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InvisibleEye
No full textWe recorded a dataset of more than 280,000 close-up eye images with ground truth annotation of the gaze location. A total of 17 participants were recorded, covering a wide range of appearances:
Gender: Five (29%) female and 12 (71%) male
Nationality: Seven (41%) German, seven (41%) Indian, one (6%) Bangladeshi, one (6%) Iranian, and one (6%) Greek
Eye Color: 12 (70%) brown, four (23%) blue, and one (5%) green
Glasses: Four participants (23%) wore regular glasses and one (6%) wore contact lenses
For each participant, two sets of data were recorded: one set of training data and a separate set of test data. For each set, a series of gaze targets was shown on a display that participants were instructed to look at. For both training and test data the gaze targets covered a uniform grid in a random order, where the grid corresponding to the test data was positioned to lie in between the training points. Since the NanEye cameras record at about 44 FPS, we gathered approximately 22 frames per camera and gaze target. The training data was recorded using a uniform 24 × 17 grid of points, with an angular distance in gaze angle of 1.45° horizontally and 1.30° vertically between the points. In total the training set contained about 8,800 images per camera and participant. The test set’s points belonged to a 23 × 16 grid of points and it contains about 8,000 images per camera and participant. This way, the gaze targets covered a field of view of 35° horizontally and 22° vertically.
The recording procedure was split into two parts for training and test data. For both parts, participants were instructed to put on the prototype and rest their head on a chin rest positioned exactly 510 mm in front of a display. The display was a 30-inch LED monitor with a pixel pitch of 0.25 mm and viewable image dimensions of 641.3 × 400.8 mm, set to 2560 × 1600-pixel resolution. On the display, the grid of gaze targets was shown, which the participants were instructed to look at. Each point appeared as a big circle 300 pixels in diameter and shrunk to a circle of 8 pixels diameter over the course of 700 ms. The small circle was then displayed for another 500 ms, until the display of the next point started. Data was only recorded during the latter 500 ms, i.e. while the small circle was shown (see Figure 7a). It is important to note that the chin rest did not fully restrain participants and we noticed that their head sometimes moved noticeably, thus resulting in a certain amount of label noise. Using the shrinking animation for the circle helps the participants to locate the circle on the screen and gives them time to relocate their gaze. Similar to [30], we also showed an “L” or an “R” in between every 20th pair of points in the sequence. The letter was displayed for 500 ms at the position of the last point. Participants were asked to confirm the letter they had seen by pressing the corresponding left or right arrow-key. This was done to ensure participants focused on the gaze targets and task at hand throughout the recording
Replication data for analyzing stable water isotopologue transport within soils using fractionation parameterizations
No full textReplication data to reproduce the results presented in J. Schneider & S. Kiemle, K. Heck, Y. Rothfuss, I. Braud, R. Helmig, J. Vanderborght (2024) Analysis of Experimental and Simulation Data of Evaporation-Driven Isotopic Fractionation in Unsaturated Porous Media. (Under review) Vadose Zone. The replication data contains numerical data sets generated via the numerical simulator tools DuMuX and SiSPAT-isotope and experimental data published by Rothfuss (2015). Further, this data set provides python scripts and a MatLAB script to reproduce the displayed figures in the linked publication.
Structure of the dataset:
input_experimental_data.tar.gz: contains the modified experimental data from Rotfuss et al.(2015). The raw data have been structured and organized to ease the evaluation and the comparison with the numerical simulation.
input_sispat_data.tar.gz: contains the numerical results derived by SiSPAT-isotope. This dataset aims to replicate the experiments.
input_dumux_data.tar.gz: contains the numerical results derived by DuMuX. This dataset aims to replicate the experiments.
input_sispat_data_sensitivity.tar.gz: contains the numerical results derived by SiSPAT-isotope. This dataset contains the data to analyze the model toward its sensitivity to the residual water saturation.
input_dumux_data_sensitivity.tar.gz: contains the numerical results derived by DuMuX. This dataset contains the data to analyze the model toward its sensitivity to the residual water saturation.
evaluation_scripts_comparison.tar.gz: contains all evaluation scripts to reproduce the figures dealing with comparing DuMuX, SiSPAT-isotope, and the experiments.
evaluation_scripts_sensitivity.tar.gz: contains all evaluation scripts to reproduce the figures dealing with comparing DuMuX and SiSPAT-isotope in terms of analyzing their sensitivity towards the residual water saturation.
The main focus lies on the folder evaluation_scripts_comparison.tar.gz and evaluation_scripts_sensitivity.tar.gz which contains all python scripts which have been used to further post-process all data which are stored in the other folders.
evaluation_scripts_comparison.tar.gz:
plot_depthprofiles_H2O18_all.py : plots the H2O18 isotope concentration over time for various depths, and compares the results for Dumux, SiSPAT-isotope and the experiments.
plot_depthprofiles_HDO_all.py : plots the HDO isotope concentration over time for various depths and compares the results for Dumux, SiSPAT-isotope, and the experiments.
plot_depthprofiles_Sat_all.py : plots the Saturation over time for various depths and compares the results for Dumux, SiSPAT-isotope, and the experiments.
plot_evaporation.py : plots the evaporation rate and the cumulative evaporation rate over the regarded time for Dumux, SiSPAT-isotope, and the experiments.
plot_evaporation_front.py : plots the evaporation front for the numerical results derived by Dumux, and compares a fine resolved resolution with different approximation methods.
plot_isotopeprofile.py : plots the isotope profiles over depth at specific days. The script can compare the solutions between Dumux, SiSPAT-isotope, and the experiments.
plot_isotopeprofile_close.py : plots a close-up near the soil surface of the isotope profile. This helps to focus on the area of interest. Currently, this only plots the results for Dumux
code_fig_10_11.R : plots the derivation process of the aerodynamic resistance in SiSPAT-isotope and the isotope profiles for different boundary conditions using SiSPAT-isotope.
evaluation_scripts_sensitivity.tar.gz:
plot_depthprofiles_all.py : compares both numerical models towards their sensitivity to the residual water saturation.
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Replication Data for: Cornuspline path planning algorithm for the fabrication of coreless wound fiber-polymer composite structures
No full textThis dataset contains all the validation data that presented within the paper "Cornuspline path planning algorithm for the fabrication of coreless wound fiber-polymer composite structures". This paper presents a spline path planning algorithm based on clothoids. This algorithm considers both the process requirements and the dynamic characteristics of the manufacturing unit. Furthermore, the algorithm is validated on a test contour and compared with established commercial methods. The test contour is not based on a specific structural element as it is usually manufactured but combines individual hallenging manufacturing situations in a systematic approach. The result is a contour that contains both sharp and slight changes in the tangent angle. A simulated Beckhoff TwinCAT controller (v3.1 Build 4024.11) is used as a reference system. This is a commercial industrial controller that is equipped with a CNC kernel and can be flexibly used for motion control of robots or machine tools. To compare the Cornuspline (CS), two programs were created to control the NC. In the first variant (NC1), the set points for the path are emulated from established methods. In the second variant (NC2), the set points are exactly on the arc along the curve. In both NC variants, the set points were connected via a B-spline function. The dataset contains three files with the position data for CS, NC1 and NC2
Supporting data for 'Collective Hall current in chiral active fluids: Coupling of phase and mass transport through traveling bands'
No full textSupporting Brownian dynamics simulation data. It includes the simulated trajectories that were processed to produce the figures in the main text of the accompanying paper. The code that was used to generate this data can be found in the provided Github repository
Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes
No full textThe datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the concepts of machine learning (ML) to predict the onset of wrinkling instabilities in dielectric elastomer (DE) bilayers as a function of its tunable geometric and material parameters. The required datasets for building the surrogate models are generated using a finite-element-based framework for structural stability analysis of DE specimens that is rooted in a saddle-point-based variational principle. For a detailed description of this finite-element framework, the sampling of data points for the training/test sets and some brief notes regarding our implementation of the ML-based surrogates, kindly refer to our article mentioned above.
Here, the datasets 'training_set.xlsx' and 'test_set.xlsx' contain the values of the critical buckling load (critical electric-charge density) and critical wrinkle count for the DE bilayer for the sampled data points, where each data point represents a unique set of four tunable input-feature values. The article above provides a description of these features, their physical units and their considered domain of values. The individual Jupyter notebooks import the training dataset and develop ML models for the different problems that are described in the article. The developed models are cross-validated and then tested on the test dataset. Extensive comments describing the ML workflow have been made in the notebooks for the user's reference. The conda environment containing all the necessary packages and dependencies for the execution of the Jupyter notebooks is provided in the file 'de_instabilities.yml'
Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models
No full textThis dataset allows to reproduce the results from the paper "Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models" submitted to GAMM Mitteilungen in September 2023.
Find more information about the structure of the dataset and the steps to reproduce the results in the readme.md file.</p
Supplementary material for `Dynamic renormalization of scalar active field theories`
No full textThe following folders have Jupyter notebook files that are used to obtain the results we present in Section I.1, I.2, III.B, III.C, IV.A and IV.B and Appendix C and D and Figures 9, 10, 11, 13. To calculate the graphical corrections we extensively use the Python package ``restflow'' (https://github.com/us-itp4/restflow). Each folder has one subfolder with figures used in the notebooks.
Description of supplementary files
1) tutorials: A folder which illustrates with examples how to use the package ``restflow''. It includes:
• model_b_simple.ipynb: A pedagogical tutorial to use restflow for the graphical corrections of the Model B (for the classic case with shifted field)
• ckpz.ipynb: A pedagogical tutorial to use restflow for the graphical corrections of the cKPZ
• neural.ipynb: A pedagogical tutorial to use restflow for the graphical corrections of a neural field model. It also analyzes the flow equations with the fixed points and plots Fig.11
2) analysis: A folder which is used for the analysis of the Active Model B+ and the Model B with the extra cubic term. It includes:
• model_b_noshift.ipynb: calculates the graphical corrections for the Model B without shifting the field (with the b model parameter) (Appendix C). It is used to plot Fig.13
• amb+_graphs.ipynb: calculates the graphical corrections of the Active Model B+ (Appendix D). It also calculates the graphical corrections leading to higher-order terms (Section III.C)
• amb+_noshift.ipynb: calculates the flow equations for the Active Model B+ without shifting the field. It also analyzes its fixed points and applies stability analysis (Section III.D.1). It optionally plots the flow equations (figure not shown in manuscript)
• amb+_shift.ipynb: calculates the flow equations for the Active Model B+ with shifting the field. It also analyzes its fixed points and applies stability analysis (Section III.D.2). It optionally plots the flow equations (figure not shown in manuscript)
3) simulations: A folder which is used for the flow equations of the modified cKPZ and for the numerical simulations of the stochastic partial differential equation using py-pde. It includes:
• modified_ckpz.ipynb: calculates the graphical corrections and analyzes the flow equations for the modified cKPZ of Section IV.A. It plots Fig.9 and simulates the stochastic partial differential equation using py-pde and plots Fig.10
• 3d_4000: A folder of the data of the modified cKPZ using py-pde. It is used for the modified_ckpz.ipyn
Saliency3D: A 3D Saliency Dataset Collected on Screen (Dataset and Experiment Application)
No full textWhile visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. Our experimental design reduces the cost and complexity of 3D saliency dataset collection. We first collect gaze data on a screen, then we map them to 3D saliency data through perspective transformation. Using this method, we collect a 3D saliency dataset (49,276 fixations) comprising 10 participants looking at sixteen objects. Moreover, we examine the viewing preferences for objects and discuss our findings in this study. Our results indicate potential preferred viewing directions and a correlation between salient features and the variation in viewing directions.
The files of this dataset are documented in README.md
D1244 sensor data (May 2024)
No full text
General information:
This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the building and used for state estimation and monitoring.
Structure of the dataset:
Each zip-file contains measurements of one day in the hdf5 format.
The hdf5-files in each zip-file contain an array of 244 signals sampled over 10^6 time steps at approximately 200Hz.
labels.csv contains auxiliary information on all measured signals, including the sensor type and the sensor's location in the building
File contents:
Each hdf5-file contains signals of the following types, arranged as stated in labels.csv:
strain: strain (in mm/m) in columns and diagonal bracing elements, measured by strain gauges.
pressure: pressure (in bar) in the piston side or the rod side chamber of a hydraulic actuator. For actuators in the diagonal bracing, the rod side chamber is permanently connected to the tank.
posenc: displacement (in meters) of each actuator, measured by a position encoder.
optic: optically measured displacement (in meters) of emitters attached to the building's facade.
The building consists of four modules spanning three stories each, and all sensors within a module are connected to a control cabinet, from which all measurements are transmitted. The cameras of the optical measurement system are placed outside the building and transmit their data separately from the sensors within the building. Therefore, there are an additional two signals per module (or camera):
timestamp: unix timestamp (seconds since 1st January 1970) of the control cabinet
numvars: number of measured variables
Missing measurements are marked as NaN. The optical measurement system is currently undergoing maintenance, which is why the corresponding signals are all NaN.
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Code for Pseudo-Riemannian Graph Convolutional Networks
No full textThis dataset is the official implementation of Pseudo-Riemannian Graph Convolutional Networks in PyTorch, based on HGCN implementation.
This code is used to reproduce the experiments of the paper. Datasets are provided in the /data directly. To execute the code, follow the instructions in the README.md file.
For more info, please check the paper or feel free to contact the authors for any inquiries. <p