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2,952 results for “limb”
SPARC Data Initiative monthly zonal mean composition measurements from stratospheric limb sounders (1978-2018)
<p>The SPARC Data Initiative dataset is the most comprehensive compilation of vertically resolved stratospheric composition measurements to date and consists of four decades of monthly zonal mean climatologies (1978-2018) from a range of satellite limb sounders including LIMS, SAGE I/II/III, HALOE, UARS-MLS, POAMII/III, OSIRIS, SMR, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, SMILES, OMPS-LP and SAGE III-ISS. The dataset includes most major long-lived trace gases (O<sub>3</sub>, H<sub>2</sub>O, N<sub>2</sub>O, CH<sub>4</sub>, CCl<sub>3</sub>F, and CCl<sub>2</sub>F<sub>2</sub>), transport tracers (HF, SF<sub>6</sub>, HCl, CO, HNO<sub>3</sub>, NOy), and shorter-lived trace gases important to stratospheric chemistry including nitrogens (NO, NO<sub>2</sub>, NOx, N<sub>2</sub>O<sub>5</sub>,and HNO<sub>4</sub>), halogens (BrO, ClO, ClONO<sub>2</sub> and HOCl), and other minor species (OH, HO<sub>2</sub>, CH<sub>2</sub>O, CH<sub>3</sub>CN). The observations considered have been compiled in units of volume mixing ratio (VMR) and on a common latitude-pressure grid, covering the region from the upper troposphere to the lower mesosphere (300-0.1 hPa) with a latitudinal resolution of 5 degrees.</p> <p> </p>
Atlas of the Solar Intensity Spectrum and its Center-to-Limb Variation
<p>The atlas of the Third Solar Spectrum (SS3) represents the ratio between the intensity spectrum at different distances from the solar limb and the intensity spectrum at disk center (µ = 1.0), both in units of the intensity of the local continuum level. The observed positions of the measurements cover 9 different µ values along the solar axis ranging from 0.1 to 0.9 in step of 0.1, where µ=cosθ is the cosine of the heliocentric angle θ. The current version of the atlas covers the range 4384- 6610 Å.</p> <p>In the PDF file, the first plot represents the spectrum at the center of the solar disk, recorded at IRSOL. The next 9 plots represent the 3rd solar spectrum for µ=0.1, µ=0.2, µ=0.3, …, µ=0.9</p> <p>Columns of the CSV file:</p> <table> <tbody> <tr> <td><strong>WL:</strong></td> <td>Wavelength, 4384-6610 Å</td> </tr> <tr> <td><strong>IC:</strong></td> <td>I/I<sub>c</sub> at disc center</td> </tr> <tr> <td><strong>RMU01:</strong></td> <td>limb / disk-center ratio at µ=0.1</td> </tr> <tr> <td><strong>RMU02:</strong></td> <td>limb / disk-center ratio at µ=0.2</td> </tr> <tr> <td><strong>…</strong></td> <td>…</td> </tr> <tr> <td><strong>RMU09:</strong></td> <td>limb / disk-center ratio at µ=0.9</td> </tr> </tbody> </table>
Hyperspectral X-ray CT datasets for a set of multiply-stained mouse limb specimens
<p><strong>General Data description:</strong></p> <p>The following are hyperspectral (energy-resolved) X-ray CT datasets for a set of mouse limb specimens, each stained with multiple contrast agents. All scans were acquired with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction of each dataset. The biological specimens were produced as they each contain multiple contrast agents, with distinct spectral markers. When measured by an energy-sensitive detector, each contrast agent may be identified and segmented individually following spectral analysis. A mouse hindlimb was double-stained with elemental iodine and BaSO<sub>4</sub>. A mouse forelimb was triple-stained with I<sub>2</sub>KI, BaSO<sub>4 </sub>and PTA.</p> <p><strong>File descriptions:</strong></p> <p>Contained are two HDF5 (.h5) data files, as well as two (.txt) metadata files and a MATLAB (.mat) file.</p> <p>Hindlimb_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the double-stained hindlimb.</p> <p>Forelimb_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the triple-stained forelimb.</p> <p>DS_Mouse_hindlimb_sinogram.h5 contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the double-stained hindlimb specimen. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied.</p> <p>TS_Mouse_forelimb_sinogram.h5 contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the triple-stained forelimb specimen. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p>
Merged SCIAMACHY-OMPS limb ozone time series
<p>This data set contains the time series of merged monthly mean ozone profiles retrieved at the University of Bremen from SCIAMACHY and OMPS-LP limb observations. The merging is performed on deseasonalized anomalies, but the data set contains also the reconstructed number density time series. The data set is longitudinally resolved, with a 5° latitude and 20° longitude resolution, and a vertical grid with 3.3 km spacing. </p>
Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al.
<p>Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al. which is to be submitted to Atmospheric Measurement Techniques. </p> <p>A comprehensive inter-comparison of seven radiative transfer models in the limb scattering geometry has been<br> performed. Every model is capable of accounting for polarisation within a fully spherical atmosphere. Three models (GSLS, SASKTRAN-HR, and SCIATRAN) are deterministic, and four models (MYSTIC, SASKTRAN-MC, Siro, and SMART-G)<br> are statistical using the Monte Carlo technique. This dataset consists of the raw radiance data used to perform the intercomparisons, atmospheric input data for the optical properties of the atmosphere, and data specifying the geometry of the test cases.</p> <p>Data is provided in NetCDF4 format with documentation present inside the variable attributes.</p> <p>More detail on the comparison scenarios can be found within the published article. (Link to be added when available).</p>
Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)
<p>These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing).</p> <p>Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the upper limb joints in planar robot-aided therapies
<p>These files contain the raw data (acquired from different users) necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the upper limb joints in planar robot-aided therapies</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Jorge A. Díez, Luis D. Lledó, Francisco J. Badesa, Nicolas Garcia-Aracil</p> <p>Conference: ICORR 2015, IEEE 14th International Conference on Rehabilitation Robotics, August 2015</p> <p><br> All the orientations are expressed regarding the origin of the robot.</p> <p>a) Robot Joints: Planar robot joints acquired during the experiment, in radians (j1-j3 columns). This robot is referenced in the paper.<br> b) Quaternion IMU shoulder: Unit quatenion acquired through a 9DoFs Inertial Measurement Unit (IMU) developed by Shimmer (qw1-qz columns).<br> c) Upper arm acceleration: Acceleration acquired from a 3-axial accelerometer developed by Shimmer (X-Z columns). It is normalized regarding the gravity (9.81m/s^2).<br> d) Quaternion Tracker onto Shoulder: unit quaternion of the tracker placed onto the shoulder acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).<br> e) Quaternion Tracker onto Upper Arm: unit quaternion of the tracker placed onto the upper arm acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).</p>
Raw data corresponding to the scientific paper: "A modular telerehabilitation architecture for upper limb robotic therapy" (Advances in Mechanical Engineering 2017, Vol. 9(1) 1-13)
<p>Acquired raw data necessary to implement the adaptive control strategy grounded on multimodal information.<br> In addition, raw data for the computation of the communication parameters needed for the assessment of the implemented telerehabilitation architecture are provided.</p> <p>a) End-effector positions and velocities (x, y, vx, vy) in three conditions: healthy (Fig 9) and constraint simulated stroke behaviour (Fig 10) without robotic assistance and simulated stroke behavior with robotic assistance (Fig 11)</p> <p>b) Performance indicators and control parameters for all the recruited subjects in both conditions healthy behaviour and simulated stroke behaviour (Fig 12a and Fig 12b)</p> <p>c) Computational values for evaluating telerehabilitation performance (Table 1)</p> <p> </p> <p> </p>
Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)
<p>There are eight files with the following information:<br> - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment<br> - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest</p> <p> </p>
Nitric oxide (NO) data set (60--160 km) from SCIAMACHY mesosphere--lower thermosphere limb scans
<p><strong>Overview</strong><br> Contains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY mesosphere--lower thermosphere (MLT, 50--150 km) limb scans.</p> <p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The Mesosphere--Lower Thermosphere (MLT) measurement mode was carried out from 07/2008 until the end of the mission for one day every 15 days. This data set comprises 84 days of SCIAMACHY MLT NO measurements, each<br> containing about 15 orbits.</p> <p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br> https://earth.esa.int/web/guest/data-access/browse-data-products.<br> The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br> https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p> <p>The SCIAMACHY NO data were compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties.</p> <p><strong>Acknowledgements</strong><br> The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products.</p> <p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various international institutions: University of Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL), University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground segment. Support with respect to mission planning and operations is given by the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>
SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Skin Vibrations Across the Upper Limb
<p>The repository contains the data for the toolbox released as part of the publication “SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb.” The toolbox and installation and usage instructions can be found on GitHub here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>. If you use these data or our toolbox please cite our publication: <a href="https://doi.org/10.1109/HAPTICS59260.2024.10520852">https://doi.org/10.1109/HAPTICS59260.2024.10520852</a>.</p> <p>Full citation: “Tummala, N., Reardon, G., Fani, S., Goetz, D., Bianchi, M., and Visell, Y. (2024) SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb. IEEE Haptics Symposium 2024. DOI: 10.1109/HAPTICS59260.2024.10520852” </p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.</p> <p><strong> </strong></p> <p><strong>Dataset Description</strong></p> <p>This dataset comprises experimental data of 3-axis surface acceleration at 72 locations on the skin in response to unit impulsive forces supplied at 20 different input locations on the palmar hand surface. For details on our experimental procedure, please see our publication. This data is intended to be used as part of the SkinSource toolbox, which can be found here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>.</p> <p><strong> </strong></p> <p><strong>Data Fields</strong></p> <p>The data is provided as a .mat file. This file contains a single variable “dataTable” of variable type “table.” The table contains 80 rows, each corresponding to a unique experimental condition (4 participants x 20 input locations), and contains the following fields:</p> <p><strong>Data </strong>(522x72x3) - 3D array containing the 3-axis skin acceleration at 522 time points (impulse responses) for each of 72 accelerometers. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for the accelerometer locations on the dorsal surface of the upper limb.</p> <p><strong>Model </strong>- The upper limb model number. This number specifies the participant that data was taken on.</p> <p><strong>Location</strong> -<strong> </strong>Number designating which input location on the palmar hand surface the data corresponds to. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for input location number mapping.</p>
Transcutaneous Kilohertz High-Frequency Alternating Current at 10 kHz for Upper-Limb Tremor in People with Parkinson's Disease: A double-blind, randomized, crossover study.
<p><strong><span>Abstract: <span>Background/Objectives:</span></span></strong><span> Preclinical studies have evidenced a peripheral nerve blockade with kilohertz high-frequency alternating current (KHFAC) stimulation. It could have a potential effect on aberrant nerve hyperactivity, such as tremor in people with Parkinson’s disease (PwPD). The objective was to investigate the effects of transcutaneous KHFAC at 10 kHz compared with sham intervention on tremor modulation, upper limb motor function, and adverse events in PwPD. <strong>Methods:</strong> This randomized, double-blind, crossover trial included PwPD, who received transcutaneous KHFAC and sham interventions, within a 48h washout period. Measurements were taken pre-intervention, during, immediately after, and 10 minutes post-intervention. The main outcomes were rest, postural, and kinetic tremor acceleration. Secondary outcomes were handgrip strength, nine-hole peg test (NHPT), movement onset time, and adverse events.<strong> </strong></span></p>
Nitric oxide (NO) data set (60--160 km) from SCIAMACHY nominal limb scans
<p><strong>Overview</strong><br> Contains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY nominal (~0--90 km) limb scans.</p> <p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The nominal limb mode was carried out daily (apart from outages and a few days dedicated to other measurement modes) from 08/2002 until the end of the mission. The limb scans were performed from ground to about 90 km tangent altitude, and the retrieval was performed on a 2.5° x 2 km latitude--altitude grid from 90°S--90°N and from 60 km--160 km. This data set comprises all SCIAMACHY nominal NO measurements sorted by date and year, each day comprised about 15 orbits. See the accompanying README for the dimension and variable descriptions.</p> <p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2017. It is adapted from the MLT NO retrieval described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br> https://earth.esa.int/web/guest/data-access/browse-data-products.<br> The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br> https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p> <p>The SCIAMACHY MLT NO data were previously compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties. This nominal data set here was not yet validated with other measurements but compares well to the SCIAMACHY MLT NO measurements below 90 km.</p> <p><strong>Acknowledgements</strong><br> The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products.</p> <p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various international institutions: University of Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL), University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground segment. Support with respect to mission planning and operations is given by the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>
First release of PLATO consortium stellar limb-darkening coefficients
<p>First official grid of stellar limb-darkening coefficients and intensity profiles computed by the consortium of the PLAnetary Transits and Oscillations of stars (PLATO) Working Package 122400.</p> <p>Linked to the paper "First release of PLATO consortium stellar limb-darkening coefficients", published on the Research Notes of the American Astronomical Society (RNAAS).</p>
Biomechanical variables and pelvic kinematics in lower limb amputees
<p>Lower limb amputation causes drastic changes in basic locomotion patterns. Currently, these patterns are analyzed using a wide variety of methods, with a focus on different variables that can describe their impact on gait conditions and normal posture. Evaluating these conditions is of paramount importance because restoring a normal gait constitutes a key objective in the physical rehabilitation of amputees. Moreover, such assessments could potentially suggest different modifications to prosthetic devices. The objective is to provide a database with values of biomechanical parameters such as body weight distribution, pelvic kinematics, and gait measurements in above-knee (AK) and below-knee (BK) amputees. <br> The data are reported in a .CSV file and are organized according to the attendance of 29 patients to the tests. Each column has the information as follows: participant number; age; gender; weight; size; body mass index (BMI); amputation level; amputation laterality; amputation date; suspension system; body weight distribution percentage in each limb, the sound limb (BWD_S) and the prosthetic one (BWD_P) and the subtraction between both of them (BWD_S-P); gait velocity, cadence; two-minute walking test distance (2MWT) and stride length for each limb (STRIDE_S and STRIDE_P). <br> Also, some gait-related indexes as follows: gait symmetry index, which represents the difference between the value (expressed as a percentage) of the sound limb and that of the prosthetic limb in the stance or swing phases, denoted as SI; quality index, it evaluates individuals’ ability to correctly divide their own gait cycle between the sound and prosthetic limb steps, named as QI_S and QI_P respectively; propulsion index, it is computed based on the gradient (in degrees) between the start and end of the monopodal support phase in the anteroposterior acceleration graph for each limb during gait, and it is denoted as PROP_S and PROP_P.</p>
Lower limb bioheat model
<p>A Lower limb bioheat COMSOL Multiphysics® (Massachusetts, USA) model. The model is based on the computed tomography dataset acquired from the Cancer Imaging Archives (Subject ID TGGA-CV-A6JU) [1,2,3]</p> <p>This COMSOL model simulates the peripheral thermal behavior using Pennes bioheat equation and by considering blood flow in the main arterial structure.</p> <p>[1] Zuley, M. L., Jarosz, R., Kirk, S., Lee, Y., Colen, R., Garcia, K., … Aredes, N. D. (2016). Radiology Data from The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma [TCGA-HNSC] collection. The Cancer Imaging Archive. <a href="http://doi.org/10.7937/K9/TCIA.2016.LXKQ47MS">http://doi.org/10.7937/K9/TCIA.2016.LXKQ47MS 6</a></p> <p>[2] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)</p> <p>[3] https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=11829589#11829589244ace71254c4bb19ade81b2783c7576</p>
Cue the chorus: Canyon treefrog calling phenology on the falling limb of spring floods and warming nights
Phenology is the timing of life events tied to environmental or abiotic cues. We used autonomous recording units (ARUs) across spring-summer months in 2022 to capture breeding calls from canyon treefrog (Hyla arenicolor). ARUs were placed in perennial and intermittent stream reaches across five Wilderness Areas within the upper Verde River basin in Arizona. We monitored streams by installing stream flow gauges (water level recorders). Treefrogs call at relatively low flow after spring floods. This suggests that stream-dwelling anurans may breed in response to flooding followed by prolonged periods of base flows which could be important for tadpole metamorphosis. Implications for stream regulation suggest maintaining the magnitude and timing of flood pulse events can benefit recruitment of stream-breeding amphibians.
ExoMove - Kinematics of Daily Activities with Lower-Limb Exoskeletons
<p>This dataset reports the lower-limb kinematics of healthy individuals during various daily activities (sitting, walking, stair ascending and descending, and transitions between them) while using two distinct lower-limb exoskeletons, eWalk and Autonomyo.<br><br>The dataset captures the biomechanical differences between the exoskeletons, offering a rich resource for advancing exoskeleton design and control for assistive and rehabilitative applications.</p>
Snow flies self-amputate freezing limbs to sustain behavior at sub-zero temperatures
<p><span>All living things are profoundly affected by temperature. In spite of the thermodynamic constraints on biology, some animals have evolved to live and move in extremely cold environments. Here, we investigate behavioral mechanisms of cold tolerance in the snow fly (<em>Chionea</em> spp.), a flightless crane fly that is active throughout the winter in boreal and alpine environments of the northern hemisphere. Using thermal imaging, we show that adult snow flies maintain the ability to walk down to an average body temperature of -7 °C. At this supercooling limit, ice crystallization occurs within the snow fly's hemolymph and rapidly spreads throughout the body, resulting in death. However, we discovered that snow flies frequently survive freezing by rapidly amputating legs before ice crystallization can spread to their vital organs. Self-amputation of freezing limbs is a last-ditch tactic to prolong survival in frigid conditions that few animals can endure. Understanding the extreme physiology and behavior of snow insects is important at this moment when the alpine ecosystems they inhabit are rapidly changing due to anthropogenic climate change.</span></p>
Fig.ç18.A mblyops surugensis sp. nov., holotype, female (NSMT-Cr 21364). A, rst thoracopodal endopod (le); B, second thoracopod (le); C, third thoracopod (le); D, distal part of endopod of the same limb (le); E, sixth thoracopod with rudimentary oostegite; F, eighth thoracopod with developed oostegite. in The Genus Amblyops (Crustacea: Mysida: Mysidae: Erythropinae) from East Asia and Australia, with Descriptions of Ten New Species
Fig.ç18.A mblyops surugensis sp. nov., holotype, female (NSMT-Cr 21364). A, rst thoracopodal endopod (le); B, second thoracopod (le); C, third thoracopod (le); D, distal part of endopod of the same limb (le); E, sixth thoracopod with rudimentary oostegite; F, eighth thoracopod with developed oostegite.
ScienceDex guides
Understand access before you commit
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.