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78 results for “time intervals”
Demographic measures of Liatris ohlingerae (Asteraceae) in 20 populations across multiple habitats and time-since-fire intervals in south central Florida from 1997-2017
Demographic data were collected on 2,858 tagged individually marked plants annually from 1997 to 2017 in 20 populations across three sites on the southern end of the Lake Wales Ridge in south central Florida, USA. Our goal was to understand demographic responses including recruitment, survival, reproduction and mortality of individuals across populations, habitat types and fire-return-intervals. Habitats include rosemary scrub, scrubby flatwoods and human created sandy roadsides. Populations spanned three sites including Archbold Biological Station (18 populations), Florida Forest Service Arbuckle Tract of the Lake Wales Ridge State Forest (1 population), and Florida Fish and Wildlife Conservation Commission Gould Road property (1 population). Annual demographic measures include survival (including plant dormancy), stage, measures of size, reproductive effort and herbivory. Plots were surveyed annually during flowering in August for previously marked plants and searched for newly recruited putative seedlings or previously missed larger adults. This landscape is managed with periodic prescribed fire impacting some populations with additional post-burn censuses completed after the burn. In addition, damage following three hurricanes in 2004 were recorded. Plants were followed through their lifecycle and after four years of no aboveground growth, plants were assumed dead and tags removed from the field.
Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals
<p>In classical cerebellar learning, Purkinje cells (PkCs) associate climbing fiber (CF) error signals with predictive granule cells (GrCs) active just prior (~150ms). Cerebellum also contributes to behaviors characterized by longer timescales. To investigate how GrC-CF-PkC circuits might learn seconds-long predictions, we imaged simultaneous GrC-CF activity over days of forelimb operant conditioning for delayed water reward. As mice learned reward timing, numerous GrCs developed anticipatory activity ramping at different rates until reward delivery, followed by widespread time-locked CF spiking. Relearning longer delays further lengthened GrC activations. We computed CF-dependent GrC→PkC plasticity rules, demonstrating that reward-evoked CF spikes sufficed to grade many GrC synapses by anticipatory timing. We predicted and confirmed that PkCs could thereby continuously ramp across seconds-long intervals from movement to reward. Learning thus leads to new GrC temporal bases linking predictors to remote CF reward signals—a strategy well-suited to learn to track long intervals common in cognitive domains.</p>
Figure. Mean pre-adult development time (in days) values for all strains. Vertical bars denote 0.95 confidence intervals. in Effects of artificial migration of susceptible individuals on resistance and fitness of a fenitrothion-resistant strain of Musca domestica (L.) Diptera
Figure. Mean pre-adult development time (in days) values for all strains. Vertical bars denote 0.95 confidence intervals.
Fig. 2. Lissamphibian biodiversity through time. A in Assessing confidence intervals for stratigraphic ranges of higher taxa: The case of Lissamphibia
Fig. 2. Lissamphibian biodiversity through time. A. Number of species found in each geologic stage. B. Standardized species numbers, calculated to account for uneven stage durations. C. Number of lineages, obtained by adding the number of recorded species and the number of ghost lineages for each geologic stage. Major biological crises identified in other taxa are shown as continuous gray lines. Minor crises that may have affected lissamphibians are shown as dashed gray lines. Since most post−Miocene species are excluded, this figure ends with the terminal Miocene. Minimum (white) and maximum (black) values have been calculated under various assumptions about the age of several middle Miocene species and the status of specimens with questionable affinities. The geologic timescale follows Gradstein et al. (2004) in all figures. Abbreviations: J, Jurassic; K, Cretaceous; M, Miocene; O, Oligocene; P, Permian; Pg, Paleogene; Tr, Triassic.
Global Mean Surface Temperatures for 100 Phanerozoic Time Intervals
<p>This set of Global Mean Surface Temperature (GMST) arrays for 100 Phanerozoic time intervals (stage level) are based on HadleyCM3L simulations (Valdes et al, 2021) that have been modified to better agree with geochemical proxy data (∂18O) and more equable pole-to-equator temperature gradients deduced from lithological indicators of climate (evaporites, calcrete, coals, bauxites, and tillites, etc.) (Scotese et al., 2021). The resolution is 1x1 degrees (latitude/longitude) in three formats: grid-reference, coordinate-list, and netcdf. The accompanying pdf, “Global Mean Temperatures during the Phanerozoic”, by C. R. Scotese, describes the content and methodology used to produce these files. For more information contact: cscotese@gmail.com.</p> <p>*C1 technical correction to the netcdf file grid (361x181).</p> <p>Please cite these following sources when using these data:</p> <p>Scotese, C. R., Song, H., Mills, B. J. W., & van der Meer, D. G. (2021). Phanerozoic paleotemperatures: The earth’s changing climate during the last 540 million years. <em>Earth-Science Reviews</em>, <em>215</em>, 103503. <a href="https://doi.org/10.1016/j.earscirev.2021.103503">https://doi.org/10.1016/j.earscirev.2021.103503</a></p> <p>Valdes, P.J., Scotese, C.R., and Lunt, D.J. (2021). Deep Ocean Temperatures through Time, Climates of the Past, Discussions, <a href="https://doi.org/10.5194/cp-2020-83">https://doi.org/10.5194/cp-2020-83</a>.</p> <p> </p> <p> </p>
League of Legends Match Data at Various Time Intervals
<p>This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.</p> <p>The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.</p> <p>This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.</p>
Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals
Open the record for dataset details and reuse information.
Data from: Climate warming prolongs the time interval between leaf-out and flowering in temperate trees: effects of chilling, forcing and photoperiod
<p><span>1. Leaf-out and flowering are two key phenological events of plants, denoting the respective onsets of visible vegetative growth and reproduction during the year. For each species, the schedule of vegetative growth and reproduction is crucial to the maximization of its fitness. Warming-induced advances of leaf-out and flowering have been reported frequently, however, it is unclear whether the responses of the two events are equal for any given species. </span></p> <p><span>2. Using long-term phenological records in Europe, we examined simultaneously the responses of both leaf-out and flowering of four common temperate tree species to climate warming and further examined the effects of winter chilling, spring forcing and photoperiod on the responses of the two events. </span></p> <p><span>3. We found that regardless whether flowering or leaf-out occurred first, the first event advanced more than the second during 1950 – 2013, resulting in a prolonged time interval between the two events. The temporal changes were also supported by a similar geographical trend that the time interval between the two events increased from cold to warm sites. Due to the warming-induced reduction in chilling, the spring forcing accumulated until the second event was increased more than the forcing accumulated until the first event, and that reduced the temperature sensitivity of the second event. In addition to the effect of chilling, the shorter photoperiod, associated with the advanced spring phenology, was also likely to substantially increase the spring forcing accumulated until the second event, which thus slowed down its advance, compared to the advance of the first event. The relative contributions of chilling and photoperiod to the increased forcing varied between species and events, with chilling mostly outweighing photoperiod. </span></p> <p><span>4. Synthesis. This study provides the large-scale empirical evidence of prolonged time interval between leaf-out and flowering with climate warming. The unequal advances of the two events may alter the partition of resources between vegetative growth and reproduction and cause different changes of spring frost damage to vegetative and reproductive tissues, which may alter species fitness and further affect ecosystem structure and function.</span></p>
Supplementary material S30: The trend in times intervals of jolting pulses for four mite individuals on various substrates (petri-dish, brood-comb and honeycomb).
<p>Jolt occurrence time intervals with respect to time. Data are shown here for a second and third mite on petri-dish (a, b and c, d respectively), a second mite on brood-comb (e, f) and the mite that produces audible jolting pulses on the empty honeycomb that is referred to in the main text (g, h). Jolt occurrences and time between consecutive instances of jolting are showcased in both linear (a, c, e, g) and logarithmic (b, d, f, h) forms. A change in the colour of the data points is indicative of the mite moving to a new position on the substrate.</p>
Real time CO2 emissions 2012-2021 at 3 minutes interval
<p>This dataset includes the power generated split by fuel and related CO2 emissions based on production and consumption. </p> <p>Geographical: Finland, Sweden, Norway, Russia, Estonia</p> <p>Time: 01-01-2013 - 01-10-2021</p> <p>time resolution: 3 minutes</p> <p>This repository is only keeping track of historical data and was used in an analysis. Real-time data can be accessed through the API of a the project. </p> <p><a href="https://app.swaggerhub.com/apis-docs/jean-nicolas.louis/emission-and_power_grid_status/1.1.0">API documentation</a></p>
Ion and electron kappa-distribution functions along the plasma sheet - Time intervals data.
<p>Data uploaded to comply with AGU Publications Data Policy.</p> <p>Article:<br> "Ion and electron kappa-distribution functions along the plasma sheet",<br> by C.M Espinoza, M. Stepanova, P.S. Moya, E. Antonova & J.A. Valdivia.<br> Submitted to Geophysical Research Letters.</p> <p>Files:<br> Contain the start of the 12-minute intervals that were considered for the results.</p> <p>See README.TXT for more details.</p> <p> </p>
Table 2 in Effects of different combinations of N, P and K at different time interval on vegetative, reproductive, yield and quality traits of mango (Mangifera Indica. L) cv. Dusehri
<p><b>Table 2.</b> Effect of different fertilizer combinations of N, P and K on reproductive physiology of mango cv. Dusehri.</p><table><tbody><tr><th><b>Treatments</b></th><th><b>Growth size (mm)</b></th><th><b>Total No. of Panicle/Tree</b></th><th><b>Total no. of flowers / Panicle</b></th><th><b>Sex Ratio (%)</b></th><th><b>Fruit Drop (%)</b></th><th><b>Fruit Retention (%)</b></th><th><b>Total no. of fruit/tree</b></th><th><b>Yield (Kg/Tree)</b></th><th><b>Fruit Length (cm)</b></th><th><b>Fruit Weight (g)</b></th><th><b>Pulp Weight (g)</b></th><th><b>Stone Weight (g)</b></th><th><b>Peel Weight (g)</b></th><th><b>TSS (%)</b></th><th><b>Total Acidity (%)</b></th><th><b>TSS/Acid Ratio</b></th><th><b>Vit. C (mg/100 mL)</b></th><th><b>Total Sugar (%)</b></th></tr></tbody><tbody><tr><th>T1 (Control)</th><td>149.34d ± 3.89</td><td>397.67h ± 3.51</td><td>543.21h ± 3.61</td><td>51.17c ± 2.10</td><td>94.85a ± 1.40</td><td>1.83e ± 0.15</td><td>186.72g ± 4.51</td><td>40.01e ± 4.51</td><td>15.4b ± 3.17</td><td>155.15e ± 6.34</td><td>76.30g ± 2.22</td><td>24.14f ± 2.02</td><td>28.61f ± 1.86</td><td>20.29d ± 1.05</td><td>0.52a ± 0.005</td><td>22.43</td><td>31.26f ± 0.92</td><td>14.52c ± 0.25</td></tr><tr><th>T2 (N)</th><td>166.67b ± 4.47</td><td>508.57f ± 4.51</td><td>612.47f ± 4.58</td><td>54.42bc ± 1.05</td><td>94.86a ± 2.41</td><td>5.57d ± 0.57</td><td>213.34f ± 3.06</td><td>52.70cd ± 4.50</td><td>16.4b ± 3.11</td><td>175.50bc ± 5.50</td><td>82.41f ±1.90</td><td>30.04de ± 2.21</td><td>33.70e ± 1.38</td><td>21.06cd ± 0.95</td><td>0.49b ± 0.004</td><td>25.16</td><td>42.22c ± 1.18</td><td>15.24bc ± 0.41</td></tr><tr><th>T3 (P)</th><td>156.26cd ± 4.85</td><td>467.33g ± 4.04</td><td>593.34g ± 3.61</td><td>53.57bc ± 1.01</td><td>91.72ab± 2.51</td><td>8.82bc ± 0.72</td><td>241.40e ± 4.04</td><td>50.14d ± 5.03</td><td>18.3ab ± 2.75</td><td>169.24cd ± 5.41</td><td>85.23f ± 1.96</td><td>28.50e ± 2.00</td><td>35.62de ±1.94</td><td>22.07bc ± 1.10</td><td>0.45c ± 0.005</td><td>29.13</td><td>39.37d ± 0.98</td><td>15.82bc ± 0.29</td></tr><tr><th>T4 (K)</th><td>164.80bc ± 4.95</td><td>634.57c ± 4.51</td><td>730.19c ± 4.56</td><td>57.39bc ± 2.38</td><td>94.26ab ± 1.79</td><td>5.83d ± 0.48</td><td>278.33d ± 3.51</td><td>55.23cd ± 4.50</td><td>19.3ab ± 2.99</td><td>182.01b ± 5.47</td><td>99.92e ± 1.89</td><td>31.26e ± 1.76</td><td>40.84c ± 1.35</td><td>21.41cd ± 1.25</td><td>0.37d ± 0.002</td><td>43.27</td><td>36.62e ± 1.21</td><td>16.89b ± 0.55</td></tr><tr><th>T5 (NP)</th><td>166.48b ± 4.98</td><td>584.47d ± 3.51</td><td>639e.46 ± 4.04</td><td>56.61bc ± 1.59</td><td>93.34ab ± 2.15</td><td>6.92cd ± 0.33</td><td>288.62c ± 4.51</td><td>57.31c ± 5.03</td><td>17.2b ± 3.29</td><td>160.46de ±4.86</td><td>107.34c ± 2.11</td><td>35.63bc ± 2.02</td><td>37.81d ± 1.88</td><td>23.43ab ± 0.93</td><td>0.35d ± 0.001</td><td>40.48</td><td>42.09b ± 0.47</td><td>16.65b ± 0.61</td></tr><tr><th>T6 (NK)</th><td>160.75bc ± 5.05</td><td>684.66b ± 4.50</td><td>810.62b ± 4.59</td><td>60.17b ± 2.53</td><td>90.28cd ± 2.71</td><td>9.74ab ± 0.66</td><td>320.32b ± 3.05</td><td>66.61b ± 4.49</td><td>18.5ab ± 2.73</td><td>180.32b ± 5.35</td><td>118.04b ± 1.94</td><td>37.21b ± 1.81</td><td>46.92b ± 1.65</td><td>23.30ab ± 0.08</td><td>0.32e ± 0.002</td><td>51.28</td><td>51.48b ±1.07</td><td>16.07b ± 0.71</td></tr><tr><th>T7 (PK)</th><td>159.42bc ± 4.98</td><td>559.71e ± 3.49</td><td>701.17d ± 3.61</td><td>55.31bc ± 1.02</td><td>92.96ab ± 2.56</td><td>7.49bcd ± 0.52</td><td>274.37d ± 2.52</td><td>54.85cd ± 4.51</td><td>18.1b ± 3.01</td><td>178.24bc ± 6.53</td><td>103.51d ± 1.79</td><td>33.07cd ± 1.95</td><td>42.14c ± 1.43</td><td>2.35bc ± 0.05</td><td>0.31e ± 0.002</td><td>52.41</td><td>51.55b ± 0.95</td><td>15.01b ± 0.37</td></tr><tr><th>T8 (NPK)</th><td>177.51a ± 4.92</td><td>845.64a ± 3.61</td><td>974.52a ± 4.58</td><td>69.18a ± 2.87</td><td>86.10e ± 2.85</td><td>13.85a ± 0.43</td><td>379.05a ± 3.00</td><td>82.35a ± 3.51</td><td>23.3a ± 3.10</td><td>197.05a ± 5.62</td><td>135.32a ± 2.09</td><td>43.53a ± 2.07</td><td>52.09a ± 1.77</td><td>24.53a ± 0.06</td><td>0.26f ± 0.001</td><td>73.53</td><td>57.63a ± 0.07</td><td>20.48a ± 0.53</td></tr></tbody></table><p>Values within each column followed by the same letter are not significantly different at P <0.5 level.</p><p>Values within each column followed by the same letter are not significantly different at <i>P</i> <0.05 level.</p><p>Values within each column followed by the same letters are not significantly different at <i>P</i> <0.05 level.</p>
Programmed Intermittent Epidural Bolus Time Interval and Injection Volume
ClinicalTrials.gov study NCT00417027. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Data from: Complementary cognitive roles for D2-MSNs and D1-MSNs during interval timing
Open the record for dataset details and reuse information.
Data from: Climate warming prolongs the time interval between leaf-out and flowering in temperate trees: effects of chilling, forcing and photoperiod
Open the record for dataset details and reuse information.
Dataset for "Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study"
<p>The dataset (EPANET file) which accompanies the publication </p> <p>Vrachimis, S. G., Eliades, D. G., and Polycarpou, M. M.: Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study, Drink. Water Eng. Sci., 2018</p>
FIG UR E 3 (a) Dated phylogeny of the genus Theodoxus constructed in BEAST based on COI, 16S and ATPα. Node labels denote divergence times in millions of years ago (Ma); node bars indicate the 95% credibility interval around these dates. Small squares at nodes indicate significant support of divergence events found with BEAST and other phylogenetic analyses (see Figures S2.1 and S2.2), as explained through the key. Where MOTUs (A–R) show conspecifics among a number of morphospecies, species names are given in order of their year of description. Morphospecies, incorporated from GenBank, where determination was potentially dubious are highlighted by an asterisk. Clades (C) and subclades (SC) are demarcated by dashed lines between MOTUs. (b) LTT plots indicating the build‐up of lineages in Theodoxus over geological time. Dashed lines surrounding the solid LTT lines indicate the 95% confidence intervals. Where intra‐ and interspecific diversity diverge, interspecific diversity is highlighted in blue and intraspecific diversity in red. Transitions in geological ages are highlighted by narrow grey lines, while the grey bar marks the period of pronounced glacial cycles (last 900 kyr) [Colour figure can be viewed at wileyonlinelibrary.com] in Contributions of biogeographical functions to species accumulation may change over time in refugial regions
FIG UR E 3 (a) Dated phylogeny of the genus Theodoxus constructed in BEAST based on COI, 16S and ATPα. Node labels denote divergence times in millions of years ago (Ma); node bars indicate the 95% credibility interval around these dates. Small squares at nodes indicate significant support of divergence events found with BEAST and other phylogenetic analyses (see Figures S2.1 and S2.2), as explained through the key. Where MOTUs (A–R) show conspecifics among a number of morphospecies, species names are given in order of their year of description. Morphospecies, incorporated from GenBank, where determination was potentially dubious are highlighted by an asterisk. Clades (C) and subclades (SC) are demarcated by dashed lines between MOTUs. (b) LTT plots indicating the build‐up of lineages in Theodoxus over geological time. Dashed lines surrounding the solid LTT lines indicate the 95% confidence intervals. Where intra‐ and interspecific diversity diverge, interspecific diversity is highlighted in blue and intraspecific diversity in red. Transitions in geological ages are highlighted by narrow grey lines, while the grey bar marks the period of pronounced glacial cycles (last 900 kyr) [Colour figure can be viewed at wileyonlinelibrary.com]
Time-restricted Eating and High Intensity Interval Training Among Women
ClinicalTrials.gov study NCT04019860. IPD Sharing: NO. Countries: 1. Publications: 3.
The Effects of High vs. Low Time Spent Near VO2max During Two Work-matched High Intensity Interval Training.
ClinicalTrials.gov study NCT05742542. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Programmed Intermittent Epidural Bolus for Labor Analgesia During First Stage of Labor: A Sequential Allocation Trial to Determine the Optimum Interval Time Between Boluses of a Fixed Volume of 2.5ml
ClinicalTrials.gov study NCT03735771. IPD Sharing: NO. Countries: 1. Publications: 1.
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