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677 results for “time of use”
Relative xylanase activities [%] obtained from fermentation by using C. phragmiteti in LB medium containing 1 w/w% of dry matter waste paper as a function of fermentation time. Average values of relative xylanase activity are presented with error bars representing standard deviations of tripli-cates.
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Prediction of creep failure time using machine learning
<p>Dataset from an elastoplastic element creep model with disorder from this publication:</p> <p>https://www.nature.com/articles/s41598-020-72969-6#author-information</p> <p>The data contains different disorder parameters, applied stresses and system sizes. Each sample is described by a csv file with four columns: The first one is a running index from zero to the number of rows in the file minus one, the second one is the number of relaxation steps performed (check the publication what this means), the third one is the index of the element being relaxed/failing and the fourth is a time increment. To get the total time, simply sum up the time increments. For information, please contact Soumyayajyoti Biswas.</p>
Figure 1 in Use of time-at-temperature data to describe dive behavior in five species of sympatric deep-diving toothed whales
Figure 1. (a) Plot of temperature as a function of depth across all types of profiles within the study area. Temperature profile types included coarse-resolution profiles of depth and temperature (PDT, 2011–2014) recovered from SPLASH tag deployments on four species of cetaceans, as well as moderate- to high-resolution sampling from neutrally buoyant Argo profiling floats (PFL, 2004–2013) and conductivity temperature depth instruments (CTD, 1990–2013) from the World Ocean Database (WOD). Modified box plots have been overlaid on these profiles showing the central tendency (median) and depth variation of the even numbered isotherms between 4°C and 24°C. (b) Map showing sampling locations of these three types of temperature profile data that were employed in statistical descriptions of thermal structure in the NW Bahamas. The study area boundary and major geographic and oceanographic features are also labeled. The line features used to calculate the distance proxies across the Providence Channels (dchan) and distance from the Florida Strait (dflst) are highlighted in bold.
Supplementary Figure 1. Comparison of diagnostic sensitivity of African swine fever virus (ASFV) real-time PCR kits using artificially spiked samples (ASSs)
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Data_Effects_and_correctability_of_pile_up_distortion_using_established_figures_of_merit_in_time_domain_diffuse_optics_at_extreme_photon_rates
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THE USE OF PHRASEOLOGICAL UNITS DEPENDING ON TIME
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Table 2 in Using photo by-catch data to reliably estimate spotted hyaena densities over time
<p><b>Table 2:</b> Summary of spotted hyaena density estimates for the Hluhluwe and iMfolozi sections in the Hluhluwe-iMfolozi Park, KwaZulu-Natal, South Africa.</p><table><tbody><tr><th><b>Year</b></th><th><b>Hluhluwe density</b></th><th><b>Hluluwe LCL</b></th><th><b>Hluhluwe UCL</b></th><th><b>iMfolozi density</b></th><th><b>iMfolozi LCL</b></th><th><b>iMfolozi UCL</b></th></tr></tbody><tbody><tr><th>2013</th><td>17.59</td><td>11.74</td><td>26.36</td><td>21.41</td><td>15.38</td><td>29.82</td></tr><tr><th>2014</th><td>14.39</td><td>9.32</td><td>22.21</td><td>27.09</td><td>20.9</td><td>35.11</td></tr><tr><th>2015</th><td>9.78</td><td>4.41</td><td>21.69</td><td>12.98</td><td>9.18</td><td>18.37</td></tr><tr><th>2016</th><td>8.54</td><td>4.69</td><td>15.55</td><td>24.31</td><td>17.j26</td><td>34.23</td></tr><tr><th>2017</th><td>12.9</td><td>8.28</td><td>20.11</td><td>21.34</td><td>15.25</td><td>29.87</td></tr><tr><th>2018</th><td>14.6</td><td>9.88</td><td>21.58</td><td>27.27</td><td>20.34</td><td>36.56</td></tr><tr><th>Mean</th><td>12.97</td><td>8.05</td><td>21.25</td><td>22.40</td><td>16.21</td><td>30.66</td></tr><tr><th>Stdev</th><td>3.34</td><td>2.94</td><td>3.50</td><td>5.30</td><td>4.75</td><td>6.63</td></tr></tbody></table><p>The SECR analysis used a uniform 12,500 m buffer effective trapping area.LCL,lower confidence limit;UCL,upper confidence limit.Estimates are measured in hyaena per 100 km <sup>2</sup>.</p>
Table 1 in Using photo by-catch data to reliably estimate spotted hyaena densities over time
<p><b>Table 1:</b> Summary of camera trapping parameters and density estimates for spotted hyaenas in the Hluhluwe-iMfolozi Park, KwaZulu-Natal, South Africa.</p><table><tbody><tr><th></th><th></th><th><b>2013</b></th><th><b>2014</b></th><th><b>2015</b></th><th><b>2016 2017</b></th><th><b>2018</b></th><th><b>Total</b></th></tr></tbody><tbody><tr><th>Survey details</th><td>View</td><td>RHS</td><td>RHS</td><td>RHS</td><td>LHS</td><td>RHS</td><td></td><td>RHS</td><td></td></tr><tr><th></th><td>Individuals</td><td>79</td><td>97</td><td>83</td><td>59</td><td>77</td><td></td><td>98</td><td>230</td></tr><tr><th></th><td>Juvenile captures</td><td>2</td><td>2</td><td>1</td><td>0</td><td>10</td><td></td><td>19</td><td></td></tr><tr><th></th><td>Independent capture events</td><td>159</td><td>281</td><td>263</td><td>113</td><td>173</td><td></td><td>294</td><td>1242</td></tr><tr><th></th><td>MMDM (m)</td><td>3047</td><td>3944</td><td>5453</td><td>2559</td><td>3424</td><td></td><td>3744</td><td></td></tr><tr><th></th><td>Trap nights</td><td>1342</td><td>1986</td><td>2039</td><td>2031</td><td>1917</td><td></td><td>1929</td><td>11,244</td></tr><tr><th></th><td>Population closure test <i>p-</i> value</td><td>>0.05</td><td>>0.05</td><td><0.05>0.05 <0.05</td><td>>0.05</td><td></td></tr><tr><th></th><td></td><td><b>2013</b></td><td><b>2014</b></td><td><b>2015</b></td><td><b>2016</b></td><td><b>2017</b></td><td><b>2018</b></td><td></td><td><b>Mean</b></td></tr><tr><th>Model results</th><td>Density per 100 km2</td><td>20.13</td><td>20.83</td><td>11.98</td><td>17.77</td><td>19.00</td><td>20.04</td><td></td><td>18.29 ± 3.27</td></tr><tr><th></th><td>SE</td><td>2.74</td><td>2.38</td><td>1.61</td><td>3.09</td><td>2.62</td><td>2.35</td><td></td><td></td></tr><tr><th></th><td>LCL</td><td>15.43</td><td>16.65</td><td>9.22</td><td>12.67</td><td>14.53</td><td>16.26</td><td></td><td></td></tr><tr><th></th><td>UCL</td><td>26.26</td><td>26.04</td><td>15.56</td><td>24.92</td><td>24.88</td><td>25.50</td><td></td><td></td></tr><tr><th></th><td>Expected population size</td><td><i>279.20</i></td><td>283.20</td><td>162.85</td><td>241.43</td><td>258.40 277.03</td><td>250.35 ± 45.66</td></tr><tr><th></th><td>SE</td><td>38.04</td><td>32.35</td><td>21.82</td><td>41.95</td><td>35.64</td><td>31.92</td><td></td><td></td></tr><tr><th></th><td>LCL</td><td>214.03</td><td>226.54</td><td>125.37</td><td>172.18</td><td>197.45 221.20</td><td></td><td></td></tr><tr><th></th><td>UCL</td><td>364.20</td><td>354.03</td><td>211.52</td><td>338.52</td><td>338.18 346.96</td><td></td><td></td></tr><tr><th></th><td>Goodness of fit test <i>p</i> -value</td><td>>0.05</td><td><0.05</td><td>>0.05</td><td><0.05</td><td>>0.05</td><td>>0.05</td><td></td><td></td></tr></tbody></table><p>LCL = 95 % lower confidence limit; UCL = 95 % upper confidence limit; RHS, right hand side photos; LHS, left hand side photos; clipped 12,500 m = the selected habitat mask used for estimating hyaena densities – see Supplementary Material for full details.</p>
Real-Time Wind Response Prediction and Load Inversion for Skyscraper Using Deep Learning
<p>Dataset and code for a forthcoming article. Any questions, please consult 2110051@tongji.edu.cn</p>
Data from: Divergence time estimation using fossils as terminal taxa and the origins of Lissamphibia
Were molecular data available for extinct taxa, questions regarding the origins of many groups could be settled in short order. As this is not the case, various strategies have been proposed to combine paleontological and neontological datasets. The use of fossil dates as node age calibrations for divergence time estimation from molecular phylogenies is commonplace. In addition, simulations suggest that the addition of morphological data from extinct taxa may improve phylogenetic estimation when combined with molecular data for extant species, and a few studies have combined morphological and molecular data to estimate combined-evidence phylogenies containing both fossil and extant taxa. However, to date, few if any studies have attempted to infer divergence time estimates using phylogenies containing both extinct and living taxa, sampled for both molecular and morphological data. Here, I infer both the phylogeny and time of origin for Lissamphibia using Bayesian methods, based on a dataset containing morphological data for extinct taxa, molecular data for a number of extant species, and molecular and morphological data for a subset of extant taxa. The results suggest that Lissamphibia is monophyletic, nested within Lepospondyli, and originated in the late Carboniferous at the earliest. This research illustrates potential pitfalls for the use of fossils as post-hoc age constraints on internal nodes, and highlights the importance of explicit phylogenetic analysis of extinct taxa. These results suggest that the application of fossils as minima or maxima on molecular phylogenies should be supplemented or supplanted by combined-evidence analyses whenever possible.
Simulation Circuits for A Time-Domain Approach to Electrical Impedance Tomography using Numerical Analysis of the Step Response
<p>SImulation circuits for the paper of the same name presented in the conference.</p>
Data from: Genome annotation improvements from cross-phyla proteogenomics and time-of-day differences in malaria mosquito proteins using untargeted quantitative proteomics
The malaria mosquito, Anopheles stephensi, and other mosquitoes modulate their biology to match the time-of-day. In the present work, we used a non-hypothesis driven approach (untargeted proteomics) to identify proteins in mosquito tissue, and then quantified the relative abundance of the identified proteins from An. stephensi bodies. Using these quantified protein levels, we then analyzed the data for proteins that were only detectable at certain times-of-the day, highlighting the need to consider time-of-day in experimental design. Further, we extended our time-of-day analysis to look for proteins which cycle in a rhythmic 24-hour ("circadian") manner, identifying 31 rhythmic proteins. Finally, to maximize the utility of our data, we performed a proteogenomic analysis to improve the genome annotation of An. stephensi. We compare peptides that were detected using mass spectrometry but are 'missing' from the An. stephensi predicted proteome, to reference proteomes from 38 other primarily human disease vector species. We found 239 such peptide matches and reveal that genome annotation can be improved using proteogenomic analysis from taxonomically diverse reference proteomes. Examination of 'missing' peptides revealed reading frame errors, errors in gene-calling, overlapping gene models, and suspected gaps in the genome assembly.
Dataset: Timing the emergence and development of arable farming in Southeastern Norway by using summed probability distribution of radiocarbon dates and a Bayesian age model
<p>The repository contains radiocarbon data and code for the paper</p> <p>TIMING THE EMERGENCE AND DEVELOPMENT OF ARABLE FARMING IN 7 SOUTHEASTERN NORWAY BY USING SUMMED PROBABILITY DISTRIBUTION 8 OF RADIOCARBON DATES AND A BAYESIAN AGE MODEL</p> <p>Published in Radiocarbon 2021 (DOI:10.1017/RDC.2021.80) </p> <p>ABSTRACT. The paper explores the emergence and development of arable farming in southeastern Norway by 12 compiling and analyzing directly dated cereals from archaeological contexts. By using summed probability 13 distributions of radiocarbon dates and Bayesian modeling, the paper presents the first comprehensive analysis of 14 the directly dated evidence for farming in the region. The models provide a more precise temporal resolution to 15 the development than hitherto presented. The results demonstrate that the introduction of arable farming to 16 southeastern Norway was a long-term development including several steps. Three different stages are pointed out 17 as important in the process of establishing arable farming: the Early and Middle Neolithic, the Late Neolithic, and 18 the Early Iron Age.</p>
Real-time detection and grading of degenerative disc disease using YO-LO-v5 model
<p>Video 1: Real-time detection and grading of degenerative disc disease using YOLO-v5 model</p>
Fig. 3 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)
Fig. 3. Example Total Ion Current (TIC) chromatograms from each sample class.
Fulldome video about using astronomy to measure time
<p>Fulldome video that explores the basics of using astronomy to measure time.</p> <p>Produced in collaboration with CeSDA (Italy) and NOESIS (Greece)</p>
Real-Time Characterizations of Diminutive Colorectal Polyps Using Narrow Band Imaging
ClinicalTrials.gov study NCT02441998. IPD Sharing: Not stated. Countries: 0. Publications: 7.
Location and Timing of Inhaler Use, Exacerbations and Physical Activity in Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT02661321. IPD Sharing: NO. Countries: 0. Publications: 1.
Study to Find Out the Timing for the Optimal Imaging of Varicose Veins Using Infrared Thermography.
ClinicalTrials.gov study NCT07334028. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Real-time fMRI Neurofeedback and Motor Imagery to Enhance Motor Timing and Precision in Cerebellar Ataxia
ClinicalTrials.gov study NCT05436262. IPD Sharing: YES. Countries: 1. Publications: 0.
ScienceDex guides
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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.