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3,225 results for “Case studies”
Data from: The scope and adaptive value of modulating aggression across breeding stages: Case study in a competitive female songbird
<p><span><span>In seasonally breeding animals, costs and benefits of territorial aggression should vary over time; however, little work thus far has directly examined the scope and adaptive value of individual-level plasticity in aggression across breeding stages. We explore these issues using </span><span>the tree swallow (</span></span><em><span><span>Tachycineta bicolor</span></span></em><span><span>), a bird species in which females compete for limited nesting sites</span> <span>before producing a single brood. We measured the aggressiveness of nearly 100 females within three different stages: (1) shortly after territory-establishment, (2) during early incubation, and (3) while caring for young chicks. </span></span><span><span>We used k-means clustering to categorize females into four distinct plasticity 'types' based on the timing, direction, and magnitude of their changes in aggression between stages. We then tested whether plasticity type and stage-specific aggression </span><span>vary</span><span> with </span><span>key</span><span> performance metrics.</span></span><span><span> Two of the four</span> <span>plasticity</span><span> types became less aggressive </span><span>across consecutive breeding stages</span><span>, consistent with population-level patterns, though these plasticity types </span><span>largely </span><span>did not differ from one another in survival or reproductive success</span></span><span><span>. A third type was characterized by high levels of among-stage plasticity</span><span>; </span><span>these females</span><span>, </span><span>had </span><span>significantly </span><span>lower body mass while parenting, </span><span>tended to hatch fewer eggs,</span> <span>and </span><span>had the lowest observed </span><span>overwinter survival </span><span>rates</span><span>. </span><span>A final type exhibited </span><span>limited</span><span> plasticity, with moderate to low levels of aggression </span><span>in all stages; </span><span>this low plasticity </span><span>-</span><span> low aggression phenotype</span><span> was not associated </span><span>with any </span><span>negative</span> <span>effects to </span><span>performance</span><span>.</span> <span>These</span><span> results reveal substantial among-individual variation in behavioral plasticity, which may reflect diverse solutions to trade-offs between current reproduction and future survival.</span></span></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>
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>
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>
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.
Figure 6a–d 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 6a–d. Quarterly emergences on Tahiti of B. dorsalis and F. arisanus per kg fruit for guava (a), Tahitian chestnut (b), tropical almond (c), and mango (d). See under
Figure 4 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 4. Annual proportion of fruit fly (B. dorsalis, B. tryoni, B. kirki) and parasitoid (F. arisanus, D. longicaudata) emergences in guava, tropical almond, Tahitian chestnut, and mango fruits for selected years.
Figure 3a, b 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 3a, b. Coconut husk block (a) and BactroMAT-ME (b) bait stations used for eradication of B. dorsalis. (Photos: L. Leblanc).
Figure 1 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 1. Monthly captures of B. dorsalis in methyl eugenol traps and quarterly percent parasitism on guava, Tahitian chestnut and tropical almond on Tahiti.
Figure 5a–d 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 5a–d. Quarterly emergences on Tahiti of B. dorsalis and F. arisanus per fruit for guava (a), Tahitian chestnut (b), tropical almond (c), and mango (d). Numbers of fruits used for each host and each year (for guava, Tahitian chestnut, tropical almond and mango, respectively) were: 1998: 1634, 16238, 5314, 67; 1999: 264, 304, 993, 404; 2000: 37, 40, 154, 64; 2001: 52, 0, 20, 74; 2002: 492, 1204, 474, 268; 2003: 1531, 1539, 2685, 977; 2004: 2252, 1324, 810, 291; 2005: 1071, 904, 4373, 436; 2006: 1927, 3343, 3140, 1044; 2007: 1537, 1525, 4200, 1814; 2008: 3255, 2648, 5045, 2052; 2009: 1515, 1972, 5475, 549.
Figure 1 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 1. Locations of the monitoring sites. A: North of Orkney Mainland and northern isles, B: Scapa Flow and southern isles. For corresponding site names refer to Supplementary material Table S1.
Figure 3 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 3. The total number of sites for which each non-native or cryptogenic species has been recorded for each sampling method for 2016 and 2017. NB: Only species recorded in these two years are reported in this figure. Abbreviations: Cm: Caprella mutica, As: Ascidiella scabra, Bh: Bonnemaisonia hamifera, Sj: Schizoporella japonica, Ce: Corella eumyota, Aa: Ascidiella aspersa, Cp: Colpomenia peregrina, Cf: Codium fragile ssp. fragile, Ti: Tricellaria inopinata, Ah: Asterocarpa humilis, Bs: Botryllus schlosseri, Bl: Botrylloides leachii, Mh: Melanothamnus harveyi, Bf: Bugulina fulva, Dj: Dasysiphonia japonica, Jm: Jassa marmorata, Mi: Monocorophium insidiosum, Ma: Monocorophium acherusicum, Cs: Ctenodrilus serratus, Tj: Telmatogeton japonicus, Pa: Potamopyrgus antipodarum, Cb: Crassicorophium bonellii, Dl: Diplosoma listerianum.
Figure 1 in A low number of introduced marine species in the tropics: a case study from Singapore
Figure 1. Map of Singapore showing localities mentioned in the text and the amount of land reclaimed since 1950.
Figure 3. Guettarda speciosa L. A in Katot yan panao: A case study of indigenous botanical nomenclature in the scientific literature
Figure 3. Guettarda speciosa L. A) Common habitus as shrubby tree, Guam (130198175). B) Leaves crowded at branch terminus, Guam (12922175). C) Flowers and buds, Saipan (7100716). D) Fruit, Aitutaki, Cook Islands (163554764). Image numbers from iNaturalist (www.inaturalist.org); photographers: C. Certeza (A), M. Freedman (B), M. Kargul (C), A. Chapman (D); licensing: CC BY-NC (A–C), CC BY-NC-SA (D).
Figure 4. A in Katot yan panao: A case study of indigenous botanical nomenclature in the scientific literature
Figure 4. A) Type specimens of Claoxylon marianum Muell.-Arg. (G 00313924). B) Gaudichaud's field number " 248 " with Chamoru name " Catud Cunau (Catoud Counao) ". C) Field number " 63 " and the Chamoru name " Panao ". D) A note indicating " Ç'est plutôt un Claxylon[sic]! Juss. " E) Gaudichaud ' s signature and date. F–G) Page six of Gaudichaud's inventory of Mariana plants in which he originally listed specimen 63 as " Guettarda ". Images © Conservatoire et Jardin botaniques de la Ville de Genève with permission (A–E) and public domain, courtesy F. Wamprechts (F–G).
Figure 2 in Katot yan panao: A case study of indigenous botanical nomenclature in the scientific literature
Figure 2. Claoxylon marianum Muell.Arg. (Euphorbiaceae), Guam. A) Common habitus as shrubby tree (94524278). B) Toothed leaves crowded at branch terminus (86964495). C) Male flowers and buds (153656635). D) Female flowers and fruit (86964496). Image numbers from iNaturalist (www.inaturalist.org); photographers: N. Sablan (A, C), M. Martinez (B, D); licensing: © the author with permission (A, C), CC BY-NC (B, D).
Figure 1 in Katot yan panao: A case study of indigenous botanical nomenclature in the scientific literature
Figure 1. Dendrocnide latifolia (Gaud.) Chew (Urticaceae). A) Common habitus as shrubby tree, Saipan (104853909). B) Leaves crowded at branch terminus, Rota (12946963). C) Female flowers and leaf abscission scars on branches, Guam (34476422). D) Male flowers, Guam (34476420). Image numbers from iNaturalist (www.inaturalist.org); photographers: H. Rogers (A), M. Freedman (B), PACN Vegetation Program (C–D); licensing: © the author with permission (A), CC BY-NC (B–D).
Fig. 5 in Post-collection taphonomy, sampling effects and the role of the collector in palaeontological collections: A case study from an early Late Triassic bone accumulation in southernmost Brazil
Fig. 5. Selected specimens from the UFRGS' collection showing the effect of trephic factors. The blocks UFRGS-PV-0453-Ta (A) and UFRGS-PV- 0453-Tb (B) were affected by fragmentation and the several fragments stored with different catalogue numbers.
Fig. 4 in Post-collection taphonomy, sampling effects and the role of the collector in palaeontological collections: A case study from an early Late Triassic bone accumulation in southernmost Brazil
Fig. 4. Comparison between the three institutions (A–C) and relationship with sampling pulses over the years. Histograms are aligned and calibrated with regards to timeline. The dashed rectangle in C indicates the period of collaboration between MCN/SEMA-RS and PUC-RS. Abbreviations: Ap, Abdala pulse; BMp, Bertoni-Machado pulse; dp, discovery pulse; PGPp, "Programa Pró-Guaíba" ("Pró-Guaíba" Program) pulse.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.