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3,370 results for “jumping”
Madison community science field campaign to assess abundance and distribution of invasive jumping worms.
Asian pheretimoid earthworms of the genera Amynthas and Metaphire (jumping worms) are leading a new wave of co-invasion into Northeastern and Midwestern states, with potential consequences for native organisms and ecosystem processes. However, little is known about their distribution, abundance, and habitat preferences in urban landscapes – areas which likely influence range expansion via human-driven spread. We led a participatory field campaign to assess jumping worm distribution and abundance in Madison, Wisconsin in September of 2017. By compressing 250 person-hours of sampling effort into a single day, we quantified presence and abundance of three jumping worm species across different land-cover types (forest, grassland, open space, residential lawns and gardens), finding that urban green spaces differed in invasibility. We show that community science can be powerful for researching invasive species while engaging the public in conservation. This approach was particularly effective here, where broad spatial sampling was required within a short temporal window.
Two Wearable Sensor Datasets recording the Countermovement Jump
<p>These datasets come from two independent studies using wearable inertial sensors to estimate countermovement jump performance. The participants were healthy sports science students, free of injury, all of whom had given their prior written consent. Ethical approval was given by the governing institutions’ ethics committees, which included further analysis of the data.</p> <ul> <li><strong>Smartphone Dataset:</strong> <ul> <li>119 valid jumps</li> <li>Peak power 40.7 +/- 8.9 W/kg</li> <li>22 males, 10 females (26.5 +/- 4.1 yrs; standing height 1.74 +/- 0.08 m; body mass 70.0 +/- 10.9 kg)</li> <li>Redmi 9T phone (Xiaomi Technology, Beijing, China)</li> <li>128 Hz sampling frequency</li> <li>Accelerometer & gyroscope</li> <li>Handheld at sternum level</li> <li>Mascia, G.; De Lazzari, B.; Camomilla, V. Machine learning aided jump height estimate democratization through smartphone measures. Frontiers in Sports and Active Living 2023, 5, 1112739. <a href="https://doi.org/10.3389/fspor.2023.1112739">https://doi.org/10.3389/fspor.2023.1112739</a>.</li> </ul> </li> <li><strong>Accelerometer Dataset:</strong> <ul> <li>347 valid jumps</li> <li>Peak power 45.1 +/- 7.6 W/kg</li> <li>48 males, 25 females (21.6 +/- 3.3 yrs; standing height 1.75 +/- 0.10 m; body mass 71.2 +/- 15.1 kg)</li> <li>Trigno sensor (Delsys Inc, MA, USA)</li> <li>250 Hz sampling frequency</li> <li>Accelerometer</li> <li>Taped to lower back (L4)</li> <li>White, M.G.E.; Bezodis, N.E.; Neville, J.; Summers, H.; Rees, P. Determining jumping performance from a single body-worn accelerometer using machine learning. PLOS ONE 2022, 17, e0263846. <a href="https://doi.org/10.1371/journal.pone.0263846">https://doi.org/10.1371/journal.pone.0263846</a></li> </ul> </li> </ul> <p>MATLAB .mat files</p> <p>This repository was used by the paper currently under review for the open journal Mathematics:</p> <p>White, M.; De Lazzari, B.; Bezodis, N., Camomilla, V. Title. Mathematics 2024, 1, 0. Wearable Sensors for Athletic Performance: A Comparison of Discrete and Continuous Feature Extraction Methods for Prediction Models</p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
HITS Inc.'s models and data for Dacon challenge, Jump AI 2023
<p>Here deposits model and data files developed during HITS Inc.'s participation in the Dacon challenge, Jump AI 2023:</p> <p><a href="https://dacon.io/competitions/official/236127/overview/description">https://dacon.io/competitions/official/236127/overview/description</a></p> <p>Note that the files here alone are less useful unless appropriate codes are employed.</p> <p><strong>File description:</strong></p> <ul> <li>pred_model_AutoGluon.tar.xz: AutoGluon model parameters for prediction.</li> <li>valid_model_AutoGluon.tar.xz: AutoGluon model parameters for validation.</li> <li>ckpts_original.tar.xz: fine-tuned <a href="https://github.com/yuyangw/MolCLR">MolCLR</a> model parameters.</li> <li>qc_out.tar.xz: molecular electronic structure files (.wfn).</li> <li>sdf_optimized.tar.xz: molecular structure files (.sdf).</li> <li>atomwfn.tar.xz: atomic electronic structure files (.wfn).</li> </ul>
Figs 194–199 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 194–199. Manzuma tanzanica gen. et sp. nov., holotype, ♂, general appearance. Scale bars: 1 mm.
Figs 182–187 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 182–187. Manzuma petroae gen. et sp. nov., general appearance of live ♂ from South Africa, photos © Vida van der Walt.
Figs 188–193 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 188–193. Manzuma tanzanica gen. et sp. nov., holotype, ♂ and paratype. 188–189. Male palp. 188. Ventral view. 189. Retrolateral view. 190–193. Embolic division. 190. Dorsal view. 191. Prolateral view. 192. Retrolateral view. 193. Ventral view. Scale bars: 0.1 mm.
Figs 161–167 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 161–167. Manzuma petroae gen. et sp. nov., paratypes, ♀♀ from Ithala GR (161, 164–165, 167, NCA 2019/712), Midrand (162, NCA 2009/3732) and Mpofu NR (163, NCA 2011/826). 161–163. Epigyne, ventral view. 164. Spermathecae, apical view. 165. Epigyne, basal view. 166. Diagrammatic course of the insemination ducts. 167. Spermathecae, dorsal view. Scale bars: 0.1 mm.
Figs 143–153 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 143–153. Manzuma nigritibia (Caporiacco, 1941) gen. et comb. nov., ♂♂ from Ethiopia (143, 145–147, 149, MRAC) and Yemen (144, 148, MRAC 212.800), holotype of Aelurillus reconditus, ♀ (150–153), general appearance. Scale bars: 1 mm.
Figs 72–82 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 72–82. Manzuma jocquei (Azarkina, Wesołowska & Russell-Smith, 2011) gen. et comb. nov., ♂♂ from Gagnoa (73–76, 78) and Bouaké (72, 77), ♀ from Bouaké (79–82, NCA 2019/730), general appearance. Scale bars: 1 mm.
Figs 4–9 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 4–9. Scanning electron micrographs of Rafalus christophori Prószyński, 1999 (4–7) and Aelurillus v-insignitus (Clerck, 1757) (8–9). 4–5. Male palp. 4. Ventral view. 5. Retrolateral view. 6–9. Embolic division. 6, 8. Retrolateral view. 7, 9. Apical view. Scale bars: 0.1 mm.
Figs 52–59 in Manzuma gen. nov., a new aelurilline genus of jumping spiders (Araneae, Salticidae)
Figs 52–59. Manzuma jocquei (Azarkina, Wesołowska & Russell-Smith, 2011) gen. et comb. nov., from Côte d'Ivoire (Gagnoa), scanning electron micrographs. 52–55. Male bulbus. 52. Dorsal view. 53. Ventral view. 54. Prolateral view. 55. Retrolateral view. 56–59. Embolic division. 56. Prolateral view. 57. Retrolatero-apical view. 58. Retrolateral view. 59. Dorsal view. Scale bars: 0.1 mm.
Figs 6-10 in New data on jumping spiders of Iran, with a new species of Salticus (Araneae: Salticidae)
Figs 6-10: Copulatory organs of holotype (6-8) and paratype (MMUE) (9-10) male Salticus lucasi sp. nov. Palp in 6. retrolateral; 7. ventral; 8. prolateral views. Bulb after expansion in KOH in 9. ventral; 10. apical views. Abbreviations: ATA anterior blade of terminal apophysis; Em embolus; EmB embolic base; PTA posterior blade of terminal apophysis; RTA retrolateral tibial apophysis. Scale bars: 0.2 mm
Figs 3-5 in New data on jumping spiders of Iran, with a new species of Salticus (Araneae: Salticidae)
Figs 3-5: Habitus of male Salticus lucasi sp. nov., holotype. 3. dorsal; 4. ventral; 5. frontal view
Figs 1-2 in New data on jumping spiders of Iran, with a new species of Salticus (Araneae: Salticidae)
Figs 1-2: Dissected epigyne of Chalcoscirtus platnicki from Rozveh, Dalan- Kouh Protected Area. 1. dorsal; 2. ventral view.
Fig. 10 in A new genus and three new species of jumping spiders (Araneae: Salticidae) from Sri Lanka
Fig. 10. Schenkelia aurantia sp. nov. A. Palp, ventral view. B. Palp, retrolateral view. C. Epigyne, ventral view. D. Vulva, ventral view. Abbreviations: see Material and methods. Scale bars: A–B = 0.2 mm; C–D = 0.1 mm.
Fig. 9 in A new genus and three new species of jumping spiders (Araneae: Salticidae) from Sri Lanka
Fig. 9. Schenkelia aurantia sp. nov., Ƌ. A. Habitus, dorsal view. B. Habitus, ventral view. C. Palp, prolateral view. D. Palp, ventral view. E. Palp, retrolateral view. Scale bars: A–B = 1 mm; C–E = 0.2 mm.
Fig. 7. Mogrus frontosus Simon, 1871 in A new genus and three new species of jumping spiders (Araneae: Salticidae) from Sri Lanka
Fig. 7. Mogrus frontosus Simon, 1871, Ƌ. A. Habitus, dorsal view. B. Ventral view. C. Palp, prolateral view. D. Palp, ventral view. E. Palp, retrolateral view. Scale bars: A–B = 1 mm; C–E = 0.2 mm.
Fig. 5. A–B. Mogrus frontosus Simon, 1871 in A new genus and three new species of jumping spiders (Araneae: Salticidae) from Sri Lanka
Fig. 5. A–B. Mogrus frontosus Simon, 1871, Ƌ. C–D. Brancus calebi sp. nov., Ƌ. A, C. Palp, ventral view. B, D. Palp, retrolateral view. Abbreviations: see Material and methods. Scale bars: 0.2 mm.
Fig. 3 in A new genus and three new species of jumping spiders (Araneae: Salticidae) from Sri Lanka
Fig. 3. Bavirecta flavopuncta sp. nov., ♀. A. Habitus, dorsal view. B. Habitus, ventral view. C. Epigyne, ventral view. D. Vulva, ventral view. Scale bars: A–B = 2 mm; C–D = 0.2 mm.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.