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Energy Security: Global analysis of Energy Matrix demand Mozambique case

<p>Energy system modelling, energy security, energy transition, renewable <br>energy, climate change, OSeMOSYS. </p>

Figures 1–10 in A new case of pedipalp regeneration in Scorpio kruglovi Birula, 1910 (Scorpiones: Scorpionidae)

Figures 1–10: Scorpio kruglovi. FIgures 1–2. Dorsal (1) and ventral (2) views. FIgures 3, 6. Normal left pedipalp, dorsal (3) and ventral (6) views. FIgures 4–5. Right pedipalp with a regenerated structure, dorsal (4) and ventral (5) views. FIgures 7–10. Right patella with a regenerated structure, ventral (7), ventral (8), internal (9) and external (10) views. Scale bar: 10 mm (1–2).

opencc-by-4.0Jan 2024View details →

Crystal structures and hkl data associated with paper 'The curious case of proton migration under pressure in the malonic acid and 4,4′-bipyridine cocrystal'

Open the record for dataset details and reuse information.

Data for Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases?

<p>This release include the datasets, R scripts and interactive maps generated for the manuscript: Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases? Authors: DA Mendicino, T Ricardo, MA Cristaldi, M Maglianesi, G Guzmán S Claussen, RG Chiaraviglio, CA Ávalos, MA Previtali. (2024).</p>

Physics-based Simulations of 3D Wave Propagation - Case study deriving from the Le Teil earthquake

<p>This dataset contains 4,000 simulation results of the 3D elastic wave equation in a setting deriving from the Le Teil earthquake (France, 2019). The elastic wave equation governs the propagation of waves in a 3D propagation medium. Two types of data are given in this dataset: a materials dataset and a velocity dataset.</p> <h2>Materials dataset</h2> <p>Each material describes the propagation domain used for one numerical simulation. It is built from non-stationary random fields added to the reference 1D velocity profile and corresponds to the velocity of shear waves. The minimum value is 1500m/s and the maximum is 4500m/s. All materials contain a 1800m-thick bottom layer with a constant velocity of 4500m/s. </p> <p>All materials are 3D arrays of shape 32 x 32 x 32.They correspond to a physical size of 9.6 x 9.6 x 9.6km³. </p> <h3>Practical use</h3> <p>Materials are provided as `.npy` arrays, readable with python: `a = np.load(‘materials0-1999.npy’)`<br>Each file contains 2000 materials. Therefore, `a` is of shape (2000, 32, 32, 32). Indices correspond to the material index, the x coordinate (from West to East), the y coordinate (from South to North), and the z coordinate (from bottom to top). </p> <h2>Velocity dataset</h2> <p>The velocity dataset contains the velocity wavefields simulated at the surface of each propagation domain. They have been generated by solving the 3D elastic wave equation with the high-performance computing code SEM3D based on the Spectral Element Method (https://github.com/sem3d/SEM). To each material described above corresponds one velocity field, obtained by the propagation of waves through this material.</p> <p>Velocity fields were recorded by a grid of 16 x 16 virtual sensors located at the surface of the propagation domain between 150m and 450m (600m between consecutive sensors). Each sensor records the 3-component velocity with a 100Hz sampling between 0s and 20s. </p> <p>Computational details: The computational mesh was designed with elements of size 300m and 7 Gauss-Lobato-Legendre quadrature points. It can accurately represent the propagation of waves up to 5Hz frequency. Waves were generated by a point-wise source placed at the bottom of the domain, inside the constant layer (the position of the source is 4800, 4800, -8400m). The seismic source derives from the Le Teil earthquake [Delouis et al., 2021, doi:10.5802/crgeos.78]. The seismic source is described by a moment tensor with fixed orientation (strike = 48°, dip = 45°, and rake = 88°) and amplitude (moment magnitude M0=2.47 · 10^16 N.m).</p> <h3>Practical use</h3> <p>Results are given in .feather dataframes, readable with pandas library in Python: v = pd.read_feather(‘velocity0-99.feather’). Each dataframe contains 100 simulation results. Each row of the dataframe has the following format: </p> <table> <tbody> <tr> <td>run</td> <td>field</td> <td>x</td> <td>y</td> <td>z</td> <td>0.0</td> <td>0.01</td> <td>0.02</td> <td>...</td> <td>19.98</td> <td>19.99</td> </tr> <tr> <td>12</td> <td>Veloc E</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>1.1e-5</td> <td>1.0e-5</td> </tr> <tr> <td>12</td> <td>Veloc N</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>3e-6</td> <td>3e-6</td> </tr> <tr> <td>12</td> <td>Veloc Z</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>-2.6e-5</td> <td>-2.7e-5</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>where `run` indicates the index of the material used in this simulation, `field` indicates the component of the velocity field (`Veloc E` for East-West, `Veloc N` for North-South, `Veloc Z` for Vertical). `x`, `y`, `z` are the coordinates of the sensor (in meters). The next 2000 columns contain the velocity field for times 0, 0.01, …, 19.99.</p> <h1>Related work</h1> <p>This dataset was used to fine-tune a Factorized Fourier Neural Operator (F-FNO, Lehmann et al. 2024, doi:10.1016/j.cma.2023.116718) to predict ground motion wavefields from 3D geologies. The code to train the F-FNO is available at https://github.com/lehmannfa/HEMEW3D</p>

Acoustic Emission dataset for impact localization: numerical and experimental case studies

<h1>Acoustic Emission dataset for Defect Detection in Aluminum plates</h1> <h2>Simulated data</h2> <h3>File name: Simulation.zip</h3> <p>Simulated AE signals based on a ray-tracing algorithm taking into consideration reflections with the mechanical boundaries of the medium (reflection up to the 4th order), which corresponds to a 1x1x0.003 m square aluminum plate.</p> <p>Each signal has been created by simulating the propagation between a transmitter (Tx, index from 1 to 40) and a Receiver (Rx, index from 1 to 25), grouped by Tx position and saved as a .mat struct ('data') containing the following fields:</p> <ul> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Rx position</li> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Tx position</li> <li>data.data = 8000 x 25 matrix containing the transmitted, propagated AE signal from Tx to Rx organized by column. Each AE instance constitutes of 8000 samples acquired at a sampling frequency of 2 MHz (indicated in the file name), one for each Rx given that Tx position.</li> <li>data.Label = 1x25 vector containing the ToA labels associated with each of the 25 Tx-rx pairs (per Tx position) computed by means of the Akaike Information Criterion. </li> </ul> <h2>Experimental data</h2> <h3>File name: Test_x0.xx_y0.yyFs2MHz_1x1x0.003_Al.csv</h3> <p>Experimental data collected with custom AE instrumentation as described in <a href="https://www.mdpi.com/1424-8220/22/3/1091">Ref 1.</a> </p> <p>One single file is a collection of 3 tests (three repetitions of the impact event at the same position), each of them containing three signals acquired simultaneously by three sensors located in proximity of three corners of a 1x1x0.003 aluminum plate having the same geometrical and numerical characteristics of the numerical one. The sensors acquire 5000 samples at a rate of 2 MHz (indicated in the file name) with a pre-trigger window of 1500 samples. The specific coordinates of the sensors are:</p> <ul> <li>s1 [x = 0.05, y = 0.95] m</li> <li>s2 [x = 0.05, y = 0.05] m</li> <li>s3 [x = 0.95, y = 0.05] m</li> </ul> <p>There are 9 files associated with as many impact positions, indicated by the "x0.xx_y0.yy" entry in the file name, with 0.xx and 0.yy corresponding to the x and y coordinate, respectively. Excitation has been provided by means of a waveform generator exciting a 3-cycle sinusoidal wave with central frequency of 250 kHz. More deatils about the electronics and the full setup are provided in the same reference above. </p> <p> </p> <p><em>This research work has been carried out within the Intelligent Sensor Systems Lab@University of Bologna, Italy. </em></p> <p><em>For any needs, warning or curiosities, please contact federica.zonzini@unibo.it</em></p>

opencc-by-4.0Mar 2019View details →

Fig. 1 A in The first autochthonous case of feline ocular thelaziosis in Austria

Fig. 1 A map showing the distribution of known cases of Thelazia callipaeda infections in Europe. Black star indicates the location where the infected cat in Austria was found

Improving the application of Important Plant Areas to conserve threatened habitats: a case study of Uganda

<p><strong>This data set relates to the publication: Richards, S. L., Kalema, J., Ojelel, S., Williams, J., & Darbyshire, I. (2024). Improving the application of Important Plant Areas to conserve threatened habitats: A case study of Uganda. Conservation Science and Practice, e13246. https://doi.org/10.1111/csp2.13246<br></strong></p> <p><strong>Abstract:</strong></p> <p>Important Plant Areas (IPAs) are a successful method of identifying priority areas for plant conservation. Assessment of IPAs, however, often relies on criteria related to species, while incorporation of habitats has been less consistent. Using Uganda as a case study, we test the application of the threatened habitat criterion – criterion C. We identified nationally threatened habitats using Red List of Ecosystems criteria and assess, for the first time, how differing application of thresholds under IPA criterion C can influence IPA network outcomes. Eleven threatened habitats were identified, with declines switching from predominantly forest to savanna after the mid-20<sup>th</sup> century. Significantly, we found current IPA guidance on use of criterion C needlessly limits the number of sites that qualify as IPAs. The “five best sites” IPA threshold is reserved for countries where quantitative data is unavailable, however, the application of the relevant numerical thresholds (site contains ≥10% of national resource or site is among the best quality examples required to collectively prioritisie up to 20% of the national resource) to quantitative data largely generated fewer than five IPAs, comparably limiting conservation opportunities identified. We recommend, therefore, that the “five best” threshold is available for application on both qualitative and quantitative data. This will bolster the value of IPAs in conserving and restoring threatened and ecologically important habitats under the Kunming-Montreal Global Biodiversity Framework.</p> <p><strong>Dataset:</strong></p> <p>Within this dataset is a shapefile of the estimated extent of threatened habitats in Uganda. Each polygon represents a single "site" for each threatened habitat, with methodology for site identification given in the manuscript. Feature area and percentage national resource are given for each site, enabling users to identify those that trigger the different IPA criterion C thresholds.</p> <p><strong>In this study, we have preliminarily identified the threatened habitats and IPAs for Uganda. However, it is important to seek the expertise and views of stakeholders, consider other IPA criteria met and any complementarity between sites when identifying IPAs. In addition, ground-truthing or more localised data could validate the threat status of a vegetation type as well as identifying which sites are best to conserve these habitats. </strong></p>

Figure 4 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 4. Typical tracks produced in the walk. The faster the walk (and the longer the limbs and the shorter the trunk), the greater is the distance (d) between the ipsilateral front- and hind hooves (i.e. the degree of overstepping increases); horizontal axis: distance covered in m. (a) Slow walk and (b) Fast walk of a German warmblood.

Figure 8 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 8. Typical footfall pattern of the canter (German: Galopp) in its three varieties. (a) Right lead of a Paso Fino at slow speed; (b) Left lead of a German warmblood horse at normal speed; (c) Right lead of a medium sized German saddle horse at fast speed. The stride length increases with increasing speed. The Paso Fino places the hind hooves between the imprints of the fore hooves, because of slow speed. The warmblood has the same limb length as the Paso Fino, but it is placing the hind hooves beneath the prints of the fore hooves, because of higher speed. The German saddle horse is medium sized and places the hind hooves in front of the fore hooves. With increasing speed in the canter, the separation between all four hoofprints becomes clearer (in the example of right lead the group HL, HR, FL, FR.).

Figure 1 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 1. Horse hooves. (a) Hind hoof and fore hoof of a horse seen from below; (b) Longitudinal section through the mechanically relevant elements of the autopodium. The hoof is shown during the middle of the stance phase, while highest loads are acting. Dots at the tips of the hooves are indicating the points used for track measurement. The difference between the imprints of hind hoof and fore hooves is not obvious, so that both are hardly discernible in most tracks.

Figure 3 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 3. Relationship between trunk length and length of the limbs. The extremities are reduced to their "functional limb lengths". Step length (s) is the product of excursion angles (α or β) and limb lengths (for example s sin αl αĮ sin αĮ l). The = fa + fr longer the limbs, the lower the ground level below the animal, and the greater the distance (s) covered during each step, without any change of trunk length. The uppermost ground level indicates a lagging of the hind hoof behind the imprint of the fore hoof; the middle level indicates capping; the lowermost indicates overstepping. Excursion angles (α and β) are determined by the resultant GRF. Among living mammals, α usually is greater than αĮ, while β is commonly smaller than β Į; l – left forelimb in anteversion; l – fa fr left forelimb in retroversion; lha – left hind in anteversion; lhr – left hind in retroversion.

Figure 7 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 7. Part of the original tracks comparing fast running pace (a) and fast trot (b). In the running pace the contralateral hoofprints are grouped together with overstepping of the fore hoof over the contralateral hind hoof. In the trot the ipsilateral hoofprints are grouped with an overstepping of the front hoof over the ipsilateral hind hoof. The horizontal axis shows the distance covered in cm.

Figure 2 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 2. Raw data of two randomly chosen trackways; horizontal axis: distance covered in cm. (a) Slow tölt (i.e. amble); (b) Fast tölt of an Icelandic horse; FR – front right; HR – hind right; FL – front left; HL – hind left.

Figure 6 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 6. Typical tracks produced in the trot of a German warmblood. With higher speed, the overstepping (d) of the ipsilateral hind hoof is increasing. (b) Slow trot: the hind hoof is placed right on top of the fore hoof imprint (capping); (a) fast (extended) trot, which leads to marked overstepping. A third possibility is the placing of the hind hoof in front of the fore hoof at very slow speed (this is rarely done and not shown here).

Figure 5 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 5. Typical track produced in the tölt (amble) of an Icelandic horse. In the amble the overstepping (d1) is greater than in the walk and the contralateral hoofprints are close to each other at fast speeds (d2). This is similar to the pace. (a) Slow tölt; (b) fast tölt.

Assessing Hydrodynamic resistance in Microfluidics: A Case Study - datasets

<p><strong><span>Abstract: </span></strong><span>Hydrodynamic resistance is a critical parameter in microfluidics, affecting device functionality and performance.</span><span> However, quantifying hydrodynamic resistance in microfluidics is a challenge due to many influencing factors and the difficulties associated with the precise measurements of low flow rates (< 10 </span><span><span>m</span></span><span>L/min) and pressure drops (< 5 kPa). This article presents a simple experimental test method for assessing hydrodynamic resistance, correlating with theoretical and numerical calculations. The results demonstrate good agreement between benchtop and theoretical data, suggesting a potential standardized method for assessing hydrodynamic resistance in microfluidic devices.</span></p> <p> </p> <p><span>In the files attached: Dataset</span></p> <p> </p> <p><strong><span>Funding:</span></strong><span> This project (20NMR02 MFMET) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. V.S. would like to acknowledge the FCT, I.P., for funding of the Research Unit INESC MN (UID/05367/2020) through pluriannual BASE and PROGRAMATICO and project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy – i4HB</span></p>

iSamples export test case

<p>This is a collection of specimen metadata records retrieved from iSamples and being used for test purposes. </p>

opencc-by-4.0Dec 2013View details →

Figure 7 in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study

Figure 7. Annual percentage of individual guavas infested with fruit flies on Tahiti. Number of fruits incubated individually each year were: 172 in 2002, 348 in 2003, 539 in 2004, 607 in 2005, 98 in 2006, 4 in 2007, 237 in 2008, and 807 in 2009.