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2,952 results for “limb”
Data from: Digit ratio and length asymmetry in calves' limbs
<p>This study considered possible sexual dimorphism in the relative lengths of the second, third and fourth digits (digit ratio), in calves. Furthermore, a different length of the bone structures of the third (3D) and of the fourth (4D) digits has been examined as an evolutionary adaptation to locomotion on soft ground. The length of the digital bones of the right fore-limb of 33 females and 15 male calves was measured in vivo using a portable X-ray machine. The vestigial structure of the second digit (2D), and 3D and 4D, from metacarpus to the third phalanx were considered in a mixed model, as well as some ratios between 2D and different parts of 3D or 4D (2D:3D and 2D:4D). A covariate for the mean finger length was considered for digit ratios to control for possible biases due to shape allometry. Shorter first phalanx and trotter were found in 3D than in 4D, and the reverse for the third phalanx. The 2D was significantly shorter in females, as well as the second phalanges of 3D and 4D. Significant sex differences in 2D:3D and 2D:4D were found for some digit parts of 3D and 4D and for the first phalanges of 3D:4D. These ratios were always shorter in females, in contrast to that found in most mammals. The asymmetry between 3D and 4D could mean a functional adaptation for locomotion. Sex differences in 2D:4D and 3D:4D were found, but with a reverse pattern than in most mammal species (males > females rather than males < females). In this regard digit ratio in calves was similar to that of Old World monkeys. This study is the first investigation of digit ratio in Ungulates, whose limbs differ from the limbs of most Mammals, maintaining five digits. The reverse pattern of sex differences (digit ratios: males > females) could be due to the peculiar nature of the vestigial dewclaw of 2D and to the hormone patterns acting on this digit during development, but further research is required around this topic.</p>
Data from: History-dependent perturbation response in limb muscle
<p>Muscle mediates movement but movement is typically unsteady and perturbed. Muscle is known to behave non-linearly and with history dependent properties during steady locomotion, but the importance of history dependence in mediating muscles function during perturbations remains less clear. To explore muscle's capacity to mitigate perturbations during locomotion, we constructed a series of perturbations that varied only in kinematic history, keeping instantaneous position, velocity and time from stimulation constant. We find that muscle's perturbation response is profoundly history dependent, varying by four fold as baseline frequency changes, and dissipating energy equivalent to ~6 times the kinetic energy of all the limbs in 5 ms (nearly 2400 W Kg<sup>-1</sup>). Muscle's energy dissipation during a perturbation is predicted primarily by the force at the onset of the perturbation. This relationship holds across different frequencies and timings of stimulation. This history dependence behaves like a viscoelastic memory producing perturbation responses that vary with the frequency of the underlying movement. </p>
Dataset of Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
<p>MATLAB Dataset for the paper. </p> <p>Paper Abstract:</p> <p>Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).</p>
Backward trajectories from AMOC lower limb
<p>Backward trajectory run from the AMOC lower limb using TRACMASS. This is a part of the previous upload https://doi.org/10.5281/zenodo.7924420 . </p>
CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 3 for limb-nadir application
<p>Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 3 (only CO and PAN files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG. </p>
"Peter" the T. rex, associated limb bone
***Tyrannosaurus rex* "Peter"** Specimen No. Peter_018; associated limb bone Peter was found in the Lance Formation in South Dakota, USA. He lived some 66 million years ago. This specimen is a limb bone of another dinosaur found with Peter. 3D model by Palaeo3D. www.palaeo3d.com Source: Objaverse 1.0 / Sketchfab
"Peter" the T. rex, associated limb bone
***Tyrannosaurus rex* "Peter"** Specimen No. Peter_034; associated limb bone Peter was found in the Lance Formation in South Dakota, USA. He lived some 66 million years ago. This bone was found together with Peter. It is a limb bone from a different dinosaur. 3D model by Palaeo3D. www.palaeo3d.com Source: Objaverse 1.0 / Sketchfab
Dataset associated with the study entitled "Dopamine lesions alter the striatal encoding of single-limb gait"
<p>There are two compressed <strong>dataset </strong>files. </p> <p>The file <strong>data.zip</strong> contains a dataset consisting of electrophysiological and behavioral measurements in the dorsal striatum of mice freely behaving in a 60 cm x 60 cm open field. Optogenetic tagging was performed to identify D1 or D2 MSNs.</p> <p>There are three experimental groups in the file <strong>data.zip</strong>:</p> <p>1. HL....the healthy group</p> <p>2. PD....the dopamine lesioned group (unilateral 6OHDA injection in the medial forebrain bundle)</p> <p>3. CT....the sham lesioned control group</p> <p>There is one experimental group in the file <strong>D2_stim_gait.zip</strong>:</p> <p>1. Behavioral measurements during optogenetic stimulation of D2 MSNs to assess changes in gait performance.</p> <p><strong>Code </strong>for analyzing the data is available at: https://github.com/LongYang10/Gait-Analysis-of-Freely-Walking-Mouse</p>
Systematic review of vertebrate limb bone content
<p>This dataset shows a systematic review of the literature examining limb bone (femur, tibia, fibula, humerus, radius, ulna) characteristics in diverse vertebrates. These characteristics include ash content, percent phosphorus, bone mineral density, bone density, and bone mineral content (defined in dataset). </p>
Residual limb models scanned using the EINScan Pro 2X Plus scanner with added HD Prime Pack
<p>This dataset contains limb models scanned using the EINScan Pro 2X Plus scanner. At each of 3 sites, operator 1 (OP1) scanned the four limb models twice (OP1-A and OP1-B) with the structured-light scanner, and operator 2 (OP2) scanned the four limb models once with the same scanner, for a total of 12 scans per site. At the European site, an additional reference scan of each positive model (4 scans) was conducted by an independent professional Operator (OP0) using the metrological scanner (EINScan Laser FreeScan 5X). </p>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</p>
Data about upper limb kinematics to test the efficacy of Action Observation Treatment
<p>The dataset contains the upper-limb kinematics collected in 40 participants before and after a short-term immobilization of the right arm. Three reach-to-grasp movements were required, targeting an object placed a) frontally at the level of the shoulders (A_low), b) frontally above the head (A_high), or c) laterally at the level of the shoulders (L_low). These movements were intended to test different degrees of freedom of the shoulder joint.</p> <p>Half of the participants underwent a virtual-reality action observation treatment (see Rizzolatti et al, Neuroscience and Biobehabioral Reviews, 2021), while the control group underwent a non-motor VR stimulation.</p>
Mechanical energy fluctuation in lower limbs during walking in participants with and without total hip replacement
<p>Mechanical energy fluctuation of the segments in the lower limbs during walking has not been fully investigated. It was hypothesised that the segments of the lower limbs may work as a pendulum, i.e., the kinetic and potential energies exchanged out of phase. This study aimed to investigate energy changes and recovery during gait in hip replacement patients. The gait data for 12 participants with total hip replacement and 12 age-matched controls were compared. The kinetic, potential, and rotative energies for the whole lower limb and thigh, calf, and foot, were calculated. The effectiveness of a pendulum effect was analysed. Gait parameters, e.g., walking speeds, cadence, and stride length were calculated. The results showed that the thigh had significant effectiveness as a pendulum during gait with an energy recovery coefficient of approximately 40% while the calf and foot were less like a pendulum during gait. In comparison, energy recoveries of the lower limbs in the two groups were not significantly different. If the centre of the pelvis was considered as approximate to the centre of mass, however, the control group had a higher energy recovery than the total hip replacement group by roughly 10%. This study concluded that the mechanical energy recovery mechanism in the lower limbs during walking exists for two groups.</p>
Exploring cell diversity and fidelity in crustacean limb regeneration using single-nucleus RNA sequencing
<p>R objects containing datasets generated by snRNA-seq on Parhyale hawaiensis limbs. These datasets have been generated during my Phd thesis.</p>
Alligator limb bone. Gray Fossil Site, TN
Alligator limb bone from Gray Fossil Site, TN. Pliocene. On exhibit at the McClung Museum, University of Tennessee, Knoxville. Source: Objaverse 1.0 / Sketchfab
A dataset for the investigation of upper limb torque prediction from EMG signals
<h1>Motivation</h1> <div>EMG-driven exoskeleton assistance requires the use of intention detection models to associate electromyographic signals with some feature of human movement, such as angular position, velocity, or joint torque. The goal of this dataset is to provide data that allows the benchmarking of such models for a variety of movements.</div> <h1>Short description</h1> <div>This dataset includes kinematic, dynamic and electromyographic data from 17 participants (11 males, age 28.2 ± 7 years, height 175.4 ± 7 cm, weight 70 ± 11 kg). These data were collected during the performance of a sagittal plane upper limb tracking task for single joint (elbow flexion/extension) and multiple joint (elbow and shoulder flexion/extension).</div> <h1>Methodology</h1> <div>A detailed description of the data collection methodology can be found here: https://www.biorxiv.org/content/10.1101/2024.01.11.575155v1</div> <h1>Data Description</h1> <div>The data set consists of 17 folders, one for each participant. Inside each folder you will find</div> <div>- A metadata file (<strong>SXX.json</strong>) containing information about the subject: age, sex, weight, height, and upper limb masses and lengths.</div> <div>- An OpenSim model file (<strong>scaledModel.osim</strong>) containing a scaled upper limb model of the given subject.</div> <div>- A <strong>MVC</strong> folder containing EMG data from maximal voluntary contraction trials</div> <div>- A <strong>SJ</strong> folder containing trial folders for the single joint condition (elbow flexion/extension).</div> <div>- A <strong>MJ</strong> folder containing test folders for the multi-joint condition (elbow and shoulder flexion/extension)</div> <h2>EMG Data</h2> <div>The files containing EMG data have the following header</div> <div><code>TIME,DELTAnt,DELTMed,DELTPost,PECT,LATI,TRILong,TRILat,TRIMed,BICLong,BICShort,BRA,BRD</code></div> <div>This corresponds to a time stamp and EMG signals from the anterior, median and posterior detloids, pectoralis major, latissimus dorsi, long, lateral and median triceps, long and short biceps, brachioradialis and brachialis.</div> <h2>MVC data</h2> <div>The MVC folder contains two files: <strong>emgMVCElbow.csv</strong> and <strong>emgMVCShoulder.csv</strong>. They were collected during the realisation of maximum voluntary contraction tasks and contain raw EMG data sampled at 2 kHz.</div> <h2>Trial data</h2> <h3>Single-joint condition</h3> <div>Single-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular position of the elbow in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocity of the elbow in rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torque of the elbow in N.m. Sampled at 100 Hz.</div> <h3>Multi-joint condition</h3> <div>Multi-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular positions of the upper limb in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocities of the upper limb rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torques of the upper limb in N.m. Sampled at 100 Hz.</div> <div>For this trial, kinematic and torque files use the following headers:</div> <div><code>TIME,elv_angle,shoulder_elv,shoulder_rot,elbow_flexion,pro_sup,deviation,flexion</code></div> <div>The columns correspond to the OpenSim model coordinates. For sagittal plane movement, columns of interest are <strong>shoulder_elv</strong> for shoulder flexion/extension and <strong>elbow_flexion</strong> for elbow flexion/extension.</div>
Research Compendium for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive is the Reproducible Research Compendium for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques.</p> <p>This compendium includes all files related to MARGE associated with the manuscript.</p> <p>NN model files are split into smaller files for convenience, given their sizes. To recombine the files, do, e.g., <br> cat cnn_weights_NH-LW.h5* > cnn_weights_NH-LW.h5</p> <p>User interested in running MARGE will need to clone the GitHub repo (https://github.com/exosports/MARGE), apply the patch file to checksum fc95b3c, organize the relevant files into directories as listed in the configuration files (Zenodo does not support organizing files into directory structures) and calculate the number of training, validation, and test cases to be stored in the relevant input file specified in the configuration file. MARGE is under the Reproducible Research Software License (https://planets.ucf.edu/resources/reproducible-research/software-license/). For more details on MARGE, see the User Manual on GitHub.</p>
CMIP6 HighResMIP HadGEM3-GC3.1 LL, MM and HH datasets for the heat transport in the lower limb of the AMOC
<p>These data include datasets for "Resolving mesoscale eddies is crucial for the heat transport in the lower limb of the AMOC", submitted to <em>Geophysical Research Letters</em>.</p> <p>Each file is prefixed with model resolution (LL, MM and HH) and experiment details (control, SSP5-8.5). Files ending with '_velocities' and '_age_pressure' contain diagnostics projected on the isopycnal surface (sigma2=36.95), depicted in Figures 2 and 3 of the submitted paper. </p>
Tables of Limb and Gravity-darkening Coefficients for the Space Mission GAIA
<p><br> File Summary:<br> --------------------------------------------------------------------------------<br> FileName Lrecl Records Explanations<br> --------------------------------------------------------------------------------</p> <p><br> TABLE1 76 574 u linear LDCs <br> PHOENIX-COND models, all metallicities,<br> v.tu=2,0 km/s for GAIA (LSM, FCM)</p> <p> TABLE2 76 1148 a, b quadratic LDCs <br> PHOENIX-COND models, all metallicities,<br> v.tu=2 km/s for GAIA (LSM, FCM) </p> <p> TABLE3 76 1148 c,d square-root LDCs <br> PHOENIX-COND models, all metallicities,<br> v.tu=2 km/s for GAIA (LSM, FCM) <br> </p> <p> TABLE4 76 1148 e,f logarithmic LDCs<br> PHOENIX-COND models, all metallicities, <br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM)</p> <p> TABLE5 49 2296 a_1,a_2,a_3,a_4 LDCs<br> PHOENIX-COND models, all metallicities, <br> v.tu=2 km/s for GAIA (LSM)</p> <p> TABLE6 76 9586 u linear LDCs <br> ATLAS models, all metallicities,<br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM)</p> <p> TABLE7 76 19172 a, b quadratic LDCs <br> ATLAS models, all metallicities,<br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM) </p> <p> TABLE8 76 19172 c,d square-root LDCs <br> ATLAS models, all metallicities,<br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM) <br> </p> <p> TABLE9 76 19172 e,f logarithmic LDCs<br> ATLAS models, all metallicities, <br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM)</p> <p> TABLE10 49 38344 a_1,a_2,a_3,a_4 LDCs<br> ATLAS models, all metallicities, <br> v.tu=2,0,1,4,8 km/s for GAIA (LSM)<br> <br> TABLE11 55 9575 y GDCs <br> ATLAS models, all metallicities,<br> v.tu=0,1,2,4,8 km/s for GAIA(BP,G,RP) <br> --------------------------------------------------------------------------------</p> <p> Byte-by-byte Description of files: TABLE 1<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- u linear LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- u linear LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- u linear LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- u linear LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- u linear LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- u linear LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 2<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- a quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- a quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- a quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- a quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- a quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- a quadratic LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- b quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- b quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- b quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- b quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- b quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- b quadratic LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 3<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- c root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- c root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- c root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- c root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- c root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- c root-square LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- d root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- d root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- d root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- d root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- d root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- d root-square LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 4<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- e logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- e logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- e logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- e logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- e logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- e logar LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- f logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- f logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- f logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- f logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- f logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- f logar LDC (GAIA RP, FCM)<br> -------------------------------------------------------------------------------- <br> <br> Byte-by-byte Description of files: TABLE 5<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a1 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a1 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a1 4 TERMS LDC (GAIA RP,LSM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a2 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a2 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a2 4 TERMS LDC (GAIA RP,LSM)<br> THIRD LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a3 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a3 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a3 4 TERMS LDC (GAIA RP,LSM)<br> FOURTH LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a4 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a4 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a4 4 TERMS LDC (GAIA RP,LSM)<br> --------------------------------------------------------------------------------</p> <p> Byte-by-byte Description of files: TABLE 6<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- u linear LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- u linear LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- u linear LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- u linear LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- u linear LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- u linear LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 7<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- a quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- a quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- a quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- a quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- a quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- a quadratic LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- b quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- b quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- b quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- b quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- b quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- b quadratic LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 8<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- c root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- c root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- c root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- c root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- c root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- c root-square LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- d root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- d root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- d root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- d root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- d root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- d root-square LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 9<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- e logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- e logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- e logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- e logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- e logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- e logar LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- f logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- f logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- f logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- f logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- f logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- f logar LDC (GAIA RP, FCM)<br> -------------------------------------------------------------------------------- <br> <br> Byte-by-byte Description of files: TABLE 10<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a1 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a1 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a1 4 TERMS LDC (GAIA RP,LSM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a2 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a2 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a2 4 TERMS LDC (GAIA RP,LSM)<br> THIRD LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a3 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a3 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a3 4 TERMS LDC (GAIA RP,LSM)<br> FOURTH LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a4 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a4 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a4 4 TERMS LDC (GAIA RP,LSM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 11<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> 2-6 F5.2 Z log [metal/H]<br> 8-13 F6.3 km/s Vel microturbulent velocity<br> 16-20 F5.2 cm/s+2 logg Surface gravity<br> 22-27 F6.3 K log Teff log Effective temperature<br> 31-37 F7.4 --- y GDC (GAIA, BP)<br> 40-46 F7.4 --- y GDC (GAIA, B)<br> 49-55 F7.4 --- y GDC (GAIA, RP)<br> --------------------------------------------------------------------------------</p>
Dataset from: Limb development in skeletally-immature large-sized dogs: a radiographic study
<p>Dataset from the publication: "Limb development in skeletally-immature large-sized dogs: a radiographic study", published in PLoS ONE, 2021.</p> <p>Despite the extreme morphological variability of the canine species, data on limb development are limited and the time windows for the appearance of the limb ossification centres (OCs) reported in veterinary textbooks, considered universally valid for all dogs, are based on dated studies. The aim of this study was to acquire up-to-date information regarding the arm, forearm and leg bone development in skeletally-immature large-sized dogs from 6 weeks to 16 weeks of age. Nine litters of 5 large-sized breeds (Boxer, German Shepherd, Labrador Retriever, Saarloos Wolfdog, White Swiss Shepherd Dog) were included, for a total of 54 dogs, which were subject to clinical and radiographic examination on a bi-weekly basis. The appearance of 18 limb OCs was recorded and 14 radiographic measurements were performed; their relationship with age and body weight was investigated and any breed differences were analysed.</p>
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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.