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638 results for “thinning”
Prediction and realisation of high mobility and degenerate p-type conductivity in CaCuP thin films Dataset
<p>Experimental and computational datasets for this publication, including README files. To unzip, in the command line paste the following command:</p> <blockquote> <p>tar -xzvf cacup_data_repository_v2.tar.gz</p> </blockquote> <p>And repeat the command:</p> <blockquote> <p>tar -xzvf <file.tar.gz></p> </blockquote> <p>for each necessary tar file.</p> <p>For any issues with accessibility, please email joe.willis.15@ucl.ac.uk.</p>
Data from: Tree growth responses to extreme drought after mechanical thinning and prescribed fire in a Sierra Nevada mixed-conifer forest, USA
<p class="MsoNormal">An estimated 128 M trees died during the 2012-2016 California drought, largely in the southern Sierra Nevada Range. Prescribed burning and mechanical thinning are widely used to reduce fuels and restore ecosystem properties, but it is unclear if these treatments improve tree growth and vigor during extreme drought. This study examined tree growth responses after thinning, prescribed burning, and extreme drought at the Teakettle Experimental Forest, a historically frequent fire mixed-conifer forest in the southern Sierra Nevada of California, USA. Mechanical thinning (no thin, understory thin, and overstory thin) and prescribed burning (unburned, fall burning) were implemented in 2000-2001. Using annual growth data from increment cores, over 10,000 mapped and measured trees, and lidar-derived metrics of solar radiation and topographic wetness, we had two primary questions. First, what were the growth responses to thinning and prescribed burning treatments, and did these responses persist during the 2012-2016 drought? Second, what tree-level attributes and environmental conditions influenced growth responses to treatments and drought?</p> <p class="MsoNormal">Thinning increased residual tree growth and that response persisted through extreme drought 10 -15 years after treatments. Growth responses were higher in overstory versus understory thinning, with differences between thinning types more pronounced during drought. Species-specific growth responses were strongest with overstory thinning, with sugar pine (Pinus lambertiana) and incense-cedar (Calocedrus decurrens) having higher growth responses compared to white fir (Abies concolor) and Jeffery pine (Pinus jeffreyi). For individual trees, factors associated with higher growth responses were declining pretreatment growth trend, smaller tree size, and post-treatment low neighborhood basal area. Growth responses were initially not influenced by topography, but topographic wetness became important during extreme drought. Mechanical thinning resulted in durable increases in residual tree growth rates during extreme drought over a decade after thinning occurred, indicating treatment longevity in mitigating drought stress. In contrast, tree growth did not improve after prescribed burning, likely due to fire effects that reduced surface fuels, but had little effect on reducing tree density. Thinning treatments promoted durable growth responses, but focusing on stand-level metrics may ignore important tree-level attributes such as localized competition and topography associated with higher water availability. Mechanical thinning was effective at improving growth in trees that had been experiencing declining growth trends, but was less effective in improving growth responses in large old higher ecological importance.</p>
Dual-comb thin-disk oscillator raw data
<p>Dual-comb thin-disk oscillator raw data:</p> <ul> <li>"DCS 625MSa.h5" Full spectrum, sampled at 625 MSa/s (dt=1.6ns)</li> <li>"DCS etalon 313msa.h5" Fabry-Pérot-etalon (fig. 7), sampled at 312.5 MSa/s (dt=3.2ns)</li> <li>"DCS acetylene 625msa.h5" Acetylene (fig. 8), sampled at 625 MSa/s (dt=1.6ns)</li> </ul> <p>Data import with Python:</p> <pre>import h5py import numpy as np trace = np.array(h5py.File(filename, "r")['Waveforms']['Channel 1']['Channel 1Data'].__array__())</pre> <p>A full description of data evaluation method can be found in the supplementary information.</p>
Long series of semi-thin transverse sections of the vascular cambium of Robinia pseudoacacia L. (a-l)
<p>A series of consecutive transverse sections of the vascular cambium of black locust used for radial, and tangential planes reconstruction in: </p> <p>Miodek, A., Gizińska, A., Włoch, W. <em>et al.</em> Intrusive growth of initials does not affect cambial circumference in <em>Robinia pseudoacacia</em>. <em>Sci Rep</em> <strong>12, </strong>7428 (2022). https://doi.org/10.1038/s41598-022-11272-y</p> <p>Scale bar = 20 μm</p>
Determination of sub-ps lattice dynamics in FeRh thin films
<p>Open Access Data for "Determination of sub-ps lattice dynamics in FeRh thin films" published in Scientific Reports <strong>12</strong>, 8584 (2022)</p> <p>https://doi.org/10.1038/s41598-022-12602-w</p> <p> </p> <p>Raw data from the XFEL experiment are accessible at https://doi.psi.ch/detail/10.16907%2F85ff2f32-f561-4413-a02a-74abc65cc82b</p>
Myzostoma cirriferum semi-thin sections stack1
<p>serial sections/1µm/cross/Toluidine blue/elastic alignment</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 03
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 03 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was positive by quantitative PCR (delta variant). One infected ciliated cell is visible in the center of the recorded area. Virus particles are visible within membrane-bound compartments of the cytoplasm. Spike visibility is poor and some virus particles appear compressed.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 04
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 04 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (negative control). The recorded area shows the profiles of four keratinocytes which are surrounded by heterogenous material (e.g. membrane lamella, needle-like crystals, round profiles with a fine-fibrous matrix). Virus partricles are not visible.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 01
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 01 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was positive by quantitative PCR (delta variant). Two, more or less, intact ciliated cells are visible and surrounded by other cells or cellular debris. The ciliated cell in the upper right corner is infected with SARS-CoV-2. Virus particles are visible within membrane-bound compartments of the cytoplasm. Several double-membrane vesicles, which are typical compartments of the coronavirus replication machinery, are also detectable.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 02
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 02 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was positive by quantitative PCR (delta variant). One ciliated cell is visible and surrounded by cellular debris. The ciliated cell is infected with SARS-CoV-2. Few virus particles are visible within membrane-bound compartments of the cytoplasm. Numerous virus particles are located at the cell surface intermingled between the cilia. The virus particles of this cell appear deformed and deviate from the oval/circular profile which is usually present.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Monte Carlo ray tracing code and simulations for photons scattered by an optically thin slab
<p>This directory contains code and data used to simulate photon path length distributions in an optically thin slab.<br> This material was used to prepare the manuscript "Photon Path Distributions in Optically Thin Slabs" by Quentin Libois and Anthony B. Davis, submitted to Optics Express</p>
Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steel battery tabs
<p>In this folder, excel files are stored with the results of signal processing that supported findings in the following paper:</p> <p>"Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steell battery tabs".</p> <p>Matlab scripts and orginal signals will be uploaded soon with more detailed description.</p> <p> </p>
WHUS2-CR, a thin cloud removal dataset for Sentinel-2 images
<p>WHUS2-CR is a thin cloud removal dataset for Sentinel-2A images. WHUS2-CR contains 36 paired cloud and corresponding clear Sentinel-2A images evenly distributed over the world.</p> <p><strong>Because the max storage limitation of one dataset is 50 GB, 5 files can not be uploaded on this dataset. They can be found on : <a href="https://doi.org/10.5281/zenodo.5616753">https://doi.org/10.5281/zenodo.5616753</a>. (The reported results in ref [1] are in CRMSS-result.rar, CRMSS-10-4.rar and CRMSS-4-4.rar, respectively.)</strong></p> <p><strong>The dataset reproducing code and model source code for ref [2] are on :</strong> <a href="https://github.com/Neooolee/WHUS2-CR">https://github.com/Neooolee/WHUS2-CR</a></p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373–389, Aug. 2020, <a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p><br> [2] J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p> <p>The training and testing image list used in reference [2] is (The training and testing small patches are listed in <a href="https://zenodo.org/api/files/bae57b05-f2cc-4729-b97c-eec7c215e9e4/filenos.xlsx">filenos.xlsx</a>):</p> <p>Training set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160403T030602_N0201_R075_T50TMK_20160403T031209</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160413T031632_N0201_R075_T50TMK_20160413T031626</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181111T053041_N0207_R105_T43RGM_20181111T083104</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181121T053121_N0207_R105_T43RGM_20181121T091419</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160925T104022_N0204_R008_T32ULB_20160925T104115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160915T104022_N0204_R008_T32ULB_20160915T104018</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160528T153912_N0202_R011_T18TWL_20160528T154746</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160518T155142_N0202_R011_T18TWL_20160518T155138</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181208T170701_N0207_R069_T14QMG_20181208T202913</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181218T170711_N0207_R069_T14QMG_20181218T203015</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180809T190911_N0206_R056_T10UFB_20180810T002400</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180819T190911_N0206_R056_T10UFB_20180820T002955</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190306T132231_N0207_R038_T22KHV_20190306T164115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190224T132231_N0207_R038_T22KHV_20190224T164104</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181012T084901_N0206_R107_T37VCC_20181012T110218</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181022T085011_N0206_R107_T37VCC_20181022T110901</td> </tr> <tr> <td>9</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190619T023251_N0207_R103_T50JKP_20190619T071925</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190629T023251_N0207_R103_T50JKP_20190629T053618</td> </tr> <tr> <td>10</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190901T032541_N0208_R018_T47NPF_20190901T070148</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190911T032541_N0208_R018_T47NPF_20190911T084555</td> </tr> <tr> <td>11</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160419T083012_N0201_R021_T36RUU_20160419T083954</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160409T083012_N0201_R021_T36RUU_20160409T084024</td> </tr> <tr> <td>12</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190218T143751_N0207_R096_T19HCC_20190218T175945</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190208T143751_N0207_R096_T19HCC_20190208T180253</td> </tr> <tr> <td>13</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180609T061631_N0206_R034_T42TWL_20180609T081837</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180530T061631_N0206_R034_T42TWL_20180530T082050</td> </tr> <tr> <td>14</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191111T025941_N0208_R032_T49QGF_20191111T055938</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191101T025841_N0208_R032_T49QGF_20191101T054434</td> </tr> <tr> <td>15</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190818T103031_N0208_R108_T31SEA_20190818T124651</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190808T103031_N0208_R108_T31SEA_20190808T124427</td> </tr> <tr> <td>16</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191202T105421_N0208_R051_T29PPP_20191202T112025</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191212T105441_N0208_R051_T29PPP_20191212T111831</td> </tr> <tr> <td>17</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190919T074611_N0208_R135_T35JPL_20190919T105208</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190929T074711_N0208_R135_T35JPL_20190929T100745</td> </tr> <tr> <td>18</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190725T142801_N0208_R053_T20LMR_20190725T175149</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190804T142801_N0208_R053_T20LMR_20190804T175038</td> </tr> <tr> <td>19</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T043931_N0208_R033_T46TDK_20191101T074915</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191022T043831_N0208_R033_T46TDK_20191022T063301</td> </tr> <tr> <td>20</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160509T065022_N0202_R020_T41UNV_20160509T065018</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160519T064632_N0202_R020_T41UNV_20160519T064833</td> </tr> <tr> <td>21</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191019T012631_N0208_R131_T53LKF_20191019T030531</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191029T012721_N0208_R131_T53LKF_20191029T040003</td> </tr> <tr> <td>22</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190503T071621_N0207_R006_T38PMB_20190503T092340</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190423T071621_N0207_R006_T38PMB_20190423T093049</td> </tr> <tr> <td>23</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190724T011701_N0208_R031_T56VLM_20190724T031136</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190714T011701_N0208_R031_T56VLM_20190714T031656</td> </tr> <tr> <td>24</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181020T012651_N0206_R074_T54TXN_20181020T032526</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181010T012651_N0206_R074_T54TXN_20181010T055606</td> </tr> <tr> <td>25</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20170224T162331_N0204_R040_T16REV_20170224T162512</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20170214T162351_N0204_R040_T16REV_20170214T163022</td> </tr> <tr> <td>26</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190613T032541_N0207_R018_T49UFT_20190613T062257</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190623T032541_N0207_R018_T49UFT_20190623T061953</td> </tr> <tr> <td>27</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190208T011721_N0207_R088_T53KLP_20190208T024521</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190129T011721_N0207_R088_T53KLP_20190129T024501</td> </tr> <tr> <td>28</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190530T184921_N0207_R113_T12VVN_20190530T222535</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190520T184921_N0207_R113_T12VVN_20190520T222900</td> </tr> </tbody> </table> <p>Testing set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20150826T084006_N0204_R064_T37UCQ_20150826T084003</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20150905T083736_N0204_R064_T37UCQ_20150905T084002</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T000241_N0208_R030_T56HLH_20191101T012241</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191111T000241_N0208_R030_T56HLH_20191111T012137</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190711T174911_N0208_R141_T13TEE_20190711T212846</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190701T174911_N0207_R141_T13TEE_20190701T212910</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190201T093221_N0207_R136_T32PRR_20190201T113425</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190211T093121_N0207_R136_T32PRR_20190211T103706</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190314T021601_N0207_R003_T52SCF_20190314T055026</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190304T021601_N0207_R003_T52SCF_20190304T042035</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180804T045701_N0206_R119_T46VDH_20180804T065907</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180725T045701_N0206_R119_T46VDH_20180725T065359</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190707T213531_N0207_R086_T05VPJ_20190707T231819</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190627T213531_N0207_R086_T05VPJ_20190628T010801</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190605T125311_N0207_R052_T24MXV_20190605T160555</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190615T125311_N0207_R052_T24MXV_20190615T142536</td> </tr> </tbody> </table>
Short-term effect of thinning on red maple transpiration in a temperate mixed forest
<p>Under climate change, forests are expected to experience drier conditions that may increase tree mortality. Silvicultural treatments, such as thinning, have been proposed to reduce moisture competition and to improve forest resistance to drought events. Most studies have investigated the effectiveness of thinning under semi-arid conditions, while little information is available regarding temperate forest responses, together with the residual basal area (BA) that is required to reap the benefits of these treatments. This research aims to understand how the residual BA influences transpiration in mixed temperate forest stands that are dominated by red maple (<em>Acer rubrum</em>) in southeastern Canada. We monitored the sap flux density (Fd) with thermal dissipation-type sensors for 18 red maples spread across nine experimental plots that were thinned to obtain a gradient of residual BA (20, 12.5, 6 m<sup>2</sup> ha<sup>-1</sup>). The study was conducted during the first growing season following treatment. Low residual BA plots (6 m<sup>2</sup> ha<sup>-1</sup>) incurred drier atmospheric conditions as shown by a greater vapor pressure deficit (VPD) compared to high residual BA plots (20 m<sup>2</sup> ha<sup>-1</sup>). At the tree scale, Fd increased with residual BA, with the most pronounced differences under dry atmospheric conditions: when daily VPD exceeded 1.1 kPa, mean Fd in high residual BA plots was respectively 20% and 75% greater than in medium (12.5 m<sup>2</sup> ha<sup>-1</sup>) and low residual BA plots. At the stand level, we simulated total transpiration considering the stand as only made of red maples. The transpiration in medium and low residual BA plots amounted to 41% and 79% of transpiration simulated in the high residual BA plot. Overall, this work highlighted broad variation in response to residual BA treatments, emphasizing the need to better model forest water budgets, and partitioning overstory and understory evapotranspiration to make more adequate residual BA prescriptions in temperate forests.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 07
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 07 comprises three stitched image montages recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (control). Ciliated cells and extracellular material, such as vesicles and needle-like crystals, are visible, but no coronavirus particles.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 06
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 06 comprises three stitched image montages recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (control). Ciliated cells and extracellular material, such as vesicles and needle-like crystals, are visible, but no coronavirus particles.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Thin-butted axe
This axe was found in Hallberg parish, Närke. Thin-butted axes are primarily connected to the Funnel Beaker Culture and dates from the early Neolithic to the Middle Neolithic A, c:a 3700-3200BC. The thin-butted axes were working axes to cut trees or other wooden elements. These axes are commonly found in megalithic tombs or in depositions. This object was not polished why traces of knapping can be seen on all sides. Cortex visible on the butt and surface. http://historiska.se/upptack-historien/object/1308346 EVA-scannern Agnes Norman. Xenter, 3D-tekniker Source: Objaverse 1.0 / Sketchfab
XPS measurements of thin film on SiO2/Si(111) and powder samples (In-foil) of di-cyano-substituted tetrazolinyl radical
<p>XPS raw data underlying Figure 10 from the publication "Thermally Ultrarobust S = 1/2 Tetrazolinyl Radicals: Synthesis, Electronic Structure, Magnetism, and Nanoneedle Assemblies on Silicon Surface" (DOI: 10.1021/jacs.3c03402)</p>
Fig. 7 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 7 Confusion matrix of predictions by YOLOv4 models. YOLO,You Only Look Once (model)
Data from: Tamm Review: a meta-analysis of thinning, prescribed fire, and wildfire effects on subsequent wildfire severity in conifer dominated forests of the Western US
<p>Increased understanding of how active forest management (i.e., mechanical thinning, prescribed burning, and managed wildfire) affects subsequent wildfire severity is urgently needed as people and forests face a growing wildfire crisis. In response, we reviewed scientific literature for the US West and completed a meta-analysis that answered three questions: (1) How much do treatments reduce wildfire severity within treated areas? (2) How do the effects vary with treatment type, treatment age, and forest type? (3) How does fire weather moderate the effects of treatments? We found overwhelming evidence that mechanical thinning with prescribed burning, mechanical thinning with pile burning, and prescribed burning only are effective at reducing subsequent wildfire severity, resulting in reductions in severity from 62% to 72% relative to untreated areas. In comparison, thinning only was less effective – underscoring the importance of treating surface fuels when mitigating wildfire severity is the management goal. The efficacy of these treatments did not vary among forest types assessed in this study and was high across a range of fire weather conditions. Prior wildfire had more complex impacts on subsequent wildfire severity, which varied with forest type and initial wildfire severity. Across treatment types, we found that effectiveness of treatments declined over time, with the mean reduction in wildfire severity decreasing nearly threefold when wildfire occurred greater than 10 years after initial treatment. Our meta-analysis provides up-to-date information on the extent to which active forest management reduces wildfire severity and facilitates better outcomes for people and forests during future wildfire events. </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
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.