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1,891 results for “Field data”
Experimental data for: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"
<p>This is the raw experimental data for the paper: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"</p> <p>Now also including code to recreate figures, and data from alignment and multi-slice ptychography reconstructions.</p> <p>The data is in zarr format and can be read with the zarr python library. It also contains metadata in a dictionary. </p>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the rendered images for HO3Dv2.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 rendered images.</div> <div>├── <a href="../api/records/13228003/draft/files/train_ho3d.zip/content" target="_blank" rel="noopener noreferrer">train_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 training set.</div> <div>├── <a href="../api/records/13228003/draft/files/full_test_dexycb.zip/content" target="_blank" rel="noopener noreferrer">full_test_dexycb.zip</a> - Contains the processed SDF files for DexYCB full test set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Bow shock crossing list at Mercury (from MESSENGER magnetic field data)
<div> <div> <div> <div> <p><strong>Version 2 (latest): </strong></p> <p>This file contains a list of bow shock crossings at Mercury, identified from magnetic field data of the MESSENGER mission with 1 second resolution (not aberrated), (https://pds-ppi.igpp.ucla.edu/). </p> <p>For a pre-selection of intervals containing bow shock crossings, we use the list of Philpott (2020): "MESSENGER bowshock and magnetopause crossings", https://doi.org/10.5683/SP2/1U6FEO. There, the first and last crossings of the inbound/outbound orbit segments are listed (boundary numbers 1 and 2 for inbound, 7 and 8 for outbound segments). For the bow shock identification, an automatic detection algorithm is applied to these intervals, extended by <strong>45</strong> seconds in either direction.</p> <p>The algorithm calculates running averages and variances of the magnetic field magnitude within adjacent 15 second intervals, separated by two seconds (gap interval). Whenever the ratio between the average magnetic fields exceed a threshold (default: 1.4), a bow shock crossing is selected. Should there be more than one ratio maximum within 10 seconds, then only the maximum is selected that corresponds to the minimal sum of the variances within the adjacent intervals. Crossings are classified with respect to detection quality depending on the ratios of the average magnetic fields and the corresponding variances. Details can be found in the algorithm script that is published alongside this file.</p> <p>Columns of the list file: <br>['time', 'orbit_number', 'x_mso_km', 'y_mso_km', 'z_mso_km', 'jump_ratio', 'indicator']</p> <p>'time': date and time in YYYY-MM-DD HH:mm:SS<br>'orbit_number': orbit number (MESSENGER Mission)<br>'x_mso_km': position (x-coordinate) in MSO coordinate system in km, not aberrated<br>'y_mso_km': position (y-coordinate) in MSO coordinate system in km, not aberrated<br>'z_mso_km': position (z-coordinate) in MSO coordinate system in km, not aberrated<br>'in/out': inbound or outbound segment of the orbit (apoherm towards periherm or periherm towards apoherm)<br>'jump_ratio': ratio of the average magnetic fields before and after selected crossings<br>'indicator': 1, 2 or 3 (1: very clear crossings, high quality, 2: clear crossings, good quality, 3: unclear crossings, poor quality)</p> <p>For further analysis it is recommended to only use the crossings with the indicators 1 and 2.<br>The uncertainty in the determination of the times is +/- 2 seconds. </p> <p>Number of analyzed orbits: 3982<br>Number of total crossings found: 13502<br>Number of crossings with indicator 1 (very clear crossings, best quality): 1765<br>Number of crossings with indicator 2 (clear crossings, good quality): 5027<br>Number of crossings with indicator 3 (unclear crossings, poor quality): 6710</p> <p> </p> <p><strong>Version 1: </strong></p> <p><br>This file contains a list of bow shock crossings at Mercury, identified from magnetic field data of the MESSENGER mission with 1 second resolution (not aberrated), (https://pds-ppi.igpp.ucla.edu/). </p> <p>For a pre-selection of intervals containing bow shock crossings, we use the list of Philpott (2020): "MESSENGER bowshock and magnetopause crossings", https://doi.org/10.5683/SP2/1U6FEO. There, the first and last crossings of the inbound/outbound orbit segments are listed (boundary numbers 1 and 2 for inbound, 7 and 8 for outbound segments). For the bow shock identification, an automatic detection algorithm is applied to these intervals, extended by 15 seconds in either direction.</p> <p>The algorithm calculates running averages and variances of the magnetic field magnitude within adjacent 15 second intervals, separated by two seconds (gap interval). Whenever the ratio between the average magnetic fields exceed a threshold (default: 1.4), a bow shock crossing is selected. Should there be more than one ratio maximum within 10 seconds, then only the maximum is selected that corresponds to the minimal sum of the variances within the adjacent intervals. Crossings are classified with respect to detection quality depending on the ratios of the average magnetic fields and the corresponding variances. Details can be found in the algorithm script that is published alongside this file.</p> <p>Columns of the list file: <br>['time', 'orbit_number', 'x_mso_km', 'y_mso_km', 'z_mso_km', 'in/out', 'jump_ratio', 'indicator']</p> <p>'time': date and time in YYYY-MM-DD HH:mm:SS<br>'orbit_number': orbit number (MESSENGER Mission)<br>'x_mso_km': position (x-coordinate) in MSO coordinate system in km, not aberrated<br>'y_mso_km': position (y-coordinate) in MSO coordinate system in km, not aberrated<br>'z_mso_km': position (z-coordinate) in MSO coordinate system in km, not aberrated<br>'in/out': inbound or outbound segment of the orbit (apoherm towards periherm or periherm towards apoherm)<br>'jump_ratio': ratio of the average magnetic fields before and after selected crossings<br>'indicator': 1, 2 or 3 (1: very clear crossings, high quality, 2: clear crossings, good quality, 3: unclear crossings, poor quality)</p> <p>For further analysis it is recommended to only use the crossings with the indicators 1 and 2.<br>The uncertainty in the determination of the times is +/- 2 seconds. </p> <p>Number of analyzed orbits: 3982<br>Number of total crossings found: 65666<br>Number of crossings with indicator 1 (very clear crossings, best quality): 4291<br>Number of crossings with indicator 2 (clear crossings, good quality): 16652<br>Number of crossings with indicator 3 (unclear crossings, poor quality): 44723</p> </div> <div> </div> <div> </div> </div> </div> </div>
Reproduction package for "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data"
<p>This is a basic reproduction package for the paper "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data" by Gourdji et al. (2021) published in MNRAS. It describes the software and settings used to obtain the final data products of the analysis. It also includes a Jupyter notebook and required data to reproduce the tables and figures of this paper.</p>
Experimental data for "Electric field drives Chern transition in Hofstadter bands of twisted double bilayer graphene"
<p>This experimental dataset was used in our study of "Electric field drives Chern transition in Hofstadter bands of twisted double bilayer graphene".</p>
Data from: Rapid, parallel evolution of field mustard (Brassica rapa) under experimental drought
<p>Climate change is driving evolutionary and plastic responses in populations, but predicting these responses remains challenging. Studies that combine experimental evolution with ancestor-descendant comparisons allow assessment of the causes, parallelism, and adaptive nature of evolutionary responses, although such studies remain rare, particularly in a climate change context. Here, we created experimental populations of Brassica rapa derived from the same natural population and exposed these replicated populations to experimental drought or watered conditions for four generations. We then grew ancestors and descendants concurrently, following the resurrection approach. Experimental populations under drought showed rapid evolution of earlier flowering time and increased specific leaf area, consistent with a drought escape strategy and observations in natural populations. Evolutionary shifts followed the direction of selection and increased fitness under drought, indicative of adaptive evolution. Evolution to drought also occurred largely in parallel among replicate populations. Further, traits showed phenotypic plasticity to drought, but the direction and effect size of plasticity varied. Our results demonstrate parallel evolution to experimental drought, suggesting that evolution to strong, consistent selection may be predictable. Broadly, our study demonstrates the utility of combining experimental evolution with the resurrection approach to investigate responses to climate change.</p>
NASA's Voyager 1 Refined Magnetic Field Data (1-10 au)
<p>This dataset contains NASA's Voyager 1 spacecraft in situ measurements of the solar wind for the Cartesian RTN magnetic field (nT), heliocentric distance (au), proton density (cm^-3), and bulk flow speed (km/s), with time resolution 1.92 seconds.<br> The original source for this refined dataset can be found via the Goddard Space Flight Center Space Physics Data Facility (SPDF), for 2 second MAG data and hourly averaged plasma data.<br> The range of heliocentric distance covered in this dataset is from 1-10 au and each file contains a time series of magnetic field measurements over two days.<br> Each file is labeled with the start and end day covered by the magnetic field time series.<br> Information regarding heliocentric distance, proton density, and bulk flow speed in each file are represented by averages over the corresponding two-day interval.<br> These averages were taken from the hourly averaged data, provided by SPDF, over the appropriate time covered by the magnetic field time series.<br> The CDF files have global attributes: 'CREATOR', 'AFFILIATION', 'S/C NAME', and 'DATE CREATED'.<br> Variable keys are 'SSEPOCH' (time), 'BR' (radial magnetic field), 'BT' (tangential magnetic field), 'BN' (normal magnetic field), 'R' (heliocentric distance), 'N' (proton density), and 'VSW' (bulk flow speed), with variable attribute 'UNITS'.<br> The 'SSEPOCH' variable are timed measurements in units of seconds since Epoch (January 1, 1970).<br> The CDF files were generated in Python using the spacepy.pycdf library.<br> For more specifics regarding the magnetic field cleaning methods utilized to produce this dataset, please refer to the Master's Thesis "Voyager 1: Cleaning Magnetic Field Data for Turbulence Studies" and contact the author for any questions related to the thesis or access.<br> Please send any questions/concerns to Manuel Enrique Cuesta at mecuesta@udel.edu for any problems related to downloading/accessing the data from the CDF files.</p>
Fatiando a Terra Data: Britain - Airborne total-field magnetic anomaly
<p>This is a digitized version of an airborne magnetic survey of Britain. Data are sampled where flight lines crossed contours on the archive maps. Contains only the total field magnetic anomaly, not the magnetic field intensity measurements or corrections.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's mean for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Datum was changed to WGS8; Year was separated from the survey name; Some fields were dropped; Exported to compressed CSV format.</p> <p><strong>Source: </strong><a href="https://www.bgs.ac.uk/datasets/gb-aeromagnetic-survey/">British Geological Survey</a></p> <p><strong>Source license: </strong><a href="https://www.bgs.ac.uk/bgs-intellectual-property-rights/open-government-licence/">Open Government Licence</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/britain-magnetic">https://github.com/fatiando-data/britain-magnetic</a></p> <p>Contains British Geological Survey materials © UKRI 2021.</p>
Fatiando a Terra Data: Osborne Mine, Australia - Airborne total-field magnetic anomaly
<p>This is a section of a survey acquired in 1990 by the Queensland Government, Australia. The data are good quality with approximately 80 m terrain clearance and 200 m line spacing. The anomalies are very visible and present interesting processing and modelling challenges, as well as plenty of literature about their geology.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Change the horizontal datum from GDA94 to WGS84. Convert terrain clearance to flight height using an SRTM grid. Keep only the coordinates, AWAGS leveled magnetic anomaly, and flight line ID. Cut to a smaller region containing only the 2 anomalies of interest.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/osborne-magnetic">https://github.com/fatiando-data/osborne-magnetic</a></p>
Data from Viroplant Belgium field trial 2018
<p>This folder contains all relevant data and results of studying the impact of a bacteriophage-based plant protection product on natural bacterial communities as they occur on plants and root material from the EU Viroplant project at a field site in Belgium in 2018. The file contains 16S rRNA sequences and scripts for their analyses, identity tables of sequence variants, qPCR data, data on physicochemical soil characteristics and other information </p>
Geodetic displacement data near North Brawley Geothermal Field, 2009-2019
<p>Supplementary Dataset for Materna, Barbour, Jiang, and Eneva (2022), "Detection of aseismic slip and poroelastic reservoir deformation at the North Brawley Geothermal Field from 2009–2019". The 2009-2019 downsampled and processed displacements for modeling, shown in their Figure 3, are presented in this repository. Processing methods are included in detail in the paper. Also included are the fault geometries used for modeling and the surface rupture trace of the M4.7 normal faulting earthquake in the 2012 Brawley Swarm, traced from a UAVSAR interferogram. </p> <p>TerraSAR-X data were ordered from the German Space Agency (DLR), using funding from grant GEO-10-001 awarded to Imageair Inc. by the California Energy Commission (CEC). InSAR/SqueeSAR processing of these data was done by TRE Altamira in Canada and Italy under CEC grant GEO-16-003 to Imageair Inc. Leveling data were obtained from the Imperial County Department of Public Works (https://publicworks.imperialcounty.org) and processed under the same grant. The Copernicus Sentinel-1 data were processed by the European Space Agency (ESA) and retrieved from the Alaska Satellite Facility (ASF) (https://search.asf.alaska.edu/). Sentinel-1 displacement time series were derived from interferograms processed in Jiang and Lohman (2021) through the support of Southern California Earthquake Center (SCEC) award 20139. SCEC is funded by NSF Cooperative Agreement EAR-1600087 & USGS Cooperative Agreement G17AC00047. UAVSAR data can be downloaded at https://uavsar.jpl.nasa.gov/. Quadtree downsampling was performed with the Kite library (Isken et al., 2017).</p>
Data to support the publication "The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment" https://doi.org/10.3390/land10090964
<p>Underlying data of soil measurements and analysis by TUC team for the publication “The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment” <a href="https://doi.org/10.3390/land10090964">https://doi.org/10.3390/land10090964</a> from the SoilCare project study sites in Crete. </p> <p>Abstract:</p> <p>The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management–or with some omissions–, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.</p>
Dataset created in the context of the project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field"
<p>The project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field", whose website is https://datause.es/, is a project funded by the Ministry of Science and Innovation - State Research Agency, with reference PID2019-105708RB-C22.</p> <p>Within the framework of the project, a bibliographic search is carried out in all thematic categories of the Web of Science (WoS) related to agriculture and related areas. The search equation included the following categories:</p> <p><strong>WC </strong>= (FOOD SCIENCE TECHNOLOGY OR PLANT SCIENCES OR FORESTRY OR AGRICULTURAL ENGINEERING OR AGRONOMY OR HORTICULTURE OR AGRICULTURE DAIRY ANIMAL SCIENCE OR AGRICULTURE MULTIDISCIPLINARY OR AGRICULTURAL ECONOMICS POLICY) </p> <p>This data set shows the distribution of journals and the quartile they occupy in each of the thematic categories in 2019, with the aim of serving researchers in this area and for future data mining.</p>
Supporting Data for "A hole spin qubit in a fin field-effect transistor above 4 kelvin"
<p>Supporting data for</p> <p><strong>"A hole spin qubit in a fin field-effect transistor above 4 kelvin"</strong>,<br> Leon C. Camenzind, Simon Geyer, Andreas Fuhrer, Richard J. Warburton, Dominik M. Zumbühl and Andreas V. Kuhlmann</p> <p>Nature Electronics (2022), <a href="https://doi.org/10.1038/s41928-022-00722-0">DOI:10.1038/s41928-022-00722-0</a></p>
Data from: Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems
<p>Measuring the individual foraging performances of pollinators is crucial to guide environmental policies that aim at enhancing pollinator health and pollination services. Automated systems have been developed to track the activity of individual honey bees, but their deployment is extremely challenging. This has limited the assessment of individual foraging performances in full-strength bee colonies in the field. Most studies available to date have been constrained to use downsized bee colonies located in urban and suburban areas. Environmental policy-making, on the other hand, needs a more comprehensive assessment of honey bee performances in a broader range of environments, including in remote agricultural and wild areas. Here we detail a new autonomous field method to record high quality data on the flight ontogeny and foraging performance of honey bees, using Radio-Frequency Identification (RFID). We separate bee traffic into returning and exiting tunnels to improve data quality, solving many previous limitations of RFID systems caused by traffic jams and the parasitic coupling of RFID antennae. With this method, we assembled a large RFID dataset made of control bee colonies from experiments conducted in different locations and seasons. We hope our results will be a starting point to understand how ontogenetic and environmental factors affect the individual performances of honey bees, and that our method will enable the large-scale replication of individual pollinator performance studies.</p>
Cyclic unrest in a geothermal field as a harbinger of the Fagradalsfjall eruption, Iceland - INSAR data repository
<p>This repository is providing the InSAR data generated from the Copernicus Sentinel-1A and 1B satellites and as published in the paper</p> <p>Flóvenz et al. (2022) Cyclic unrest in a geothermal field as a harbinger of the Fagradalsfjall eruption, Iceland. Nature Geosciences, NGS-2021-06-01178</p> <p>We analyse deformation and seismicity for one year prior to the March 2021 Fagradalsfjall eruption in Iceland. We generate a high-resolution catalogue of 39,500 earthquakes using optical cable recordings and develop a poroelastic model to describe three pre-eruptional uplift and subsidence cycles at the Svartsengi geothermal field, 8 km west of the eruption site. We find the observed deformation is best explained by cyclic intrusions into a permeable aquifer by a fluid injected at 4 km depth below the geothermal field, with a total volume of 0.11±0.05 km3 and a density of 850±350 kg/m3.</p> <p>The geodetic data relevant for the publication and provided here include:</p> <p>1. Displacement from ascending geometry</p> <p>2. Displacement from descending geometry</p> <p>3. Vertical displacement component</p> <p>4. Horizontal displacement component</p> <p>These data are available for the following bands and dates:</p> <p>band date<br> 58 07-01-2020<br> 57 13-01-2020<br> 56 19-01-2020<br> 55 25-01-2020<br> 54 31-01-2020<br> 53 06-02-2020<br> 52 12-02-2020<br> 51 18-02-2020<br> 50 24-02-2020<br> 49 01-03-2020<br> 48 07-03-2020<br> 47 13-03-2020<br> 46 19-03-2020<br> 45 25-03-2020<br> 44 31-03-2020<br> 43 06-04-2020<br> 42 12-04-2020<br> 41 18-04-2020<br> 40 24-04-2020<br> 39 30-04-2020<br> 38 06-05-2020<br> 37 12-05-2020<br> 36 18-05-2020<br> 35 24-05-2020<br> 34 30-05-2020<br> 33 05-06-2020<br> 32 11-06-2020<br> 31 17-06-2020<br> 30 23-06-2020<br> 29 29-06-2020<br> 28 05-07-2020<br> 27 11-07-2020<br> 26 17-07-2020<br> 25 23-07-2020<br> 24 29-07-2020<br> 23 04-08-2020<br> 22 10-08-2020<br> 21 16-08-2020<br> 20 22-08-2020<br> 19 28-08-2020<br> 18 03-09-2020<br> 17 09-09-2020<br> 16 15-09-2020<br> 15 21-09-2020<br> 14 27-09-2020<br> 13 03-10-2020<br> 12 09-10-2020<br> 11 15-10-2020<br> 10 21-10-2020<br> 9 27-10-2020<br> 8 02-11-2020<br> 7 08-11-2020<br> 6 14-11-2020<br> 5 20-11-2020<br> 4 26-11-2020<br> 3 02-12-2020<br> 2 08-12-2020<br> 1 14-12-2020</p> <p> </p>
Microstructure and velocity data collected near Velasco Reef during the June 2016 FLEAT field program
<p>Data used in Wynne-Cattanach et al paper, "Measurements of turbulence generated by wake eddies near a steep headland"</p>
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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)
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DANDI Archive for NWB datasets
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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.