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Figure 6 in Cloudy with a chance of speciation: integrative taxonomy reveals extraordinary divergence within a Mesoamerican cloud forest bird
Figure 6. Results of tests for strong niche divergence on multivariate niche axis in relationship to the phylogeny. Boxes show whether each niche axis was more divergent than background divergence (diverged), more similar than background divergence (conserved), or was similar to background divergence and therefore failed to reject the null hypothesis (null). Percentages indicate the amount of variation explained by that axis.
Figure 5 in Cloudy with a chance of speciation: integrative taxonomy reveals extraordinary divergence within a Mesoamerican cloud forest bird
Figure 5. Phylogenies of Aphelocoma unicolor based on mitochondrial DNA and ultraconserved elements (UCEs). For the Bayesian time-calibrated mitochondrial DNA phylogeny generated in BEAST, the mean estimated split dates are provided on the nodes, with the 95% highest probability density shown below in square brackets. For both phylogenies, nodes with perfect support are shown with black dots.
Figure 4 in Cloudy with a chance of speciation: integrative taxonomy reveals extraordinary divergence within a Mesoamerican cloud forest bird
Figure 4. Results of a discriminant function (DF) analysis and normal mixture models on all morphological and plumage traits. A, differences among all five Aphelocoma unicolor subspecies in the first two DF axes. B, differences between only the A. u. unicolor and A. u. griscomi subspecies in the third and fourth DF axes. C, D, results of normal mixture modelling to determine the objective number of phenotypic clusters among individuals west (C) and east (D) of the Isthmus of Tehuantepec, with inset showing the assignment of individuals to each cluster with respect to their a priori subspecies assignment.
Figure 3 in Cloudy with a chance of speciation: integrative taxonomy reveals extraordinary divergence within a Mesoamerican cloud forest bird
Figure 3. Scatterplot of hue and colour saturation (chroma) for the five Aphelocoma unicolor subspecies.
Figure 2 in Cloudy with a chance of speciation: integrative taxonomy reveals extraordinary divergence within a Mesoamerican cloud forest bird
Figure 2. Differences among the five Aphelocoma unicolor subspecies for six morphological traits (measured in millimetres).
Figure 1. A, a in Cloudy with a chance of speciation: integrative taxonomy reveals extraordinary divergence within a Mesoamerican cloud forest bird
Figure 1. A, a unicolored jay (Aphelocoma unicolor unicolor) from Reserva de Biósfera Sierra de las Minas, Guatemala (Macauley Library ML85163771, photograph by Daniel Aldana). B, specimens representing A. u. guerrerensis (MLZ 45972), A. u. concolor (NMNH A9096), A. u. oaxacae (MLZ 33558), A. u. unicolor (MLZ 45360) and A. u. griscomi (AMNH 327521). C, distribution map of A. unicolor subspecies drawn from eBird observations.
PointFaces: Point Cloud Dataset of Facial Landmarks and Blendshapes
<p>The dataset is based on 1000 facial landmark captures performed on the FFHQ facial image dataset.<br> The 3D blendshape models are sculpted based on the MediaPipe canonical model.</p>
Data and scripts for Fudan-NSA cloud fraction scheme
<p>Data and scripts for preparing the Fudan-NSA cloud fraction scheme.</p>
A prototyping software engineering approach for designing and implementing model-based cloud mobile application for rationalized energy consumption
<p>The dataset used in this study comprises four files containing household consumers' energy consumption records. These records are collected at hourly intervals, providing detailed information on the energy usage patterns of the households. The dataset serves as a valuable resource for analyzing energy consumption trends, developing energy management strategies, and exploring the potential for energy efficiency improvements in residential settings.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Archival bundle of the data used for "A 2-phase Strategy For Intelligent Cloud Operations"
<p>This archive contains the data used for the paper</p> <p><strong>A 2-phase Strategy For Intelligent Cloud Operations</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Remo Andreoli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella</p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/intelligent-cloud-operations">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-disk-load-<INCREMENTAL-ID>/</p> </td> <td> <p>distwalk runs data</p> </td> </tr> <tr> <td> <p>FINAL-rl3-*/</p> </td> <td> <p>Cassandra runs data</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models used for the validation</p> </td> </tr> <tr> <td> <p>test_load_disk_01-2tpi.dat</p> </td> <td> <p>distwalk load trace used to generate the workload</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-server-distwalk-683d9e7.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>* <em>contact author</em></p>
Data for: UK redwoods terrestrial laser scanner point clouds
<p class="MsoNormal">Giant redwoods (<em>Sequoiadendron giganteum) </em>are some of the UK's largest trees, despite only being introduced in the mid-19th century. Given recent interest in planting redwoods in the UK, partly due to their carbon sequestration potential and also their undoubted public appeal, an understanding of their viability is important. However, little or no research has been conducted to quantitatively estimate their carbon uptake in UK conditions. We used 3D terrestrial laser scanning (TLS) to make detailed structure measurements of individual <em>S. giganteum</em> trees at three sites, to estimate aboveground biomass (AGB) and annual biomass accumulation rates. We show that UK-grown <em>S. giganteum</em> can sequester carbon at a rate of 80 - 100 kg C year<sup>-1</sup>, varying with climate, management and age. This accumulation rate is 2.5 and 20 times faster than commonly-grown UK plantation tree species. We develop new UK-specific allometric models for <em>S. giganteum</em>which fit observed AGB with r<sup>2</sup> > 0.93 and bias < 2% and can be used to estimate <em>S. giganteum</em> AGB more generally. <em>S. giganteum</em> appears to represent a small but potentially important addition to the UK's carbon sequestration efforts and this work provides a baseline for estimating their longer term AGB and carbon sequestration capacity.</p>
Stability of Nucleic Acid Bases in Concentrated Sulfuric Acid: Implications for the Habitability of Venus' Clouds
<p><strong>Supplementary datasets for the article accepted in PNAS entitled: </strong>Stability of Nucleic Acid Bases in Concentrated Sulfuric Acid: Implications for the Habitability of Venus’ Clouds.</p> <p>Dataset S1. Dataset S1 contains original UV-Vis data. </p> <p>The folder “UV-VIS_plots-data-code-tables DATASET S1.zip” contains the original UV-Vis data</p> <p>Dataset S2. Dataset S2 contains original NMR data. </p> <p>The folder “ORIGINAL NMR DATA_DATASET S2.zip” contains the original NMR data.<br> </p>
NOAA Open Data Dissemination: Petabyte scale Earth System Data in the Cloud
<p>NOAA Open Data Dissemination (NODD) makes NOAA environmental data publicly and freely available on Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP). These data can be accessed by anyone with an internet connection and span key datasets across the Earth system including satellite imagery, radar, weather models and observations, ocean databases, and climate data records. NODD works closely with NOAA stakeholders to make these data available consistently and performantly, often with near-real-time latency. Since its inception, NODD has grown to provide public access to more than 24PB of NOAA data and can support billions of requests and petabytes of access daily. NODD growth is driven by use; stake- holders routinely access more than 5PB of NODD data every month. NODD continues to grow to support open petabyte-scale Earth system data science in the cloud by investing in onboarding additional NOAA data, performant data formats, and streaming data platforms. Here, we document how this pro- gram works with a focus on data provenance, discuss the key datasets available through NODD, show how these data are being used, and highlight ways these data can be accessed with the goal of accelerating use of NOAA resources in the cloud.</p>
ICON-D2-ART output for "Aerosol-cloud-radiation interaction during Saharan dust episodes: the dusty cirrus puzzle"
<p>ICON-D2-ART output for the three dusty cirrus episodes that are discussed in section 3 of the paper.</p> <p>Some grib and netcdf data of ICON-D2-ART is provided for 16 March 2022 (Figure 4), 3 March 2021 (Figure 12) and 6 May 2022 (Figure 16). In addition, the sounding data of Figures 7 and 8 is included as well as the CERES statistics of Figures 6, 14, 17 and 18.</p>
Welcome again, Magellanic Clouds!
<p>This repository contains data for the simulations of Large Magellanic Cloud (LMC)'s<br> interaction with the Milky Way (MW).<br> Some of the data and all scripts require the Agama framework for stellar dynamics.<br> Units are: length = 1 kpc, velocity = 1 km/s, time = 0.978 Gyr (for simplicity,<br> referred to as Gyr throughout the text), mass = 232500 Msun, G = 1.<br> <br> There are six simulations with different combinations of initial LMC and MW mass<br> (see Section 2 in Vasiliev 2023 for a definition of initial profiles of both galaxies):<br> <br> name M_LMC[Msun] M_MW[Msun] time[Gyr] comment<br> L2M10 2e11 10e11 11 second passage, Tperi ~ 8.8 Gyr<br> L2M11 2e11 11e11 10 --"--, Tperi ~ 6.5 Gyr<br> L3M10 3e11 10e11 11 --"--, Tperi ~ 8.2 Gyr<br> L3M11 3e11 11e11 10 --"--, Tperi ~ 6.3 Gyr<br> L2M10first 2e11 10e11 4 first passage starting from apocentre<br> L3M10rad 3e11 10e11 11 radially anisotropic MW halo (beta=0.5)<br> <br> Each simulation's folder contains the following data:<br> - potential.ini: time-dependent potential of the entire system represented by<br> two series of Multipole expansions for each galaxy (the one for the LMC is moving),<br> plus the fixed MW stellar potential, plus non-inertial acceleration of the MW-centric<br> reference frame. This potential can be used to integrate orbits (resimulation).<br> - potential_bse.ini: same, but with potentials represented by BasisSet expansions<br> (not recommended, because it is generally slower to evaluate, but this format can<br> be more readily used in other galaxy modelling packages).<br> - potential_lmc_bound.ini: time-dependent moving potential of particles still bound<br> to the Magellanic system (defined so that their total energy in this potential is<br> negative).<br> - potential_lmc_init.ini: LMC potential at the start of the simulation.<br> - potential_mw_now.ini: Milky Way potential at present time (without the non-inertial<br> acceleration term).<br> - potential_mw_init.ini: Milky Way potential at the start of the simulation.<br> - trajlmc.txt: Galactocentric trajectory of the LMC (also used in the potentials).<br> - boundmass.txt: evolution of bound mass of the LMC (in units of Msun).<br> - snapshot.npz: numpy zip archive with the following arrays:<br> * pos: Nx3 array of particle positions at present time (t=0) in the standard<br> Galactocentric coordinate system from Astropy (where the Sun is at -8.122,0,0.02);<br> * vel: Nx3 array of particle velocities;<br> * mass: array of particle masses of length N;<br> * tstrip: time at which the particle became unbound from the LMC (length Nlmc),<br> if greater than zero, it is still bound at present time.<br> The first Nlmc = 2e6 particles represent the Magellanic system, the next 7e6<br> represent the MW halo, and the remaining 1e6 represent the stellar disc+bulge;<br> all particles from the same category have identical masses.<br> <br> The folder "scripts" contains the following Python scripts or Jupyter notebooks:<br> - find_lmc_orbit.ipynb: notebook illustrating the method for finding orbital initial<br> conditions by running a bunch of simulations with slightly different initial points.<br> Instead of running an actual simulation, it creates a fake trajectory by integrating<br> equations of motion of LMC and MW as two extended rigid bodies, taking into account<br> dynamical friction (but note that this approximation is not particularly realistic).<br> One may adapt the code to actual simulations by replacing one specific routine.<br> - resimulate_orbits.py: script for performing orbit integration in the recorded<br> approximated potential of both galaxies; the actual particle trajecties in the<br> simulation may differ individually, but on average this gives a realistic result.<br> - membership_analysis.py: script for computing the probability of Magellanic<br> association for nearby dwarf galaxies, using data from the text file satellites.txt<br> (positions and distances taken from the catalogue of McConnachie,<br> https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/community/nearby/<br> and proper motions from Battaglia et al.2022).<br> - plot_snapshot.py: script for plotting the spatial distribution and velocity map of<br> Magellanic debris, together with the observed positions and velocities of dwarfs.<br> - adaptive_histogram.py: auxiliary module for variable-bandwidth KDE construction.</p>
The spectral energy distributions of classical Cepheids in the Magellanic Clouds
<p>The spectral energy distributions (SEDs) of a sample of 142 LMC and 77 SMC fundamental mode classical Cepheids (CCs) were constructed using photometric data in the literature.<br> The sample was build from stars that have a metallicity determination from high-resolution spectroscopy,<br> have been used in Baade-Wesselink type of analysis, have a radial velocity curve published in {\it Gaia} DR3, have Walraven photometry, or have their light- and radial-velocity curves modelled by pulsation codes.<br> <br> The SEDs were fitted with stellar photosphere models to derive the best-fitting luminosity and effective temperature.<br> Distance and reddening were taken from the literature.<br> <br> Only one star with a significant infrared (IR) excess was found in the LMC and none in the SMC, contrary to earlier work on the Milky Way (MW) suggesting that IR excess may be more prominent in MW cepheids than in the Magellanic Clouds.</p> <p>The stars were plotted in a Hertzsprung-Russell diagram (HRD) and compared<br> to evolutionary tracks for CCs and to theoretical instability strips.<br> For the large majority of stars, the position in the HRD is consistent with the instability strip.<br> <br> Period-luminosity ($PL$) and period-radius relations are derived and compared to these relations in the MW.</p>
WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Train part
<p>The training parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons.</p> <p>The validation and testing parts can be found on: <a href="https://doi.org/10.5281/zenodo.8035349">https://doi.org/10.5281/zenodo.8035349</a></p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,<a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p>[2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p> <p>[3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, <a href="http://10.1109/JSTARS.2022.3211857">http://doi.org/10.1109/JSTARS.2022.3211857</a>.</p>
Supporting data for ''Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions"
<p>Supporting data for our work on Nature Climate Change. You will find:</p> <p> - python codes for generating the plots in the manuscript.</p>
ACA CO(J=2-1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
<p>(1) M33_ACA7m+IRAM_12CO21_cube_pbcor_kelvin.fits.tar.gz (661 MB): <sup>12</sup>CO(<em>J</em>=2-1) data cube obtained with the ACA 7m array combined with the IRAM 30m telescope, 7".31 x 6".50 beam, 0.7 km/s resolution<br> (2) M33_ACA7m-only_12CO21_cube_pbcor_kelvin.fits.tar.gz (660.6 MB): <sup>12</sup>CO(<em>J</em>=2-1) data cube obtained with the ACA stand-alone mode (7m array only), 7".31 x 6".50 beam, 0.7 km/s resolution<br> (3) M33_ACA7m-only_13CO21_cube_pbcor_kelvin.fits.tar.gz (331.5 MB): <sup>13</sup>CO(<em>J</em>=2-1) data cube obtained with the ACA stand-alone mode (7m array only), 7".72 x 6".86 beam, 1.4 km/s resolution<br> (4) M33_ACA7m-only_C18O21_cube_pbcor_kelvin.fits.tar.gz (289.9 MB): C<sup>18</sup>O(<em>J</em>=2-1) data cube obtained with the ACA stand-alone mode (7m array only), 7".82 x 6".96 beam, 1.6 km/s resolution</p>
Data for the publication: Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients
<p>The files contain the datasets shown in the publication "Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients" to appear in J. Geophys. Res. Atmos. (revised manuscript submitted). The files are xmgrace plot files containing the research data (ASCII) shown in all figures in the main text and Appendix A.</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.