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4,276 results for “Estimation”
Data from: Non-invasive estimation of absorbed ionizing radiation dose in mice using Near-Infrared Spectroscopy (NIRS) and aquaphotomics
<p>Accurate measurement of ionizing radiation exposure, whether therapeutic or accidental, is of utmost importance in various scenarios. This paper presents a study that addresses this critical need by utilizing near-infrared (NIR) spectroscopy and aquaphotomics to estimate radiation dose exposure in mouse models subjected to X-ray irradiation. The analysis of NIR spectra acquired from the mouse abdomen enabled non-invasive estimation of radiation doses ranging from 0.5 to 6.5 Gy, immediately following the irradiation exposure. The findings were consistent with the impact of total body irradiation in mice, as evidenced by measures such as animal survival rate, alterations in body weight observed over a 30-day post-exposure period, and changes in hematocrit levels. The spectroscopic measurements were based on detecting changes in the molecular structure of body water after radiation exposure, utilizing the water spectral pattern as a multidimensional biomarker. While further validation in nonhuman primates is necessary, the findings demonstrate a simple, non-destructive, and rapid method that holds promise for the estimation of radiation exposure across a range of doses, applicable to both clinical applications and catastrophic radiation events. These advancements in radiation dose quantification have significant implications for the timely and precise assessment of radiation exposure in humans.</p>
Models for rapid estimates of leaf litter chemistry using reflectance spectroscopy
<p>Measuring the chemical traits of leaf litter is important for understanding plants' roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validation <em>R<sup>2</sup></em> = 0.491-0.946; %RMSE = 7.00-19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.</p>
TABLE 5 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 5 — Fossil locations from Southern South America and age in million of years.Studied geological formations and sites used for comparison in the discussion.</p><table><thead><tr><th><b>Fossil Site</b></th><th><b>Geological Formation</b></th><th><b>Age (Ma)</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th>Pico Quemado</th><td>Ñirihuau</td><td>middle Miocene?</td><td>Caviglia 2018</td></tr><tr><th>Cancha Carreras, Estancia Tres Marías</th><td>Río Guillermo</td><td>≤21.7 ± 0.3 to ≤23.5 ± 0.3</td><td>Fosdick <i>et al.</i> 2011; 2015a, b</td></tr><tr><th>Aluminé Basin</th><td>Rancahué</td><td>25.0 ± 1.4 to 26.0 ± 1.5</td><td>Brea <i>et al.</i> 2015; Franzese <i>et al.</i> 2011</td></tr><tr><th>Sierra Baguales</th><td>Río Leona</td><td>33.0 ± 2.8</td><td>Gutiérrez <i>et al.</i> 2017, 2019</td></tr><tr><th>Upper Río Turbio</th><td>Río Turbio</td><td>≤26.6 ± 0.2 to ≤33.4 to</td><td>Fosdick <i>et al.</i> 2015a</td></tr><tr><th>Lower Río Turbio</th><td>Río Turbio</td><td>≤46.3 ± 1.3 to ≤47.1 ± 2.7</td><td>Fosdick <i>et al.</i> 2015a</td></tr><tr><th>Río Pichileufú</th><td>Ventana</td><td>47.46 ± 0.05</td><td>Wilf <i>et al.</i> 2005</td></tr><tr><th>Laguna del Hunco</th><td>La Huitrera</td><td>51.91 ± 0.22</td><td>Wilf <i>et al.</i> 2005</td></tr><tr><th>Ligorio Márquez</th><td>Ligorio Márquez</td><td><57</td><td>Suárez <i>et al.</i> 2000; Hinojosa 2005</td></tr><tr><th>Palacio de los Loros</th><td>Salamanca</td><td>61.7</td><td>Iglesias <i>et al.</i> 2007</td></tr></tbody></table>
TABLE 2 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 2 — Estimated values of temperature and precipitation for the upper Río Turbio Formation member. *Scarce fossil material.</p><table><thead><tr><th><b>Upper RTF</b></th><th></th><th><b>Equation</b></th><th><b>Dataset</b></th><th><b>R</b> <b>2</b></th><th><b>Error</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th>Temperature (°C)</th><td>15.3</td><td colspan="2">MAT = 3.25 + 0.24*% non-tooth CLAMP 3B SA</td><td>0.9</td><td>2.1°C</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Temperature (°C)</th><td>14.3</td><td>MAT = 26.03pE + 1.31</td><td>SA</td><td>0.82</td><td>2.8°C</td><td>Hinojosa <i>et al.</i> 2011</td></tr><tr><th>Temperature (°C)</th><td>14.8</td><td>MAT = 0.204*E + 4.6</td><td>LMA</td><td>0.58</td><td>4.8°C</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Precipitation (mm)</th><td>*</td><td>Ln(MAP) = 1.63 + 0.49*MLnA</td><td>CLAMP 3B SA</td><td>0.6</td><td>Ln(0.5) cm</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Precipitation (mm)</th><td>*</td><td>lnMAP = 0.283(MlnA) + 2.92</td><td>LAA</td><td>0.23</td><td>0.61</td><td>Peppe <i>et al.</i> 2011</td></tr></tbody></table>
TABLE 3 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 3 — Estimated values of temperature and precipitation for the Río Guillermo Formation.</p><table><thead><tr><th><b>RGF</b></th><th></th><th><b>Equation</b></th><th><b>Dataset</b></th><th><b>R</b> <b>2</b></th><th><b>Error</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th>Temperature (°C)</th><td>5.3</td><td>MAT = 3.25 + 0.24*% non-tooth</td><td>CLAMP 3B SA</td><td>0.9</td><td>2.1°C</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Temperature (°C)</th><td>3.5</td><td>MAT = 26.03pE + 1.31</td><td>SA</td><td>0.82</td><td>2.8°C</td><td>Hinojosa <i>et al.</i> 2011</td></tr><tr><th>Temperature (°C)</th><td>6.3</td><td>MAT = 0.204E + 4.6</td><td>LMA</td><td>0.58</td><td>4.8°C</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Precipitation (mm)</th><td>682</td><td>Ln(MAP) = 1.63 + 0.49*MLnA</td><td>CLAMP 3B SA</td><td>0.6</td><td>Ln(0.5) cm</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Precipitation (mm)</th><td>829</td><td>lnMAP = 0.283(MlnA) + 2.92</td><td>LAA</td><td>0.23</td><td>0.61</td><td>Peppe <i>et al.</i> 2011</td></tr></tbody></table>
TABLE 1 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 1 — Estimated values of temperature and precipitation for the lower Río Turbio Formation member.</p><table><thead><tr><th><b>Lower RTF</b></th><th></th><th><b>Equation</b></th><th><b>Dataset</b></th><th><b>R</b> <b>2</b></th><th><b>Error</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th>Temperature (°C)</th><td>16.5</td><td>MAT = 3.25 + 0.24*% non-tooth</td><td>CLAMP 3B SA</td><td>0.9</td><td>2.1°C</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Temperature (°C)</th><td>15.7</td><td>MAT = 26.03pE + 1.31</td><td>SA</td><td>0.82</td><td>2.8°C</td><td>Hinojosa <i>et al.</i> 2011</td></tr><tr><th>Temperature (°C)</th><td>15.6</td><td>MAT = 0.204E + 4.6</td><td>LMA</td><td>0.58</td><td>4.8°C</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Temperature (°C)</th><td>16.9</td><td>See manuscript (1)</td><td>DiLP</td><td>0.7</td><td>4°C</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Precipitation (mm)</th><td>1764</td><td>Ln(MAP) = 1.63 + 0.49*MLnA</td><td>CLAMP 3B SA</td><td>0.6</td><td>Ln(0.5) cm</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Precipitation (mm)</th><td>1435</td><td>lnMAP = 0.283(MlnA) + 2.92</td><td>LAA</td><td>0.23</td><td>0.61 cm</td><td>Peppe <i>et al.</i> 2011</td></tr><tr><th>Precipitation (mm)</th><td>1303</td><td>See manuscript (2)</td><td>DiLP</td><td>0.27</td><td>0.6 cm</td><td>Peppe <i>et al.</i> 2011</td></tr></tbody></table>
Estimating global transpiration from TROPOMI SIF with angular normalization and separation for sunlit and shaded leaves
<p>All three types of SIF-driven T models integrate canopy conductance (gc) with the Penman-Monteith model, differing in how gc is derived: from a SIFobs driven semi-mechanistic equation, a SIFsunlit and SIFshaded driven semi-mechanistic equation, and a SIFsunlit and SIFshaded driven machine learning model. </p> <p>The difference between a simplified SIF-gc equation and a SIF-gc equation is the treatment of some parameters and is shown in <a href="https://doi.org/10.1016/j.rse.2024.114586" rel="noreferrer">https://doi.org/10.1016/j.rse.2024.114586</a>.</p> <p>In this dataset, the temporal resolution is 1 day, and the spatial resolution is 0.2 degree.</p> <p>BL: SIFobs driven semi-mechanistic model</p> <p>TL: SIFsunlit and SIFshaded driven semi-mechanistic model</p> <p>hybrid models: SIFsunlit and SIFshaded driven machine learning model.</p>
A Scene-Level Method for Estimating Small River Widths in Complex Terrain Using Remote Sensing
<p><strong>Files and Descriptions:</strong></p> <p>1. <strong>TP_Lake.csv</strong>: This CSV file contains identified lakes in the Tibetan Plateau.</p> <p>2. <strong>TP_River_Monthly_Statistics.csv</strong>: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders.</p> <p>3. <strong>S2RiverWidth.py</strong>: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths.</p> <p>4. <strong>best_model_vCloud10.pth</strong>: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL).</p> <p>5. <strong>Example.tif</strong>: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model.</p> <p><strong>Model Input Requirements:</strong><br>The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.</p>
Data sets and code for "Suprachiasmatic Nucleus-wide Estimation of Oscillatory Temporal Dynamics" (Yao et al, 2024)
<ul> <li>Data from iDISCO clearing and scanning of three adult mouse suprachaismatic nuclei. Brains are labeled as b1, b2, and b3. Each lobe of the SCN is recorded in a separate csv file. Animals were sacrificed at ZT 19. </li> <li>Data for PER2::LUC recordings of ix adult mouse suprachaismatic nuclei. For each slice there are two files: the time series data (labeled "slice-[orientation]-time-series-#" and the coordinates of the pixels represented (labeled "slice-[orientation]-pixel-coords-#."</li> <li>Code in MATLAB to perform phase extraction, linear modeling, phase estimation, and dynamical simulation.</li> </ul>
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>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Estimates of molecular convergence reveal genes with intermediate pleiotropy underlying adaptive variation across teleost fish
<p>This dataset comprises raw sequence data, output of analyses, code used to reproduce the study, figures, and supplementary materials.</p> <p>Code and input files are in Datasets.zip</p> <p>Use the README to navigate this folder. </p> <p>Data to reproduce the CSUBST analysis are in the .tar.gz folder.</p> <p> </p> <p>For more information please check: </p> <p>https://github.com/agneeshbarua/Teleost_convergence</p>
Water_Estimation_Dataset
<p>This dataset contains measures of the wather of the diferent sweet whater sources in spaing with its coordinates and the nerest meteorogicak station that measures the rain , this dataset would be useful for estimate the evolution of the weather depending on the weather.</p>
Estimating wolf density from cameras
<p>DNA recapture data used to estimate wolf density in three study areas in Idaho, USA 2016-2018. </p>
Estimating historic N- and S-deposition with publicly available data – An example from Central Germany
<p>The deposition of reactive nitrogen and sulphur has profound effects on ecosystem functioning. In the last decades, monitoring networks providing high resolution spatio-temporal deposition estimates have been set up, but equivalent information on historic deposition is mostly missing. However, understanding vegetation change and mitigate future loss of biodiversity and ecosystem functioning is only possible evaluating the effects of its strongest drivers, which includes deposition in many ecosystems. Here, we combine different data sources to provide estimates of historic deposition in forested ecosystems on a high spatio-temporal scale for a federal state in Central Germany from 1880 to present.</p> <p>We make use of data from field measurement stations together with elevation and precipitation data from the last three decades to build a simple deposition model, validate this model with a model publicly available covering the time range from 2000 to present, and extrapolate deposition from this joint model to the past using European deposition trends from the last 150 years.</p> <p>Our approach can easily be adapted to other data and spatial areas shows how to use raw deposition data together with publicly available data on elevation and precipitation to construct simple deposition models covering recent and historic times in areas and for times for which no data are available.</p>
Sex differences in behavioural and anatomical estimates of visual acuity in the green swordtail Xiphophorus helleri
<p>Among fishes in the family Poeciliidae, signals such as colour patterns, ornaments, and courtship displays play important roles in mate choice and male-male competition. Despite this, visual capabilities in Poeciliids are understudied, in particular visual acuity, the ability to resolve detail. We used three methods to quantify visual acuity in male and female green swordtails (Xiphophorus helleri), a species in which body size and the length of the male's extended caudal fin ('sword') serve as assessment signals during mate choice and agonistic encounters. Topographic distribution of retinal ganglion cells (RGC) was similar in all individuals and characterized by areas of high cell densities located centro-temporally and nasally, as well as a weak horizontal streak. Based on the peak density of RGC in the centro-temporal area, anatomical acuity was estimated to be approximately 3 cycles/degree (cpd) in both sexes. However, a behavioural optomotor assay found significantly lower mean acuity in males (0.8 cpd) than females (3.0 cpd), which was not explained by differences in eye size between males and females. An additional behavioural assay, in which we trained individuals to discriminate striped gratings from grey stimuli of the same mean luminance, also showed lower acuity in males (1-2 cpd) than females (2-3 cpd). Thus, although retinal anatomy predicts identical acuity in males and females, two behavioural assays found higher acuity in females than males, a sexual dimorphism which is rare outside of invertebrates. Overall, our results have implications for understanding how Poeciliids perceive visual signals during mate choice and agonistic encounters.</p>
Fast and accurate spectral estimation axial super resolution optical coherence tomography
<p>This depository contains the data and code underlying the results of the publication '<em>Fast and accurate spectral estimation axial super resolution optical coherence tomography</em>' in Optics Express (doi.org/<em>10.1364</em>/<em>OE</em>.<em>439761). </em>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.</p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes the Matlab scripts and functions in this depository that were used in the publication. For reproducing the figures of the publication, refer to the scripts <em>SE_OCT_figure(..).m</em>. For understanding the method and applying it on other datasets from the reader, <em>Bscan_reconstruction.m </em>and <em>Cscan_reconstruction.m</em> are the most convenient scripts to start with. For simulating OCT data as presented in the publication, <em>OCT_simulations.m</em> could be applied. Details on the variables and parameters, such as number of iterations, grid interpolation factor and number of data chunks are commented on in the code itself and should be understandable with the publication as reference. </p> <p>Table 2 describes the datasets that have been used for the publication and are free for the readers to be used with their methods. Table 3 then gives a brief explanation of the variables that are contained in the dataset <em>.mat</em> files.</p> <table> <caption>Table 1. The Matlab scripts in this depository with brief description.</caption> <thead> <tr> <th scope="col">script name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>Bscan_reconstruction.m</td> <td>This script loads a B-scan .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>Cscan_resonstruction.m</td> <td>This script loads a 3Ddata .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>oct_ar.m</td> <td>This function applies the auto-regressive spectral estimation on the input data.</td> </tr> <tr> <td>oct_iaa.m</td> <td>This function applies RFIAA or FIAA on the input data.</td> </tr> <tr> <td>fiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying FIAA (without the recursive scheme) and within rfiaa_oct.m for the first line. This function applies FIAA on a single A-scan.</td> </tr> <tr> <td>rfiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying RFIAA (with the recursive scheme) on a chunk of data. It initializes the first line of the chunk with fiaa_oct.m, and then it applies rec_fiaa_oct.m with the initialization taken from the previous scanline.</td> </tr> <tr> <td>rec_fiaa_oct.m</td> <td>This function applies RFIAA on a single A-scan, taking the initialization from the previous scanline as extra input parameter. </td> </tr> <tr> <td>RayleighThreshold.m</td> <td>This function automatically determines the lower limit of the dynamic range for plotting an OCT image. It fits a Rayleigh distribution on the input data (preferably noise, but also a full image could be used) and returns a threshold in dB. </td> </tr> <tr> <td>morgenstemning.m</td> <td>This function defines the colormap as used in the publication.</td> </tr> <tr> <td>Bscan_reconstruction_function.m</td> <td>This function takes the interference OCT signal, reference spectra and reconstruction parameters as input and returns the reconstructed images according to the four methods in the publication. This function is used in the scripts for reproducing the figures in the publication. It follows the same structure as the script <em>Bscan_reconstruction.m.</em></td> </tr> <tr> <td>SE_OCT_figure3.m</td> <td>This script does the processing for and plots figure 3 in the manuscript. For this script, the .zip file <em>wedge_simulation_data </em>needs to be unpacked and placed as folder in the folder where this script is executed.</td> </tr> <tr> <td>SE_OCT_figure4.m</td> <td>This script reproduces figure 4 in the publication.</td> </tr> <tr> <td>SE_OCT_figure5.m</td> <td>This script reproduces figure 5 in the publication.</td> </tr> <tr> <td>SE_OCT_figure6.m</td> <td> <p>This script reproduces figure 6 in the publication</p> </td> </tr> <tr> <td>OCT_simulations.m</td> <td>This script reproduces the OCT simulations as described in the publication. As the noise is random, any new realization might slightly differ from the data in the publication.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Table 2. The OCT datasets contained in this depository with a brief description. Table 3 describes the variables that are contained in each of these datasets.</caption> <thead> <tr> <th scope="col">dataset name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>wedge_Bscan_data.mat</td> <td>Experimental data from the wedge phantom as visualized in figure 3 of the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>wedge_simulation_data.zip</td> <td>This zipped folder contains 16 simulation datasets with different noise levels, which form the basis of Figure 3 (f) in the publication.</td> </tr> <tr> <td>interfaces_simulation_Bscan_data.mat</td> <td>This file contains the simulation data for 8 interfaces with decreasing intensity and forms the basis of Figure 4 in the publication.</td> </tr> <tr> <td>layered_phantom_Bscan_data.mat</td> <td>This file contains the experimental data from the layered phantom, as used in Figure 4 (c-d) in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>onion_Bscan_data.mat</td> <td>This file contains the experimental data from the onion sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>skin_Bscan_data.mat</td> <td>This file contains the experimental data from the skin sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>intralipid_Bscan_data.mat</td> <td>This file contains the experimental data from the intralipid sample as used in Figure 6 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>speckle_simulation_Bscan_data.mat</td> <td>This file contains simulation data for 3 speckle regions as used in Figure 6 in the publication.</td> </tr> <tr> <td>reference_spectrum.mat</td> <td>This file just contains a spectrum from the used experimental setup which is used as input for the simulations.</td> </tr> <tr> <td>onion_3Ddata.mat</td> <td>This file contains 3D data of the onion sample, which is used for visualization 1. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> <tr> <td>skin_3Ddata.mat</td> <td>This file contains 3D data of the skin sample, which is used for visualization 2. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> </tbody> </table> <table> <caption>Table 3. This table contains the variables in the .mat files and their description.</caption> <thead> <tr> <th scope="col">variable name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td><em>iRawdata</em></td> <td>OCT interference spectra interpolated to a linear grid in k-domain, before subtracting the reference spectrum</td> </tr> <tr> <td><em>sk</em></td> <td>the reference spectrum, interpolated to a linear grid in k-domain</td> </tr> <tr> <td><em>phasep</em></td> <td>4 polynomial coeficients, which can be used in 'polyval' to correct for dispersion</td> </tr> <tr> <td><em>sizeX</em></td> <td>the lateral size of the scan in mm</td> </tr> <tr> <td><em>sizeY</em></td> <td>(only for 3D datasets) the lateral size in the direction perpendicular to x in mm</td> </tr> <tr> <td><em>sizeZ</em></td> <td>the axial field of view (one-sided) before range reduction in mm </td> </tr> <tr> <td><em>ROIp</em></td> <td>the best axial region of interest for this dataset to apply RFIAA on a reduced reconstruction range (in pixels of the DFT reconstruction without zero-padding)</td> </tr> </tbody> </table> <p> </p>
An estimate for thermal diffusivity in highly irradiated tungsten using Molecular Dynamics simulation
<p>The changing thermal conductivity of an irradiated material is among the principal design considerations for any nuclear reactor, but at present few models are capable of predicting these changes starting from an arbitrary atomistic model. Here we present a simple model for computing the thermal diffusivity of tungsten, based on the conductivity of the perfect crystal and resistivity per Frenkel pair, and dividing a simulation into perfect and athermal regions statistically. This is applied to highly irradiated microstructures simulated with Molecular Dynamics. A comparison to experiment shows that simulations closely track observed thermal diffusivity over a range of doses from the dilute limit of a few Frenkel pairs to the high dose saturation limit at 3 displacements per atom (dpa).<br> </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.