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1,255 results for “High-resolution”
Figure 1 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography
Figure 1. Three-dimensional reconstruction of the skull of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, lateral view; B, dorsal view; C, ventral view with lower jaw digitally removed; D, anterior view; and E, posterior view. Scale bar = 1 mm. See key for abbreviations.
Data from: The first high-resolution aerosol pH change since industrial revolution constructed by the nitrogen isotopes of ice core ammonium
<p><span><span>Aerosol acidity has broad significance in the atmosphere and ecosystems; however, a reliable way to quantify its changes over long time is lacking. Here, we propose a n</span><span>ew</span><span> approach calculating aerosol pH based on the nitrogen stable isotope composition (δ15N) of aerosol ammonium (NH4+) and for the first time reconstruct historical trajectory of aerosol pH over the last two centuries via δ15N of NH4+ achieved in a Tibetan ice core. We observed a significant decrease in δ15N of ice core NH4+ by 13‰ from the preindustrial to modern era, corresponding to a decreased in aerosol pH by 0.75 units. The decline in pH demonstrates a dominant role of anthropogenic emissions in acid gases over alkaline gases since Industrial Revolution. Our study also suggests that spatiotemporal patterns of aerosol acidity could be widely revealed by future nitrogen stable isotope of aerosol</span><span> or ice core</span><span> ammonium measurement, which in turn will promote the understanding in aerosol chemistry in the context of global environmental changes.</span>y in the context of global environmental changes.</span></p>
SolSysELTs2022 Part II: High-resolution Near-Infrared Spectra of Cometary Organics in Inner Coma
<p>Contributed talk: presentation and video recording</p>
Kümmerer et al.; Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass; open data
<p>The file contains data on cover crop biomass and canopy heights determined by ruler method, high-, and low-resolution imaging via unmanned aerial vehicle and structure-from-motion approach from an experimental field site in Triesdorf, Germany. The article to this data set is published in the journal MDPI Remote Sensing: Kümmerer, R.; Noack, P.O.; Bauer, B. Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass. Remote Sens. <strong>2023</strong>, 15, 1520. https://doi.org/10.3390/rs15061520</p>
The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 - 2021: Low-resolution gridded outputs for 2019 - 2021
<p><strong>The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 – 2021: Low-resolution gridded outputs for 2019 - 2021</strong></p> <p><strong>Roger Teoh, Zebediah Engberg, Marc Shapiro, Lynnette Dray and Marc E.J. Stettler</strong></p> <p>These files contain the low-resolution monthly sum of the global flight distance flown, fuel consumption, and various pollutants from 2019 to 2021. The data is provided in a 4D grid with spatiotemporal resolution of 0.5° (longitude) x 0.5° (latitude), at altitude intervals of 1000 feet, and at a monthly temporal resolution.</p> <p>The global air traffic activity in GAIA was derived from Spire Aviation data. Non-commercial use and research purposes only.</p> <p>For further details, see README.txt</p> <p><strong>Change log</strong></p> <p>- 6-June-2023: The missing file "2021-08-monthly.nc" has been added and can be found in "Zenodo.zip"</p>
Validation of E-region Model Electron Density Profiles using High-Resolution Cross Sections
<p>Zenodo Dataset:</p> <ol> <li> <p>Arecibo ISR observation on February 9, 2012</p> </li> <li> <p>COSMIC-1 GPS radio occultation on February 9, 2012</p> </li> <li> <p>Millstone Hills ISR observation on February 16, 2012</p> </li> <li> <p>IRI-2016 output</p> </li> <li> <p>AURIC model outputs</p> </li> <li> <p>Geomagnetic and Solar indices on February 2012</p> </li> <li> <p>Associated python scripts to read and plot data</p> </li> </ol>
Synthetic Airborne Intruder Dataset: A dataset based on High-Resolution Inpainting for Safety Critical Detect and Avoid
<p>Modern machine learning techniques have shown tremendous potential, especially for object detection on camera images. For this reason, they are also used to enable safety-critical automated processes such as autonomous drone flights. We present a study on object detection for Detect and Avoid, a safety critical function for drones that detects air traffic during automated flights for safety reasons. An ill-posed problem is the generation of good and especially large data sets, since detection itself is the corner case. Most models suffer from limited ground truth in raw data, e.g. recorded air traffic or frontal flight with a small aircraft. It often leads to poor and critical detection rates. We overcome this problem by using inpainting methods to bootstrap the dataset such that it explicitly contains the corner cases of the raw data. We provide an overview of inpainting methods and generative models and present an example pipeline given a small annotated dataset. We validate our method by generating a high-resolution dataset and present it to an independent object detector that was fully trained on real data.</p> <p>This dataset is represented in the following repository. The dataset is structured as follows:</p> <p># Synthetic Airborne Intruder Dataset</p> <p>This dataset was syntheticaly generated using an adapted [Pix2Pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) with different background images and object segementations. Each image contains one object instance.</p> <p>The annotations are in the COCO annotation format.</p> <p>## Data Structure<br> Synthetic Dataset Root:<br> --train<br> |--images<br> |--instances.json<br> --val<br> |--images<br> |--instances.json<br> --test<br> |--images<br> |--instances.json<br> --Background_Sources<br> |--sources_train.csv<br> |--sources_val.csv<br> |--sources_test.cs<br> --README.md</p> <p>## Categories</p> <p>| Id | Name | Instances over all splits |<br> | ---| --- | --- |<br> | 0 | large airplane | 1695 | <br> | 1 | small airplane | 1255 | <br> | 2 | very small airplane | 46 | <br> | 3 | helicopter | 2201 | <br> | 4 | drone | 961 |<br> | 5 | hot air balloon | 315 |<br> | 6 | paraglider | 565 |<br> | 7 | airship | 42 |<br> | 8 | UFO | 0 |</p> <p>### Note:<br> UFO is a placeholder for future expansion of the dataset.</p> <p>## Splits<br> The dataset consists of 3 splits: train 5900 images, val 590 images, test 590 images.<br> The Number of instances per class and per split can be seen in the table below:</p> <p>Class | train | val | test<br> -------|-------|-----|--------<br> large airplane | 1416 | 142 | 137<br> small airplane | 1046 | 96 | 113<br> very small airplane | 38 | 2 | 6<br> helicopter | 1812 | 206 | 183<br> drone | 800 | 86 | 75<br> hot air balloon | 268 | 21 | 26<br> paragliders | 492 | 32 | 41<br> airship | 28 | 5 | 9<br> UFO | 0 | 0 | 0</p> <p>## Sources<br> The sources of the background images can be found in the files [here](./Background_Sources/).</p>
Data and code for "High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra"
<p><strong>Repository structure</strong></p> <p>The zipped folder includes the following subfolders:</p> <p><em>data</em></p> <p>In-situ measurement data from the plots. Remotely-sensed data could not be included in the repository due to their large size. </p> <p><em>src</em></p> <p>R codes to reproduce the data cleaning, prosessing, and statistical analysis steps.</p> <p><em>results</em></p> <p>Model parameters, performance statistics, model files, figures, edited tables together with some summary tables produces from upscaling results.</p> <p><em>raster data and upscaled results</em></p> <p>Averaged flux, soil moisture and temperature maps for the growing season (July 1-August 2nd, 8 am - 8 pm) as well as static maps produced in this study. All the upscaled results could not be included in the repository due to their large size. </p> <p>Note that the analysis to produce the vegetation classification map are described here: https://github.com/poniitty/kilpisjarvi_vegclass</p>
Data for: Impact of islands on tidally dominated river plumes: a high-resolution modelling study
Open the record for dataset details and reuse information.
Use of High-Resolution Microendoscopy (HRME) in Patients With Adenocarcinoma In-Situ (AIS) of the Cervix
ClinicalTrials.gov study NCT02206048. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Mitochondrial Function in Myofascial Trigger Points Cohort Pilot Study Using High-resolution Respirometry
ClinicalTrials.gov study NCT03704311. IPD Sharing: NO. Countries: 0. Publications: 1.
Mobile High-Resolution Microendoscopy (mHRME) for the Detection of Cervical Dysplasia in El Salvador
ClinicalTrials.gov study NCT04472455. IPD Sharing: YES. Countries: 0. Publications: 28.
Evaluation of the High-resolution, Contrast Enhanced Low-dose Breast-CT
ClinicalTrials.gov study NCT02798562. IPD Sharing: NO. Countries: 0. Publications: 6.
A High-resolution Peripheral Quantitative Computed Tomography Study in Postmenopausal Women Previously Treated With Denosumab
ClinicalTrials.gov study NCT00890981. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Clinical Evaluation of A New High-speed and High-resolution Intravascular Ultrasound System
ClinicalTrials.gov study NCT04739657. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Role of High-resolution Anorectal Manometry in the Diagnosis of Functional Anorectal Disorders in Patients With Inactive Inflammatory Bowel Disease
ClinicalTrials.gov study NCT05351034. IPD Sharing: Not stated. Countries: 0. Publications: 6.
Artifact-Free High-Resolution Myocardial Perfusion MRI in Subjects With Abnormal Nuclear Myocardial Perfusion Studies
ClinicalTrials.gov study NCT01949844. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High-resolution residual dry matter (RDM) map for a California oak savanna/annual grassland derived from drone multispectral remote sensing imagery and in-situ grass biomass data
Open the record for dataset details and reuse information.
Data and code for a high-resolution summary of Cambrian to Early Triassic marine invertebrate biodiversity
Open the record for dataset details and reuse information.
Data from: A new approach using high-resolution computed tomography to test the buoyant properties of chambered cephalopod shells
Open the record for dataset details and reuse information.
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.