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opennotspecifiedJun 2024View details →

FIGURE 3 in Contribution to the understanding of the life history of Cyllopsis hilaria (Godman, 1901) (Lepidoptera: Nymphalidae: Satyrinae) through observations in Costa Rica

FIGURE 3. Adult of Cyllopsis hilaria. A, recently emerged adult following 12 days of pupal stage; B, close-up showing black submarginal ocelli.

opennotspecifiedJun 2024View details →

FIGURE 2 in Contribution to the understanding of the life history of Cyllopsis hilaria (Godman, 1901) (Lepidoptera: Nymphalidae: Satyrinae) through observations in Costa Rica

FIGURE 2. Late immature stage of Cyllopsis hilaria. A, frontal view of the head capsule; B, close-up of the two distinct tails and the dark green dorsal line; C, lateral view of the body, highlighting subspiracular yellow lines.

opennotspecifiedJun 2024View details →

FIGURE 1 in Contribution to the understanding of the life history of Cyllopsis hilaria (Godman, 1901) (Lepidoptera: Nymphalidae: Satyrinae) through observations in Costa Rica

FIGURE 1. Costa Rican specimens of Cyllopsis hilaria preserved at the National Museum of Natural History, US. A, female specimen from Monteverde, Puntarenas (underside view); B, male specimen from Poas Volcano, Costa Rica (underside view). Photographs by Brian Harris.

BVOC observations in Kleiner Feldberg (2011)

<p>The dataset contains both ambient measurements and emission rates as described in:</p> <p>Bourtsoukidis et al. (2012): https://doi.org/10.5194/bg-9-4337-2012 </p> <p>Bourtsoukidis et al. (2014): https://doi.org/10.5194/acp-14-6495-2014 </p>

The VISTA Variables in the Vía Láctea eXtended (VVVX) ESO public survey: Completion of the observations and legacy - Appendix A

<p><strong>Table A.1. </strong>List the VVV+VVVX tiles and observed number of epochs. We present the tile names and centre coordinates in Equatorial and Galactic systems (see Fig. A.1 at the end for the spatial distribution). Columns 6 to 8 present the number of epochs observed in J, H and Ks during the VVV and VVVX campaigns. The first number is the total of epochs, and in parentheses the number of epochs observed in VVV and VVVX, respectively.</p>

Data using in "Moored observations of the North Pacific deep western boundary current east of the Gagua Ridge"

<p>Data on manuscript entitled "Moored observations of the North Pacific deep western boundary current east of the Gagua Ridge" in Matlab format. Details about the variables in the mat files are described in Readme.txt.</p>

Magnetometer and Ionospheric Observations from Indian Sector During 10-11 May 2024 Storm

<p>This data repository includes magnetometer observations from 12 ground stations in the Indian sector. The repository also consist of <span>VLF receiver and Ionosonde data operated by Indian Institute of Geomagnetism. </span></p>

Dataset for Investigation of Thermospheric Response to Geomagnetic Storms Using GITM-OVATION Prime and -FTA model With Comparison to GOLD and SABER Observations

<div> <div> <div> <div> <h4>Dataset Overview</h4> <p>This dataset accompanies the research paper titled " Investigation of Thermospheric Response to Geomagnetic Storms Using GITM-OVATION Prime and -FTA model With Comparison to GOLD and SABER Observations" and contains all the necessary data and scripts required to reproduce the results presented in the paper. The dataset is organized by figure numbers, corresponding directly to the figures in the paper, making it straightforward to locate and generate the specific results.</p> <h4>Structure of the Dataset</h4> <p>The dataset is divided into multiple folders, each named according to the figure numbers in the paper (e.g., Figure_1, Figure_2, etc.). Inside each of these folders, you will find:</p> <ul> <li><strong>Data Files</strong>: These files contain the raw and processed data used to generate the figures.</li> <li><strong>Scripts</strong>: MATLAB scripts (e.g., Figure_2*.m) that process the data and generate the respective figures.</li> <li><strong>Readme.txt</strong>: A text file providing detailed instructions on how to use the data and scripts, including any dependencies or specific steps required.</li> </ul> <h4>Instructions for Reproducing Figures</h4> <ol> <li> <p><strong>Download the Dataset</strong>:</p> <ul> <li>Download the entire dataset or specific figure folders as needed.</li> </ul> </li> <li> <p><strong>Prepare Your Environment</strong>:</p> <ul> <li>Ensure that MATLAB is installed on your local machine.</li> <li>Verify that all necessary MATLAB toolboxes and dependencies are installed, as specified in the Readme.txt files within each figure folder.</li> </ul> </li> <li> <p><strong>Generate Figures</strong>:</p> <ul> <li>Navigate to the directory where the dataset is saved.</li> <li>Open MATLAB and set the current directory to the folder containing the downloaded data and scripts.</li> <li>Run the script corresponding to the figure you wish to generate. For example, to generate Figure 1, navigate to the Figure_1 folder and run the <code>Figure_1*.m.</code></li> </ul> </li> <li> <p><strong>Refer to Readme.txt for Further Details</strong>:</p> <ul> <li>Each figure folder contains a Readme.txt file with additional details, including specific instructions, data descriptions, and any figure-specific requirements or notes.</li> </ul> </li> </ol> <p>By following these steps, you can successfully reproduce the figures and results presented in the paper "Paper 1 vs Paper 2" using the provided dataset and scripts. If you encounter any issues or have questions, refer to the Readme.txt files or contact the authors for further assistance.</p> </div> </div> </div> </div>

DYAMOND-II Model Outputs for "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations"

<p>This dataset contains outputs from the DYAMOND Phase-II simulations, focusing on Global Storm-Resolving Models (GSRMs) at various Atmospheric Radiation Measurement (ARM) sites. The dataset supports the analysis presented in the manuscript "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations."</p> <p>Included are high-resolution model outputs from nine GSRMs, detailing simulations of atmospheric processes at six ARM sites, including variables of clouds, temperature, humidity, wind, and surface fluxes. This dataset allows for a direct comparison between GSRM simulations and ARM field observations, facilitating the evaluation of PBL-coupled and decoupled clouds. For further inquiries or assistance regarding the dataset, please contact the corresponding author at su10@llnl.gov.</p>

Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"

<p>Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"</p>

Interview and Observation Result for Post-metaverse Class Implementation "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects""

<p>This data file is open for viewing as proof of Author; Brandon Lie - BINUS University for conffrence paper<strong> "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects" <br><br>Data can be use an reffrence with author permission*</strong></p>

Observing scenarios simulations for HLVK-Configuration for O4 Runs, using 20 million injections. This simulation led to 17,009 BNS useful for training Parameter Estimations of EM counterparts of GW. (July 2024 edition).

<p>We have conducted a simulation of the HLVK-configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>Due to the large file sizes, we have split them into three parts and uploaded them to Zenodo with the following DOIs:<br><br></p> <ol> <li><strong>The first files is located :</strong>           <code>runs_part_aa</code> and   <code>runs_part_ab</code>  : <a title="https://zenodo.org/doi/10.5281/zenodo.12693652" href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a></li> <li><strong>The second files is located :     </strong><code>runs_part_ac</code> and   <code>runs_part_ad</code>  : <a href="../doi/10.5281/zenodo.12694779">https://zenodo.org/doi/10.5281/zenodo.12694779</a></li> <li><strong>The third files is located :          </strong><code>runs_part_ae</code> and   <code>runs_part_af</code>  : <a href="../doi/10.5281/zenodo.12696695">https://zenodo.org/doi/10.5281/zenodo.12696695</a></li> </ol> <blockquote> <p>Download them or use this Python script from GitHub to download all of them by running the script: </p> <p> <a href="https://github.com/weizmannk/ObservingScenariosInsights/blob/main/chunk-xml/zenodo-process/download_split_chunk_data.py">hchunk-files-downloader</a>.</p> </blockquote> <p>After downloading them  (6 files ), you will need to combine them  in a single file using the following process:</p> <blockquote> <p>1.Combine the parts:<br><code>cat runs_part_* > runs.zip</code></p> </blockquote> <blockquote> <p>2.Verify the combined file:<br><code>ls -lh runs.zip</code><br><code>file runs.zip</code></p> </blockquote> <blockquote> <p>3.Unzip the combined file:<br><code>unzip runs.zip</code></p> </blockquote> <p> </p> <p>In the <code>runs</code> folder, we have three subfolders:</p> <ul> <li><code>O4</code>: This contains the <code>.fits</code> files for skymap localization and all GW parameters of the CBCs that passed the threshold cutoff of 8.</li> <li><code>statistics_results</code>: This contains the summary results of the statistical predictions of GW detections.</li> <li><code>subpopulations</code>: This folder is the split of BNS, NSBH, and BBH events. It is useful for those who need quick parameters of BNS and NSBH for their lightcurve simulations or EM counterpart statistical estimation.</li> </ul> <p> </p> <p>For more information, visit: <a href="https://github.com/lpsinger/observing-scenarios-simulations" target="_new" rel="noreferrer">Observing Scenarios Simulations</a></p> <p>Contact: <a rel="noreferrer">weizmann.kiendrebeogo@oca.eu</a> or <a rel="noreferrer">kiend.weizman7@gmail.com</a></p>

Observing Runs for Binary Neutron Star and Neutron Star-Black Hole mergers in the HLVK-Configuration during O4, using 20 million CBC injections (July 2024 edition)

<p>We have conducted a simulation of the HLVK configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>However, due to size constraints, this repository contains only the simulation data and sky maps of BNS and NSBH detections that have passed the cutoff threshold. For the complete dataset, including BBH, please refer to   <a href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a> .</p>

In situ observations used in GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"

<p>Observations taken by</p> <p>Mika Aurela, Tarek S. El-Madany, Marika Honkanen, Anna Kontu, Juha Lemmetyinen, and Susan C. Steele-Dunne</p> <p>and used in the GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"</p>

Data release of the Swift-LVK subthreshold search during the third LIGO-Virgo-KAGRA observing run

<div> <div>Here we describe the structure of the data release.</div> <br> <div>1. The folder fits contains all the upper limit maps in the for of fits files. Run the code plot_maps_paper.py to obtain the figures 3 and 4 of the paper</div> <br> <div>2. The folder pdf contains all the upperlimit maps, with the GW sky localizations, in pdf format</div> <br> <div>3. The file lum.csv contains the data to reproduce Fig. 6. If the 'cred' colums is empty, then the event is only detected in low latencyl. If 'no' then the event has p_astro<0.5, otherwise it has p_astro>0.5.</div> <br> <div>4. The file joint_far.cvs contains the data to reproduce the Figure 7. Columns are self explanatory</div> <br> <div>5. The file data_BBH.txt, data_BBH_highpastro.txt, data_BBH_allreal.txt, data_BBH_allreal_earth.txt contain the likelihood to produce Figs 8-9-10-11. The plots are produced runnning read.py and read_allreal.py</div> <br> <div>6. All the tables are provided in cvs format</div> </div>

Raw Data for Publication "Earth observations reveal impacts of climate variability on maize cropping systems in Sub-Saharan Africa"

<p>Phenological metrics extracted for all agricultural fields used in the study. Data also includes the coordinates of the fields.</p>

An observation-based estimate of the Atlantic meridional freshwater transport

Open the record for dataset details and reuse information.

Distance sampling visual observation sightings data for cetaceans from Antarctic Tourist vesssels

<p>The following is two summer sampling seasons of distance sampling data collected by trained observer teams of two from Antarctic tourist vessels. The data set contains 5 key dataframes. This data has be cleaned and quality controlled. </p> <p>Effort - details the type of visual observation effort, the observer on effort and other information </p> <p>Environment - details the environmental conditions under which the data were collected. </p> <p>Sightings - details the observations made, and distance estiamtes (relative to the ship) for cetceans. </p> <p>Resightings - used in select cases, see protocols.</p> <p>gpsData - automated collection of location data at 5 sec intervals, some gaps exists, which were interpolated later for analysis</p> <p>Work is ongoing with the dataset, please contact authors about its use. </p>

Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"

<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>

Symmetry Enforced Fermi Surface Degeneracies Observed in Time-Reversal Symmetry-Breaking Superconductor LaNiGa2

<p>ARPES data for manuscript "Symmetry Enforced Fermi Surface Degeneracies Observed in Time-Reversal Symmetry-Breaking Superconductor LaNiGa2"</p>