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3,916 results for “reconstruction”
Reconstruction of the 1938 Hurricane in New England and Hurricane Hugo in Puerto Rico
This study examined landscape and regional impacts of the 1938 Hurricane in New England and Hurricane Hugo in Puerto Rico, with a focus on the Harvard Forest and the Luquillo Expermental Forest. For details on methods and results, please see the published paper (Boose, E. R., D. R. Foster and M. Fluet. 1994. Hurricane impacts to tropical and temperate forest landscapes. Ecological Monographs 64(4): 369-400). The Abstract from the paper is reproduced below. "Hurricanes represent an important natural disturbance process to tropical and temperate forests in many coastal areas of the world. The complex patterns of damage created in forests by hurricane winds result from the interaction of meteorological, physiographic, and biotic factors on a range of spatial scales. To improve our understanding of these factors and of the role of catastrophic hurricane wind as a disturbance process, we take an integrative approach. A simple meteorological model (HURRECON) utilizes meteorological data to reconstruct wind conditions at specific sites and regional gradients in wind speed and direction during a hurricane. A simple topograhic exposure model (EXPOS) utilizes wind direction predicted by HURRECON and a digital elevation map to estimate landscape-level exposure to the strongest winds. Actual damage to forest stands is assessed through analysis of remotely sensed, historical, and field data. "These techniques were used to evaluate the characteristics and impacts of two important hurricanes: Hurricane Hugo (1989) in Puerto Rico and the 1938 New England Hurricane, storms of comparable magnitude in regions that differ greatly in climate, vegetation, physiography, and disturbance regimes. In both cases patterns of damage on a regional scale were found to agree with the predicted distribution of peak wind gust velocities. On a landscape scale there was also good agreement between patterns of forest damage and predicted exposure in the Luquillo Experimental Forest in Puerto Rico and t
Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks.
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Dynamic Reconstructions of Sagittarius A* with Resolve from 2017 EHT data
<p>This repository contains the dynamic reconstructions of Sagittarius A* (SgrA*) from (EHT) data using the Resolve framework, as presented in "Resolving Horizon-Scale Dynamics of Sagittarius A*".</p>
Avizo reconstruction of skull of Siamogale melilutra dataset
<p><em><strong>Siamogale melilutra</strong></em> is an extinct species of giant <a href="https://en.wikipedia.org/wiki/Otter">otter</a> from the late <a href="https://en.wikipedia.org/wiki/Miocene">Miocene</a> from <a href="https://en.wikipedia.org/wiki/Yunnan">Yunnan</a> province, <a href="https://en.wikipedia.org/wiki/China">China</a>. Ranking among the largest fossil otters, <em>Siamogale</em> represents a feeding <a href="https://en.wikipedia.org/wiki/Ecomorphology">ecomorphology</a> with no living analog. Its giant size and high mandibular strength confer <a href="https://en.wikipedia.org/wiki/Durophagy">shell-crushing capability</a> matched only by other extinct <a href="https://en.wikipedia.org/wiki/Molluscivore">molluscivores</a>, such as the marine bear <em><a href="https://en.wikipedia.org/wiki/Kolponomos">Kolponomos</a></em>.<sup><a href="https://en.wikipedia.org/wiki/Siamogale_melilutra#cite_note-1">[1]</a> </sup>The skull reveals a combination of otter-like and badger-like cranial and dental characteristics. The new species belongs to the <a href="https://en.wikipedia.org/wiki/Lutrinae">Lutrinae</a> because of its possession of a large infraorbital canal and ventral expansion of the mastoid process, among other traits.<sup><a href="https://en.wikipedia.org/wiki/Siamogale_melilutra#cite_note-2">[2]</a></sup> <em>Siamogale melilutra</em> was about 1.9 m (6.25 ft) in overall length and weighed at least 40 kg (88 pounds).<sup><a href="https://en.wikipedia.org/wiki/Siamogale_melilutra#cite_note-3">[3]</a></sup> The remains of the skull were found in China and were re-created with a special program called the CT scan which is able to reconstruct the skeleton without being damaged. (From https://en.wikipedia.org/wiki/Siamogale_melilutra).</p> <p>This dataset was generated by Avizo 2020.2. It represents our reconstruction of the skull of Siamogale melilutra. The original CT files are available on MorphoSource.org, media 000413248, DOI <a href="https://doi.org/10.17602/M2/M413248">10.17602/M2/M413248</a>. A mesh PLY file of the reconstructed skull generated from this Avizo dataset is also available at MorphoSource.org, media 000413543, DoI <a href="https://doi.org/10.17602/M2/M413543">10.17602/M2/M413543</a>.</p>
Reconstructed high-rate SEIS data recorded during HP3 hammering from the NASA InSight mission to Mars
<p>The NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we provide data that was reconstructed at a higher sampling rate of 2000 samples per second using a dedicated de-aliasing algorithm described in the accompanying article. This archive will be updated regularly with new data acquired on Mars. </p> <p>For a detailed data description and instructions on how to cite this dataset, please refer to the README file. </p>
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
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Fe/Sb2Te3 Interface Reconstruction through Mild Thermal Annealing (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Fe/Sb<sub>2</sub>Te<sub>3</sub> Interface Reconstruction through Mild Thermal Annealing</em>" by E. Longo et al., Adv. Mat. Interfaces (2020): <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/admi.202000905">https://onlinelibrary.wiley.com/doi/full/10.1002/admi.202000905</a></p> <p>Note for users: </p> <p>CEMS data of Figure 5 can be treated with the open source software Vinda: <a href="https://e-ms.web.cern.ch/content/vinda">https://e-ms.web.cern.ch/content/vinda</a>, H. P. Gunnlaugsson, "<em>Spreadsheet based analysis of Mössbauer spectra</em>", Hyperfine Interact., 237 (1) (2016), p. 79, <a href="https://doi.org/10.1007/s10751-016-1271-z">10.1007/s10751-016-1271-z</a></p>
Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
<p>This dataset provides various acquisitions for T2 mapping of the MnCl2 array of the NIST phantom at 1.5T. Data were acquired on a MAGNETOM Sola (Siemens Healthcare, Erlangen, Germany), with an 18-channel body coil and a 32-channel spine coil (12 elements used). It gathers original acquisitions from Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12.</p> <p>The dataset is composed of DICOM images from:</p> <p>i) Gold-standard single-echo spin echo (SE) sequences acquired at variable TE;</p> <p>ii) Alternative reference multi-echo spin echo (MESE) acquisitions;</p> <p>iii) Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images at variable TE in three orthogonal orientations.</p> <p>The acquisition parameters are further detailed in the ReadMe.txt file provided along with the images.</p> <p>These acquisitions were repeated independently on three different days during the month of January 2020.</p> <p>These data are made publicly available as a support for further reproducibility studies as well as for the validation of new T2 relaxometry strategies.</p> <p>Works using any of these data should cite the following two references:</p> <p>- Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12</p> <p>- Lajous, Hélène, Ledoux, Jean-Baptiste, Hilbert, Tom, van Heeswijk, Ruud B., & Bach Cuadra, Meritxell. (2020). Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3931812</p>
RADIT: A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean
<p>The Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset provides daily records of river discharge, temperature, and heat flux for 25 major Arctic-draining rivers from 1950 to 2023. Using machine learning methods and ERA5-Land reanalysis data, we reconstructed these key hydrological variables with high accuracy (most NSEs > 0.8).</p> <p>Due to licensing restrictions and to encourage adherence to the stated licenses of the original input data, this dataset only provides the reconstructed (filled) values. Users can obtain the complete historical observational data from their original publicly available sources as detailed in our documentation. By combining these original observations with our reconstructed data, a comprehensive and continuous daily dataset from 1950 to 2023 can be assembled. Clear instructions and links for downloading the original observational data used in this study can be found at: <a href="https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data" target="_blank" rel="noopener">https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data</a>. Should you encounter any issues or have questions, please feel free to contact the first author, Zihan Wang (zhwang2018@163.com).</p>
Processing of 3-D Polygon Mesh Model and Radio Propagation Simulations in a Cave: Surface Reconstruction from Point Cloud, Simplification of the Mesh, and Ray Tracing
<p><strong>ABOUT</strong></p><p>This repository includes mesh data from cave geometry scanning and processing, and radio propagation data from ray tracing simulations.</p><p>The geometry data is obtained with laser scanning in a cave in Slovenija. </p><p>The geometry processing includes (i) 3-D shape reconstruction - surface reconstruction from point cloud data and (ii) simplification - reduction of the geometric complexity of the 3-D mesh model. </p><p>The radio propagation data is obtained using CloudRT [1] ray-tracing simulator. </p><p>The obtained propagation-related quantities include information about the propagation mechanism, interactions with the geometry, received power, delay, azimuth and elevation angles of arrival and departure, and path loss. </p><p> </p><p><strong>AUTHORS</strong></p><p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p><p>Department of Communication Systems</p><p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p><p>teodora.kocevska@ijs.si</p><p> </p><p><strong>GEOMETRY PROCESSING</strong></p><p>The cave segment used for the propagation calculations is selected from a point cloud obtained in a cave in Litia, Slovenia. The point cloud is obtained with 3-D laser scanning of the environment. The selected segment is approx. 58 m long. Several parameter configurations were considered for 3-D shape reconstruction, including Poisson surface reconstruction with octree depths of 8, 10, and 12. Geometries that represent the cave shape and have different levels of complexity were created and studied. In the simplification process, one and two-stage simplification was explored using the Quadric Edge Collapse Decimation approach. </p><p> </p><p><strong>RADIO SETUP</strong></p><p>The transmitter (Tx) is fixed at the entrance of the cave and the receiver (Rx) is moved along the cave in 40 positions with a step of 1 m.</p><p>Omnidirectional antennas at the Tx and Rx sites and vertical polarization are considered. The antenna is mounted 1.5 m above the ground.</p><p>The start frequency is 3.5 GHz, the end frequency is 3.6 GHz and the step is 10 MHz. Direct propagation and first-order reflection are considered. </p><p>The cave geometry is represented by a triangular mesh, and the material of the cave is wet earth. The material electromagnetic properties are selected according to the specifications presented in [2].</p><p> </p><p><strong>FOLDER STRUCTURE</strong></p><p>The folder structure is:</p><p> - Polygon_Mesh_Models</p><p> <i># 3-D environment models with varying </i>levels<i> of geometry complexity</i></p><p> - Reconstruction_Segmen1_Poisson_Surface_Reconstruction</p><p> - Simplification_Segment1_Quadric_Edge_Collapse_Decimation</p><p> - Propagation_Data</p><p> <i># Propagation quantities of all rays between a transmitter and receiver</i></p><p> - AllRay_PropData</p><p> - PathLoss</p><p> - readme.txt</p><p> - RayTracing_EnvironmentModel</p><p> <i> # Final environment model used for ray tracing simulations</i></p><p> - Cave_MeshModel.json</p><p> - Cave_MeshModel.skb</p><p> - Cave_MeshModel.skp</p><p> - RayTracing_MaterialProperties</p><p> <i># Properties of the materials in the environment</i></p><p> - materials.json</p><p> - materials.mtl</p><p> - readme.txt</p><p> - Cave_Length.txt</p><p> <i># Length between selected locations in the environment</i></p><p> - Cave_Segment1_visual.png</p><p> <i> # Visualization of the environment segment used for propagation calculation</i></p><p> - readme.txt</p><p> <i># Overall description </i></p><p><strong>REFERENCES</strong></p><p>[1] D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, "The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, First quarter 2019, doi: 10.1109/COMST.2018.2865724.</p><p>[2] R. sector of International Telecommunication Union (ITU-R), "Effects of building materials and structures on radio wave propagation above about 100 MHz," International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p><p> </p><p><strong>ACKNOWLEDGEMENT</strong></p><p>This work was supported by the Slovenian Research Agency under grant <strong>J2-3048</strong>.</p><p> </p>
Sentinel-5P Methane Density at 2 km from 2021-12 to 2023-11 Monthly Aggregation Time-series Reconstructed
<p><strong>General Description</strong></p><p>The <i>monthly aggregated Methane Volume Mixing Ratio </i>dataset is derived from Sentinel-5P to generate a time-series reconstructed monthly aggregated map. The dataset time spans from December 2021 to November 2023 and provides data that covers the entire globe. The mission is still underway and expected to update periodically.</p><p>For more info about the s5p Methane product see: <a href="">https://maps.s5p-pal.com/ch4/</a>.</p><p>The dataset can be used in many applications like emission tracing, livestock monitor, and greenhouse gas monitor.</p><ul><li><strong>Monthly time-series:</strong></li></ul><p>Methane monthly average value December 2021 – November 2023. Derived using the <a href="https://eumap.readthedocs.io/en/latest/">eumap</a> and <a href="https://github.com/openlandmap/scikit-map">scikitmap</a> package in Python . We derived three standard statistics: (1) 10th percentile (p10), median (p50), and 90th percentile (p90).</p><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> December 2021 – November 2023</li><li><strong>Type of data:</strong> Methane Volume Mixing Ratio (Unit: ppbv)</li><li><strong>How the data was collected or derived:</strong> Derived from 2km Sentinel-5P Menthane using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> and <a href="https://eumap.readthedocs.io/en/latest/">eumap </a>Python package.</li><li><strong>Statistical methods used:</strong> percentiles 10, 50, and 90.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset is not completed gap-filled. Certain areas have no data in the whole time series</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -61.9966697, 180.0000072, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/60 d.d. = 0.016666667 (2km)</li><li><strong>Image size:</strong> 21,600 x 8,962</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li><strong>generic variable name:</strong> ch4.vmr = methane density methane volume mixing ratio</li><li><strong>variable procedure combination:</strong> m.seacov = monthly aggregated and gap filled by seasonal convolution</li><li><strong>Position in the probability distribution / variable type:</strong> p10/p50/p90 = 10th/50th/90th percentile</li><li><strong>Spatial support:</strong> 2km</li><li><strong>Depth reference:</strong> a = above surface</li><li><strong>Time reference begin time:</strong> 20211201 = 2021-12-01</li><li><strong>Time reference end time:</strong> 20231131 = 2023-11-31</li><li><strong>Bounding box:</strong> go = global (without Antarctica)</li><li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li><li><strong>Version code:</strong> v20230628 = 2023-12-08 (creation date)</li></ol>
muBrain - a 3D volumetric reconstruction of the mid-fetal brain
<h2><strong>File descriptions</strong></h2> <h3><strong>Volumes:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain-volume.nii.gz</strong></td> <td>microBrain template volume. A 3D reconstruction of the right hemisphere of a mid-fetal brain. Voxel size: 0.15mm.</td> </tr> <tr> <td><strong>uBrain-atlas-labels.nii.gz</strong></td> <td>microBrain brain tissue labels. Brain tissue labels (n=20) for the microBrain volume.</td> </tr> <tr> <td><strong>brain-tissue-labels.txt</strong></td> <td>LUT for brain tissue labels</td> </tr> </tbody> </table> <h3><strong>Surfaces:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain.R.outer.surf.gii</strong></td> <td>outer (pial) cortical surface of the microBrain volume</td> </tr> <tr> <td><strong>uBrain.R.inner.surf.gii</strong></td> <td>inner (white) cortical surface of the microBrain volume</td> </tr> <tr> <td><strong>uBrain.cortical-atlas.fetal36w-template.label.gii</strong></td> <td>microBrain cortical atlas labels projected onto the 36w timepoint of the <a href="https://gin.g-node.org/kcl_cdb/dhcp_fetal_brain_surface_atlas">dHCP fetal surface template</a></td> </tr> <tr> <td><strong>cortical-labels.txt</strong></td> <td>LUT for cortical atlas labels.</td> </tr> </tbody> </table> <h3><strong>Microarray data:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain-processed-lmd-data.csv</strong></td> <td>LMD microarray data from the <a href="https://www.brainspan.org/lcm/search/index.html">BrainSpan</a> atlas aligned to the microBrain cortical labels. </td> </tr> </tbody> </table>
Gedminiene_ea_Dukstelis_paleolake_climate reconstructions
<p>Supplementary dataset related to the Gedminiene et al. manuscript, describing multiproxy analyses from a Late Glacial- Holocene core sequence from Dukštelis paleolake, SE Lithuania. This file includes:</p> <ul> <li>Palynological data (pollen, using calibration data taxonomy, in percentages, for Ensemble of six calibrations models and for calibration with Modern analogue technique (MAT)</li> <li>Pollen-based reconstructions (temperatures and precipitation)</li> </ul>
Water levels at tide gauges from: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>Data to reproduce the analysis of the Hourly Coastal water levels with Counterfactual (HCC) dataset, presented in the publication "<strong>Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution</strong>" published in Earth System Science Data (ESSD). </p><p>Note that in this repository, water levels are only provided tide gauge locations which were used for the analysis presented in the paper. The full Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><h2>File Descriptions</h2><h4>HCC_analysis_and_plots.ipynb</h4><p>This jupyter-notebook contains all scripts to produce the plots presented in the paper. Make sure that all necessary python packages are installed. The script assumes all netCDF files from this repository to be stored in a sub-directory called "data".</p><h3>hcc_gesla3_99pctl_surge_2011_2015.nc</h3><p>Extreme surge levels from 2011-2015 at 999 GESLA-3 tide gauge stations with at least 90 percent of data in the considered period. As astronomical tides are removed from the modeled and observed water levels to yield the surge component. The file also contains monthly relative water levels and monthly geocentric water levels from 1900-2015 from the HCC dataset.</p><h4>Variables:</h4><ul><li><i>observed_99pctl_surge_level_anomaly</i> -- 99th percentile of daily maximum surge level anomalies from 2011-2015</li><li><i>hcc_99pctl_surge_level_anomaly -- </i>HCC surge level anomalies at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_counterfactual_99pctl_surge_level_anomaly</i> -- HCC counterfactual surge levels at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_water_level_monthly</i> – Monthly relative water level from 1900-2015</li><li><i>hcc_geocentric_water_level_monthly</i> – Monthly geocentric water level from 1900-2015</li></ul><h3>hcc_hr_psmsl_water_level_monthly_1900_2015.nc</h3><p>Monthly water levels at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. The file contains data from the HCC, HR and PSMSL datasets. To align PSMSL and HR with HCC, the 1993-2012 average from PSMSL and HR is removed from each of those datasets respectively and the 1993-2012 average of HCC is added. The average is calculated only over all time steps where the associated observational record has valid data.</p><h4>Variables:</h4><ul><li><i>hcc_water_level_monthly</i> – Monthly relative water level from the HCC dataset</li><li><i>hr_aligned_water_level_monthly</i> -- Monthly relative water level from the HR dataset, aligned with <i>hcc_water_level_monthly</i></li><li><i>psmsl_aligned_water_level_monthly</i> -- Monthly relative water level from the PSMSL database, aligned with <i>hcc_water_level_monthly</i></li></ul><h3>hcc_codec_hr_gesla3_water_level_hourly_monthly_1979_2015.nc</h3><p>Hourly water levels at 1040 GESLA-3 tide gauge stations which have at least 30 percent of valid observations between 1979 and 2015. The file contains data from the HCC, CoDEC, HR and GESLA-3 datasets. The different records are not vertically aligned.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li><li><i>codec_water_level_hourly</i> -- Hourly relative water level from the CoDEC dataset</li><li><i>hr_water_level_monthly</i> -- Monthly relative water level from the HR dataset</li></ul><h3> </h3><h3>hcc_gesla3_water_level_hourly_2011_2015.nc</h3><p>Water levels from the HCC and GESLA-3 datasets, only for tide gauge stations with a complete record in the period 2011-2015 and associated HCC grid points.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li></ul><h3>slr_ds_psmsl_selected.nc</h3><p>Linear estimates of relative sea level rise from 1900 to 2015. Data is provided at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. Estimates are calculated for the HCC, HR and PSMSL datasets.</p><h4>Variables:</h4><ul><li><i>psmsl_rslr, psmsl_rslr_lower, psmsl_rslr_upper</i> -- Relative sea level rise for PSMSL with lower and upper bounds for a 95 percent confidence interval</li><li><i>hcc_long_rslr, hcc_long_rslr_lower, hcc_long_rslr_upper </i>-- Relative sea level rise for HCC with lower and upper bounds for a 95 percent confidence interval</li><li><i>hr_rslr, hr_rslr_lower, hr_rslr_upper</i> -- Relative sea level rise for HR with lower and upper bounds for a 95 percent confidence interval</li></ul><h3>reg_mask_xr.nc</h3><p>Split of the world into 7 ocean basins: Indian Ocean - South Pacific, Northwest Pacific, East Pacific, South Atlantic, Subtropical North Atlantic, Subpolar North Atlantic West and Subpolar North Atlantic East.</p><h4>Variables:</h4><p><i>reg_mask</i> – Float value, representing the ocean basins</p><p> </p>
Paleomagnetic data for Beaver, Kent & Dalziel in Tectonics (2022), "Paleomagnetic Constraints From South Georgia On The Tectonic Reconstruction Of The Early Cretaceous Rocas Verdes Marginal Basin System Of Southernmost South America"
<p>Text data files of paleomagnetic data from Tables in: Beaver, D. G., D. V. Kent, and I. W. D. Dalziel (2022), Paleomagnetic Constraints From South Georgia On The Tectonic Reconstruction Of The Early Cretaceous Rocas Verdes Marginal Basin System Of Southernmost South America: Tectonics, in press.</p> <p><strong>Table 1.</strong> Site Mean Stable Paleomagnetic Directions from South Georgia.</p> <p><strong>Table 2.</strong> Site Mean Stable Directions for Differential Tilt Test of South Georgia Sites With Structural Control.</p> <p><strong>Table 3.</strong> Tectonic Rotations Inferred from Available Paleomagnetic Results from Rocas Verde Rock Units of Late Cretaceous Age in Fuegian Andes and South Georgia.<br> </p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158710
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158709
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158708
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158707
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</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)
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
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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
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