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1,940 results for “pulses”
Data to accompany: Seedling responses to soil moisture amount versus pulse frequency in a dominant semi-arid shrub
<p>The timing, frequency and quantity of rainfall is rapidly changing in dryland regions, leading to profound alterations to dryland plant communities. Understanding dryland plant responses to future rainfall scenarios is crucial for implementing proactive management strategies, particularly in light of intensive changes to land cover concurrent with climate change. One such change is woody plant encroachment, an increasing abundance of woody plant species in areas formerly dominated by grasslands or savannas. The continued encroachment of <em>P.</em> <em>velutina</em> will depend, in part, on seedling capacity to establish and thrive under future climate conditions. Seedling performance is primarily impacted by soil moisture conditions that are governed by precipitation amount (quantity) and frequency. We hypothesized that H1) seedling performance would be enhanced by both greater soil moisture and greater pulse frequency, such that seedlings with similar mean soil moisture would perform best under high moisture pulse frequency. Alternatively, H2) mean soil moisture would have greater influence than pulse frequency, such that at a given pulse frequency would have little influence on seedling performance. The hypotheses were tested by growing 256 <em>P. velutina </em>seedlings under two distinct soil moisture treatments, each of which was maintained by two different pulse frequency treatments. Contrary to H1, mean soil moisture had far greater impact than pulse frequency on seedling growth, photosynthetic gas exchange, leaf chemistry and biomass allocation. These results indicate that <em>P. velutina</em> seedling establishment may be more responsive to rainfall amount than rainfall frequency.</p>
Data from: Predation probabilities and functional responses: How piscivorous waterbirds respond to pulses in fish abundance
<p>How predators respond to changes in prey abundance (i.e. functional responses) is foundational to consumer-resource interactions, predator-prey dynamics, and the stability of predator-prey systems. Predation by piscivorous waterbirds on out-migrating juvenile steelhead trout (<em>Oncorhynchus mykiss</em>) is considered a factor affecting the recovery of multiple Endangered Species Act-listed steelhead populations in the Columbia River basin. Waterbird functional responses, however, may vary by predator species and location, with important implications to predator management strategies. We used a 13-year dataset on waterbird abundance across seven breeding colonies (three Caspian tern [<em>Hydroprogne caspia</em>], two double-crested cormorant [<em>Nannopterum auritum</em>], and two California and ring-billed gull [<em>Larus californicus</em> and <em>L. delawarensis</em>] colonies) and steelhead tag-recovery data (>645,000 tagged and >32,000 recovered steelhead) to quantify weekly predation probabilities and functional responses across waterbird species, colonies, and years. Weekly predation probabilities were highly variable, ranging from 0.01–0.30 at tern colonies, 0.01–0.20 at cormorant colonies, and 0.03–0.13 at gull colonies. Per capita predation probabilities were an order of magnitude higher at inland tern and cormorant colonies relative to estuary colonies of the same species. Terns displayed Type II functional responses across colonies and years, where predation probabilities peaked at low steelhead abundances and declined as steelhead abundance increased (i.e., predator swamping). Cormorants nesting at the large estuary colony (several thousand birds) displayed a Type III functional response, but cormorants nesting at the smaller inland colony (several hundred birds) displayed a Type II response. Consumption probabilities of steelhead by gulls remained consistent across a large range of steelhead availability, suggesting a Type I or a Type III functional response, but a lack of colony abundance data prevented quantifying functional responses. The level of tern predation combined with Type II functional responses indicate possible population-level impacts that could destabilize small or declining prey populations. Conversely, the apparent Type III functional responses of gulls and estuary nesting cormorants are indicative of prey switching behaviors targeted at periods of high steelhead abundance. Our results illustrate the complexity of predator-prey interactions and the importance of quantifying predator- and location-specific functional responses when predicting the efficacy of management strategies to enhance prey populations.</p>
Calibration of a movable heat pulse probe in borehole for measuring horizontal groundwater flow velocity in deep aquifers
<ol> <li>The first file is the flow profile data calculated by analytical solution in the steady-state flow. The calculation region is 30cm×30cm.</li> <li>The secend file is the temperature response data calculated by finite difference method. The simulated temperature response data corresponding to all heat pulse experiments are calculated and listed here.</li> </ol>
Calibration of a movable heat pulse probe in borehole for measuring horizontal groundwater flow velocity in deep aquifers
<ul> <li>In the laboratory, 42 heat pulse experiments with 7 different Darcy’s velocities and 6 different heating powers had been done. The results are summarized in a relationship of temperature increase with time.</li> <li>The temperature responses were continuously monitored over a 30min period. Data were collected every 2s throughout the recording period. In order to strictly monitor the actual power to the heater, the voltage and current delivered to the heater were also recorded simultaneously with the temperature.</li> <li>The heater was switched on and lasted for 1 min to generate a heat pulse. The time when the heater was switched on was selected as the initial time of data analysis.</li> </ul>
Dataset for "Long-wavelength pulse generation via light-sail backscattering"
<p>Dataset for "Long-wavelength pulse generation via light-sail backscattering", paper submitted to PPCF</p>
Pulsed supplies of small fish facilitate short-term intraguild predation in salmon-stocked streams
<p class="MsoNormal"><span>Pulsed supplies of prey generally increase predator food intake. However, it is unclear whether this holds true when predators and pulsed prey are in same guild (i.e., intraguild [IG] predators and prey). IG prey may increase IG-predator food intake through predation, but they may decrease food intake through competition. To test these hypotheses, we compared the food intake of white-spotted charr (<em>Salvelinus leucomaenis</em>) (IG predator) in streams that were stocked or unstocked with masu salmon (<em>Oncorhynchus masou</em>) fry (IG prey) in streams in Hokkaido, Japan. One day after stocking, mean stomach content weight of charr was six-times higher than in unstocked streams due to fry consumption. In particular, large charr showed intense piscivory. However, predation on fry was rare on other days. Decreasing small fry may be partially responsible for the short-term occurrence of predation. In addition, acquisition of predator-avoidance behavior by fry and/or a lack of accommodation by charr to the sudden emergence of a new prey source may explain this time-limited intraguild predation. In days other than the first day post-stocking, food intake by charr did not differ between stocked and unstocked streams. No effects of interspecific competition on charr food intake were observed.</span></p>
Temporally Programmable Hybrid MOPA Laser with Arbitrary Pulse Shape and Frequency Doubling
<p>Video S1: SBS pulse distortion_40kHz_40ns. Video S2: SBS pulse distortion_100kHz_168ns. Video S3: SBS_freqency shift_100 kHz_168ns.</p>
Data for: Alfvén Pulse-Driven Spicule-like jets in the presence of thermal conduction and ion-neutral collision in a two-fluid regime
<p>The uploaded folder consists of the numerical simulation data and analyses routines of two-fluid JOANNA code that studied the Alfvén pulse-driven spicule-like jets in the presence of thermal conduction and Ion-neutral collision. The code produced the data in .xmf and .h5 formats, which are available for the analysis in the Data folder. The slices folder within Data consists of grid information in X- and -Y, as well as time. Apart from that, these slices consist of the temporal variations of various physical variables, e.g., pressure, density, velocity for ions and neutrals, magnetic field, etc. These slices are utilized in making the distance-time maps as presented in Figs 4-5 in the paper. Each physical variable is finally converted from code units to physical units (S.I or C.G.S. as required) and presented in the paper. The data and its analysis tree are self-descriptive, and each folder contains the instruction files in this context.</p>
Data for droplet frequencies in atomizing pulsed jet
Open the record for dataset details and reuse information.
Simulations of Pulsed Over-Pressure Jets: Formation of Bellows and Ripples in Galactic Environments
<p>Movies in the form of animated gifs.</p> <p>Corresponding to figures, tables and notation of paper submitted to MNRAS with the same title.</p> <p>Movies of jets exiting a circular nozzle with an overpressure K and density E relative to the ambient medium. </p> <p>The Mach number is set to 2 and an initial ramp up inspeed over 10 time units.</p> <p>All simulations run to 200 time units.</p> <p>These are Mach 2 jets with superimposed velocity pulsations with pulse period P and amplitude V-1( so V1.4 is 40% and V2 is 100% relative amplitude) A ramp up of the velocity from 0 is applied over an initial period R. Adiabatic gas with specific heat ratio of 5/3. </p> <p>The animated graphs called PROFILES here are radial cross-cuts of the physical parameters as a function of time.</p> <p> </p> <p>The pressure movies are collated into four streams according to the over-pressure and density.</p> <p>Within each zipped folder are movies with pulse periods of 2, 10 and 40 time units.</p> <p> </p> <p>The fixed speed movies are non-pulsed and both density and pressure movies are included.</p> <p> </p> <p>The preview movie is chosem to illustrate the ripples in the environment.</p> <p> </p> <p> </p> <p> </p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACoA)
<p>This repository contains the dataset for the Missing ACoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Supporting information for "Discerning TGF and leader current pulse in ASIM observation"
<p>Data files for the paper "Discerning TGF and leader current pulse in ASIM observation".</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database:Missing PCoA)
<p>This repository contains the dataset for the Missing PCoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)
<p>This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACA A1)
<p>This repository contains the dataset for the Missing ACA A1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoAs)
<p>This repository contains the dataset for the Missing PCoAs described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Long pulse surface NMR
<p>Data set for the paper: Feasibility of surface nuclear magnetic resonance with absurdly long excitation pulses</p>
Data for: Slip-pulses drive frictional motion of dissimilar materials: universality, dynamics, and evolution
<p>Here, we present data for: Slip-pulses drive frictional motion of dissimilar materials: universality, dynamics, and evolution.</p> <p>Spreadsheets correspond to figures in the paper (SI). Data is organized within a spreadsheet by subfigures, and the '.I' index stands for insets data.</p> <p> </p> <p>For further questions, you may contact me - yonatan.poles@mail.huji.ac.il.</p> <p> </p> <p> </p> <p> </p>
Data used in the paper "Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser".
<p>Data recorded for the experiment at the European XFEL are available at <strong>doi:10.22003/XFEL.EU-DATA-900331-00</strong> and <strong>doi:10.22003/XFEL.EU-DATA-900383-00.</strong></p>
Data accompanying "High order coherent communications using mode-locked dark-pulse Kerr combs from microresonators"
<p>This dataset contains figure data for the publication "High order coherent communications using mode-locked dark-pulse Kerr combs from microresonators".</p> <p>To complement the main transmission results in the paper, the figure 3c subfolder also includes the related raw measurement data as well as the digital signal processing (DSP) code that is required to calculate the bit error ratios. The program code is distributed under a GPLv3 license.</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)
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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.