Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
210
datasets available to search
ShareScore release 0.9.0
Dataset results
210 results for “field observations”
Current Full-Waveform Inversion of the Return Stroke Channel based on Single-Station Electric Field Observations
<p>In manuscript entitled "Current Full-Waveform inversion of the Return Stroke Channel Based on Single-Station Electric Field Oberbations", the data of rocket-triggered flash o901 was obtained during the SHATLE was used. The data of our results are including in the Data- for- figrue-x.fig. These files can be opened later. The data supports the aforementioned manuscript and can bue used freely for scientific purposed with appropriate citation.</p>
Data in Field observations of turbulence, sand suspension and cross-shore transport under spilling and plunging breakers
<p>The files include all the raw data, such as the results in numerical simulation, flow and sand deposition data in wind tunnel simulation and some remote sense data. </p>
Associated modeling data & materials for manuscript: Observing the evolution of the Sun's global coronal magnetic field over eight months
<p>This archive contains the magnetohydrodynamic (MHD) modeling materials associated with the manuscript:</p> <p>"<em>Observing the evolution of the Sun’s global coronal magnetic field over eight months</em>"</p> <p>by Zihao Yang, Hui Tian, Steven Tomczyk, Xianyu Liu, Sarah Gibson,<br>Richard Morton, and Cooper Downs</p> <p>Science, 386(6717), 76-82, <strong>2024</strong>, DOI: <a title="Observing the evolution of the Sun&rsquo;s global coronal magnetic field over eight months" href="http://doi.org/10.1126/science.ado2993" target="_blank" rel="noopener">10.1126/science.ado2993</a></p> <p>This archive is intended for transparency and reproduceability purposes. It contains the MHD model source code, run inputs, run outputs, and example python scripts for working with the model data.</p> <p># Contents<br>The subfolders are organized as follows:</p> <p>### source<br>This folder contains the the high-performance MHD code "Magnetohydrodynamic Algorithm outside a Sphere" (MAS) and associated files. MAS is written in Fortran. The dependencies are very straightforward. See `README_MAS.txt` and the associated Makefile for compilation instructions.</p> <p>### runs<br>This folder contains the three MHD model runs that are described in the manuscript. See `README_Runs.txt` for more information on the inputs and outputs. Each folder contains all files required for recreating the run.</p> <p>### scripts<br>This folder contains some example python scripts that illustrate how to read the model data files. This includes an example that will convert the raw 3D data to physical units and place all variables on a common, non-staggered mesh. See `README_Scripts.txt` for more information.</p> <p># Additional Notes<br>MAS is developed and maintained by Predictive Science Inc. (PSI) in San Diego California.</p> <p>The version of MAS in the source folder is not the most recent version. It is the exact version of MAS from the main branch that was used for MHDweb CORHEL runs circa 2022 when similar runs were first posted to PSI's website. For this reason, we used this exact version of MAS for the runs described in the manuscript. As such, MAS is licensed here using the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. Please see the LICENSE file or visit https://creativecommons.org/licenses/by-nc-nd/4.0/ for details. </p> <p>We are currently working on a project that includes a public, open source release of MAS on GitHub, which will be licensed appropriately. This archive is not intended for that purpose.</p> <p>If you have questions, concerns, or issues installing or running this code for reproduceability purposes, please contact Cooper Downs <cdowns@predsci.com>.</p>
FIGURE 5 in Reassessment of Begonia arboreta and B. sonlaensis (Begoniaceae) based on field observation and type examination
FIGURE 5. The materials of the type specimens of Begonia pseudodryadis C.Y.Wu (A–C) and Begonia sonlaensis Aver. (D–G) A. Holotype; B. Specimen from type locality; C. & F. Rhizome and stipule; G. Dry Capsule. (Specimen no.: A, B & C from P-I Mao 2389 (KUN [0371714]!); D from the holotype of B. sonlaensis; E, F & G from Y. M. Shui et al. CK 1801 (KUN!)).
FIGURE 4. Begonia pseudodryadis C.Y in Reassessment of Begonia arboreta and B. sonlaensis (Begoniaceae) based on field observation and type examination
FIGURE 4. Begonia pseudodryadis C.Y.Wu (A–E) & Begonia sonlaensis Aver. (F–I) A. Habitat; B. Adaxial leaf surface; C. Abaxial leaf surface; D. Male flower and close-up of anthers; E. Fruit; F. Habitat; G. Adaxial leaf surface; H. Abaxial leaf surface; I. Dried fruit. (A–E, photographs by Yu-Min Shui, F–I, by Shi-Wei Guo)
FIGURE 2 in Reassessment of Begonia arboreta and B. sonlaensis (Begoniaceae) based on field observation and type examination
FIGURE 2. Living plants of Begonia garrettii Craib (A–B) and Begonia arboreta Y.M.Shui (C–H) A. Plant, adaxial leaf and fruit; B. Plant, abaxial leaf and fruit; C. & D. Habitats; E. Plant, abaxial leaf and inflorescence; F. Male flower and close up of anther; G. Inflorescence; H. Fruit, side and front views (A & B photographs by D. J. Middleton, C–H photographs by Yu-Min Shui)
FIGURE 1 in Reassessment of Begonia arboreta and B. sonlaensis (Begoniaceae) based on field observation and type examination
FIGURE 1. The distribution of Begonia arboreta Y.M.Shui (z), Begonia garrettii Craib (Δ), Begonia pseudodryadis C.Y.Wu (•) and Begonia sonlaensis Aver. (¢).
FIGURE 3 in Reassessment of Begonia arboreta and B. sonlaensis (Begoniaceae) based on field observation and type examination
FIGURE 3. Specimens of Begonia garrettii Craib (A, C, D, E, G) and Begonia arboreta Y.M.Shui (B, F) A. Type of B. garrettii; B. Holotype of B. arboreta; C. Close-up leaf; D. Close-up of male flowers; E. Close-up of female flowers; F. Close-up of fruits; G. Closeup of fruit with obtuse apex of the wings. (Specimens no.: A & E, K [000761183]!; B & F, KUN [0773200]!; C, K [000761184]!; D, K [000761184]!; G, K [000761185]!).
The Dataset of the paper "Observations of the near-field Yellow River plume, multiple fronts and their biogeochemical effects"
<p>The data for "Observations of the near-field Yellow River plume, multiple fronts and their biogeochemical effects". The file names correspond to the figures in the paper.</p>
Data for 3 Swarm-E Fast Auroral Imager Passes Observing the ICEBEAR Radar Field of View
<p>Files containing data for 3 Swarm-E satellite passes observing the Ionospheric Continuous-wave E-region Bistatic Experimental Auroral Radar (ICEBEAR) field of view during semi-active geomagnetic conditions using the Fast Auroral Imager. The dates and times of the passes are:</p> <p>2018-03-10 05:21:00-05:26:00 UT<br> 2019-10-27 04:03:00-04:13:00 UT<br> 2020-03-19 09:00:00-09:04:00 UT</p>
Adopt a Pixel 3 km: A Multiscale Data Set Linking Remotely Sensed Land Cover Imagery with Field Based Citizen Science Observation
<p>These datasets were used in an article submitted to the journal Frontiers in Climate in 2021: <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full">https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full</a></p> <p>Further supplemental links (including general information about GLOBE data) can be accessed at <a href="https://observer.globe.gov/get-data/mosquito-habitat-data">https://observer.globe.gov/get-data/mosquito-habitat-data</a>.</p>
Four-dimensional wind fields retrieved from FY4B GIIRS during 15-minute interval intensified observations from 24 to 26 June 2022
<p>This dataset is the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4B GIIRS observations with a 15-minute interval during a northeastern cold vortex event in China.</p>
Four-dimensional wind fields retrieved from FY4A GIIRS during 30-minute interval intensified observations on typhoons Ampil (2018), Mangkhut(2018) and Lekima(2019)
<p>This dataset includes the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4A GIIRS with 30-minute interval during Ampil (2018), Mangkhut(2018) and Lekima(2019).</p>
Numerical simulations reproduce field observations showing transient weakening during shear zone formation by diffusional hydrogen influx and H2O inflow
<p>Abstract of the corresponding paper:</p> <p>Exposures on Holsnøy island (Bergen Arcs, Norway) indicate fluid infiltration through fractures into a dry, metastable granulite, which triggered a kinetically delayed eclogitization, a transient weakening during fluid-rock interaction, and formation of shear zones that widened during shearing. It remains unclear whether the effects of grain boundary-assisted aqueous fluid inflow on the duration of granulite hydration were influenced by a diffusional hydrogen influx accompanying the fluid inflow. To better estimate the fluid infiltration efficiencies and the parameter interdependencies, a 1D numerical model of a viscous shear zone is utilized and validated using measured mineral phase abundance distributions and H<sub>2</sub>O-contents in nominally anhydrous minerals (NAMs) of the original granulite assemblage to constrain the hydration by aqueous fluid inflow and diffusional hydrogen influx, respectively. Both hydrations are described with a diffusion equation and affect the effective viscosity. Shear zone kinematics are constrained by the observed shear strain and thickness. The model fits the phase abundance and H<sub>2</sub>O-content profiles if the effective hydrogen diffusivity is approximately one order of magnitude higher than the diffusivity for aqueous fluid inflow. The observed shear zone thickness is reproduced if the viscosity ratio between dry granulite and deforming, re-equilibrating eclogite is ~10<sup>4</sup> and that between dry granulite and hydrated granulite is ~10<sup>2</sup>. The results suggest shear velocities <10<sup>-2</sup> cm/a, hydrogen diffusivities of ~10<sup>-13±1</sup> m<sup>2</sup>/s, and a shearing duration of <10 years. This study successfully links and validates field data to a shear zone model and highlights the importance of hydrogen diffusion for shear zone widening and eclogitization.</p> <p> </p> <p>The files uploaded here represents the numerical codes and dataset utilized to produce the results presented in the associated paper.<br> Find a description of the single code files ([<em>name</em>].m) in the ST1.doxc file.</p>
Emissivity spectra retrieved from airborne observations collected during the MACSSIMIZE field campaign
<p>Emissivity values retrieved from observations made using the MARSS and ISMAR radiometers on board the UK's BAe-146-301 (FAAM) atmospheric research aircraft during the MACSSIMIZE field campaign.</p>
Organic aerosol sources in the Milan metropolitan area – Receptor modelling based on field observations and air quality modelling
<p>This is the data related to the paper "Organic aerosol sources in the Milan metropolitan area – Receptor modelling based on field observations and air quality modelling" in Atmospheric Environment</p> <p> </p>
Clustering dark energy imprints on cosmological observables of the gravitational field
<p>This file contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clustering dark energy imprints on cosmological observables of the gravitational field" (<a href="https://arxiv.org/abs/2007.04968">https://arxiv.org/abs/2007.04968</a>).</p> <p><br> This paper has also been published in MNRAS which can be accessed at this link: <a href="https://doi.org/10.1093/mnras/staa3589">https://doi.org/10.1093/mnras/staa3589</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory includes the codes to generate and post-process the simulation data.</li> <li><strong>data</strong>: This directory contains the data generated as part of the project.</li> <li><strong>jupyter_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>jupyter_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>data</strong>" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Observations of multiple fronts in the near-field Yellow River plume
<p>The observed data for"Observations of multiple fronts in the near-field Yellow River plume"</p>
Extending aquatic spectral information with the first radiometric IR-B field observations
<p>This dataset contains absolute radiometric observations of aquatic and celestial targets spanning a spectral range of 313 to 1640nm. Absolute radiometric observations were obtained using analog and digital instruments with similar hardware and processing. Observations at individual wavebands were obtained objectively and independently. Radiometric observations of the Moon and the Sun were obtained at Mauna Loa (HI) and Mount Laguna (CA), and radiometric observations of aquatic targets were obtained in the Southern Ocean, Mission Bay (CA), San Pablo Bay (CA), Grizzly Bay (CA), and Lake Tahoe (CA and NV). Additional verification (e.g., from satellite matchups, model results, or alternate processing methodologies) are included when appropriate. Additional methodological details are provided in the associated publication.</p>
EVA Nexus Vitrectomy Device Field Observation Study
ClinicalTrials.gov study NCT05229094. IPD Sharing: NO. Countries: 1. Publications: 1.
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.