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FIGURE 5 in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 5. Anticyphon oyonensis sp. nov., abdomen. A) male, B) female.
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset (Version 2).
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This is an update of the previous zenodo.231216 upload. This update includes revised PDF atlases for Be59 and IC348 (with revised vales of the SED slope and [3.6] and [4.5]-band magnitudes).</p>
Preliminary Breakdown Process of Winter Positive Cloud-to-Ground Lightning Flash and Its relation to the Following First Return Stroke
<p>The file <em>+CG statistics.xlsx</em> contains various statistical parameters for 60 +CG events.</p> <p>The files <em>3D_UPB.dat</em>, <em>3D_DPB.dat</em>, <em>3D_IRPB1.dat</em>, and <em>3D_IRPB2.dat</em> provide the 3D location results for the four example events discussed in the main text. Each file includes data organized in four lines, representing time (ms), x (m), y (m), and z (m), respectively.</p> <p><strong> </strong></p>
Refractive Indices For Virga Exoplanet Cloud Model
<p><strong>Difference between v1 and v2? </strong></p> <p>The optical constants were updated based on Batalha et al (in prep). The default values and references are located in the <a href="https://github.com/natashabatalha/virga/blob/master/virga/ior_factory.py">IOR Factory</a>. Additionally the specific radii for which the mieff parameters were computed have also changed. Therefore if using Virga V1 we recommend using the Mieff's computed in V2. </p> <p><strong>Additional Details</strong></p> <p>These files are used to compute parameterized cloud models with the <a href="http://github.com/natashabatalha/virga">Virga Exoplanet Cloud Model</a>. The <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#">documentation is available</a> here for specifically how these files are included into virga.</p> <p><strong>1. REFRACTIVE INDICES: (.refrind): </strong>contain the refractive indices of each condensate species. If you are running cloud models utilizing this data, please cite the corresponding source for each species listed below. All files are 4 columns structured as : </p> <blockquote> <p>index, wavelength (micron), real part, imaginary part. </p> </blockquote> <p>Virga reads in these files <a href="https://github.com/natashabatalha/virga/blob/32665150fc83769cae46fd3290f5a237f959712c/virga/calc_mie.py#L452">in this routine</a> but you can simply use this python code:</p> <pre><code>filename = "H2O.refrind" idummy, wave, nn, kk = np.loadtxt(open(filename,'rt').readlines(), unpack=True, usecols=[0,1,2,3])#[:-1] </code></pre> <p> </p> <p><strong>2. MIE PARAMETERS: (.mieff): </strong>There are specific tutorials and functions in virga that will guide you through computing these on your own. However, we provide them here for completeness. The program that calculates these is <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Creating-the-Mie-scattering-Database">demonstrated here</a>. Note that each Mie parameters are averaged 6 points within the wavelength bin. You can use this function here to <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Analyzing-Mie-Parameters">parse the data</a>. Or, you can simply read the mieff files with this code: </p> <pre><code>import pandas as pd gas = "H2O" df = pd.read_csv(gas+".mieff",names=['wave','qscat','qext','cos_qscat'], delim_whitespace=True) </code></pre> <p> </p> <p><strong>CITATIONS TO REFERENCE FOR EACH SPECIES (See Table 1 Batalha et al. submitted):</strong></p> <p> </p>
Data for: Low temperature ice nucleation of sea spray and secondary marine aerosols under cirrus cloud conditions
<p>Sea spray aerosols (SSA) represent one of the most abundant aerosol types on a global scale and have been observed at all altitudes including the upper troposphere. SSA has been explored in recent years as a source of ice nucleating particles (INPs) in cirrus clouds due to the ubiquity of cirrus clouds and the uncertainties in their radiative forcing. This study expands upon previous works on low temperature ice nucleation of SSA by investigating the effects of atmospheric aging of SSA and the ice nucleating activity of newly formed secondary marine aerosols (SMA) using an oxidation flow reactor. Polydisperse aerosol distributions were generated from a Marine Aerosol Reference Tank (MART) filled with 120 L of real or artificial seawater and were dried to very low relative humidity to crystallize the salt constituents of SSA prior to their subsequent freezing, which was measured using a Continuous Flow Diffusion Chamber (CFDC). Results show that for both primary SSA (pSSA), and the aged SSA and SMA (aSSA+SMA) at temperatures > 220 K, homogeneous conditions (92–97 % relative humidity with respect to water (RHw)) were required to freeze 1 % of the particles. However, below 220 K, heterogeneous nucleation occurs for both pSSA and aSSA+SMA at much lower RHw, where up to 1 % of the aerosol population freezes between 75–80 % RHw. Similarities between freezing behaviors of the pSSA and aSSA+SMA at all temperatures suggest that the contributions of condensed organics onto the pSSA or alteration of functional groups in pSSA via atmospheric aging did not hinder the major heterogeneous ice nucleation process at these cirrus temperatures that has previously been shown to be dominated by the crystalline salts. Occurrence of 1% frozen fraction of SMA, generated in the absence of primary SSA, was observed at/near water saturation below 220 K, suggesting it is not an effective INP at cirrus temperatures, similar to findings in the literature of other organic aerosols. Thus, any SMA coatings on the pSSA would only decrease the ice nucleation behavior of pSSA if the organic components were able to significantly delay water uptake of the inorganic salts, and apparently, this was not the case. Results from this study demonstrate the ability of lofted primary sea spray particles to remain an effective ice nucleator at cirrus temperatures, even after atmospheric aging has occurred over a period of days in the marine boundary layer prior to lofting. We were not able to address aging processes under upper tropospheric conditions.</p>
MODIS-VIIRS Cloud Regimes
<p>The MODIS-VIIRS Cloud Regimes are a discrete classification of cloud fields at the mesoscale as observed by the VIIRS sensors aboard the Suomi-NPP and NOAA-20 satellites, as well as the MODIS sensor aboard the Aqua satellite using the Continuity algorithm. Derived by applying the <i>k</i>-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. This MODIS-VIIRS Cloud Regimes dataset is an independently derived dataset which can be viewed as an extension of the work that produced the MODIS Cloud Regimes (Cho et al. 2021, Oreopoulos 2021).</p><p>The MODIS-VIIRS Cloud Regimes are derived similarly by applying a <i>k</i>-means clustering algorithm to the joint-histograms at equal-area grids of 110 km every 3-h generated from granule (Level-2) cloud products available from the Continuity Algorithm "CLDPROP" applied on observations by the MODIS Aqua, VIIRS Suomi-NPP, and VIIRS NOAA-20 sensors during the period of March 2018 to February 2023. A cluster number of <i>k</i> = 11 was selected, which when including a clear sky regime gives a total of 12 MODIS-VIIRS Cloud Regimes. Each histogram is then assigned to the nearest <i>k</i>-means centroid to obtain its regime membership. Two variants of the VIIRS Cloud Regimes are available: (1) a 3-h product in the original 110-km native resolution of its derivation, and (2) a 3-h product derived from the 110-km product by nearest neighbor interpolation to 1°.</p><p>Cho, N., J. Tan, and L. Oreopoulos, 2021: Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes. <i>J. Appl. Meteorol. Climatol.</i>, <strong>60</strong>, 981–997, https://doi.org/10.1175/JAMC-D-20-0247.1.</p><p>Oreopoulos, L., 2021: MODIS CR Equal Area 3-Hour. https://doi.org/10.5067/MEASURES/MODISCR/EQAR3H/DATA301.</p>
Contrasting Activation Characteristics of Biomass Burning and Fossil Fuel Combustion Aerosols in Fogs and Clouds: Implications for Regional Air Quality and Climate
<p>The key 'jul' in data use 2021-01-01 as the referece day, for example, 2021-01-02 12:00:00 corresponding to jul of 2.5. </p>
Hard Point Cloud Localization Dataset
<p>This is a dataset to evaluate the robustness of point-cloud-based localization algorithms in extremely severe situations including aggressive sensor motion, point cloud degeneration, and data interruptions.</p><p>The dataset contains five indoor sequences recorded with a Microsoft Azure Kinect and three outdoor sequences recorded with a Livox MID360. Each outdoor sequence is split into two consecutive rosbags.</p><p>indoor_easy_01 & 02 : "Easy" sequences without aggressive sensor motion and data interruptions.<br>indoor_hard_01 : "Hard" sequence involving quick sensor motion and point cloud degeneration.<br>indoor_kidnap_01 & 02 : Sequences that involve kidnapping situations (the sensor was moved to another room while its view was completely occluded several times)<br>outdoor_hard_01 & 02 : "Hard" outdoor sequence involving quick sensor motion and point cloud degeneration.<br>outdoor_kidnap : Outdoor sequence that involve kidnapping situations.<br><br>Each rosbag corresponds to an environmental map file as follows:<br>indoor_easy : map_indoor_easy.ply<br>indoor_kidnap : map_indoor_easy.ply<br>indoor_hard : map_indoor_hard.ply<br>outdoor_kidnap : map_outdoor_kidnap.ply<br>outdoor_hard : map_outdoor_hard.ply<br><br>Each indoor sequence contains the following ROS2 messages:<br>- /points2/decompressed : sensor_msgs/msg/PointCloud2 : Point cloud data<br>- /imu : sensor_msgs/msg/Imu : Imu data<br><br>Each outdoor sequence contains the following ROS2 messages:<br>- /livox/points : sensor_msgs/msg/PointCloud2 : Point cloud data<br>- /livox/lidar : livox_ros_driver2/msg/CustomMsg : Point cloud data in the Livox format<br>- /livox/imu : sensor_msgs/msg/Imu : Imu data<br><br>We used the follwoing LiDAR-IMU transformation parameters (T_lidar_imu : [tx, ty, tz, qx, qy, qz, qw]) for the indoor and outdoor sequences:<br>- indoor : [0.003, 0.004, -0.051, -0.476, 0.474, 0.524, 0.525]<br>- outdoor: [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]<br><br>Groundtruth trajectories were obtained through batch optimization of point cloud registration residuals and IMU motion residuals. Each line in a GT trajectory file represents the LiDAR pose in the map frame [time, tx, ty, tz, qx, qy, qz, qw] (TUM format). We recommend evo toolkit (https://github.com/MichaelGrupp/evo) for quantitative evaluation.</p>
Data for "Statistical characteristics of thunderstorm activity in the middle reaches of the Yangtze River Basin based on a five-year cloud-to-ground lighting dataset"
<p>Data for "Statistical characteristics of thunderstorm activity in the middle reaches of the Yangtze River Basin based on a five-year"</p>
The diurnal data of the aerosol extinction coefficient of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma
<p><strong>The diurnal data of the the vertical average aerosol extinction coefficient of 0.15-2.5 km of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma in July 2018 and July 2019.</strong></p>
2DVD dataset for GMD publication - Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign
<p>This archive contains the 2DVD measurement of graupel particles used in the GMD paper "Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign".</p><p>For each identified graupel particle, the following are included:</p><ul><li>Volume-equivalent diameter (mm)</li><li>Density (g cm-3)</li><li>Fall velocity (m s-1)</li></ul>
Data and code for Spaulding-Astudillo and Mitchell (2023c), "Clear-sky convergence and the origin of tropical congestus clouds"
<p>Data and code for Spaulding-Astudillo and Mitchell (2023c), "Clear-sky convergence and the origin of tropical congestus clouds".</p><p>The Python code used to create Figures 1, 3-6 and supplementary Figure 1 are included in separate directories. Within each directory, we include experimental data generated by the Reference Forward Model (RFM; Dudhia 2017) that is needed to reproduce the figures. To create the figures, run the provided code in a Jupyter notebook or from the command line. Helper functions for data processing within the figure scripts have the nomenclature ..._data.py. Note that the provided code only allows you to visualize RFM data, not produce it yourself. To do so, you must download and run RFM yourself (https://eodg.atm.ox.ac.uk/RFM/). In addition, Figure 1 requires spatio-temporally averaged data from MERRA-2, ERA5, and CloudSat/CALIPSO (Betrand et al. 2023), which we provide in pickle files. See the main text for data citations, which will allow you to access the original, non-averaged climate data. </p><p>RFM data directories have the nomenclature /ctrl-..... For example, ctrl-RHmid-75-zmid-7-uniform-1 where:<br>RHmid=75% is the relative humidity at a mid-tropospheric height zmid=7.5 km. Uniform=True indicates that the tropospheric relative humidity is constant with height. When uniform=False, the relative humidity follows a C-shaped distribution between the surface and the tropopause. The helper functions in ..._data.py allow you to read the RFM output from .asc files. The nomenclature of the RFM output files is standardized: https://eodg.atm.ox.ac.uk/RFM/. </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
Data from "Compact Disks in a High-resolution ALMA Survey of Dust Structures in the Taurus Molecular Cloud"
<p>Continuum fits images for all disks in our ALMA Cycle 4 Taurus disk survey - see details in Long et al., 2018, ApJ, 869, 17 and Long et al., 2019, ApJ, 882, 49</p>
Dynamically coupled kinetic chemistry in brown dwarf atmospheres - II. Cloud and chemistry connections in directly imaged sub-Jupiter exoplanets
<p>Gifs of GCM output from the paper, model is Teff = 1000 K, log g = 3, M/H = 1. </p><p>The atmos_daily_2980.nc file contains the GCM NETCDF output at 2080 days.</p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 2
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
An LES perturbed parameter ensemble of free-tropospheric cloud-controlling factors on stratocumulus
<p>This dataset contains a perturbed parameter ensemble of large-eddy simulations to assess the effect of two free-tropospheric cloud-controlling factors on stratocumulus clouds properties. The simulations were run on the UK Met Office/NERC cloud model (MONC) for the DYCOMS-II RF01 nocturnal stratocumulus case (Stevens et. al., 2005). Each simulation had the same initial conditions except for the two perturbed parameters, which were the jumps in moisture and temperature at the temperature inversion at cloud top. The corresponding analysis code can be found at this <a href="https://github.com/eers1/dycoms_analysis">dycoms_analysis Github page</a>. </p><p> </p><p>Stevens, B., Moeng, C. H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de Roode, S., . . . Zhu, P. (2005). Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Monthly Weather Review , 133 (6), 1443–1462. doi: 10.1175/MWR2930.1</p>
LiDAR and Photogrammetry Point Clouds and R code
<p>Some examples of point clouds from lidar and photogrammetry, from drone and from helicopter surveys.</p> <p>These data are used in the course "Remote Sensing" to forestry students at University of Padova, TESAF Department</p> <p> </p>
Ethiopia Street - Point cloud to Mesh
This is a test to convert an iPad LIDAR point cloud into a PLY mesh using CloudCompare software. Source: Objaverse 1.0 / Sketchfab
Point Cloud -- Umm an-Nar Tomb
Mleiha 3 – Umm an-Nar Tomb "Close to the Mleiha Archaeological Centre lies the Umm an-Nar tomb, which is considered to be the most impressive grave building among the many ancient funeral sites in the Mleiha area. Constructed around 2300 BCE, the burial site was used for approximately 200 years. Measuring in at a huge 13.85m in diameter, this circular Bronze Age grave is one of the largest from the Umm an-Nar period throughout the whole of the UAE. The grave chamber is separated into an eastern and western half, each then further sub-divided into four units with doorways connecting one chamber to another. The discovery of a single well-shaped rectangular stone block (typical of the tomb buildings of the Umm an-Nar Early Bronze Age culture)" from: http://www.discovermleiha.ae/archaeological-sites/ Point cloud was made using two FARO Focus3D laser scanners. Source: Objaverse 1.0 / Sketchfab
Dreamland-castle-stylized-point-cloud-2600k-pts
Nara Dreamland Castle - stylized point cloud 2600k pts [[ low resolution input test from last year, low resolution point cloud ]] 100 captured frames from the following video were used to create this point cloud last year: https://www.youtube.com/watch?v=2u1gcSbrTWw more information about this location can be found here: https://www.timetravelturtle.com/nara-dreamland-abandoned-japan/ and here: https://en.wikipedia.org/wiki/Nara_Dreamland Tools used: youtube-dl, ffmpeg, python, colmap, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
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.