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52 results for “ice particle”
Ice Nucleating Particle number concentration from low-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract </strong></p> <p>Ice nucleating particles (INP) are a subclass of atmospheric aerosol particles, which can force heterogeneous freezing of cloud droplets at temperatures above -38 degrees C. In contrast, ice particles form from cloud droplets at temperatures below -38 degrees C due to homogeneous freezing, without INP. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as INP. During the Antarctic Circumnavigation Expedition (ACE) around the Southern Ocean, off-line filter sampling was performed. Filters were stored on the ship and analysed after the cruise at Leibniz-Institute for Tropospheric Research (TROPOS) concerning INP abundance. Here, we give INP number concentrations for sampling of 8 hour periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ice_nucleating_particles_frozen_fraction_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>ACESPACE_ice_nucleating_particles_number_concentration_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>data_file_header_frozen_fraction.txt, metadata, text format</li> <li>data_file_header_number_concentration.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>change dataset title to reflect low-volume sampling method</li> <li>addition of INP number concentration data from different temperatures</li> <li>addition of fraction of frozen droplets data</li> <li>addition of field blank filter data</li> <li>create separate data_file_header files</li> <li>add change log</li> </ul> <p>v1.0 - initial release of dataset</p> <p> </p>
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
Supporting data for manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging"
<p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers micron-resolution 2D chemical imaging, which has been adapted recently to ice core analysis. The datasets are supporting information for the manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging" accepted for publication at Geochemistry, Geophysics, Geosystems (10.1029/2022GC010595). Measurements were performed at the Ca’Foscari University of Venice, considered as analytes are 23Na, 24Mg, 27Al, 29Si, 43Ca, 56Fe and 88Sr. Background and drift correction as well as image construction were performed using the software HDIP (Teledyne Photon Machines, Bozeman, MT, USA). Impurity maps are acquired as a pattern of lines, without overlap in the direction perpendicular to that of the scan, and without any further spatial interpolation. In a sample of the EGRIP Greenland ice core (from about 1256.95 m depth), maps were obtained over 3 adjacent areas. For each of the maps, for every chemical channel the intensities (in counts, after background and drift correction) are provided as a separate file, named as “ds01_Area1_Na.csv”, etc. These maps were obtained using a 20 µm square spot. This data can be used to obtain the images shown in the manuscript. For the additional map shown as Figure 9 in the manuscript, data were obtained using a LA-ICP-TOFMS for imaging a sample of the last glacial period in the EPICA Dome C (EDC) ice core, bag 1065. The maps were acquired using a 35 µm square spot, with 50% overlap between neighboring pixels to increase the spatial resolution horizontally.</p>
ICELEARNING - Detection of ice core particles via deep neural networks
<p>This dataset refers to the ICELEARNING project - Detection of ice core particles via deep neural networks, by Maffezzoli N. et al., <em>The Cryosphere</em>, 10.5194/tc-17-539-2023, 2023.</p> <p>The main folder contains all TRAINING data. </p> <p>The TEST data are contained in the folder /test. </p> <p>Please refer to the <a href="https://github.com/nmaffe/icelearning">icelearning GitHub</a> repository for instructions. </p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Measurements of Ice Nucleating Particles in Beijing, China - Data and processing code
<p>Dataset needed to replicate findings published in the journal article "Measurements of Ice Nucleating Particles in Beijing, China, published in the Journal of Geophysical Research. The dataset contains the following:</p> <p>1. Data files containing raw data from a Continuous Flow Diffusion Chamber - Ice Activation Spectrometer (CFDC-IAS), in comma-delimited format).</p> <p>2. Data and processing files for analysis of backward air trajectories as an Igor Pro 8 packed experiment package file. Igor Pro is available from www.wavemetrics.com and a free 30-day trial version can be used to export data to other formats.</p> <p>3. Data and processing files for analysis of CFDC, APS and meteorological data, including data in the form of waves, as part of an Igor Pro 8 packed experiment package.</p>
Dataset to: Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems
<p>The dataset contains supplementary information to the manuscript "Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems"</p> <p>The file <a href="https://zenodo.org/api/records/14988900/draft/files/INP_data_all_samples.csv/content" target="_blank" rel="noopener noreferrer">INP_data_all_samples.csv</a> contains information on the ice nucleation measurements for all samples presented.</p> <p>The file "<a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_16S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_16S.xlsx</a>" contains a list of the bacterial taxa that significantly correlated with the concentration of INPs observed in the samples, while the file <a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_18S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_18S.xlsx</a> contains the same information for the microalgae. </p> <p>The relative abundance of the taxa in each sample is indicated in the columns "F" to "T". </p>
Dataset for "Bioaerosol diversity and Ice nucleating particles in the North-Western Himalayan Region"
<p>Dataset for "Bioaerosol diversity and Ice nucleating particles in the North-Western Himalayan Region". Contains data and scripts to reproduce the figures in the manuscript. See README.md for more information.</p>
CNN for the classification of ICE-CAMERA images of Antarctic ice particles
<p>-The file 'ZENODO_FILES.rar' contains the GoogleNet Convolutional Neural Network (CNN) trained to classify pre-processed ICE-CAMERA images (224*224*3) into 14 classes. CNN was developed for (Mathworks) MATLAB® R2020b.</p> <p>-The ICE-CAMERA images used for training, validation and testing the CNN are also contained in specific folders.</p> <p> </p>
Scattering matrices and integral scattering quantities of laboratory-characterized silicate and ice particles
<p>Mueller scattering matrices and integral scattering quantities of four different scattering particle shapes, two refractive indices, and various size parameters computed using ADDA (v1.2; Yurkin and Hoekstra, JQSRT 112, pp. 2234-2247, 2011). The file name consists of the particle shape designation (A-D), the refractive index designation ("m254" depicts a silicate-rich particle: m = 2.54 + 0.01i; "m178" depicts an ice-rich particle with impurities: m = 1.78 + 0.001i; both at microwave frequencies), and the volume-equivalent size parameter (spherical-volume-equivalent particle perimeter length per wavelength) ranging from 0.25 to 7-16 depending on the shape type. "CS" refers to Cross Sections, which includes the extinction cross section, the extinction efficiency, the absorption cross section, and the absorption efficiency. The cross sections assume a wavelength of 6.283185307. All Mueller scattering matrices are 4 x 4 matrices for 180 different scattering angles, and orientation-averaged over hundreds or thousands of orientations. The scattering matrix files include a descriptive header line. In the ADDA computations, the polarizability prescription is "Lattice dispersion relation" when |m| < 2 and "Filtered coupled dipoles" when |m| > 2. The Interaction term prescription is "Point dipoles" when |m| < 2 and "Filtered Green's tensor" when |m| > 2.</p> <p>The corresponding scatterer shape models are given in the files ending "dipoles_x12.out". ADDA uses scatterer shape models that have been discretized into equally-sized cubic voxels. The shape model files list the x, y, and z coordinates of each voxel. The shape models have been scaled to optimize computation times but ensuring that the number of dipoles per wavelength is greater than 10|m| for each size parameter. The given shape model fulfills this condition for a size parameter of 12. The particle shape models have been derived from atmospheric dust particles by scanning-electron microscopes (Lindqvist et al., Atmos. Chem. Phys. 14, 143-157, 2014).</p>
Dataset to: Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland
<p>This repository contains the datasets used in the study 'Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland'. Detailed information and technical aspects of the data can be found in the publication.</p>
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Data for figures in "Next-generation ice nucleating particle sampling on aircraft: Characterization of the High-volume flow aERosol particle filter sAmpler (HERA)"
<p>Atmospheric ice nucleating particle (INP) concentration data from the free troposphere are sparse, but urgently needed to understand vertical transport processes of INPs and their influence on cloud formation and properties. Here, we introduce the new High-volume flow aERosol particle filter sAmpler (HERA) which was specially developed for installation on research aircraft and subsequent offline INP analysis. HERA is a modular system constisting of a sampling unit and a powerful pump unit and has several features which were integrated specifically for INP sampling. Firstly, the pump unit enables sampling at flow rates exceeding 100 L min<sup>−1</sup>, which is well above typical flow rates of aircraft INP sampling systems described in the literature (~10 L min<sup>−1</sup>). Consequently, required sampling times to capture rare, high-temperature INPs (≥-15 °C) are reduced in comparison to other systems and potential source regions of INPs can be confined more precisely. Secondly, the sampling unit is designed as a seven-way valve, enabling switching between six filter holders and a bypass with one filter being sampled at a time. In contrast to other aircraft INP sampling systems, the valve position is controlled remotely via software so that manual filter changes in-flight are eliminated and the potential for sample contamination is decreased. This design is compatible with a high degree of automation, i.e., triggering filter changes depending on parameters like flight altitude, geographical location, temperature, or time. In addition to the design and principle of operation of HERA, this paper presents laboratory characterization experiments with size-selected test substances, i.e., SNOMAX® and Arizona Test Dust. The particles were sampled on filters with HERA, varying either particle diameter (300 nm to 800 nm) or flow rate (10 L min<sup>−1</sup> to 100 L min<sup>−1</sup>) between experiments. The subsequent offline INP analysis showed good agreement with literature data and comparable sampling efficiencies for all investigated particle sizes and flow rates. Furthermore, the deposition efficiency of atmospheric INPs in HERA was compared to a straightforward filter sampler and good agreement was found. Finally, results from the first campaign of HERA on the High Altitude and LOng range research aircraft (HALO) demonstrate the functionality of the new system in the context of aircraft application.</p> <p>The given csv files contain the data for reproducing the figures in the publication. The data structure of the csv files is explained in the README file.</p>
Experimental data for "Fragmentation of ice particles: laboratory experiments on graupel-graupel and graupel-snowflake collisions"
<p>Graupel-graupel and graupel-snowflake collisions<br> experiments were carried out in still air conditions in the cold room of the Wind tunnel laboratory of the<br> Johannes Gutenberg University, Mainz. Graupel particles were first exposed to supersaturated air conditions, under which dendritic ice crystals have grown on their surface.<br> All fragments resulting from graupel-graupel collisions were<br> collected and investigated under a digital microscope. Fragments resulting from graupel-snowflake collisions were observed and recorded instantly after collision using a<br> holographic instrument. Number, size, area, and aspect ratio distributions of fragments were derived. </p> <p>These files contain data collected during the graupel-graupel and graupel-snowflake collisions. These data served for generating the figures in the publication.</p> <p>The files Graupel_graupel_number_CKE_mass_size.csv and Graupel_snowflake_CKE_mass_size give information on the experimental properties of the graupel-graupel and graupel-snowflake collisions, respectively.<br> For these two files, the first column gives the name of the experiment (e.g. snow1). Each line is corresponding to one collision experiment.</p> <p>The concrete data for each collision experiment is provided in the data file having the corresponding name, e.g., snow_data1.csv contains the data of collision experiment snow1, etc.</p> <p>Readme_graupel_data.txt and Readme_snow_data.txt contain collision kinetic energies of graupel-graupel and graupel-snowflake collisions, respectively.</p> <p>Graupel_crystal_surface.csv gives the estimated number of crystal for defined bins (Fig. 12 dashed line) on the surface of the 2 mm graupel before the collision.</p> <p> </p>
Supplement to the article "Predicting the morphology of ice particles in deep convection using the super-droplet method" (Shima et al., 2020, GMD)
<p>This is a supplement to the article "Predicting the morphology of ice particles in deep convection using the super-droplet method" authored by Shin-ichiro Shima, Yousuke Sato, Akihiro Hashimoto, and Ryohei Misumi, published in Geosci. Model Dev., 2020.</p> <p>Typical realization of CTRL, simulated by SCALE-SDM 0.2.5-2.2.0</p> <ul> <li>Movie01.QHYD_TYP-CTRL_2.2.0.gif: Spatial structure of the cumulonimbus</li> <li>Movie02.M-D_TYP-CTRL_2.2.0.gif: Mass-dimension relationship of the ice particles</li> <li>Movie03.phi-D_TYP-CTRL_2.2.0.gif : Aspect ratio–dimension relationship of the ice particles</li> <li>Movie04.rho-D_TYP-CTRL_2.2.0.gif : Apparent density–dimension relationship of the ice particles</li> <li>Movie05.V-D_TYP-CTRL_2.2.0.gif : Velocity-dimension relationship of the ice particles</li> </ul> <p>One realization of DX/2, simulated by SCALE-SDM 0.2.5-2.2.0</p> <ul> <li>Movie06.QHYD_DXx0.5_2.2.0.gif: Spatial structure of the cumulonimbus</li> </ul> <p>The same setup as Moves 1-5 (typical realization of CTRL) is used, but simulated by SCALE-SDM 0.2.5-2.2.1</p> <ul> <li>Movie07.QHYD_TYP-CTRL.2.2.1.gif: Spatial structure of the cumulonimbus</li> <li>Movie08.M-D_TYP-CTRL_2.2.1.gif: Mass-dimension relationship of the ice particles</li> <li>Movie09.phi-D_TYP-CTRL_2.2.1.gif: Aspect ratio–dimension relationship of the ice particles</li> <li>Movie10.rho-D_TYP-CTRL_2.2.1.gif: Apparent density–dimension relationship of the ice particles</li> <li>Movie11.V-D_TYP-CTRL_2.2.1.gif: Velocity-dimension relationship of the ice particles</li> </ul> <p>The same setup as Moves 1-5 (typical realization of CTRL) is used, but simulated by SCALE-SDM 0.2.5-2.2.2</p> <ul> <li>Movie12.QHYD_TYP-CTRL.2.2.2.gif: Spatial structure of the cumulonimbus</li> <li>Movie13.M-D_TYP-CTRL_2.2.2.gif: Mass-dimension relationship of the ice particles</li> <li>Movie14.phi-D_TYP-CTRL_2.2.2.gif: Aspect ratio–dimension relationship of the ice particles</li> <li>Movie15.rho-D_TYP-CTRL_2.2.2.gif: Apparent density–dimension relationship of the ice particles</li> <li>Movie16.V-D_TYP-CTRL_2.2.2.gif: Velocity-dimension relationship of the ice particles</li> </ul>
Ice-Nucleating Particle Concentrations from the MC2/ISLAS 2021 campaign in Andenes, and NorESM2 simulations with observationally constrained INPs
<p>This dataset containts the data for the article <strong>Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model </strong>(https://doi.org/10.5194/acp-25-1617-2025), published in Atmospheric Chemistry and Physics (ACP). It consists of ice-nucleating particle (INP) measurements collected as part of the MC2/ISLAS campaign in Andenes, Norway (69<em>° </em>18’ N, 16<em>°</em> 07’ E) in 2021. Additionally, it consists of simultaneous aerosol measurements, back trajectories for the INP measurement times, and model data from the Norwegian Earth System model (Seland et al., 2020) where INP concentrations where constrained in the Arctic using these INP measurements. </p> <p><strong>Abstract:</strong></p> <p><em>Projections of global climate change and Arctic amplification are sensitive to the representation of low-level cloud phase in climate models. Ice-nucleating particles (INPs) are necessary for primary cloud ice formation at temperatures above approximately -38 °C, and thus significantly affect cloud phase and cloud radiative effect. Due to their complex and insufficiently understood variability, INPs constitute an important modelling challenge, especially in remote regions with few observations, such as the Arctic. In this study, INP observations were carried out at Andenes, Norway in March 2021. These observations were used as a basis for an Arctic-specific and purely temperature-dependent INP parameterization, and implemented into the Norwegian Earth System Model. This implementation results in an annual average increase in cloud liquid water path (CLWP) of 70 % for the Arctic, and improves the representation of cloud phase compared to satellite observations. The change in CLWP in boreal autumn and winter is found to likely be the dominant contributor to the annual average increase in net surface cloud radiative effect of 2 W m<sup>-2</sup>. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases with 0.7 °C in boreal autumn. These findings indicate that INPs could have a significant impact on Arctic climate, and that a region-specific INP parameterization can be a useful tool to improve cloud representation in the Arctic region.</em></p> <p>The dataset contains three subsets:</p> <p>i) <strong>aerosol_data:</strong> Observations of Ice-Nucleating Particles (INPs) in Andenes, Norway, March 2021, as well as simultaneous aerosol measurements</p> <p>ii) <strong>INP_trajectories: </strong>Back trajectories at the time of INP measurements</p> <p>iii) <strong>model_data: </strong>Simulations with NorESM2 using the Andenes 2021 INP observations to constrain INPs in the Arctic</p> <p>Additionally, scripts for visualizing the data and for reproducing the NorESM2 model setup can be found in the folder <strong>scripts. </strong>The data folders and scripts folder should be in the same repository when running the scripts.</p> <p>Some of the scripts use other openly available datasets. The availability of these are listed below. All the specific datasets can also be provided to the user upon request. </p> <p>The CALIOP L2 data used to derive SLF metrics (used in Fig07.py) and the CERES EBAF data (used in Fig12.py) can be downloaded freely at <a href="https://search.earthdata.nasa.gov/" rel="nofollow">https://search.earthdata.nasa.gov/</a>. The derived SLF metrics can also be found at Bruno (2022), and are also described in Hofer et al. (2024) and Shaw et al. (2022). The CALIPSO-GOCCP data product (used in Fig08.py) can be downloaded from https://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html. The surface radiation flux (used in Fig13.py) can be downloaded freely at <a href="https://www.pangaea.de/" rel="nofollow">https://www.pangaea.de/</a>. The ERA5 data used to produce the back trajectories can be found at https://doi.org/10.24381/cds.bd0915c6. The colormap from Crameri et al. (2020) was used when preparing the figures. </p> <p>References:</p> <ul> <li>Bruno, O. (2022). Distributions of supercooled liquid fraction from CALIOP V4 [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8289058" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8289058</a></li> <li>Crameri, F., Shephard, G.E. & Heron, P.J. The misuse of colour in science communication. <em>Nat Commun</em> <strong>11</strong>, 5444 (2020). https://doi.org/10.1038/s41467-020-19160-7</li> <li>Hofer, S., Hahn, L.C., Shaw, J.K. et al. Realistic representation of mixed-phase clouds increases projected climate warming. Commun Earth Environ 5, 390 (2024). <a href="https://doi.org/10.1038/s43247-024-01524-2" rel="nofollow">https://doi.org/10.1038/s43247-024-01524-2</a></li> <li>Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geoscientific Model Development, 13, 6165–6200, <a href="https://doi.org/10.5194/gmd-13-6165-2020" rel="nofollow">https://doi.org/10.5194/gmd-13-6165-2020</a>, 2020.</li> <li>Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. (2022). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49, e2021GL096191. <a href="https://doi.org/10.1029/2021GL096191" rel="nofollow">https://doi.org/10.1029/2021GL096191</a></li> </ul>
Data for the figures in "In-cloud characteristics observed in US Northeast and Midwest non-orographic winter storms with implications for ice particle mass growth and residence time"
<div> <div>This repository contains data shown in the figures in Allen, L. R., Yuter, S. E., Crowe, D. M., Miller, M. A., and Thornhill, K. L.: "In-cloud characteristics observed in US Northeast and Midwest non-orographic winter storms with implications for ice particle mass growth and residence time," to be submitted to Atmospheric Chemistry and Physics.</div> <div> </div> <div> <div> <div>Description of the original data sources: All of the NASA IMPACTS data are archived by GHRC at https://ghrc.nsstc.nasa.gov/uso/ds_details/collections/impactsC.html (McMurdie et al., 2019). The NSF PLOWS 1-second flight-level data are archived by the UCAR Earth Observing Laboratory at https://data.eol.ucar.edu/dataset/113.063 (UCAR/NCAR - Earth Observing Laboratory, 2011). ERA5 hourly data on pressure levels are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview (Hersbach et al., 2023b). ERA5 hourly single-level data are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview (Hersbach et al., 2023a).</div> </div> </div> </div>
Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback
<p>This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. </p> <p>Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files.</p> <p>Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article. </p>
Supplement to "Microwave and submillimeter wave scattering of oriented ice particles"
<p>This data set contains the simulated brightness temperatures and the corresponding atmospheric states for the radiative transfer simulations for the journal article "<a href="https://doi.org/10.5194/amt-2019-382">Microwave and submillimeter wave scattering of oriented ice particles</a>", which is on Atmospheric Measurement Techniques (AMTD).</p>
Data and metadata associated with ice-nucleating particles during the 2022 Arctic Cold Air Outbreak Campaign
<p>Data and metadata associated with analysis of ice-nucleating particle concentrations and aerosol-size distributions measured during the March 2022 Arctic Cold Air Outbreak campaign.</p> <p>Contains INP concentrations and aerosol-size normalisations of these, parametrisations of these, SEM data and metadata and backtrajectory modelling inputs and results.</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.