Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Dataset of publication "Speed of Sound Measurements in Helium at Pressures from 15 to 100 MPa and Temperatures from 273 to 373 K"

<p>This is a dataset of the speed of sound in helium, which was measured along five isotherms in a temperature range from 273 to 373 K at pressures from 15 to 100 MPa with a relative expanded uncertainty (k = 2) from 0.02 to 0.04%. A dual-path pulse-echo device was utilized to conduct these measurements.</p>

Dataset for the paper "Aircraft wake vortices affecting airport wind measurements"

<p>Dataset in support of the paper "Aircraft wake vortices affecting airport wind measurements". The dataset contains the results of the manual classification as discussed in section 2 and 3 of the paper, details can be found there.</p><p>For each take-off, one row exists in the dataset. The columns are:</p><ul><li><i>takeoff_no</i>: int, Incrementing integer</li><li><i>timestamp</i>: string, UTC time the flight passes by the anemometer</li><li><i>flight_id</i>: string, Unique identifier for the flight</li><li><i>typecode</i>: string, ICAO aircraft typecode of the flight</li><li><i>wtc</i>: string: ICAO wake turbulence category of the flight</li><li><i>groundspeed_kts</i>: float, Groundspeed [kts] at the moment of passing by the anemometer</li><li><i>alt_above_thr_m</i>: float, Altitude above runway threshold [m] at the moment of passing by the anemometer</li><li><i>wind_speed_kts</i>: float, Wind speed [kts]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>wind_dir_deg</i>: float, Wind direction [°]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>is_event_visual_assessor_1</i>: int, Classification of assessor 1 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_2</i>: int, Classification of assessor 2 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_3</i>: int, Classification of assessor 3 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_sum</i>: int, Sum of classifications of 3 assessors (0 to 3)</li><li><i>is_event_wake_model</i>: float, Classification of wheather the flight caused a wake that hit the anemometer based on P2P wake model output (only applied to flights with a sum of classifications of 2 and more)</li><li><i>is_event</i>: int, Final classification of wheather the flight caused a wake that hit the anemometer</li></ul><p> </p>

Bridge admittance measurements of a Moroccan rabāb

<p>Instrument: <i>rabāb </i><br>Country of origin: Morocco <br>Place of origin: Fès <br>Instrument maker: Abdessalam Chiki <br>Year of manufacture: 2015 <br>Location: Basel, private property of Thilo Hirsch</p><p>Dimensions: <br>Total length: 513.2 mm <br>Max. Body width: 114.8 mm <br>Width at the upper end of the skin: 96.2 mm <br>Width at top nut: 31.6 mm <br>Body depth at the upper end of the skin: approx. 80 mm</p><p>Vibrating string lengths: <br>d-string: 410 mm <br>G-string: 403 mm</p><p>Materials: <br>Body: walnut <br>Pegbox: walnut <br>Fingerboard: acajou (mahogany) <br>Decoration: mother-of-pearl <br>Bars: spruce <br>Top nut, tailpiece button: bone <br>Bridge: bamboo <br>Top: goatskin</p><p>Bridge admittance measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 18.2.2020</p><p>Force: Impact hammer exciting at the bass side of the bridge.<br>ACC: Acceleration measured on the same side.<br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>________________________</p><p>How to read VIA-Files: <br>Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: <br>1st col: Frequency [Hz]          2nd col: Magnitude [as Factor not dB!]        3rd col: Phase [rad]    4th col:  Real part [as Factor not dB!] 5th col: Imaginary part [as Factor not dB!] (so only first 3 columns are needed)</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. <br>Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>

Laser interferometry measurements of a Moroccan rabāb

<p>Instrument: <i>rabāb</i><strong> </strong><br>Country of origin: Morocco <br>Place of origin: Fès <br>Instrument maker: Abdessalam Chiki <br>Year of manufacture: 2015 <br>Location: Basel, private property of Thilo Hirsch</p><p>Dimensions: <br>Total length: 513.2 mm <br>Max. Body width: 114.8 mm <br>Width at the upper end of the skin: 96.2 mm <br>Width at top nut: 31.6 mm <br>Body depth at the upper end of the skin: approx. 80 mm</p><p>Vibrating string lengths: <br>d-string: 410 mm <br>G-string: 403 mm</p><p>Materials: <br>Body: walnut <br>Pegbox: walnut <br>Fingerboard: Acajou (mahogany) <br>Decoration: mother-of-pearl <br>Bars: spruce <br>Top nut, tailpiece button: Bone <br>Bridge: bamboo <br>Top: goatskin</p><p>Laser interferometry measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 18.2.2020</p><p>Each figure (Filenames: A_####Hz.jpg) corresponds to the frequency of excitation on the bass side of the bridge (shaker: Frederiksen Vibration Generator Nr 2185.00). Images are made by time-averaging of laser speckle interference at the camera (time-average ESPI approach). Operating deflection shapes (roughly translating to theoretical vibrating modes) are observed at each frequency.</p><p>Photo of the setup: Thilo Hirsch</p>

Transfer function measurements of a Moroccan rabāb

<p>Instrument: <i>rabāb </i><br>Country of origin: Morocco <br>Place of origin: Fès <br>Instrument maker: Abdessalam Chiki <br>Year of manufacture: 2015 <br>Location: Basel, private property of Thilo Hirsch</p><p>Dimensions: <br>Total length: 513.2 mm <br>Max. Body width: 114.8 mm <br>Width at the upper end of the skin: 96.2 mm <br>Width at top nut: 31.6 mm <br>Body depth at the upper end of the skin: approx. 80 mm</p><p>Vibrating string lengths: <br>d-string: 410 mm <br>G-string: 403 mm</p><p>Materials: <br>Body: walnut <br>Pegbox: walnut <br>Fingerboard: acajou (mahogany) <br>Decoration: mother-of-pearl <br>Bars: spruce <br>Top nut, tailpiece button: bone <br>Bridge: bamboo <br>Top: goatskin</p><p>Transfer function measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 18.2.2020</p><p>Transfer function from shaker to microphone 1 meter in front of the instrument. <br>Frequency range specified in the Filename. <br>Shaker exciting with a frequency sweep on the bass side of the bridge (shaker type: Minishaker by BNK). <br>Pressure measurement with a ROGA RG50 microphone.</p><p>Photos of the setup: Thilo Hirsch 18.2.2020</p><p>________________________</p><p>How to read VIA-Files: <br>Line 1 to 9: Header, Line 8 holds the number of values <br>Data is organized as followed: 1st col: Frequency [Hz]          2nd col: Magnitude [as Factor not dB!]        3rd col: Phase [rad]    4th col:  Real part [as Factor not dB!] 5th col: Imaginary part [as Factor not dB!] (so only first 3 columns are needed)</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>

Acoustical measurements of a rabab reconstructed after a pictorial source from the 13th century (Cantigas de Santa María)

<p>Instrument: rabab<strong> </strong><br>Pictorial source: <i>Cantigas de Santa Maria</i>, <i>E</i>-Codex (<i>Códice de los músicos</i>), ca. 1284, fol. 118r, <i>Cantiga</i> 110, Madrid, San Lorenzo de El Escorial, Real Biblioteca del Monasterio del Escorial, Ms. b-I-2 <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2021 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 468 mm <br>Max. Body width: 104 mm <br>Body depth: approx. 80 mm</p><p>Vibrating string lengths: <br>a-string: 403 mm <br>d-string: 401 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: maple <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: maple <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the two open rosettes on the fingerboard and then the upper one was sealed with a piece of wood (see photos of the setup).</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder cantigas_rosette_o_offen: <br>Files: cantigas_roo_1 to 6  (Description: upper rosette open) <br>File: cantigas_roo_do (Description: upper rosette open / damper moved between 2 rosettes)    </p><p>Folder cantigas_rosette_o_zu: <br>Files: cantigas_roz_1 to 6 (Description: upper rosette closed with wooden sheet)</p><p>________________________</p><p>How to read VIA-Files: <br>Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad]    4th col:  Real part [as Factor not dB!] 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. </p><p>Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>

Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures

<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>

Acoustical measurements of a rabab reconstructed after a pictorial source from the 14th century

<p>Instrument: rabab<strong> </strong><br>Pictorial source: Francesc Comes, <i>Madonna and Child with angel musicians</i>, around 1394, Gold and tempera on wood, Pollença (Mallorca), Museu de Pollença <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2022 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 579 mm <br>Max. Body width: 112 mm <br>Body depth: approx. 95 mm </p><p>Vibrating string lengths: <br>d-string: 500 mm <br>G-string: 497 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: serviceberry <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: boxwood <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the upper rosette and the two holes in the body closed, then with both rosettes and the body holes open, and finally only with the body holes closed.</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder comes_rosette_o_loecher_zu <br>Files: comes_rolz_1 to 6 (Description: upper rosette closed with wooden sheet, body-holes closed)</p><p>Folder comes_rosette_oO_loecher_offen <br>Files: comes_roo_lo_1 to 6 (Description: rosette open , body-holes open)</p><p>Folder comes_rosette_oO_loecher_zu <br>Files: comes_roo_lz_1 to 6 (Description: rosette open , body-holes closed)          </p><p>________________________</p><p>How to read VIA-Files:</p><p>Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad], 4th col:  Real part [as Factor not dB!], 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed!</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>

Acoustical measurements of a rabab reconstructed after a pictorial source from the 16th century

<p><strong>Acoustical measurements of a rabab reconstructed after a pictorial source from the 16th century</strong></p><p>Instrument: rabab<strong> </strong><br>Pictorial source: Jorge Affonso (attr.), <i>The Adoration of the Shepherds</i>, 1515, oil on wood, Lissabon, Museu Nacional de Arte Antiga <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2022 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 510 mm <br>Max. Body width: 110 mm <br>Body depth: approx. 93 mm</p><p>Vibrating string lengths: <br>d'-string: 351 mm <br>a-string: 350 mm <br>e-string: 348 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: cerry <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: boxwood <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the rosette and the two holes in the body open, then with the body holes closed.</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder modell_affonso <br>Files: affonso_1 to 6 (Description: holes open)</p><p>Folder modell_affonso_loecher_zu <br>Files: affonso_hc_1 to 6 (Description: both holes closed)</p><p>________________________</p><p>How to read VIA-Files: Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad], 4th col:  Real part [as Factor not dB!], 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed!</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p> Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>

Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder

<p>This database corresponds to the results of the paper:</p><p>Lacruz-Pérez, I., Pastor-Cerezuela, G., Tárraga-Mínguez, R. & Lüke, T. (2023): Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder, <i>European Journal of Special Needs Education</i>. <a href="https://doi.org/10.1080/08856257.2023.2185858">https://doi.org/10.1080/08856257.2023.2185858</a> </p><p> </p>

SPLEEN - High Speed Turbine Cascade – Test Case Database - PIV Measurements

<p>This is an open-access database of Particle Image Velocimetry (PIV) measurements of the flow in the <strong>high-speed low-pressure turbine cascade SPLEEN C1</strong>. </p><p>This database complements aerodynamics measurements reported in the database "SPLEEN - High Speed Turbine Cascade – Test Case Database" which can be found at <a href="https://zenodo.org/doi/10.5281/zenodo.7264761">10.5281/zenodo.7264761</a>.  </p><p>The data have been collected at the <strong>von Karman Institute for Fluid Dynamics</strong> during 2022 within the H2020 Clean Sky 2 project <strong>SPLEEN </strong>– Secondary and Leakage Flow Effects in High-Speed Low-Pressure Turbines.</p><p>This database contains documents that describe the experimental setup, instrumentation, measurement uncertainties, geometries, and the dataset structure related to the SPLEEN C1 PIV measurements.</p><p>The database contains experimental data of the test campaign conducted on the linear cascade codenamed SPLEEN C1 in the VKI high-speed wind tunnel S1/C.</p><p>PIV measurements were performed on the upstream blade-to-blade plane at cascade midspan, passage blade-to-blade plane at cascade midspan, and on a cascade outlet axial plane located 50% of the airfoil axial chord downstream the cascade trailing edge, near the cascade endwall (0-18% of the cascade span).</p><p>The turbine cascade geometry is representative of designs of high-speed low-pressure turbines for next-generation geared turbofan engines.</p><p>The measurement datasets describe the flow through the turbine cascade tested at on- and off-design conditions (cascade exit Reynolds and Mach numbers), with and without a turbulence grid located upstream of the cascade. The database includes measurements of flow velocity, flow angles, Mach numbers, and turbulence quantities.</p><p>The PIV measurements were performed on the <strong>SPLEEN C1 turbine cascade</strong> equipped <strong>WITH a REFERENCE CAVITY ENDWALL</strong> (Cavity Aref), <strong>WITHOUT WAKE GENERATOR</strong> (steady inlet flow), <strong>WITH and WITHOUT TURBULENCE GRID</strong> (different inlet turbulence level).</p><p>The project has received Funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under the grant agreement 820883.</p>

Ostwald_colour-atlas_reflectance-measurements

<p>This repository contains the results of visible reflectance spectroscopy that have been performed on three colour atlases in the 1920s made by Verlag Unesma under the supervision of Wilhelm Ostwald. These colour atlases are currently held at the Rijksmuseum Research Library in Amsterdam under the following inventory numbers (339D32 / 339D33 and GF386G3). </p><p>The atlases are physical representations of the colour space developed by Wilhelm Ostwald in the 1910s. They are composed of hundreds of small swatches of paint, where each one represents a specific colour and can be characterised by a hue number (ranging from F01 to F24) and a two-letter code that indicates the position of the swatch in the Ostwald colour space. </p><p>In addition to photographs stored in the zip file, each colour swatch were measured three times with a spectrophotometer from Konica Minolta (CM-2600d), where the UV radiation had been cut-off. Subsequently, the mean and standard deviation were calculated and stored inside the Ostwald_DB.csv file. The Lab values were calculated with the help of the Colour Science python package (https://www.colour-science.org/) according to a 10° observer and a D65 illuminant.</p><p>A Jupyter notebook along with a python script have been created to help users to manipulate the data contains in the Ostwald_DB.csv file.</p>

Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures

<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>

Data for "An autonomous quantum machine to measure the thermodynamic arrow of time"

<p>Numerical simulation data from the article "An autonomous quantum machine to measure the thermodynamic arrow of time"</p> <p>J. Monsel, C. Elouard, A. Auffèves <em>npj Quantum Inf</em> <strong>4</strong>, 59 (2018). <a href="https://doi.org/10.1038/s41534-018-0109-8" target="_blank" rel="noopener">https://doi.org/10.1038/s41534-018-0109-8</a></p> <p>See the jupyter notebook for the data analysis and figures.</p> <p>The code to perform the numerical simulations is given in the repository <a href="https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system" target="_blank" rel="noopener">https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system</a>.</p>

opencc-by-4.0Sep 2020View details →

PsPM-SF: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS (monophones/triads), performed during MRI scanning

<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 20 healthy unmedicated participants (10 females and 10 males, age range: 19 - 35 years, mean age: 24.2 +/- 4.9) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less. NOTE: In Staib et al. 2015, this dataset is denoted as SC1F</p>

opencc-by-4.0Jan 2024View details →

Kuopio gait dataset: motion capture, inertial measurement and video-based sagittal-plane keypoint data from walking trials

<p>This dataset contains motion capture (3D marker trajectories, ground reaction forces and moments), inertial measurement unit (wearable Movella Xsens MTw Awinda sensors on the pelvis, both thighs, both shanks, and both feet), and sagittal-plane video (anatomical keypoints identified with the OpenPose human pose estimation algorithm) data.<br>The data is from 51 willing participants and collected in the HUMEA laboratory in the University of Eastern Finland, Kuopio, Finland, between 2022 and 2023. All trials were conducted barefoot.</p> <p>The file structure contains an Excel file containing information of the participants, data folders under each subject (numbered 01 to 51), and a MATLAB script.</p> <p>The Excel file has the following data for the participants:</p> <ul> <li><strong>ID</strong>: ID of the participants from 1 to 51</li> <li><strong>Age</strong>: age of the participant in years</li> <li><strong>Gender</strong>: biological sex as M for male, F for female</li> <li><strong>Leg</strong>: the participant's dominant leg, identified by asking which foot the participant would use to kick a football; R for right, L for left</li> <li><strong>Height</strong>: height of the participant in centimeters</li> <li><strong>Invalid_trials</strong>: list of invalid trials in the motion capture data (MOCAP) data, usually classified as such because the participant did not properly step on the middle force plate</li> <li><strong>IAD</strong>: inter-asis distance in millimeters, the distance between palpated left and right anterior superior iliac spine, measured with a caliper</li> <li><strong>Left_knee_width</strong>: width of the left knee from medial epicondyle to lateral epicondyle in millimeters, palpated and measured with a caliper</li> <li><strong>Right_knee_width</strong>: same as above for the right knee</li> <li><strong>Left_ankle width</strong>: width of the left ankle from medial malleolus to lateral malleolus in millimeters, palpated and measured with a caliper</li> <li><strong>Right_ankle_width</strong>: same as above for the right ankle</li> <li><strong>Left_thigh_length</strong>: the distance between the greater trochanter of the left femur and the lateral epicondyle of the left femur in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_thigh_length</strong>: same as above for the right thigh</li> <li><strong>Left_shank_length</strong>: the distance between the medial epicondyle of the femur and the medial malleolus of the tibia in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_shank_length</strong>: same as above for the right shank</li> <li><strong>Mass</strong>: mass in kilograms, measured on a force plate just before the walking measurements</li> <li><strong>ICD</strong>: inter-condylar distance of the knee of the dominant leg, measured from low-field MRI</li> <li><strong>Left_knee_width_mocap</strong>: distance between reflective MOCAP markers on the medial and lateral epicondyles of the knee in millimeters, measured from a static standing trial; -1 for missing (subject did not have those markers)</li> <li><strong>Right_knee_width_mocap</strong>: same as above for the right knee</li> </ul> <p>The folders under each subject (folders numbered 01 to 51) are as follows:</p> <ul> <li><strong>imu</strong>: "Raw" inertial measurement unit (IMU) data files that can be read with Xsens Device API (included in Xsens MT Manager 4.6, which may be unavailable these days, not sure). You won't need this if you use the data in the imu_extracted folder.</li> <li><strong>imu_extracted</strong>: IMU data extracted from those data files using the Xsens Device API, so you don't have to. <ul> <li>The data is saved as MATLAB structs where the fields are named as a sensor ID (e.g., "B42D48"). The sensor IDs and their corresponding IMU locations are as follows: <ul> <li>pelvis IMU: B42DA3</li> <li>right femur IMU: B42DA2</li> <li>left femur IMU: B42D4D</li> <li>right tibia IMU: B42DAE</li> <li>left tibia IMU: B42D53</li> <li>right foot IMU: B42D48</li> <li>left foot IMU: B42D51 (except for subjects 01 and 02, where left foot IMU has the ID B42D4E)</li> </ul> </li> <li>Some of the data are just zeros as they couldn't be read from these sensors, but under each sensor, the fields "calibratedAcceleration", "freeAcceleration", "time", "rotationMatrix", and "quaternion" contain usable data. <ul> <li>time: Contains time stamps of the measurement at each frame recorded at 100 Hz, so if you remove the first value from all values in the time vector and divide the result by 100, you will get the time in seconds from the beginning of the walking trial.</li> <li>calibratedAcceleration and freeAcceleration: Contain triaxial acceleration data from the accelerometers of the IMU. freeAcceleration is just calibratedAcceleration without the effect of Earth's gravitational acceleration.</li> <li>rotationMatrix: Orientations of the IMU as rotation matrices.</li> <li>quaternion: Orientations of the IMU as quaternions.</li> </ul> </li> </ul> </li> <li><strong>openpose</strong>: Trajectories of the keypoints identified from sagittal plane video frames, saved as json files. <ul> <li>The keypoints are from the BODY_25 model of OpenPose (https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_02_output.html).</li> <li>Each frame in the video has its own json file.</li> <li>You can use the function in the script "OpenPose_to_keypoint_table.m" in the root folder to read the keypoint trajectories and confidences of all frames in a walking trial into MATLAB tables. The function takes as argument the path to the folder containing the json files of the walking trial.</li> </ul> </li> <li>Note that some subjects (11, 14, 37, 49) do not have keypoint and IMU data.</li> </ul> <p>The folders under each subject are divided into three ZIP archives with 17 subjects each.</p> <p>The script "OpenPose_to_keypoint_table.m" is a MATLAB script for extracting keypoint trajectories and confidences from JSON files into tables in MATLAB.</p> <p><br><strong>Publication in Data in Brief</strong>: <a href="https://doi.org/10.1016/j.dib.2024.110841" target="_blank" rel="noopener">https://doi.org/10.1016/j.dib.2024.110841</a></p> <p><br><strong>Contact</strong>: Jere Lavikainen, jere.lavikainen@uef.fi</p>

opencc-by-4.0Apr 2023View details →

HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous United States

<p>Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.</p> <p>Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022). </p> <p><strong>At-a-station Hydraulic Geometry</strong></p> <p>We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:</p> <p>\(w={aQ^b}\)</p> <p>\(d=cQ^f\)</p> <p>\(v=kQ^m\)</p> <p>where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:</p> <ol> <li>We considered only measurements which reported Q, v, w and A.</li> <li>For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.</li> <li>We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which  \( Q\neq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.</li> <li>Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables. <ol> <li>We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.</li> <li>Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.</li> <li>Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).</li> </ol> </li> <li>If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.</li> </ol> <p>Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.</p> <p><strong>At-a-station Manning's n</strong></p> <p>We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by</p> <p>\(n = {{R^{2/3}S^{1/2}} \over v}\)</p> <p>where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10<sup>-5</sup> m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q<sub>50</sub>; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10<span>—</span>which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.</p> <p><strong>Daily discharge percentiles</strong></p> <p>We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:</p> <ol> <li>For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.</li> <li>A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.</li> </ol> <p>We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q<sub>1</sub> and Q<sub>5</sub>, increments of 5% (e.g. Q<sub>10</sub>, Q<sub>15</sub>, Q<sub>20</sub>, etc.) between Q<sub>5</sub> and Q<sub>95</sub>, increments of 1% between Q<sub>95</sub> and Q<sub>99</sub>, and increments of 0.1% between Q<sub>99</sub> and Q<sub>100</sub> in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.</p> <p><strong>HG Regionalization</strong></p> <p>We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:</p> <p>\(log(Q_i) = \beta_1log(DA) + \beta_0\)</p> <p>where Q<sub>i</sub> is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\),  \(\beta_0\) , and the r<sup>2</sup> value of the regression relationship for Q percentiles Q<sub>10</sub>, Q<sub>25</sub>, Q<sub>50</sub>, Q<sub>75</sub>, Q<sub>90</sub>, Q<sub>95</sub>, Q<sub>99</sub>, and Q<sub>99.9</sub>. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).</p> <p><strong>Dataset description</strong></p> <p>We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r<sup>2</sup> values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.</p> <p> </p> <p><strong>Errata</strong></p> <p>In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.</p>

opencc-by-4.0Feb 2024View details →

In Silico Local Electrical Impedance Measurements in the Atria

<div>This document describes a dataset provided in the context of the manuscript “In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human” [1].</div> <div> </div> <div>Authors: Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dössel O, Luik A</div> <div> </div> <div>The dataset contains in silico simulation setups and results from forward electrical impedance simulations with EIDORS. Geometrical models include the commercially available ablation catheters IntellaNav MiFi and IntellaNav StPt catheter measuring local impedance (LI). </div> <div>Catheter geometries were embedded in different surrounding conditions of clinical importance. Catheter tissue interaction with and without scar, the insertion of the catheter into a pulmonary vein (PV), the withdrawal into a transeptal sheath, and catheter irrigation were modeled to quantify the respective effect on LI measurements. In vitro and clinical data used for validation purposes are included in the dataset as well.</div> <div> </div> <div>Abbreviations: </div> <div>LI: local impedance, all numbers are given in Ohms</div> <div>MiFi: IntellaNav MiFi catheter</div> <div>PV: pulmonary vein</div> <div>StPt: IntellaNav StPt catheter</div> <div> </div> <div> </div> <div>Simulation results, in vitro measurements and clinically measured traces are provided in the following MATLAB files in the subdirectory &bdquo;results_LI“:</div> <div> </div> <div>• impConductivities.mat</div> <div>In vitro measurements and simulation results for MiFi and StPt in NaCl solutions of different concentrations as described in section III A  of the related publication [1]. The struct "impedance" includes the following fields:</div> <div>⁃ conc: concentration of NaCl solutions from in vitro measurements in weight percentages</div> <div>⁃ cond: conductivities of the NaCl solutions from in vitro measurements in S/m</div> <div>⁃ temp: interpolated temperature curves from in vitro measurements in °C</div> <div>⁃ condSim: different conductivities of the NaCl solutions from in silicon experiments in S/m</div> <div>⁃ LI_MiFi_iV: 41x9 matrix with interpolated in vitro LI measurements with the MiFi catheter in 9 different NaCl solutions and at 41 interpolated temperature values</div> <div>⁃ LI_StPt_iV: 41x9 matrix with interpolated in vitro LI measurements with the StPt catheter in 9 different NaCl solutions and at 41 interpolated temperatures values</div> <div>⁃ LI_MiFi_iV_RT: LI values for different NaCl solutions at  room temperature interpolated from in vitro MiFi measurements</div> <div>⁃ LI_MiFi_iV_BT: LI values for different NaCl solutions at  body temperature interpolated from  in vitro MiFi measurements</div> <div>⁃ LI_StPt_iV_RT: LI values for different NaCl solutions at  room temperature interpolated from  in vitro StPt measurements</div> <div>⁃ LI_StPt_iV_BT: LI values for different NaCl solutions at body temperature  interpolated from  in vitro StPt measurements</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different NaCl solutions</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different NaCl solutions</div> <div> </div> <div>• impSheath.mat</div> <div>Simulation results and clinical measurements of LI with MiFi and StPt for different overlaps with a transeptal sheath as described in section III B of the related publication [1]. The struct &bdquo;impSheath“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and distal edge of the sheath in mm. Negative distances describe an insertion of the catheter into the sheath</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_MiFi_cd: 281x2 matrix containing clinical LI measurements with the MiFi catheter in the second column and corresponding time steps in the first column</div> <div>⁃ LI_StPt_cd: 301x2 matrix containing clinical LI measurements with the StPt catheter in the second column and corresponding time steps in the first column</div> <div> </div> <div>• impTissue.mat</div> <div>Simulation results for MiFi and StPt with variable distance and angle between catheter and tissue as described in section III C of the related publication [1]. The struct &bdquo;impTissue“ contains the following fields:</div> <div>⁃ distance: 25 different distances between catheter tip and endocardial surface in mm</div> <div>⁃ distanceSel: 5 selected distances between catheter tip and endocardial surface in mm</div> <div>⁃ angle: 13 different angles between catheter and endocardial tissue surface in degrees</div> <div>⁃ LI_MiFi_d_alpha: 5x13 matrix with simulated LI values for the MiFi catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_MiFi_d_90: 25 simulated LI values for the MiFi catheter for different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div>⁃ LI_StPt_d_alpha: 5x13 matrix with simulated LI values for the StPt catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_StPt_d_90: 25 simulated LI values for the StPt catheter different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div> </div> <div>• impTissueScar.mat</div> <div>Simulation results for MiFi and StPt interacting with tissue in the presence of scar as described in section III C of the related publication [1]. The struct &bdquo;impTissueScar“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and endocardial surface for all simulation setups in mm</div> <div>⁃ centerX: horizontal distance between the catheter tip and the center of the line of scar for all simulation setups in mm</div> <div>⁃ LI_MiFi3mm: simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_StPt3mm: simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_MiFi6mm:  simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div>⁃ LI_StPt6mm:  simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div> </div> <div>• impPV.mat</div> <div>Simulation results for MiFi and StPt insertion into a pulmonary vein (PV) as described in section III D of the related publication [1]. The struct &bdquo;impPV“ includes the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and tissue surface in mm. Negative distances describe an insertion of the catheter into the vein.</div> <div>⁃ radius: inner radius of the PV in mm</div> <div>⁃ thickness: thickness of the PV tissue in mm</div> <div>⁃ LI_MiFi_d_r_th: 31x4x4 matrix containing the LI simulation results for the MiFi catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div>⁃ LI_StPt_d_r_th: 31x4x4 matrix containing the LI simulation results for the StPt catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div> </div> <div>• impFlush.mat</div> <div>Simulation results for MiFi and StPt flush with NaCl at different flow rates as described in section III E of the related publication [1]. The struct &bdquo;impFlush“ includes the following fields:</div> <div>⁃ radius: radius of the NaCl spheres at the irrigation holes in mm</div> <div>⁃ LI_MiFi_NaCl: LI extracted from simulations with MiFi catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div>⁃ LI_StPt_NaCl: LI extracted from simulations with StPt catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div> </div> <div>Additionally, exemplary geometrical setups and results are provided as VTK files in the subdirectory &bdquo;selectedGeometriesAndSimResults“:</div> <div> </div> <div>Each VTK file contains the following data fields:</div> <div>⁃ Ids (point data): integer specifying the Id of the respective vertex</div> <div>⁃ Voltage (point data): electric potential of the respective vertex with respect to a reference potential in mV</div> <div>⁃ Conductivity (cell data): conductivity of the material of the respective cell in S/mm</div> <div>⁃ Current (cell data): current density of the respective cell in nA/mm^2</div> <div>⁃ Ids (cell data): integer specifying the Id of the respective cell</div> <div>⁃ Material (cell data): integer specifying the material of the respective cell (for MiFi setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: mini electrode 1, 8: insulator mini electrode 1, 9: mini electrode 2, 10: insulator mini electrode 2, 11: mini electrode 3, 12: insulator mini electrode 3, 13: tissue, 14: blood, 15: sheath, 16:NaCl, 17: scar tissue; for StPt setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: tissue, 8: blood, 9: NaCl, 10: scar tissue)</div> <div> </div> <div>• mifi.vtk: MiFi catheter in blood </div> <div>• stpt.vtk: StPt catheter in blood</div> <div>• mifiTissue_dist000_angle0000.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 0°</div> <div>• mifiTissue_dist000_angle0450.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 45°</div> <div>• mifiTissue_dist000_angle0900.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 90°</div> <div>• mifiTissue_dist000_angle1350.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 135°</div> <div>• mifiTissue_dist000_angle1800.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 180°</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line3mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 3mm width</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line6mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 6mm width</div> <div>• mifi_PV_d0060_r030_th20.vtk: MiFi catheter 6mm above the endocardial surface with a PV of 3 mm radius and 2mm PV tissue thickness</div> <div>• mifi_PV_d-070_r030_th20.vtk: MiFi catheter inserted into a PV of 3 mm radius and 2mm PV tissue thickness; insertion depth = 7mm</div> <div>• mifi_flush_050-0.50.vtk: MiFi catheter within blood with NaCl spheres of 0.5mm radius at irrigation holes</div> <div>• mifi_sheath_0100.vtk: MiFi catheter within transeptal sheath extracted by 10mm</div> <div> </div> <div>[1] Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dossel O, Luik A. In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human. IEEE Trans Biomed Eng. 2023 Feb;70(2):533-543. doi: 10.1109/TBME.2022.3196545. Epub 2023 Jan 19. PMID: 35925848.</div> <p> </p>

opencc-by-4.0Mar 2024View details →

Acer pseudoplatanus pot experiment measuring data of single leaves in LandKlif project

<p><span>We cultivated 168 saplings of Acer pseudoplatanus of four Bavarian provenances under varying shading (two treatments: sun-exposed and shaded) and watering (three treatments: regular watering / drought period in summer / drought periods in spring and summer). The experiment took place in a half-open greenhouse between March and August 2021. For each sapling, we measured the increase in height and stem diameter, as well as specific leaf area (SLA) and leaf dry matter content (LDMC) of three leaves. This dataset contains measuring data of fresh/dry weight and area of each single leaf.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif).  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>