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.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “differential phase contrast”
Improving magnetic STEM-differential phase contrast imaging using precession
<p>Scanning transmission electron microscopy datasets and processing files used in the journal publication "<strong>Improving Magnetic STEM-Differential Phase Contrast Imaging using Precession</strong>".</p> <p>DOI link to publication: <a href="https://doi.org/10.1093/micmic/ozad001">https://doi.org/10.1093/micmic/ozad001</a></p> <p> </p> <p><strong>Prerequisites</strong></p> <p>To run the scripts necessary to process the files, the open source packages JupyterLab, HyperSpy, pyXem, and fpd need to be installed. These notebooks were created with these package versions:</p> <ul> <li>hyperspy 1.6.4</li> <li>pyxem 0.13.3</li> <li>fpd 0.2.0</li> <li>jupyterlab 3.2.0</li> </ul> <p> </p> <p><strong>Data files and processing scripts</strong></p> <p>Data files are collected in .zip folders and have names that start with "d00..", while processing scripts are in the Jupyter Notebook .ipynb data format whose names start with "p00..". These files are divided into three main processing steps, outlined as follows:</p> <ol> <li><strong>Processing of raw data</strong>: Raw data files can be found in the d001_scans.zip folder. These are processed with the p002_get_dpc_raw.ipynb script which uses either the center of mass or phase correlation methods.</li> <li><strong>D-scan correction</strong>: The processed files from the previous step are saved in the d002_dpc_raw.zip folder. The p003_get_dpc_cor.ipynb script performs a d-scan correction on these files and saves the output in the d003_dpc_cor.zip folder. <ul> <li><strong>Virtual segmented detector algorithm</strong>: For comparison purposes to the other processing algorithms, a virtual segmented detector algorithm was developed and can be found in the p004_segmented_detector.ipynb script. This algorithm extracts a linear d-scan plane from already processed phase correlation files found in d002_dpc_raw.zip, subtracts it from the raw data files found in d001_scans.zip, and finally performs the processing algorithm.</li> </ul> </li> <li><strong>Plotting files</strong>:<strong> </strong>The p005_plot_dpc_images.ipynb script creates the figures as seen in the journal publication. The input files are those found in d003_dpc_cor.zip from the previous processing step.</li> </ol>
On the effect of precession for magnetic differential phase contrast imaging
<p>4D-STEM datasets and processing scripts used for the journal publication "<strong>On the effect of precession for magnetic differential phase contrast imaging</strong>".</p> <p>DOI link to publication: <a href="https://doi.org/10.1016/j.micron.2024.103761">10.1016/j.micron.2024.103761</a></p> <p> </p> <p><strong>Prerequisites</strong></p> <p>The scripts presented below were created using open-source Python packages with specific versions:</p> <ul> <li>hyperspy 1.7.1</li> <li>pyxem 0.14.2</li> <li>fpd 0.2.5 (with scikit-image 0.18)</li> <li>jupyterlab 4.0.7</li> </ul> <p>There is no guarantee that other versions of the listed packages will run the code.</p> <p><strong>Data files</strong></p> <p>In total five zipped (.7z extension) files are included in this repository. The files contain data from the different samples studied with different precession electron diffraction calibrations where applicable. The three samples studied are LSMO, and the corresponding 4D-STEM datasets taken away from a high-symmetry zone axis (<em>LSMO_LZ_data.7z</em>) and on the zone axis (<em>LSMO_HZ_data.7z</em>), FeAl sample (<em>FeAl_data.7z</em>), and scans from an Au TEM alignment grid (<em>AuGrid_data.7z</em>). The <em>precession_azimuths.7z</em> files contains the precession azimuthal circles from the three precession calibrations performed.</p> <p><strong>Processing scripts</strong></p> <p>Three scripts are included in the IPython notebook format (.ipynb extension). The <em>Precession_azimuth_plot.ipynb</em> script plots the precession azimuth for comparison. The <em>Segmentation_script.ipynb</em> is used to process the precession path segmented 4D-STEM datasets, and the <em>DPC_processing.ipynb</em> includes code to perform center of mass and phase correlation processing on the 4D-STEM data to extract differential phase contrast signals.</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.