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464 results for “swimming”
Endurance swimming performance and physiology of juvenile Green Sturgeon (Acipenser medirostris) at different temperatures, CA, 2022
This dataset provides information on the endurance swimming performance and physiological responses of juvenile Green Sturgeon (Acipenser medirostris), reared and tested at the University of California, Davis, in 2022. Fish were acclimated to two temperature treatments (13°C and 18°C) for 14 days prior to swimming trials. Endurance tests were conducted at 47–53 days post-hatch (DPH) in modified swim tunnels at fixed water velocities (25–55 cm s⁻¹) to measure time-to-fatigue (End.min), station-holding behavior (Station_holding), and swimming type (Swim.type). Fish morphometrics (e.g., weight, fork length, total length) were recorded before trials. Post-swim physiological analyses included whole-body measurements of cortisol, glucose, lactate, and protein. Tissue homogenates were processed to determine concentrations normalized to fish weight (e.g., Cortisol_ng_g, Glucose_ug_g, Lactate_ug_g). Standard curves showed high assay linearity (R² > 0.98) and low variability (CV < 10%). This dataset contributes to understanding sturgeon endurance and physiological stress under different environmental conditions, providing insights into their resilience to temperature and flow changes relevant to river management and conservation efforts. Variables include: species, developmental stage (DPH), rearing and trial conditions (tank, temperature, velocity), fish morphometrics (weight, fork length, total length), and physiological metrics (cortisol, protein, glucose, and lactate).
Endurance swimming performance and physiology of juvenile sturgeon at different temperatures, 2022-2024
Endurance swimming trials were conducted on both Green Sturgeon (Acipenser medirostris) and White Sturgeon (Acipenser transmontanus) across multiple size classes to assess the effects of temperature and velocity on swimming performance. Fish were exposed to various water temperatures for at least 14 days and swam at fixed-water velocities (cm/s) in controlled swim tunnels. Data were collected on parameters such as species, size class, trial temperature, velocity, recovery time, time-to-fatigue, swim type classifications, and whether the trials were completed. Additional metadata included fish morphometrics such as fork length, total length, weight, and trial dates, enabling comparisons across species, size, and treatment conditions. Physiological responses were measured post-swimming to evaluate the impacts of endurance trials on the smallest size class of Green Sturgeon (5cm fork length): - In 2022, whole-body cortisol, glucose, and lactate concentrations were measured immediately following endurance trials (0 min recovery). - In 2023, recovery dynamics were incorporated, with physiological responses assessed at multiple time points (0 min, 15 min, 30 min, and 60 min post-trial). - In 2022 and 2023 the baseline physiological metrics (whole-body cortisol, glucose, and lactate concentrations) of control fish (not subjected to swimming trials) were measured across all temperatures, years, species, and size classes. This dataset provides information on sturgeon swimming performance under varied temperature conditions, as well as the associated physiological stress responses, offering valuable insights into their endurance capabilities and recovery processes. The findings can inform conservation strategies, habitat management, and aquaculture practices for these ecologically and economically important species.
3D reconstruction of a horse swimming
<p>3D reconstruction of a horse swimming and visualisation of the joint angles during two cycles of swimming.</p>
High water temperature significantly influences swimming performance of New Zealand migratory species
<p>Our study examined how temperature variations affect critical swimming speeds of four migratory species. Higher temperatures (26°C) significantly reduced swimming speeds for three species, emphasising the need for fish passage solutions that consider temperature fluctuations. This is crucial for habitat restoration and freshwater fish preservation, especially in a changing climate. </p>
Deeptangle Dataset: Labelled Experimental and Synthetic Videos of Swimming and Overlapping C. elegans worms
<p>This repository contains the dataset employed in the paper <a href="https://arxiv.org/abs/2301.04460">Fast spline detection in high density microscopy data</a>.</p> <p>Three files are provided:</p> <p>1. <em>videos.zip</em>: raw experimental videos.<br> 2. <em>labeled_data.zip</em>: labelled sections of experimental videos used for evaluation<br> 3. <em>syntehthic_dataset.zip</em>: synthetic dataset used for training</p> <p><strong>Labelled data</strong></p> <p>Sections are named as VIDEONAME_FRAMENUMBER_SECTIONID.<br> All labels are in labels.json and correspond to the middle frame of the clip (05.png).<br> Example plotting script is provided (<em>plot_data.py</em>).</p> <p><strong>Synthetic data</strong></p> <p>Clips are named as NUMBEROFWORMS_ID.<br> Labels (for all frames) are stored in labels.npy.<br> Example plotting script is provided (plot_data.py).</p> <p> </p> <p><strong>Related</strong></p> <p>Paper: <a href="https://arxiv.org/abs/2301.04460">https://arxiv.org/abs/2301.04460</a></p> <p>Deeptangle code: <a href="https://github.com/kirkegaardlab/deeptangle">https://github.com/kirkegaardlab/deeptangle</a></p> <p>Labelling tool: <a href="https://github.com/kirkegaardlab/deeptanglelabel">https://github.com/kirkegaardlab/deeptanglelabel</a></p> <p>---</p> <p>If used, please cite</p> <p><em>Albert Alonso & Julius B. Kirkegaard. Fast spline detection in high density microscopy data. 2023.</em></p>
Fig. 6. Prochilodus costatus swimming speeds measured a in Upstream and downstream migration speed of Prochilodus costatus (Characiformes: Prochilodontidae) in upper São Francisco basin, Brazil
Fig. 6. Prochilodus costatus swimming speeds measured a. in this study in stretch 2 and b. in the laboratory by Santos et al. (2012). Central points are medians, boxes represent percentiles 25 and 75 and whiskers represent amplitude. Dashed lines separate different kinds of fish movements. BL/s = swimming velocity in standard length of fish per second.
Figure 2 in Swimming and bipedal bottom-running in the pig-nosed turtle Carettochelys insculpta Ramsay, 1886
Figure 2. Detail of hindlimb action during bipedal bottom running in large Carettochelys insculpta. Numerals indicate field sequence. Tail is stippled. Short horizontal lines indicate substratum beneath hindlimbs. Note that, at field 6, neither rear limb is in contact with the substratum.
Convergence of undulatory swimming kinematics across a diversity of fishes
<p>Fishes exhibit an astounding diversity of locomotor behaviors, from classic swimming with their body and fins to jumping, flying, walking, and burrowing. Fishes that use their body and caudal fin (BCF) during undulatory swimming have been traditionally divided into modes based on the length of the propulsive body wave and the ratio of head:tail oscillation amplitude: anguilliform, sub-carangiform, carangiform and thunniform. This classification was first proposed based on key morphological traits, such as body stiffness and elongation, to group fishes based on their expected swimming mechanics. Here, we present a comparative study of 44 diverse species quantifying kinematics and morphology of BCF-swimming fishes. Our results reveal that most species we studied share similar oscillation amplitude during steady locomotion that can be modeled using a second-degree order polynomial. The length of the propulsive body wave was shorter for species classified as anguilliform and longer for those classified as thunniform, although substantial variability existed both within and among species. Moreover, there was no decrease in head:tail amplitude from anguilliform to thunniform mode of locomotion as we expected from the traditional classification. While the expected swimming modes correlated with morphological traits, they did not accurately represent the kinematics of BCF locomotion. These results indicate that even fish species differing as substantially in morphology as tuna and eel exhibit statistically similar two-dimensional midline kinematics and point toward unifying locomotor hydrodynamic mechanisms that can serve as the basis for understanding aquatic locomotion and controlling biomimetic aquatic robots.</p>
Fig. 2. Glycera nicobarica Grube, 1866. A in First Record of Epitokous Metamorphosis and Swimming Behaviour of Glycera nicobarica (Polychaeta: Glyceridae), in the Seto Inland Sea, Western Japan
Fig. 2. Glycera nicobarica Grube, 1866. A, Whole body, epitokous male (NSMT-Pol 111422), dorsal view; B, enlargement of proboscis and prostomium, epitokous male (NSMT-Pol 111422), dorsal view; C, anterior part of an atoke with everted proboscis (NSMT-Pol 111429), lateral view; D, spermatozoon obtained from an epitokous male (NSMT-Pol 111426). Scale bars: 5 mm (A, C); 1 mm (B); 5 µm (D).
Fig. 6. Glycera nicobarica Grube, 1866 in First Record of Epitokous Metamorphosis and Swimming Behaviour of Glycera nicobarica (Polychaeta: Glyceridae), in the Seto Inland Sea, Western Japan
Fig. 6. Glycera nicobarica Grube, 1866. Comparison of parapodia and chaetal arrangement in chaetiger 110 between epitoke (A) and atoke (B), posterior view. A, Epitokous female, 1.9 mm BW, NSMT-Pol 111427; B, atoke, 1.8 mm BW, NSMT-Pol 111428. Scale bar: 0.2 mm.
Fig. 1 in First Record of Epitokous Metamorphosis and Swimming Behaviour of Glycera nicobarica (Polychaeta: Glyceridae), in the Seto Inland Sea, Western Japan
Fig. 1. Collection sites of epitokes (solid circles) and atokes (open circles) of Glycera nicobarica Grube, 1866 in the Seto Inland Sea and Ariake Sea, Japan. 1, Uno Port, Okayama Prefecture; 2, Tadanoumi, Hiroshima Prefecture; 3, Imabari Port, Ehime Prefecture; 4, Yanai Port, Yamaguchi Prefecture; 5, off Nagashima Island, Kaminoseki, Yamaguchi Prefecture; 6, Himeshima Port, Oita Prefecture; a, Kasaoka Bay, Okayama Prefecture; b, Kure, Hiroshima Prefecture; c, Bouchi-no-su, Ehime Prefecture; d, Nukari-no-seto, Hiroshima Prefecture; e, off Iwaijima Island, Yamaguchi Prefecture; f, Kojiro-nagahama, Isahaya Bay, Nagasaki Prefecture.
Fig. 4. Glycera nicobarica Grube, 1866 in First Record of Epitokous Metamorphosis and Swimming Behaviour of Glycera nicobarica (Polychaeta: Glyceridae), in the Seto Inland Sea, Western Japan
Fig. 4. Glycera nicobarica Grube, 1866. Scanning electron micrographs of two kinds of papillae on proboscis. A, B, Epitokous male (NSMT-Pol 111423); C, D, atoke (NSMT-Pol 111429). A, C, conical papillae with 3 U-shaped ridges; B, D, oval papilla. Scale bars: 10 µm.
Fig. 9 in First Record of Epitokous Metamorphosis and Swimming Behaviour of Glycera nicobarica (Polychaeta: Glyceridae), in the Seto Inland Sea, Western Japan
Fig. 9. Small holes (one arrowed) on ventral surface of bases of parapodia of spent G. nicobarica Grube, 1866 (MS). Scale bar: 0.1 mm.
Fig. 8 in First Record of Epitokous Metamorphosis and Swimming Behaviour of Glycera nicobarica (Polychaeta: Glyceridae), in the Seto Inland Sea, Western Japan
Fig. 8. Timing of reproductive swimming of epitokes of Glycera nicobarica Grube, 1866 at six sites in July to November in 2009 to 2011. Numbers above black squares indicate the number of epitokes collected on each ocassion. The locality numbers () correspond to those in Fig. 1.
Fig. 7 in First Record of Epitokous Metamorphosis and Swimming Behaviour of Glycera nicobarica (Polychaeta: Glyceridae), in the Seto Inland Sea, Western Japan
Fig. 7. Presumed parasites attached to epitokous males of Glycera nicobarica Grube, 1866. A, Copepod (arrow) (NSMT-Cr 22387) attached to parapodia of male (NSMT-Pol 111425); B, enlargement of copepod, dorsal view; C, nematodes (arrows) (NSMT-As 3962) attached to parapodia in mid-body of another male (NSMT-Pol 111424); D, enlargement of nematode. Scale bars: 0.5 mm (A–C); 0.1 mm (D).
Dataset: Latham Group, Inc. (SWIM) 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.
Figure 9 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 9. Simulation results for the lift forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 8 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 8. Simulation results for the drag forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.
Figure 2 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 2. An illustration of the Model A tag in the computational domain used for the simulations of all tag designs. The fluid flow is from left to right and representative orientations of the tag to the flow are shown at the bottom of the figure.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.