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1,723 results for “Reactivation”
Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
Open the record for dataset details and reuse information.
Image analysis data for the study of the reactivity of the phases in Nd-Fe-B magnets etched with HCl-saturated Cyphos IL 101
<p>Scanning electronic microscopy (SEM), Energy dispersive X-rays Spectroscopy (EDS) and image analysis have been used as techniques to analyse the results of etching experiments carried on NdFeB permanent magnets by using the ionic liquid Cyphos IL 101 saturated with HCl. Image analysis is for the first time reported in the literature as a technique for corrosion studies.</p> <p>Operational conditions of the analysis equipment were the following:</p> <p>- The samples were analyzed via electron microscopy and image analysis. Scanning electron microscope (SEM) pictures and energy dispersed spectra (EDS) were collected with a JEOL JSM 5800 microscope, operating at 20 kV. The polished samples were made conductive by spraying a carbon layer on them using a Balzer SCD 050 sputter coater.</p> <p>- The EDS analysis were collected as average on 5 points per each SEM picture</p> <p>- A commercial software, ImageJ®, was used for the image analysis. Two data were analysed: the Feret diameter, d<sub>F</sub>, and the percentage of etched area, %area. The SEM images were converted to 8-bit grayscale, from 0 to 255 number of grey ranges. Simple linear scaling was applied</p>
Input files and simulation results for reactive two-phase transport calculations in a waste package with OGS6-MP-LT-Drum
<p>The folders contain the original input and output files for reactive two-phase flow calculations with OpenGeoSys6-MP-LT-drum (doi:10.5281/zenodo.4060885), as well as the GEM-Selektor V3.3 project for look-up table generation, state<br> files for Paraview, R-script and jupyter notebook files used for post-processing the simulation output, which were used for creating the results in:</p> <p>Huang, Y., Shao, H., Wieland, E. <em>et al.</em> Two-phase transport in a cemented waste package considering spatio-temporal evolution of chemical conditions. <em>npj Mater Degrad</em> <strong>5, </strong>4 (2021). https://doi.org/10.1038/s41529-021-00150-z</p>
Machine learning reactive mixing dataset-0
<p>Reactive-mixing dataset-0 for machine learning analyses</p>
Machine learning reactive mixing dataset-2
<p>Reactive-mixing dataset-1.1 for machine learning analyses</p>
Machine learning reactive mixing dataset-1
<p>Reactive-mixing dataset-1 for machine learning analyses</p>
Machine learning reactive mixing dataset-9
<p>Reactive-mixing dataset-3 for machine learning analyses</p>
Machine learning reactive mixing dataset-4
<p>Reactive-mixing dataset-1.3 for machine learning analyses</p>
Machine learning reactive mixing dataset-3
<p>Reactive-mixing dataset-1.2 for machine learning analyses</p>
Machine learning reactive mixing dataset-8
<p>Reactive-mixing dataset-2.3 for machine learning analyses</p>
Machine learning reactive mixing dataset-7
<p>Reactive-mixing dataset-2.2 for machine learning analyses</p>
Machine learning reactive mixing dataset-6
<p>Reactive-mixing dataset-2.1 for machine learning analyses</p>
Machine learning reactive mixing dataset-5
<p>Reactive-mixing dataset-2 for machine learning analyses</p>
Machine learning reactive mixing dataset-12
<p>Reactive-mixing dataset-3.3 for machine learning analyses</p>
Machine learning reactive mixing dataset-10
<p>Reactive-mixing dataset-3.1 for machine learning analyses</p>
Machine learning reactive mixing dataset-16
<p>Reactive-mixing dataset-4.3 for machine learning analyses</p>
Machine learning reactive mixing dataset-15
<p>Reactive-mixing dataset-4.2 for machine learning analyses</p>
Machine learning reactive mixing dataset-13
<p>Reactive-mixing dataset-4 for machine learning analyses</p>
Machine learning reactive mixing dataset-21
<p>Reactive-mixing dataset-6.1 for machine learning analyses</p>
Machine learning reactive mixing dataset-14
<p>Reactive-mixing dataset-4.1 for machine learning analyses</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.