeorgRamer/anasys-python-tools">anasyspythontools</a> python library. FTIR files (".txt") are CSV files. The first column represents wavenumbers, the second column represents absorption.</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.