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58 results for “field application”
Datasets to "Compressible test-field method and its application to shear dynamos"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Compressible test-field method and its application to shear dynamos" by M. J. Kapyla, M. Rheinhardt, & A. Brandenburg (Astrophys. J., in press, arXiv:2106.01107). If anything turns out to be incomplete, please email maarit.kapyla@aalto.fi or brandenb@nordita.org. </pre>
Soil organic carbon and nutrient dynamics in response to anaerobic digestate application to farm fields, Eastern Iowa, 2011-2023
This dataset documents a long-term, field-scale study of anaerobic digestate application on commercial croplands in eastern Iowa, USA. It includes detailed records of digestate composition, application rates, and timing, as well as soil test results collected over a 12-year period (2011–2023) from 14 agricultural fields. The dataset supports analysis of soil organic carbon (SOC), nutrient dynamics, and isotopic composition in response to digestate inputs. It contains 421 georeferenced soil samples, digestate nutrient profiles, field management histories, and spatial boundaries. The data were collected as part of a collaborative effort between researchers at Iowa State University and Sievers Family Farms to evaluate the agronomic and environmental implications of integrating anaerobic digestion into row crop and livestock systems.
Emissions of nitrous oxide and methane after field application of liquid organic fertilizers and biochar
<p>This dataset corresponds to the open access article "Emissions of nitrous oxide and methane after field application of liquid organic fertilizers and biochar" published in Agriculture, Ecosystems & Environment (<a href="https://doi.org/10.1016/j.agee.2023.108642">https://doi.org/10.1016/j.agee.2023.108642</a>) funded by the Swiss Federal Offices for the Environment (BAFU), Agriculture (BLW) and Energy (BFE).</p> <p> </p> <p> </p>
Supplementary Dataset for "Extracting near-field seismograms from ocean-bottom pressure gauge inside the focal area: application to the 2011 Mw 9.1 Tohoku-Oki earthquake"
<p>Datasets S1 contains the results obtained by the analysis in this study, such as the spatial and temporal configuration of the basis functions. Dataset S2 contains the ocean-bottom pressure gauge data used in this study.</p> <p>The manuscript is available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091664</p> <p> </p> <p> </p> <div> </div>
Calibration of non-local damage models from full-field measurements: application to discrete element fields.
<p>The codes, datasets, and results from the manuscript 'Calibration of non-local damage models from full-field measurements: application to discrete element fields' are available here.</p> <p> </p> <p>This repository is organized into four folders:</p> <ol> <li><strong>'Ideal Case' folder</strong>: This corresponds to Section ‘3. Application of the calibration method on a 1D ideal case' of the manuscript. In this folder, you will find the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM+</a> and <a href="https://www.python.org/" target="_blank" rel="noopener">Python</a> scripts and a makefile to run them. These codes reproduce all the figures, as well as the complete dataset associated with Figures 1-7.</li> <li><strong>'Virtual Testing Machine' folder</strong>: This corresponds to Section 4, 'Introduction of a Virtual Testing Machine' of the manuscript. Here, you will find: <ul> <li>The data for Figure 9, contains the force vs. CMOD response for all sizes and geometries.</li> <li>The data for Figure 10, contains the scores associated with this parametric identification.</li> </ul> </li> <li><strong>'Green Functions' folder</strong>: This includes the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM++</a> code to solve the boundary value problem for obtaining Green's function of the Eikonal equation. The code is demonstrated with both a non-damaged case and a polynomial damage case. In the first case, Green’s function corresponds to the weighting functions of the Implicit Gradient method (<a href="https://doi.org/10.1002/(SICI)1097-0207(19961015)39:19<3391::AID-NME7>3.0.CO;2-D" target="_blank" rel="noopener">Peerlings et al., 1996</a>).</li> <li><strong>'Real Case' folder</strong>: This corresponds to Section 5, 'Application of the Proposed Calibration Procedure to Virtual Tests.' Here, you will find: <ul> <li>The complete 'Damage vs. Strain' Dataset was generated with the Virtual Testing Machine. Figures 12-15 correspond to this dataset, processed as explained in the manuscript.</li> <li>The 'damage vs. damage driving variable' dataset for each characteristic length. Figure 16 in the manuscript corresponds to this dataset.</li> <li>The data for Figures 17 and 18, contain the evolution of the error with the characteristic length for different evolution laws.</li> </ul> </li> </ol>
Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties
<p>This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties".</p> <p>It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output.</p> <p>In this dataset, you will find two types of files:</p> <p>- the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on the simulated images at different time steps.</p> <p>- the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps.</p> <p>Finally, metadata_simulations.csv gather the information relative to the simulations.</p>
A New Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications
<p>This database is created by the Realistic 3D research group at Mid Sweden University, Sundsvall, Sweden. The database details are explained thoroughly in the publication which was published at the 16th International Conference on Quality of Multimedia Experience (QoMEX) in 2024. This database was also reviewed as part of the submission and publication process.</p> <p>You can use this database in your work under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence, provided that you cite the database as below:</p> <blockquote> <p>Zerman, E., Gond, M., Takhtardeshir, S., Olsson, R., & Sjöström, M. (2024). A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications. <em>The 16th International Conference on Quality of Multimedia Experience (QoMEX)</em>. IEEE. DOI: <a href="https://ieeexplore.ieee.org/abstract/document/10598264">10.1109/QoMEX61742.2024.10598264</a></p> </blockquote> <p>BibTeX:</p> <blockquote> <p>@inproceedings{zerman2024spherical,<br> title = {A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications},<br> author = {Zerman, Emin and Gond, Manu and Takhtardeshir, Soheib and Olsson, Roger and Sj{\"o}str{\"o}m, M{\aa}rten},<br> booktitle = {The 16th International Conference on Quality of Multimedia Experience (QoMEX)},<br> year = {2024},<br> organization = {IEEE},<br> doi = {10.1109/QoMEX61742.2024.10598264}<br>}</p> </blockquote> <p>This database contains 20 spherical light fields of 1 x 60 views, captured with a consumer-grade 360-degree camera: Insta360 X3. The capture was done using a dolly to ensure the separation between consecutive views is exactly 1 cm. In addition to the original captures, this database also provides outputs for two different use cases: compression and view synthesis. Several parameters, features, and objective quality metric values are also included.</p> <p>N.B. Only the README file and this description have been updated after the initial submission on 2024-02-09.</p>
Analysis of variance for the effect of insecticides as a contact and systemic applications and Analysis of variance for the effect of insecticides tested under field condition
<p>Analysis of variance for the effect of insecticides as a contact and systemic applications and Analysis of variance for the effect of insecticides tested under field condition </p> <p>The mean number of <em>H. armigera</em> live larvae were transformed into square-root values before the statistical analysis. The one-way analysis of variance (ANOVA) was used for both transformed values under laboratory conditions. Means were compared using Fisher’s least significant differences (LSD) test at P< 0.05. Under field conditions, a two-way repeated measures analysis of variance (ANOVA) was used to determine the effects of insecticides and exposure time. The computations were carried out using GenStat (19th Edition, VSN International, UK). </p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Hubble eXtreme Deep Field that has been clipped and processed for ML applications
<p>This is a set of FITS files downloaded from https://archive.stsci.edu/ .</p> <p> </p> <p>The *.npy file has been clipped to the 99.99th percentile and then minmax normalised along each channel.</p>
MESSENGER magnetic field data with Mercury's magnetic main field removed through application of the Chapman-Miller method
<p>The MESSENGER (Mercury Surface, Space Environment, Geochemistry and Ranging) spacecraft followed a highly elliptical orbit about Mercury. Therefore, attenuation with radial distance of the dipole and higher order terms of Mercury’s core-generated, steady main field led to MESSENGER’s low-noise, triaxial ring-core fluxgate magnetometer registering magnetic field variations of several hundred nanoteslas. These variations swamp Mercury’s significantly smaller time-varying induction signal. Generally, the steady main field of a planetary body can be removed using a model derived through spherical harmonic analysis. However, MESSENGER’s highly eccentric orbit with near-polar perihermian leads to models of Mercury’s magnetic main field that are inadequately characterised for this purpose. Instead, novel application of the Chapman-Miller method, a geophysical processing technique, better models and removes Mercury’s magnetic main field from MESSENGER data. Three-component magnetic field time series sampled at 10 s intervals were downloaded from NASA’s Planetary Data System (Korth and Anderson, 2016) and processed by applying the Chapman-Miller method to 20 pairs of MESSENGER orbits, yielding 40 events of 256 data points per magnetic component that provide a basis for studying electromagnetic induction in Mercury’s deep crust and mantle.</p>
Figure 1 in Field application of six commercial essential oils against Date Palm mite, Phyllotetranychus aegypticus (Acari: Tenuipalpidae) in Egypt
Figure 1. The relationship (regression modeling at P = 0.05) between X (temperature recorded in spring, summer, and autumn), and Y (predicted value which representing mite populations before and after oil treatments). The line is the linear model, significant part till temperature ≤ 27 (which represents spring and autumn treatments). While the circle in the end part, temperature> 27.50 (representing effect of oil in summer) is not significant, P = 0.202ns.
Figure 5 in Field application of six commercial essential oils against Date Palm mite, Phyllotetranychus aegypticus (Acari: Tenuipalpidae) in Egypt
Figure 5. Independent-samples test of ordered alternatives (Jonckheere-Terpstra Test) on the relation between tested commercial essential oils of thyme, spearmint, jasmine, banana, camphor and clove across the temperature recorded in the field application 2018, at 0.05 significance level.
Figure 4 in Field application of six commercial essential oils against Date Palm mite, Phyllotetranychus aegypticus (Acari: Tenuipalpidae) in Egypt
Figure 4. Independent-samples median (Kolmogorov-Smirnov Test) resulted the distribution and the median of tested commercial essential oils of thyme, spearmint, jasmine, banana, camphor and clove across the three categories of season (spring, summer, and autumn), at 0.05 significance level.
Figure 2 in Field application of six commercial essential oils against Date Palm mite, Phyllotetranychus aegypticus (Acari: Tenuipalpidae) in Egypt
Figure 2. The relationship between numbers of Phyllotetranychus aegyptiacus active stages/50 leaflets of palm trees (treated and control), and the optimum temperature recorded (°C) during spring, summer, and autumn 2018 in Giza.
From Electron Tomography of Dislocations to Field Dislocation Mechanics: application to olivine
<p>Aligned and numerically filtered tilt-series of the PoEM9 specimen (double-tilt sample-holder + weak-beam dark-field conditions + precession angle = 0.1°+ objective aperture = 20 micron):</p> <p>Tilted-series 1: Projection angles from -48° to 52° every 2° (from image 01.tif to 51.tif)</p> <p>Tilted-series 5: Projection angles from -56° to 50° every 2° (from image 01.tif to 54.tif)</p>
Water application method influences survival or growth of Escherichia coli on bulb onions during field curing.
<p>These data are presented in figure 2, 3, 4 and 5 of the publication entitled "Water application method influences survival or growth of Escherichia coli on bulb onions during field curing" submitted to Journal of Food Protection</p> <p> </p> <p> </p> <p> </p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </p>
Data for MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications
<p>This repository contains the results of simulations exposed in the article <strong>MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications.</strong></p> <p>Notebooks for easy plotting of the results are contained in this repository. The README.txt contains light explainations regarding how to use those notebooks.</p> <p>This article is yet to be published at the time of writing.</p> <p> </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.