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Dataset for "Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry"
<p>This repository contains data from the sonic test experimental campaign carried out in eight structures at various location in Croatia.</p> <p>The data set is structured in 3 levels of folders:</p> <p>- At first level, the 8 folders correspond to the 8 tested buildings.</p> <p>- At second level, for each building, each folder corresponds to a different test location within the building, e.g. "Data FJ1".</p> <p>- At third level, for each location, each folder corresponds to a different setup (i.e. distance and location of hammer and accelerometer), , e.g. "FJ1 1-2".</p> <p>Sonic data are presented in .txt files in three columns corresponding to the time, the hammer (emitter) and the accelerometer (receptor), respectively.</p> <p>Please cite the following related publication:</p> <p>Ortega J, Stepinac M, Lulic L, Nuñez Garcia M, Saloustros S, Aranha C, Greco F, Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry, under review (2022)</p>
The Meltwater Pulse1A Triggered an Extreme Cooling Event: Evidence From Southern China. Meltwater Pulse Cooling Event (MCE). Winter temperature data during the last deglacial of Huguangyan Maar lake, Surface water temperature and seasonal diatom assemblage data of Huguangyan and Yunlong Lake.
<p>Here we present results of The lake averaged monthly mean surface water temperature over the period from September 2013 to August 2015 from Yunlong Tianchi Lake(YL)(25°52.2′N, 99°16.8′E, altitude: 2551 m a.s.l), southwestern China. The dataset include sediment trap main diatom percentages over the period from September 2013 to August 2015 from YL. Lake water temperature profiles at different depths (1, 3, 6, 9, 11, 13, 16 m) from November 2008 to May 2009 in Huguang Maar Lake (HML)(21°9′N, 110°17′E), Southern China. AMS radiocarbon dates of plant remains and bulk sediment samples for Huguangyan Maar Lake over the last ~17 cal ka BP. The main diatom assemblage percentages (%) from 17 to 10 cal ka BP at Huguangyan Maar Lake. Diatom-based reconstruction of winter temperature (WT) from 17 to 10 cal ka BP at Huguangyan Maar Lake.</p>
Data from: Functional diversity buffers the effects of a pulse perturbation on the dynamics of tritrophic food webs
<p>Biodiversity decline causes a loss of functional diversity, which threatens ecosystems through a dangerous feedback loop: this loss may hamper ecosystems' ability to buffer environmental changes, leading to further biodiversity losses. In this context, the increasing frequency of human-induced excessive loading of nutrients causes major problems in aquatic systems. Previous studies investigating how functional diversity influences the response of food webs to disturbances have mainly considered systems with at most two functionally diverse trophic levels. We investigated the effects of functional diversity on the robustness, i.e. resistance, resilience and elasticity, using a tritrophic ---and thus more realistic---plankton food web model. We compared a non-adaptive food chain with no diversity within the individual trophic levels to a more diverse food web with three adaptive trophic levels. The species fitness differences were balanced through trade-offs between defense/growth rate for prey and selectivity/half-saturation constant for predators. We showed that the resistance, resilience and elasticity of tritrophic food webs decreased with larger perturbation sizes and depended on the state of the system when the perturbation occurred. Importantly, we found that a more diverse food web was generally more resistant and resilient but its elasticity was context-dependent. Particularly, functional diversity reduced the probability of a regime shift towards a non-desirable alternative state. The basal-intermediate interaction consistently determined the robustness against a nutrient pulse despite the complex influence of the shape and type of the dynamical attractors. This relationship was strongly influenced by the diversity present and the third trophic level. Overall, using a food web model of realistic complexity, this study confirms the destructive potential of the positive feedback loop between biodiversity loss and robustness, by uncovering mechanisms leading to a decrease in resistance, resilience and potentially elasticity as functional diversity declines.</p>
Data presented in "A buffer gas beam source for short, intense and slow molecular pulses"
<p>Data presented in our paper "A buffer gas beam source for short, intense and slow molecular pulses". The files give the data shown in figures 4 and 5 of the paper.</p>
Supplementary Materials "Isolated proton bunch acceleration by a petawatt laser pulse": 3D3V Input Files and Plot Data Figure 3
<p><strong>PIConGPU Simulation Input + Code</strong></p> <p>3D3V simulations are based on a pre-release of PIConGPU 0.2.0 [1]</p> <p>Directory: hilz-darmstadt-2707-3D<br> - branch with all applied & backported patches<br> - input files: code/examples/SphereDarmstadt/</p> <p>[1] DOI:10.5281/zenodo.168390</p> <p> </p> <p><strong>Data</strong></p> <p>data behind simulation plots in Fig 2c and Fig 3.</p>
FIGURE 4 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 4 | Trophic niche breadth of Pimelodus blochii collected in different hydrological periods in the middle Xingu River region (Eastern Amazon, Brazil). Based on centroid distances between groups from the Permutational Multivariate Dispersion Analysis (PERMDISP).
FIGURE 3 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 3 | Non-metric Multidimensional Scaling (nMDS) graphical representation of the diet of Pimelodus blochii collected in different hydrological periods in the middle Xingu River region, Eastern Amazon, Brazil.
FIGURE 2 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 2 | Alimentary index (Ai) of the diet of Pimelodus blochii collected in different hydrological periods in the middle Xingu River region, Eastern Amazon, Brazil. *Less than 5% of contribution.
FIGURE 1 in Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River
FIGURE 1 | Map depicting the Volta Grande do Xingu (Xingu River, Brazil), with emphasis on the reduced flow section created by the construction of the Belo Monte Dam (including the Pimental Dam). The orange circles represent the sampling sites where Pimelodus blochii specimens were collected, and the arrows indicate the direction of water flow. The orange star and triangle represent the Pimental Dam and the Belo Monte Dam, respectively.
Supplementary Dataset for `Imbalanced speciation pulses sustain the radiation of mammals`
<p>Supplementary Dataset for Quintero, I., Lartillot, N. and Morlon, H. `Imbalanced speciation pulses sustain the radiation of mammals`, Science. This dataset contains all the simulations and the empirical data and results for Mammals using the birth-death diffusion (BDD) diversification models. These dataset were produced with the Tapestree.jl package for the Julia software. Moreover, it contains the fossil data and resulting extinction curves using PyRate as well as other files. Please read the README.md file for detailed description.</p>
Miniaturization and expansion of the contactless temperature measurement system. Facial temperatures in relation to age, pulse and gender.
<p><span>The dataset contains temperature measurements on the surface of the face taken on 109 people. Each patient (identified by Patient ID in dataset) acclimatized in a room with a temperature of 22-24 degrees Celsius. Then the person completed a survey, during which they provided their:</span></p> <ul> <li><span>age (column Survey - age [years]),</span></li> <li><span>gender (column Survey - Gender),</span></li> <li><span>temperature measurement using a pyrometer thermometer (column Survey - temperature [°C]),</span></li> <li><span>and pulse measurement using a pulse oximeter (column Survey - measured pulse [BPM]).</span></li> </ul> <p><span>After that, the examined person stood in front of the contactless temperature measurement system (using a thermal camera), which was continuously calibrated to the black body at a distance of 1.5-3 meters (column Distance between camera and patient [m]). Then, several hundred temperature measurements were taken on each person in the following ways:</span></p> <ul> <li><span>Median temperature on face [°C]</span></li> <li><span>Median temperature on face, 1% of pixels with max temperature [°C]</span></li> <li><span>Median temperature on face, 5% of pixels with max temperature [°C]</span></li> <li><span>Median temperature on face, 10% of pixels with max temperature [°C]</span></li> <li><span>Median temperature in the center of the eyes (3x3 pixels) [°C]</span></li> <li><span>Median temperature measured at the corners of the eyes (3x3 pixels) [°C]</span></li> </ul> <p><span>Additionally, the system automatically estimated:</span></p> <ul> <li><span>the age of the examined person (column Estimated Age [years]),</span></li> <li><span>the pulse of the examined person (column Estimated Pulse [BPM]),</span></li> <li><span>and gender (Estimated Gender).</span></li> </ul> <p><span>According to [1], the measured temperature on the surface of the face is influenced by the age of the measured person. As part of the project, a Binary Regression Tree was developed, which considers (estimated) age when calculating the temperature on the surface of the face (column Temperature calculated by Binary Tree Regression algorithm [°C]).</span></p> <p><span>[1] Cheung, Ming & Chan, Lung & Lauder, I & Kumana, Cyrus. (2012). Detection of body temperature with infrared thermography: accuracy in detection of fever. Hong Kong medical journal = Xianggang yi xue za zhi / Hong Kong Academy of Medicine. 18 Suppl 3. 31-4.</span></p>
Dataset: Pulse Biosciences, Inc. (PLSE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 4 in Effects of the interannual variations in the flood pulse mediated by hypoxia tolerance: the case of the fish assemblages in the upper Paraná River floodplain
Fig. 4. Ordination of the samples of the upper Paraná River floodplain, through the detrended correspondence analysis (DCA), in years of short (diamond: 2000 white, 2001 gray) and moderate floods (square: 2002 gray, 2003 black). Numbers 1-6 are codes of the sampling stations (see Fig. 1).
Fig. 5 in Effects of the interannual variations in the flood pulse mediated by hypoxia tolerance: the case of the fish assemblages in the upper Paraná River floodplain
Fig. 5. Fish assemblage attributes in the main habitats of the upper Paraná River floodplain in years of short (2000 and 2001) and moderate (2002 and 2003) floods. The black area of the bars represents the proportion of STH. Numbers 1-6 on the abscissa are codes of the sampling stations (see Fig. 1).
Fig. 2 in Effects of the interannual variations in the flood pulse mediated by hypoxia tolerance: the case of the fish assemblages in the upper Paraná River floodplain
Fig. 2. Monthly (a) and daily level (b, between January and March) of the upper Paraná River recorded in Porto São José municipality. In b (right axis), the number of days between January and March, when the upper Paraná River surpassed the threshold of 350 cm (horizontal braked line). The years 2000 and 2001 were considered as years of short floods and 2002 and 2003 as years of moderate floods. Source: National Department of Waters and Electric Energy.
Fig. 6 in Effects of the interannual variations in the flood pulse mediated by hypoxia tolerance: the case of the fish assemblages in the upper Paraná River floodplain
Fig. 6. Relationships between each assemblage attribute and dissolved oxygen, in years of short (white) and moderate floods (black). Attributes where either calculated for the entire fish assemblage (STH+SIH) (a-c) and for the subsets of STH (d-f) and SIH (g-i). Numbers 1-6 are codes of the sampling stations (see Fig. 1).
The impact of pulsed Electromagnetic Fault Injection on true random number generators
<p>Random number acquisition from HECTOR daughter board and the clock signals of both decimator and Priority Encoder. The acquisition where done with a shift register between the output of Priority Encoder’s output and the scope (the same applies for the decimator’s output).</p> <p>Folder is class by type of faults:</p> <ol> <li>tmp_00_wb means two of Priority Encoder’s bits are stuck at 00.</li> <li>tmp_01_wb means two of Priority Encoder’s bits are stuck at 01.</li> <li>tmp_10_wb means two of Priority Encoder’s bits are stuck at 10.</li> <li>tmp_11_wb means two of Priority Encoder’s bits are stuck at 11.</li> <li>tmp_0_wb means one of Priority Encoder’s bit is stuck at 0.</li> <li>tmp_1_wb means one of Priority Encoder’s bit is stuck at 1.</li> <li>Decim_0_wb means one bit of decimator’s output stuck at 0.</li> <li>Decim_1_wb means one bit of decimator’s output stuck at 1.</li> </ol> <p>Curves are class as follows:</p> <p> (<em>Index</em> means the acquisition number)</p> <ol> <li>CIdecimIndex_0.trc files are the clock signal before decimation, i.e. the clock used to sample Priority encoder’s output.</li> <li>COdecimIndex_0.trc files are the clock signal after decimation, i.e. the clock used to sample decimator’s output.</li> <li>IrandDecim_Index_0.trc is the output of the Priority Encoder.</li> <li>OrandDecim_Index_0.trc is the output of the Priority Encoder.</li> </ol> <p>All trc files are binary files.</p> <p>The file conditions_wb recap all the injection parameter used to obtain the different fault.</p>
Bayesian Time-Resolved Spectra of GRB Pulses
<p>Spectral analysis results of 38 gamma-ray burst pulses for two empirical models: the cutoff powerlaw (CPL) and the Band function (BAND). Standard FITS file format. Bayesian inference done by the astrophysical data analysis software 3ML.</p>
Simulated Arterial Pulse Waves Database (preliminary version)
<p> </p> <p><em>This provides a brief overview of the database. Further details are provided at: <a href="https://peterhcharlton.github.io/pwdb/ppwdb.html">https://peterhcharlton.github.io/pwdb/ppwdb.html</a></em></p> <p><strong>Background:</strong> The shape of the arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health, since it is influenced by both the heart and the vasculature. Consequently, many algorithms have been proposed to estimate clinical parameters from PWs. However, it is difficult and costly to acquire comprehensive datasets with which to assess their performance. We are aiming to address this difficulty by creating a database of simulated PWs under a range of CV conditions, representative of a healthy population. The database provided here is an initial version which has already been used to gain some novel insights into haemodynamics.</p> <p><strong>Methods:</strong> Baseline PWs were simulated using 1D computational modelling. CV model parameters were varied across normal healthy ranges to simulate a sample of subjects for each age decade from 25 to 75 years. The model was extended to simulate photoplethysmographic (PPG) PWs at common measurement sites, in addition to the pressure (ABP), flow rate (Q), flow velocity (U) and diameter (D) PWs produced by the model.</p> <p><strong>Validation:</strong> The database was verified by comparing simulated PWs with in vivo PWs. Good agreement was observed, with age-related changes in blood pressure and wave morphology well reproduced.</p> <p><strong>Conclusion:</strong> This database is a valuable resource for development and pre-clinical assessment of PW analysis algorithms. It is particularly useful because it contains several types of PWs at multiple measurement sites, and the exact CV conditions which generated each PW are known.</p> <p><strong>Future work:</strong> However, there are two limitations: (i) the database does not exhibit the wide variation in cardiovascular properties observed across a population sample; and (ii) the methods used to model changes with age have been improved since creating this initial version. Therefore, we are currently creating a more comprehensive database which addresses these limitations.</p> <p><strong>Accompanying Presentation:</strong> This database was originally presented at the BioMedEng18 Conference. The presentation describing the methods for creating the database, and providing an introduction to the database, is available at: <a href="https://www.youtube.com/watch?v=X8aPZFs8c08">https://www.youtube.com/watch?v=X8aPZFs8c08</a> . The accompanying abstract is available <a href="https://kclpure.kcl.ac.uk/portal/en/publications/a-database-for-the-development-of-pulse-wave-analysis-algorithms(d14a02f7-ae79-4761-b17e-621d45094591).html">here</a>.</p> <p><strong>Accompanying Manual: </strong>Further information on how to use the PWDB datasets, including this preliminary dataset, are provided in the <a href="https://github.com/peterhcharlton/pwdb/wiki">user manual</a>. Further details on the contents of the dataset files are available <a href="https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database">here</a>.</p> <p><strong>Citation: </strong>When using this dataset please cite <a href="https://kclpure.kcl.ac.uk/portal/en/publications/modelling-arterial-pulse-wave-propagation-during-healthy-ageing(6579ac0c-f092-4ab5-9dc6-2a7aeda6c78d).html">this publication</a>:</p> <p><a href="https://kclpure.kcl.ac.uk/portal/en/publications/modelling-arterial-pulse-wave-propagation-during-healthy-ageing(6579ac0c-f092-4ab5-9dc6-2a7aeda6c78d).html">Charlton P.H. <em>et al.</em> <strong>Modelling arterial pulse wave propagation during healthy ageing</strong>, In <em>World Congress of Biomechanics 2018</em>, Dublin, Ireland, 2018.</a></p> <p><strong>Version History:</strong></p> <p>- v.1.0: Originally uploaded to PhysioNet. This is the version which was used in the accompanying presentation.</p> <p>- v.2.0: The initial upload to this DOI. The database was curated using the <a href="https://doi.org/10.5281/zenodo.3271512">PWDB Algorithms</a> v.0.1.1. It differs slightly from the originally reported version in that: (i) the augmentation pressure and index were calculated at the aortic root rather than the carotid artery.</p> <p><em>Text adapted from: Charlton P.H. et al., '<a href="https://kclpure.kcl.ac.uk/portal/en/publications/a-database-for-the-development-of-pulse-wave-analysis-algorithms(d14a02f7-ae79-4761-b17e-621d45094591).html">A database for the development of pulse wave analysis algorithms',</a> BioMedEng18, London, 2018.</em></p> <p> </p>
Observations and Simulations of Tin Plasmas Formed by CO2 Laser Pulses
<p>This is the experimental and simulation data associated with an article submitted to Physics of Plasmas. It contains two folders and one data file. </p> <p>Folder #1: dual probe csv files</p> <p>This folder contains the data from a complete experimental run with two probes in the chamber.</p> <p>Each csv file has 4 columns of data from Row 22.</p> <ol> <li>Column 1 contains the time data.</li> <li>Column 2 contains the laser pulse data</li> <li>Column 3 contains the planar probe data</li> <li>Column 4 contains the wire probe data</li> </ol> <p>Please note that the planar probe is positioned only at 100 mm, 80 mm, 60 mm, 40 mm and 20 mm from the target, while the wire probe is positioned at a greater range of distances from the target.</p> <p>Please see the data naming convention below which explains how to determine the distance of each probe from the target for each data file.</p> <p>Folder #2: planar probe csv files</p> <p>This folder contains the data from a complete experimental run with the planar probe inside the chamber.</p> <p>Each csv file has 3 columns of data from Row 22.</p> <ol> <li>Column 1 contains the time data.</li> <li>Column 2 contains the laser pulse data</li> <li>Column 3 contains the planar probe data</li> </ol> <p>Please note that the planar probe is positioned a range of distances from the target.</p> <p>Please see the data naming convention below which explains how to determine the distance of the probe from the target for each data file.</p> <p>File: complete_541ns.plt</p> <p>This is a text file which contains the simulation data for a range of parameters which were outputted from the 541 ns simulation at different edit times. All units are cgs.</p> <p>Data Naming Convention</p> <p>As each data sample was taken, it was important to name the saved correlated files in line with a strict convention, which would later enable the analysis of concomitant data sets and the proper association of probe-response with laser voltage and probe distances. Periods were avoided within the naming convention to avoid subsequent potential software conflicts.</p> <p>The naming convention selected contained reference to the following (see Figure 1).</p> <ul> <li>The base position of the probe: from 100 mm to 20 mm in steps of 20 mm. (In the dual probe csv files, the planar probe is held at this base position)</li> <li>The bias on the probe: ±15 V.</li> <li>The laser driving voltage: 22.5 kV, 25.0 kV, 27.5 kV.</li> <li>The actuator offset. In Figure 1, posn120 indicates that the probe has been moved 12 mm towards the target, closer than its starting position 020 mm i.e. it is 8 mm from the target. (The wire probe moves with this offset in the dual probe csv files. The planar probe moves with this offset in the planar probe csv files.)</li> <li>The sequence number of the shot within the group.</li> </ul> <p>e.g. : 020mm_pos15_27-5kV_posn120_003.csv</p> <p>Questions for clarification may be directed to Frank McQuillan at +353 87 7109085, or frank@frankmcquillan.com.</p> <p> </p>
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