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191 results for “ICON”
ICON-LEM Ny-Ålesund low-level clouds polar night and polar day 2021/2022
<h3>Low-level clouds during the polar night and polar day simulated in ICON-LEM for Ny-Ålesund </h3> <p>This data set was created using the ICON-LEM model with ca. 600m resolution and a diagnostic tool "microphysical wrapper". It contains the meteogram output of the Ny-Ålesund column (Svalbard) and the microphysical process rates. The data was created for the polar night (Nov 2021- Feb 2022) and polar day (May - Aug 2022). Clouds are classified as low-level if their cloud top height (CTH) is below 2.5 km and the distance between any cloud with CTH above 2.5 km is at least 500 m higher. The data set was first used and described in the <em>publication: </em></p> <p>T. Kiszler, D. Ori, V. Schemann<em>. </em>(preprint) Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds. <em>Atmospheric Physics and Chemistry, </em>https://doi.org/10.5194/egusphere-2023-2986<em><br></em></p> <p>This data is related to the repository <a href="https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics">https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics</a></p> <p><em>File description:</em></p> <p>*_PN is polar night data</p> <p>*_PD is polar day data</p> <p>LLC_<em>meteo_<yyyymm>_ICONv1</em>_v6.nc : Contains the meteogram variables (thermodynamics, surface variables, hydrometeors)</p> <p>LLC_wrapper_mass_<yyyymm>_ICONv1_v6.nc : Contains hydrometeors masses after diagnostic run of a microphysical wrapper</p> <p>LLC_wrapper_tend_<yyyymm>_ICONv1_v6.nc : Contains the mircophysical process rates showing the mass change per timestep </p> <p>low_cloud_times_v6_*.csv : Contains the date and time when a low-level cloud was detected</p>
Meteograms of Ny-Ålesund for ICON-LEM maritime aerosols simulations
<p>This data contains the simulation data as meteogram from ICON-LEM simulations with ca. 600 m resolution. The output location is Ny-Ålesund. The data is for the months Aug and Oct 2021. This data was used in the PhD thesis of Theresa Kiszler. Thesis title: "Improving our understanding of cloud phase-partitioning using long-term cloud-resolving simulations of Svalbard".</p> <p>The original simulation setup is is described in the method section of the paper "A Performance Baseline for the Representation of Clouds and Humidity in Cloud-Resolving ICON-LEM Simulations in the Arctic" by Kiszler et al. (2023). <a href="https://doi.org/10.1029/2022MS003299">https://doi.org/10.1029/2022MS003299</a></p> <p>The following adaptation has been made to the simulation settings: The CCN activation is based on a version by Segal and Khain (2006) using the lowest possible number concentration, i.e. maritime aerosols. The INP nucleation follows the paper by Phillips et al. (2008) only using dust as aerosol. The implementation of the mentioned schemes was not done by us, only the settings were changed to use these schemes instead of the default version.</p>
Lightning Potential Index Using ICON Simulation at the km-scale over the Third Pole Region: ISS-LIS events and ICON-CLM simulated LPI
<p>This dataset contains records of lightning events recorded by the International Space Station (ISS) Lightning Imaging Sensor (LIS) from October 2019 to September 2022 in the Third Pole region. Furthermore, the Icosahedral Nonhydrostatic Weather and Climate Model in Climate Limited-Area Mode (ICON-CLM) was utilized to simulate the hourly Lightning Potential Index (LPI) over the Third Pole region for the same duration. The aforementioned dataset was utilized in the creation of the research article titled "Modeling Lightning Activity in the Third Pole Region: Performance of a km-scale ICON-CLM Simulation" authored by Prashant Singh and Bodo Ahrens. The paper has been submitted to the journal Atmosphere. In CORDEX-FPS-CPTP contribution no. 17 (GUF), you can find more data from ICON-CLM, such as precipitation, CAPE, wind vectors, and more.</p>
Meteograms of Ny-Ålesund for ICON-LEM default aerosols simulations
<p>This data contains the simulation data as meteogram from ICON-LEM simulations with ca. 600 m resolution. The output location is Ny-Ålesund. The data is for the months Aug and Oct 2021. This data was used in the PhD thesis of Theresa Kiszler. Thesis title: "Improving our understanding of cloud phase-partitioning using long-term cloud-resolving simulations of Svalbard".</p> <p>The simulation setup is is described in the method section of the paper "A Performance Baseline for the Representation of Clouds and Humidity in Cloud-Resolving ICON-LEM Simulations in the Arctic" by Kiszler et al. (2023). <a href="https://doi.org/10.1029/2022MS003299">https://doi.org/10.1029/2022MS003299</a></p>
Drone orthomosaics for 'Livestock impacts on an iconic Namib Desert plant are mediated by abiotic conditions'
<p>Drone orthomosaics used in the analyses and figures of: Livestock impacts on an iconic Namib Desert plant are mediated by abiotic conditions, accepted in Oecologia.</p> <p>These drone orthomosaics are a data supplement to the code and data repository here: https://github.com/jtkerb/Nara_Paper_Repo</p>
Official GO FAIR Foundation icons for the Three-Point FAIRification Framework
<p>The official icons for the Three-Point FAIRification Framework (3PFF): Metadata for Machines Workshops, FAIR Implementation Profiles and FAIR Orchestration, created by the GO FAIR Foundation.</p>
Icons of the tipping points in the Earth System from the H2020 COMFORT project (820989)
<p>Triple threat processes and/or other forcings can lead to changes in the ocean happening fast and abruptly. These changes, referred to as “tipping points”, are critical thresholds in a marine system that, when exceeded, can lead to a significant change in the state of the system, which often can be irreversible. This product has been prepared with the financial support of Norges forskningsråd (Research Council of Norway) (309382) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT, Our common future ocean in the Earth system – quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points). The work reflects only the author’s/authors’ view; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains.</p>
Icons illustrating aspects of data organization from the Data Literacy Initiative (DaLI) at TH Köln
<p>Icons created as part of the research project Data Literacy Initiative (DaLI) at TH Köln - University of Applied Sciences in Cologne, Germany. They illustrate aspects of best practices for data organization as recommended in Karl W. Broman & Kara H. Woo (2018) Data Organization in Spreadsheets, The American Statistician, 72:1, 2-10, DOI: 10.1080/00031305.2017.1375989.<br> </p> <p>When using any of the images please include the following attribution together with the DOI: This image was created by Jule Marie Schacht and Juliane Piecha for the Data Literacy Initiative (DaLI) at TH Köln and is used under a CC-BY license.</p> <p>The Data Literacy Initiative (DaLI) at TH Köln develops an interdisciplinary, modular program offering data literacy training to students from all fields.</p> <p>More information on the Data Literacy Initiative (DaLI) (in German): https://www.th-koeln.de/dali</p> <p>Illustrations created by: Jule Marie Schacht</p>
FIG. 2. — Oil painting from a in The wonder whale: a commodity, a monster, a show and an icon
FIG. 2. — Oil painting from a private collection depicting the shore-based sperm-whaling off the Azores (19 th century). Photo by the authors.
FIG. 1 in The wonder whale: a commodity, a monster, a show and an icon
FIG. 1. — Beached fin whale (Balaenoptera physalus (Linnaeus, 1758)) in Parede (nearby Lisbon, Portugal), January 2016. Photo by Inês Carvalho.
FIG. 5 in The wonder whale: a commodity, a monster, a show and an icon
FIG. 5. — "There are two whales in this watercolour. One, its tail raised, is diving. The back of the second is indicated by a long, curved pencil line below the tail of the first. A harpoon appears to have struck one of them, for the sea is stained red with blood." A Harpooned Whale (1845) by Joseph Mallord William Turner (1775–1851), part of Ambleteuse and Wimereux Sketchbook. © Tate Collection, CC-BY-NC-ND 3.0 (Unported). http://www.tate.org.uk/art/artworks/turner-aharpooned-whale-d35391, last consultation: 16/01/2019.
Conjunctions between ICON-MIGHTI and 4 meteor radars, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to meteor radars" by Harding et al. (2020, Submitted)
<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are los_wind (the line of sight wind profiles observed by ICON-MIGHTI) and los_wind_r (the wind profiles observed by the meteor radar, interpolated in time and altitude to the MIGHTI sample, and projected onto the MIGHTI line of sight). Dimensions are "time" and "row" (which refers to the row of the MIGHTI CCD, roughly equivalent to altitude. Velocity units are m/s, distances are km, and lat/lon are in degrees. More information can be found in the paper.</pre>
Dataset and trained models belonging to the article 'Distant reading patterns of iconicity in 940.000 online circulations of 26 iconic photographs'
<p>Quantifying Iconicity - Zenodo</p> <p><br> ## The Dataset<br> This dataset contains the material collected for the article "Distant reading 940,000 online circulations of 26 iconic photographs" (to be) published in New Media & Society (DOI: 10.1177/14614448211049459). We identified 26 iconic photographs based on earlier work (Van der Hoeven, 2019). The Google Cloud Vision (GCV) API was subsequently used to identify webpages that host a reproduction of the iconic image. The GCV API uses computer vision methods and the Google index to retrieve these reproductions. The code for calling the API and parsing the data can be found on GitHub: https://github.com/rubenros1795/ReACT_GCV.</p> <p>The core dataset consists of .tsv-files with the URLs that refer to the webpages. Other metadata provided by the GCV API is also found in the file and manually generated metadata. This includes:<br> - the URL that refers specifically to the image. This can be an URL that refers to a full match or a partial match<br> - the title of the page<br> - the iteration number. Because the GCV API puts a limit on its output, we had to reupload the identified images to the API to extend our search. We continued these iterations until no more new unique URLs were found<br> - the language found by the ``langid`` Python module [link](https://github.com/saffsd/langid.py), along with the normalized score.<br> - the labels associated with the image by Google<br> - the scrape date</p> <p>Alongside the .tsv-files, there are several other elements in the following folder structure:</p> <p>```<br> ├── data<br> │ ├── embeddings<br> │ └── doc2vec<br> │ └── input-text<br> │ └── metadata<br> │ └── umap<br> │ └── evaluation<br> │ └── results<br> │ └── diachronic-plots<br> │ └── top-words<br> │ └── tsv<br> ```</p> <p>1. The ```/embeddings``` folder contains the doc2vec models, the training input for the models, the metadata (id, URL, date) and the UMAP embeddings used in the GMM clustering. Please note that the date parser was not able to find dates for all webpages and for this reason not all training texts have associated metadata.<br> 2. The ```/evaluation``` folder contains the AIC and BIC scores for GMM clustering with different numbers of clusters.<br> 3. The ```/results``` folder contains the top words associated with the clusters and the diachronic cluster prominence plots.</p> <p>## Data Cleaning and Curation<br> Our pipeline contained several interventions to prevent noise in the data. First, in between the iterations we manually checked the scraped photos for relevance. We did so because reuploading an iconic image that is paired with another, irrelevant, one results in reproductions of the irrelevant one in the next iteration. Because we did not catch all noise, we used Scale Invariant Feature Transform (SIFT), a basic computer vision algorithm, to remove images that did not meet a threshold of ten keypoints. By doing so we removed completely unrelated photographs, but left room for variations of the original (such as painted versions of Che Guevara, or cropped versions of the Napalm Girl image). Another issue was the parsing of webpage texts. After experimenting with different webpage parsers that aim to extract 'relevant' text it proved too difficult to use one solution for all our webpages. Therefore we simply parsed all the text contained in commonly used html-tags, such as ```<p>```, ```<h1>``` etc.</p>
Data from: The acacia ants revisited: convergent evolution and biogeographic context in an iconic ant/plant mutualism
Phylogenetic and biogeographic analyses can enhance our understanding of multispecies interactions by placing the origin and evolution of such interactions in a temporal and geographical context. We use a phylogenomic approach—ultraconserved element sequence capture—to investigate the evolutionary history of an iconic multispecies mutualism: Neotropical acacia ants (Pseudomyrmex ferrugineus group) and their associated Vachellia hostplants. In this system, the ants receive shelter and food from the host plant, and they aggressively defend the plant against herbivores and competing plants. We confirm the existence of two separate lineages of obligate acacia ants that convergently occupied Vachellia and evolved plant-protecting behaviour, from timid ancestors inhabiting dead twigs in rainforest. The more diverse of the two clades is inferred to have arisen in the Late Miocene in northern Mesoamerica, and subsequently expanded its range throughout much of Central America. The other lineage is estimated to have originated in southern Mesoamerica about 3 Myr later, apparently piggy-backing on the pre-existing mutualism. Initiation of the Pseudomyrmex/Vachellia interaction involved a shift in the ants from closed to open habitats, into an environment with more intense plant herbivory. Comparative studies of the two lineages of mutualists should provide insight into the essential features binding this mutualism.
Figure 3. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 3. - TimelineThis Gantt chart provides an estimate of the relative timing and duration for achieving each of the Aims.
Figure 1. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 1. - First pass at a VPDRS static graphicFigure 1 corresponds to the first self-administered MDS-UPDRS question:1.7 SLEEP PROBLEMS.Over the past week, have you had trouble going to sleep at night or staying asleep through the night? Consider how rested you felt after waking up in the morning.0: Normal: No problems.1: Slight: Sleep problems are present but usually do not cause trouble getting a full night of sleep.2: Mild: Sleep problems usually cause some difficulties getting a full night of sleep.3: Moderate: Sleep problems cause a lot of difficulties getting a full night of sleep, but I still usually sleep for more than half the night.4: Severe: I usually do not sleep for most of the night."
Figure 2. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 2. - Prototype for the mobile phone appThis screen shows a pre-release version of Node, which will support the VPDRS/UPDRS modules. Here we present a means by which a person administering a questionnaire can securely log into and manipulate patient information locally and through cloud services and lastly an example clinician-administered UPDRS question.
MAGE Model Simulation of the Pre-reversal Enhancement and Comparison with ICON and Jicamarca ISR Observations
The dataset contains the MAGE simulations files and observational data used in the paper along with a plotting routine to read the files. The dataset covers a 1.25 degree by 1.25 degree space horizontally, 0.25 degree space vertically, at altitudes between 97 and 600 kilometers globally.
Supporting data for Wu et al. "Penetrating Electric Field Simulated by the MAGE and Observed by ICON" paper
This dataset contains the necessary data supporting the paper titled "Penetrating Electric Field Simulated by the MAGE and Observed by ICON", to be submitted by Wu et al., 2022. The dataset includes the Multiscale Atmosphere-Geospace Environment (MAGE) model simulation for two intervals to support 2020 Sep 26 9-10 UT and 2020 Sep 24 5-6 UT. The data contain neutral wind, ion drift, and electron density output.
Output from ICON v2.6.2.2 cloud locking simulations: 3D radiative fluxes and additional atmospheric variables
<p>Simulation output from a cloud locking experiment carried out by A. Voigt with ICON version 2.6.2.2, originally for use in M. Huber’s PhD thesis (<a href="https://utheses.univie.ac.at/detail/63548/">https://utheses.univie.ac.at/detail/63548/</a>) and described therein. This subset was processed by E.K. Van de Koot for use in a study by McGraw et al (submitted 2024). Vertically-resolved radiative flux output is in the ‘phy_3d’ files, while ‘atm_2d’ and ‘atm_3d’ include additional atmospheric quantities, such as temperatures, specific humidity, and 2D radiative fluxes at the top-of-atmosphere and surface. Each file name is prefaced with the name of the relevant simulation (e.g. ‘amip_T1C1W1’), which follows nomenclature described in the Huber thesis.</p> <p>*Updated Dec 4, 2024 to fix a very small issue on vertical levels in the 'phy' files.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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