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                                          <http://schema.org/name>        "Kuck, Maximilian"
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                "This dataset contains process data from milling operations performed on a three-axis milling machine (CMX 600V, DMG Mori), complementing DOI:10.35097/hvvwn1kfwf7qt48z. To reflect the diversity and variability of real-world milling scenarios, the dataset includes components, tools and machining strategies that are relevant to the industry. A total of 54 milling experiments are included. To support reproducibility and benchmarking applications, the dataset includes NC programs, labelled pictures, and detailed documentation. During processing, signals were acquired from the Siemens SINUMERIK 840D CNC system via the SINUMERIK Edge app 'Analyse MyWorkpiece/Capture', which enables the export of high-frequency data at 500 Hz in JSON format (.json). Additionally, force and acceleration data recorded by external sensors is provided in MATLAB format (.mat). Each experiment also includes a combined MATLAB file in which all recorded signals are synchronized." , "This dataset contains process data from milling operations performed on a three-axis milling machine (CMX 600V, DMG Mori) and is complementary to DOI:10.35097/hvvwn1kfwf7qt48z. The data was collected in a controlled laboratory setting using industrially relevant components, tools, and machining strategies to reflect the diversity and variability of practical milling scenarios. Process signals from the machine controller were recorded using a Siemens Industrial Edge device at a sampling rate of 500 Hz. Additional high-frequency force and acceleration data were measured using a force platform (Kistler Type Z 3393) and a spindle-mounted acceleration sensor (PCB Type 356A33). The analog signals were amplified via a charge amplifier (Kistler Type 5015A1000 K) and acquired using a Data Translation DT9836 data acquisition card at 10 kHz.\nRaw data from the force and acceleration sensors are provided as MATLAB timetable files (.mat). The process signals from the Siemens Industrial Edge are stored in JSON format (.json).\nIn addition, each experiment includes a consolidated MATLAB file in which all available signals (Edge and sensor data) are temporally aligned and stored as a single timetable object. To achieve this, the 500 Hz Edge signals were interpolated using the PCHIP method (Piecewise Cubic Hermite Interpolating Polynomial) to match the 10 kHz resolution of the sensor data. This unified file enables direct signal comparison across all sources and simplifies further analysis. \nIn total, 54 milling experiments are included. NC programs, labeled pictures, and detailed documentation are provided to support benchmarking applications.\n--------------------------------------------------------------------------------------------------------------------------\nDocuments:\n- Dataset: All data files organized by component type and trial ID\n- Descriptive: Design of Experiment, NC programs executed during the experiments (G-code), labeled pictures organized by component type and tools\nExperimental Setup:\n- Machines: CMX 600V (3-axis milling center)\n- Materials: S235JR (unalloyed structural steel), Al2007 T4 (aluminum alloy), POM-C (polyoxymethylene copolymer)\n- Cutting Tools:\n - HSS-Co8 end mill (TiAlN coating, Ø 5 mm, k10 tolerance)\n - Solid carbide end mill (TiSi coating, Ø 10 mm, f8 tolerance)\n - Solid carbide end mill (uncoated, Ø 5 mm, e8 tolerance)\n- Workspace blank dimensions: 150x75x50mm" , "Der vorliegende Datensatz enthält Prozessdaten, die auf einer dreiachs Fräsmaschine (CMX 600V, DMG Mori) durchgeführt wurden, und ergänzt DOI:10.35097/hvvwn1kfwf7qt48z. Um die Vielfalt und Variabilität realer Fräsbearbeitungen widerzuspiegeln, wurden industrierelevante Bauteile, Werkzeuge und Bearbeitungsstrategien angewendet. Insgesamt sind 54 Fräsversuche enthalten. Um eine Reproduzierbarkeit und Benchmarking-Anwendungen zu ermöglichen, umfasst der Datensatz NC-Programme, beschriftete Bilder und eine detaillierte Dokumentation. Während der Bearbeitung wurden die Signale der SINUMERIK 840D Steuerung über die SINUMERIK Edge App „Analyse MyWorkpiece/Capture” erfasst. Diese ermöglicht einen Datenexport mit 500 Hz im JSON-Format (.json). Zusätzlich werden die von externen Sensoren aufgezeichneten Kraft- und Beschleunigungsdaten im MATLAB-Dateiformat (.mat) bereitgestellt. Jeder Versuch umfasst zudem eine MATLAB-Datei, in der alle aufgezeichneten Signale synchron abgespeichert sind.";
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                "This dataset consists of raw and processed images obtained in droplet impingement experiments in a shadowgraphy method with additional color-coded glare points. The raw images are saved in the uncompressed file format .tif and processed images are saved as .png." , "The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas-liquid interface on the basis of monocular images obtained via optical measurement techniques.\nThe method is tested an evaluated at the example of liquid droplets impacting on structured solid substrates.\nThe droplet dynamics are captured through high-speed imaging in an extended shadowgraphy setup with additional glare points from lateral light sources that encode further three-dimensional information of the gas-liquid interface in the images.\nA neural network is trained for the physically correct reconstruction of the droplet dynamics on a labelled dataset generated by synthetic image rendering on the basis of gas-liquid interface shapes obtained from direct numerical simulation.\nThe employment of synthetic image rendering allows for the efficient generation of training data and circumvents the introduction of errors resulting from the inherent discrepancy of the droplet shapes between experiment and simulation. \nThe accurate reconstruction of the three-dimensional shape of the gas-liquid interface during droplet impingement on the basis of images obtained in the experiment demonstrates the practicality of the presented approach.\nThe introduction of glare points from lateral light sources in the experiments is shown to improve the reconstruction accuracy, which indicates that the neural network learns to leverage the additional three-dimensional information encoded in the images for a more accurate depth estimation.\nBy the successful reconstruction of obscured areas in the input images, it is demonstrated that the neural network has the capability to learn a physically correct interpolation of missing data from the numerical simulation.\nFurthermore, the physically reasonable reconstruction of unknown gas-liquid interface shapes for drop impact regimes that were not contained in the training dataset indicates that the neural network learned a versatile model of the involved two-phase flow phenomena during droplet impingement.";
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                "For decades, scanning/transmission electron microscopy (S/TEM) techniques have been employed to analyze shear bands in metallic glasses and understand their formation in order to improve the mechanical properties of metallic glasses. However, due to a lack of direct information in reciprocal space, conventional S/TEM cannot characterize the local strain and atomic structure of amorphous materials, which are key to describe the deformation of glasses. For this work, we applied 4-dimensional STEM to map and directly correlate the local strain and the atomic structure at the nanometer scale in deformed metallic glasses. We observe residual strain fields with quadrupolar symmetry concentrated at dilated Eshelby inclusions. The strain fields percolate in a vortex-like manner building up the shear band. This provides a new understanding of the formation of shear bands in metallic glass." , "The data format is DM4 (Gatan Microscopy Suite Software) and processed by lab-written Matlab code. 4D-STEM measurements were conducted using a Themis Z double-corrected TEM (Thermofisher Scientific) operated at 300 kV in microprobe STEM mode with spot size 6 and a semi-convergence angle of 0.26 mrad giving rise to a diffraction-limited probe size of ~5 nm. 4D-STEM records local 2D diffraction patterns over a 2D array of probe positions by stepwise scanning of the probe. The method is called 4D-STEM referring to its typical 4D dataset (2D diffraction pattern on a 2D array of the sample). We used a OneView camera (Gatan Inc.) with a camera length of 1.15 m to record the diffraction patterns. This camera length was chosen to capture the first diffuse diffraction ring with a sufficient diameter on the camera to enhance the sensitivity for measuring distortions. The 2nd diffuse diffraction ring was also included. This preserves the capability for PDF analysis. 4D-STEM maps were acquired with a step size of 5.8 nm and a frame size of 900×500 pixels for the Fe85.2Si0.5B9.5P4Cu0.8 metallic glass ribbon and a step size of 9.7 nm and a frame size of 350×270 pixels for the Zr46Cu38Al8Ag8 bulk metallic glass with an exposure time of 3.3 ms per frame (frame rate of ~300 f/s). \nThe diffraction pattern of a typical amorphous material shows a diffuse ring pattern (Figure 1a). As described in previous works [23-24, 27], the local stress in the metallic glass induces a structural anisotropy, which results in an elliptic distortion of the diffraction ring leading to a deviation from the ideal circle as illustrated in Figure 1b (the diffraction pattern was artificially elongated for easy presentation). Therefore, the strain can be mapped by determining the ellipticity of the diffraction ring in each local diffraction pattern of the 4D-STEM dataset. Different from high-resolution (HR)TEM-based strain mapping methods such as geometric phase analysis (GPA), which analyzes real space atomic lattice displacements [49], the strain measurement used here analyzes the diffraction ring in the 4D-STEM data. It thus enables the capability to measure strain for amorphous materials and a large field of view (up to micrometers).";
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                "This dataset includes the simulation model of the demonstrator vehicle \"elf++\" for vehicle dynamics simulation in CarMaker. The demonstrator vehicle was developed in the research project e²-Lenk at the Institute of Vehicle System Technology (FAST) and at Schaeffler Hub for Advanced Research at KIT (SHARE at KIT) and subsequently used in the projects SmartLoad and FLOOW. The CarMaker model includes the vehicle geometry, the chassis kinematics, the powertrain, and the tire model, supplemented by the modeling of the steering system, the localization module, and the longitudinal and lateral dynamics control in MATLAB-Simulink. Other modules such as vehicle guidance controllers can be added to the Simulink model. The modeling status corresponds to the vehicle state at the end of the SmartLoad project." , "# Vehicle Dynamics Model of Demonstrator Vehicle \"elf++\"   \nEnglish version see below.\n## Einleitung\nDies ist ein Fahrdynamik-Simulationsmodell für das Demonstratorfahrzeug „elf++“, das mit CarMaker 8.0 und Matlab 2018b kompatibel ist. Das CarMaker-Projekt umfasst das Fahrzeugmodell *Demonstrator_vehicle_ELFpp* und den Testrun *Test_Runs_DrivingDynamicsArea*.\nDas Modell ermöglicht die Simulation von Fahrmanövern durch Vorgabe des zeitlichen Verlaufs von Lenkradwinkel und Geschwindigkeit. Weitere Funktionalitäten können im Simulink-Modell modular ergänzt und angepasst werden. Beispielhaft wurde ihm Rahmen folgender Veröffentlichung ein Fahrzeugführungsregler in das Modell integriert: [Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay](https://doi.org/10.3390/vehicles5020034).\nBei dem Demonstratorfahrzeug „elf++“ handelt es sich um ein Fahrzeug im Maßstab 1:1,5, das über eine Ackermannlenkung mit redundant verbautem Lenkaktor und radselektive Antriebe an der gelenkten Vorderachse verfügt.\n## Aufbau des integrierten Simulink-Modells *Demonstrator_vehicle_ELFpp*  \n### *Vehicle_Control*\nDieser Block umfasst die Funktionen der Fahrzeugregelung von der Sollwertvorgabe bis zur Ausgabe der Aktorstellgrößen.\n- #### *Target*\n Vorgabe des Lenkradwinkelverlaufs in rad und der Geschwindigkeit in m/s des zu fahrenden Manövers.\n- #### *Steering_controller*\n Lenkradwinkelregelung zur Vorgabe des Gesamtlenkmomentes.\n- #### *Steering_Actuator_Allocation*\n Aufteilung des Gesamtlenkmomentes auf die redundant verbauten Lenkaktoren.\n- #### *Velocity_controller*\n Geschwindigkeitsregelung zur Vorgabe des Gesamtantriebsmomentes.\n- #### *Driving_Actuator_Allocation*\n Aufteilung des Gesamtantriebsmomentes auf die radindividuellen Antriebe der Vorderachse.\n### *CarMaker*\nDieser Block stellt die Schnittstelle zu CarMaker dar. Die Aktorstellgrößen, die die Regler vorgeben, werden im *Torque_Limiter* den physikalischen Limits entsprechend beschränkt. Weitere Modifikationen gegenüber der CarMaker-Vorlage betreffen die Modellierung des Lenksystems. Ausgehend von den Aktormomenten und den Spurstangenkräften wird mittels eines 2DOF Modells die resultierende Lenkbewegung berechnet.\n### *Localization*\nZur Abbildung des auf dem Fahrzeug verbauten GNSS RTK Systems werden in diesem Block die Positionsmessgrößen in ihrer Frequenz reduziert.\n## Parameter und Einstellungen\n### *sim_settings.m*\nMit den Vorgaben in dieser Dateikönnen die Dauer des Simulationsdurchlaufes, sowie die Parameter des Modells angepasst werden, die die Einstelllungen der Regler und die fahrdynamischen Grenzen des Manövers betreffen.\n### *init_parameter* in Ordner *vehicle_control_data*\nDiese Datei definiert Konstanten und enthält fahrzeugspezifische Parameter.\n## Verwendung\nUm dieses Simulationsmodell zu verwenden, öffnen Sie das Simulink-Modell *Demonstrator_vehicle_ELFpp* und starten daraus CarMaker durch Klicken auf das CarMaker Symbol. In CarMaker öffnen Sie den Testrun *Test_Runs_DrivingDynamicsArea*. Definieren Sie dann die Fahrmanöver im *Target* Block, indem Sie den zeitlichen Verlauf von Lenkradwinkel und Geschwindigkeit angeben. Starten Sie die Simulation über die CarMaker GUI.\n## English version:\n## Introduction\nThis is a driving dynamics simulation model for the demonstrator vehicle “elf++” compatible with CarMaker 8.0 and Matlab 2018b. The CarMaker project includes the vehicle model *Demonstrator_vehicle_ELFpp* and the test run *Test_Runs_DrivingDynamicsArea*.\nThe model supports the simulation of driving maneuvers by specifying the course of the steering wheel angle and velocity. Further functionalities can be added and adapted modularly in the Simulink model. For example, a vehicle guidance controller was integrated into the model as part of the following publication: [Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay](https://doi.org/10.3390/vehicles5020034).\nThe demonstrator vehicle “elf++” is a vehicle on a scale of 1:1.5 that has Ackermann steering with a redundantly installed steering actuator and wheel-selective drives on the steered front axle.\n## Structure of the integrated simulink model *Demonstrator_vehicle_ELFpp*\n### *Vehicle_Control*\nThis block includes the functions of the vehicle control from the setpoint specification to the output of the actuator target values.\n- #### *Target*\n    Specification of the steering wheel angle course in rad and the speed in m/s of the maneuver to be driven.\n- #### *Steering_controller*\n Steering wheel angle control that outputs the total steering torque.\n- #### *Steering_Actuator_Allocation*\n Allocation of the total steering torque to the redundantly installed steering actuators.\n- #### *Velocity_controller*\n Velocity control that outputs the total drive  torque.\n- #### *Driving_Actuator_Allocation*\n Allocation of the total drive torque to the individual wheel drives of the front axle.\n### *CarMaker*\nThis block represents the interface to CarMaker. The actuator target values which the controllers specify are limited in the *Torque_Limiter* according to their physical limits. Further modifications compared to the CarMaker template concern the modeling of the steering system. Starting from the actuator torques and the tie rod forces, the resulting steering movement is calculated by means of a 2DOF model.\n### *Localization*\nTo represent the GNSS RTK system installed on the vehicle, the frequency of the position measurement variables is reduced in this block.\n## Parameter and Settings\n#### *sim_settings.m*\nIn this file, the duration of the simulation run can be adjusted, as well as the parameters of the model, which affect the settings of the controllers and the driving dynamic limits of the maneuver input.\n#### *init_parameter* in Ordner *vehicle_control_data*\nThis file contains constants and parameters specific to the vehicle.\n## Usage\nTo use this simulation model, open the Simulink model *Demonstrator_vehicle_ELFpp* and start CarMaker from it by clicking on the CarMaker icon. In CarMaker, open the test run *Test_Runs_DrivingDynamicsArea*. Then define the driving maneuvers in the *Target* block by specifying the course of steering wheel angle and velocity. Start the simulation via the CarMaker GUI." , "Dieser Datensatz enthält das Simulationsmodell des Demonstratorfahrzeugs \"elf++\" für die Fahrdynamiksimulation in CarMaker. Das Demonstratorfahrzeug wurde im Forschungsprojekt e²-Lenk am Institut für Fahrzeugsystemtechnik (FAST) und am Schaeffler Hub for Advanced Research am KIT (SHARE am KIT) entwickelt und anschließend in den Projekten SmartLoad und FLOOW eingesetzt. Die Modellierung in CarMaker umfasst die Fahrzeuggeometrie, die Fahrwerkskinematik, den Antriebsstrang und das Reifenmodell, ergänzt um die Modellierung des Lenksystems, des Lokalisierungsmoduls und der Längs- und Querdynamikregelung in MATLAB-Simulink. Das Simulink-Modell kann um weitere Module wie z.B. einen Fahrzeugführungsregler ergänzt werden. Der Modellierungsstand entspricht dem Fahrzeugzustand zum Ende des Projektes SmartLoad.";
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                "These are the experimental data for the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\npublished on [arXiv](https://arxiv.org/) in 2023.\nYou can find the paper [here](https://doi.org/10.48550/arXiv.2307.11607) and the code [here](https://github.com/jakob-bach/alternative-feature-selection).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text." , "# Experimental Data for the Paper \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\nThese are the experimental data for the paper> Bach, Jakob. \"Finding Optimal Diverse Feature Sets with Alternative Feature Selection\"\npublished at [arXiv](https://arxiv.org/) in 2023.\nIf we create multiple versions of this paper in the future, these experimental data will cover at least the first version.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Alternative-Feature-Selection) for the code and instructions to reproduce the experiments.\nThe data were obtained on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 128 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the experimental pipeline (`run_experiments.py`) took about 255 h.\nThe commit hash for the last run of the experimental pipeline (`run_experiments.py`) is [`2212360a32`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/2212360a320121862556bc8d54da45cb6dc8ee93).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation.py`) is [`487b8ba04d`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/487b8ba04d513881be48fb2bc7b1c1dca416d122).\nWe also tagged both commits (`run-2023-06-23` and `evaluation-2023-07-04`).\nThe experimental data are stored in two folders, `datasets/` and `results/`.\nFurther, the console output of `run_evaluation.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the experimental pipeline `run_experiments.py`, i.e., prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 30 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth files are CSVs only containing numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column corresponds to a feature.\nThe first line in each CSV contains the names of the features as strings; for `_y.csv` files, there is only one column, always named `target`.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `results/`\nThese are the output data of the experimental pipeline in the form of CSVs, produced by the script `run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `run_evaluation.py`.\nThe remaining files are subsets of the results, as the experimental pipeline parallelizes over 30 datasets, 5 cross-validation folds, and 5 feature-selection methods.\nThus, there are `30 * 5 * 5 = 750` files containing subsets of the results.\nEach row in a result file corresponds to one feature set.\nOne can identify individual search runs for alternatives with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- feature-selection method `fs_name`\n- search method `search_name`\n- objective aggregation `objective_agg`\n- feature-set size `k`\n- number of alternatives `num_alternatives`\n- dissimilarity threshold `tau_abs`\nThe remaining columns mostly represent evaluation metrics.\nIn detail, all result files contain the following columns:\n- `selected_idxs` (list of ints, e.g., `[0, 4, 5, 6, 8]`): The indices (starting from 0) of the selected features (i.e., columns in the dataset).\n  Might also be an empty list, i.e., `[]` if no valid solution was found.\n  In that case, the two `_objective` columns and the four `_mcc` columns contain a missing value (empty string).\n- `train_objective` (float in `[-1,1]`): The training-set objective value of the feature set.\n  Three methods (FCBF, MI, Model Importance) have the range `[0,1]`, while two methods (mRMR, Greedy Wrapper) have the range `[-1,1]`.\n- `test_objective` (float in `[-1,1]` ): The test-set objective value of the feature set.\n- `optimization_time` (non-negative float): Time for alternative feature selection in seconds.\n  For white-box feature-selection methods, this measures one solver call.\n  In contrast, wrapper feature selection can call the solver multiple times, and we record the total runtime of the \"Greedy Wrapper\" algorithm.\n- `optimization_status` (int in `{0, 1, 2, 6}`): The status of the solver; for wrapper feature selection, this is only the status of the last solver call.\n  - 0 = (proven as) optimal\n  - 1 = feasible (valid solution, but might be suboptimal)\n  - 2 = (proven as) infeasible\n  - 6 = not solved (no valid solution found, but one might exist)\n- `decision_tree_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `decision_tree_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `random_forest_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `random_forest_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `k` (int in `{5, 10}`): The number of features to be selected.\n- `tau_abs` (int in `{1, ..., 10}`): The dissimilarity threshold for alternatives, corresponding to the absolute number of features (`k * tau`) that have to differ between feature sets.\n- `num_alternatives` (int in `{1, 2, 3, 4, 5, 10}`): The number of desired alternative feature sets, not counting the original (zeroth) feature set.\n  A number from `{1, 2, 3, 4, 5}` for simultaneous search and always `10` for sequential search.\n- `objective_agg` (string, 2 different values): The name of the quality-aggregation function for alternatives (`min` or `sum`).\n  Min-aggregation or sum-aggregation for simultaneous search but always sum-aggregation for sequential search.\n  (In fact, sequential search only optimizes individual feature sets anyway, so the aggregation does not matter for the search.)\n- `search_name` (string, 2 different values): The name of the search method for alternatives (`search_sequentially` or `search_simultaneously`).\n- `fs_name` (string, 5 different values): The name of the feature-selection method (`FCBFSelector`, `MISelector`, `ModelImportanceSelector` (= Model Gain), `MRMRSelector`, or `GreedyWrapperSelector`).\n- `dataset_name` (string, 30 different values): The name of the `PMLB` dataset.\n- `n` (positive int): The number of features of the `PMLB` dataset.\n- `split_idx` (int in `[0,4]`): The index of the cross-validation fold.\n- `wrapper_iters` (int in `[1,1000]`): The number of iterations in case wrapper feature selection was used, missing value (empty string) in the other cases.\n  This column does not exist in result files not containing wrapper results.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```\nAll result files are comma-separated and contain plain numbers and unquoted strings, apart from the column `selected_features` (which is quoted and represents lists of integers).\nThe first line in each result file contains the column names.\nYou can use the following code to make sure that lists of feature indices are treated as such (rather than strings):\n```python\nimport ast\nresults['selected_idxs'] = results['selected_idxs'].apply(ast.literal_eval)\n```";
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                "This code has been used for the numerical experiments in the thesis \"High-frequency wave-propagation: error analysis for analytical and numerical approximations\" by Julian Baumstark, see https://www.doi.org/10.5445/IR/1000149719." , "The scripts are intended to reproduce the figures from the thesis\n\"High-frequency wave-propagation: error analysis for analytical and numerical approximations\"\nby Julian Baumstark\n################################################################################\n#### Requirements\nThe codes are tested on \n    Ubuntu 18.04 LTS\nwith \n    Matlab R2020a \n################################################################################\n#### Generation of figures\nWith this code Figures \n    3.1, 3.2, 4.1, 4.2, 4.3, 5.1, and 5.2\ncan be generated. \nThe calculation of the data for Figure 4.1, 4.2, 4.3, 5.1 and 5.2 takes a few days and requires a storage space of about 140 GB.\n#### Figure 3.1 and 3.2\nIn order to obtain Figure 3.X (X=1 or 2) run the command\n    visualize_Fig3X.m\nin Matlab. \n#### Figure 4.1, 4.2 and 4.3\nOpen folder \"Chapter_4\" in Matlab.\nIn order to obtain Figure 4.X (X=1 or 2 or 3) first run the command\n    kleingordon_analytical_approx.m \nto compute the data. Then, run\n    visualize_Fig4X.m\nin Matlab. \n#### Figure 5.1 and 5.2\nOpen folder \"Chapter_5\" in Matlab.\nIn order to obtain Figure 5.X (X=1 or 2) first run the command\n    kleingordon_numerical_approx.m \nto compute the data. Then, run\n    visualize_Fig5X.m\nin Matlab.";
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                "The data set consists of five types. \n1) In table AnthroDemoMaxForcesTable.mat the anthropometric data, the gender and the maximum grip and push forces of the subjects are listed. \n2) In Matrix resultsMatrix.mat the mechanical impedance and the phase angle of the measurements of the subjects study are summarized. The resulting mechanical impedance and the phase angle are already calibrated in each direction, and can be used directly from the matrix. The matrix consists of three dimensions, the first is the mechanical impedance and the phase angle in the direction of excitation (zh), the second dimension is the cross-axial mechanical impedance and the phase angle in xh-direction, and the third dimension is the cross-axial mechanical impedance and the phase angle in yh-direction. The directions are named according to the coordinate system of DIN 5349:2001. Each row of the matrix represents one measurement. The following data information and results are in the corresponding columns:\n- 1: number of the subect\n- 2: is 0 if the grip and push forces during the measurements are within the range of 5N and 14%, otherwise a 1 is at this position. This can be used for data exclusion.  \n- 3: predefined push force value\n- 4: predefined grip force value\n- 5: experiment number according to the resultsMatrix for each subject\n- 6: sequence of the taken measurements of each subject, due to a randomized test plan\n- 7: mean measured grip force\n- 8: median measured grip force\n- 9: standard deviation of the measured grip force\n- 10: mean measured push force\n- 11: median measured push force\n- 12: standard deviation of the measured push force\n- 13: mechanical impedance at 10 Hz\n- 14: phase angle at 10 Hz\n- 15: mechanical impedance at 15 Hz\n- 16: phase angle at 15 Hz\n- 13:2:209 mechanical impedance between 10-500 Hz in 5 Hz steps\n- 14:2:210 phase angle of the mechanical impedance between 10-500 Hz in 5 Hz steps\n3) The VPAmatrix.mat is according to resultsMatrix.mat, instead of the mechanical impedance in positions 13:2:209 the vibration power absorption values are listed and in positions 14:2:210 the weighted vibration power absorption according to ISO10068:2012 is listed. Here, only the direction of excitation is evaluated. \n4) Raw data, the data name consists of the time stamp and is named afterwards in the same order as the matrix: Px, where x is the number of the subject, followed by the push and then the grip force and the position in the matrix. In each struct the metadata, the time and the signals are saved. The excitation in the subject study was applied along the forearm axis (zh according to DIN5349:2001). Therefore, the signals az_top and az_bottom are the acceleration at the top and bottom of the measuring handle in excitation direction. The signals Fz_top and Fz_bottom are the forces in the direction of excitation, both are used to calculate the mechanical impedance and vibration power absorption in the excitation direction (dimension 1 in the resultsMatrix.mat file). The signals used for the second dimension of the resultsMatrix are Fx_top, Fx_bottom, az_top, az_bottom, for the third dimension Fy_top, Fy_bottom, az_top, az_bottom are used.\n5) emptyMeasurement.mat is a measurement of the unloaded handle while excited with the same signal as in the subject study. This file can be used for the calibration of the handle." , "The data set contains the raw as well as the evaluated data from a subject study with 6 subjects to measure the mechanical impedance and the vibration power absorbtion while varying the grip and push forces in ten levels between 10-135N and 10-110N respectively. The measuring handle on which the study was conducted complies with the specifications in ISO10068:2012. The subjects were randomised to hold the force levels while vibrations were applied translatory along the lower arm axis (zh-axis according to DIN5349:2001) to their hand-arm system via a shaker system, with the posture of the subjects lying within the ranges specified in ISO 10819:2022. The excitation was a multisine with frequencies between 10 to 500 Hz in 5 Hz steps with an root mean square value of 40m/s². The acquisition time of each measurement is 16.5 sec, for the evaluation the data is cutted to exclude the first 4 sec and the last 1.5 sec.";
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                "These are the experimental data for the paper> Bach, Jakob, and Klemens Böhm. \"Alternative feature selection with user control\"\npublished in the [International Journal of Data Science and Analytics](https://link.springer.com/journal/41060) in 2024.\nYou can find the paper [here](https://doi.org/10.1007/s41060-024-00527-8) and the code [here](https://github.com/jakob-bach/alternative-feature-selection).\nSee the `README` for details.\nThe datasets used in our study (which we also provide here) originate from [PMLB](https://epistasislab.github.io/pmlb/).\nThe corresponding [GitHub repository](https://github.com/EpistasisLab/pmlb) is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nPlease see the file `LICENSE` in the folder `datasets/` for the license text." , "# Experimental Data for the Paper \"Alternative feature selection with user control\"\nThese are the experimental data for the paper> Bach, Jakob, and Klemens Böhm. \"Alternative feature selection with user control\"\npublished in the [International Journal of Data Science and Analytics](https://link.springer.com/journal/41060) in 2024.\nCheck our [GitHub repository](https://github.com/Jakob-Bach/Alternative-Feature-Selection) for the code and instructions to reproduce the experiments.\nThe data were obtained on a server with an `AMD EPYC 7551` CPU (32 physical cores, base clock of 2.0 GHz) and 128 GB RAM.\nThe operating system was `Ubuntu 20.04.6 LTS`.\nThe Python version was `3.8`.\nWith this configuration, running the experimental pipeline (`run_experiments.py`) took about 255 h.\nThe commit hash for the last run of the experimental pipeline (`run_experiments.py`) is [`2212360a32`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/2212360a320121862556bc8d54da45cb6dc8ee93).\nThe commit hash for the last run of the evaluation pipeline (`run_evaluation_journal.py`) is [`758b71e70b`](https://github.com/Jakob-Bach/Alternative-Feature-Selection/tree/758b71e70b941a54c88bda17a5591698dafcc03c).\nWe also tagged both commits (`run-2023-06-23` and `evaluation-2024-03-19`).\nThe experimental data are stored in two folders, `datasets/` and `results/`.\nFurther, the console output of `run_evaluation_journal.py` is stored in `Evaluation_console_output.txt` (manually copied from the console to a file).\nIn the following, we describe the structure and content of each data file.\n## `datasets/`\nThese are the input data for the experimental pipeline `run_experiments.py`, i.e., prediction datasets.\nThe folder contains one overview file, one license file, and two files for each of the 30 datasets.\nThe original datasets were downloaded from [PMLB](https://epistasislab.github.io/pmlb/) with the script `prepare_datasets.py`.\nNote that we do not own the copyright for these datasets.\nHowever, the [GitHub repository of PMLB](https://github.com/EpistasisLab/pmlb), which stores the original datasets, is MIT-licensed ((c) 2016 Epistasis Lab at UPenn).\nThus, we include the file `LICENSE` from that repository.\nAfter downloading from `PMLB`, we split each dataset into the feature part (`_X.csv`) and the target part (`_y.csv`), which we save separately.\nBoth files are CSVs only containing numeric values (categorical features are ordinally encoded in `PMLB`) except for the column names.\nThere are no missing values.\nEach row corresponds to a data object (= instance, sample), and each column corresponds to a feature.\nThe first line in each CSV contains the names of the features as strings; for `_y.csv` files, there is only one column, always named `target`.\n`_dataset_overview.csv` contains meta-data for the datasets, like the number of instances and features.\n## `results/`\nThese are the output data of the experimental pipeline in the form of CSVs, produced by the script `run_experiments.py`.\n`_results.csv` contains all results merged into one file and acts as input for the script `run_evaluation_journal.py`.\nThe remaining files are subsets of the results, as the experimental pipeline parallelizes over 30 datasets, 5 cross-validation folds, and 5 feature-selection methods.\nThus, there are `30 * 5 * 5 = 750` files containing subsets of the results.\nEach row in a result file corresponds to one feature set.\nOne can identify individual search runs for alternatives with a combination of multiple columns, i.e.:\n- dataset `dataset_name`\n- cross-validation fold `split_idx`\n- feature-selection method `fs_name`\n- search method `search_name`\n- objective aggregation `objective_agg`\n- feature-set size `k`\n- number of alternatives `num_alternatives`\n- dissimilarity threshold `tau_abs`\nThe remaining columns mostly represent evaluation metrics.\nIn detail, all result files contain the following columns:\n- `selected_idxs` (list of ints, e.g., `[0, 4, 5, 6, 8]`): The indices (starting from 0) of the selected features (i.e., columns in the dataset).\n  Might also be an empty list, i.e., `[]` if no valid solution was found.\n  In that case, the two `_objective` columns and the four `_mcc` columns contain a missing value (empty string).\n- `train_objective` (float in `[-1,1]`): The training-set objective value of the feature set.\n  Three feature-selection methods (*FCBF*, *MI*, *Model Importance*) have the range `[0,1]`, while two methods (*mRMR*, *Greedy Wrapper*) have the range `[-1,1]`.\n- `test_objective` (float in `[-1,1]` ): The test-set objective value of the feature set.\n- `optimization_time` (non-negative float): Time for alternative feature selection in seconds.\n  The interpretation of this value depends on the feature-selection method.\n  For white-box feature-selection methods, this value corresponds to one solver call.\n  For wrapper feature selection, we record the total runtime of the *Greedy Wrapper* algorithm, which calls the solver and trains prediction models multiple times.\n- `optimization_status` (int in `{0, 1, 2, 6}`): The status of the solver; for wrapper feature selection, this is only the status of the last solver call and only refers to a satisfiability problem rather than the (black-box) optimization problem.\n  - 0 = (proven as) optimal\n  - 1 = feasible (valid solution, but might be suboptimal)\n  - 2 = (proven as) infeasible\n  - 6 = not solved (no valid solution found, but one might exist)\n- `decision_tree_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `decision_tree_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a decision tree trained with the selected features.\n- `random_forest_train_mcc` (float in `[-1,1]`): Training-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `random_forest_test_mcc` (float in `[-1,1]`): Test-set prediction performance (in terms of Matthews Correlation Coefficient) of a random forest trained with the selected features.\n- `k` (int in `{5, 10}`): The number of features to be selected.\n- `tau_abs` (int in `{1, ..., 10}`): The dissimilarity threshold for alternatives, corresponding to the absolute number of features (`k * tau`) that have to differ between feature sets.\n- `num_alternatives` (int in `{1, 2, 3, 4, 5, 10}`): The number of desired alternative feature sets, not counting the original (zeroth) feature set.\n  A number from `{1, 2, 3, 4, 5}` for simultaneous search, but always `10` for sequential search.\n- `objective_agg` (string, 2 different values): The name of the quality-aggregation function for alternatives (`min` or `sum`).\n  Min-aggregation or sum-aggregation for simultaneous search but always sum-aggregation for sequential search.\n  (In fact, sequential search only optimizes individual feature sets anyway, so the aggregation does not matter for the search.)\n- `search_name` (string, 2 different values): The name of the search method for alternatives (`search_sequentially` or `search_simultaneously`).\n- `fs_name` (string, 5 different values): The name of the feature-selection method (`FCBFSelector`, `MISelector`, `ModelImportanceSelector` (= *Model Gain* in the paper), `MRMRSelector`, or `GreedyWrapperSelector`).\n- `dataset_name` (string, 30 different values): The name of the `PMLB` dataset.\n- `n` (positive int): The number of features of the `PMLB` dataset.\n- `split_idx` (int in `[0,4]`): The index of the cross-validation fold.\n- `wrapper_iters` (int in `[1,1000]`): The number of iterations in case wrapper feature selection was used, missing value (empty string) in the other cases.\n  This column does not exist in result files not containing wrapper results.\nYou can easily read in any of the result files with `pandas`:\n```python\nimport pandas as pd\nresults = pd.read_csv('results/_results.csv')\n```\nAll result files are comma-separated and contain plain numbers and unquoted strings, apart from the column `selected_features` (which is quoted and represents lists of integers).\nThe first line in each result file contains the column names.\nYou can use the following code to make sure that lists of feature indices are treated as such (rather than strings):\n```python\nimport ast\nresults['selected_idxs'] = results['selected_idxs'].apply(ast.literal_eval)\n```";
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                "Ziel des Datensatzes ist das Training sowie die Validierung von Modellen zur Prognose von Zeitreihen für Fräsprozesse. Hierfür wurden an einer DMG CMX 600 V durch eine Siemens Industrial Edge Prozesse mit einer Abtastrate von 500 Hz aufgenommen. Es wurde ein Prozess für das Modelltraining und ein Prozess für die Validierung aufgenommen, welche sowohl für die Bearbeitung von Stahl sowie von Aluminium verwendet wurden. Es wurden mehrere Aufnahmen mit und ohne Werkstück (Aircut) aufgenommen, um möglichst viele Fälle abdecken zu können." , "Documents:\n-Design of Experiments: Information on the paths such as the technological values of the experiments\n-Recording information: Information about the recordings with comments\n-Data: All recorded datasets. The first level contains the folders for training and  validation both with and without the workpiece. In the next level, the individual test executions are located. The individual recordings are stored in the form of a JSON file. This consists of a header with all relevant information such as the signal sources. This is followed by the entries of the recorded time series.\n-NC-Code: NC programs executed on the machine\n-Workpiece: Pictures of the raw parts as well as the machined workpieces. The pictures show the unfinished part on the left, the training part in the middle and a part with two validation runs on the right.\nExperimental data:\n-Machine: DMG CMX 600 V\n-Material: S235JR, 2007 T4\n-Tools:\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 5mm\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 10mm\n   -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 20mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 10mm\n   -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm\n-Workpiece blank dimensions: 150x75x50mm\nLicense: This work is licensed under a Creative Commons Attribution 4.0 International License. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)." , "The aim of the data set is the training as well as the validation of models for the prediction of time series for milling processes. For this purpose, processes with a sampling rate of 500 Hz were recorded on a DMG CMX 600 V by a Siemens Industrial Edge. A process for model training and a process for validation were recorded, which were used for both steel and aluminum machining. Several recordings were made with and without the workpiece (aircut) in order to cover as many cases as possible.";
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                "Numerous business workflows involve printed forms, such as invoices or receipts, which are often manually digitalized to persistently search or store the data. As hardware scanners are costly and inflexible, smartphones are increasingly used for digitalization. Here, processing algorithms need to deal with prevailing environmental factors, such as shadows or crumples. Current state-of-the-art approaches learn supervised image dewarping models based on pairs of raw images and rectification meshes. The available results show promising predictive accuracies for dewarping, but generated errors still lead to sub-optimal information retrieval. In this paper, we explore the potential of improving dewarping models using additional, structured information in the form of invoice templates. We provide two core contributions: (1) a novel dataset, referred to as Inv3D, comprising synthetic and real-world high-resolution invoice images with structural templates, rectification meshes, and a multiplicity of per-pixel supervision signals and (2) a novel image dewarping algorithm, which extends the state-of-the-art approach GeoTr to leverage structural templates using attention. Our extensive evaluation includes an implementation of DewarpNet and shows that exploiting structured templates can improve the performance for image dewarping. We report superior performance for the proposed algorithm on our new benchmark for all metrics, including an improved local distortion of 26.1 %. We made our new dataset and all code publicly available at https://felixhertlein.github.io/inv3d." , "Each sample contains the following files: \n\"flat_document.png\" (2200x1700x3, uint8, 0-255), showcasing a document in perfect condition. \n\"flat_information_delta.png\" displays all texts which represent invoice data (2200x1700x3, uint8, 0-255). \n\"flat_template.png\" is an empty invoice template (2200x1700x3, uint8, 0-255). \n\"flat_text_mask.png\" visually presents all texts shown in the given document (2200x1700x3, uint8, 0-255). \n\"warped_angle.png\" shows warping-induced x- and y-axis angle (1600x1600x2, float32, -Pi to Pi). \n\"warped_albedo.png\" is an albedo map (1600x1600x3, uint8, 0-255). \n\"warped_BM.npz\" stores backward mapping, i. e. the realtive pixel shift from warped to normalized image for each pixel  shifts (1600x1600x2, float32, 0-1). \n\"warped_curvature.npz\" has pixel-wise curvature of the warped document (1600x1600x1, float32, 0-inf). \n\"warped_depth.npz\" holds per-pixel depth between camera and document (1600x1600x3, float32, 0-inf). \n\"warped_document.png\" displays the warped document (1600x1600x3, uint8, 0-255). \n\"warped_normal.npz\" contains warped document normals (1600x1600x3, float32, -inf to inf). \n\"warped_recon.png\" features a chess-textured warped document (1600x1600x3, uint8, 0-255). \n\"warped_text_mask.npz\" is a boolean text pixel mask (1600x1600x1, bool8, True/False). \n\"warped_UV.npz\" stores warped texture coordinates (1600x1600x3, float32, 0-1). \n\"warped_WC.npz\" includes document coordinates in the 3D space (1600x1600x3, float32, -inf to inf). \nFor more details see https://github.com/FelixHertlein/inv3d-generator.\nReleased under CC BY-NC-SA 4.0. Excluded files are listed in 'restricted-license-files.txt' (located in record with DOI 10.35097/1730, \"Inv3D: a high-resolution 3D invoice dataset for template-driven Single-Image Document Unwarping - Metadata\"). These are for academic use only.";
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                "Drug-induced differential gene expression analysis (DGEA) is an essential tool for uncovering the molecular basis of phenotypic changes in cells upon drug treatment, and ultimately for understanding the mechanisms of individual tumor responses to anticancer drugs. Performing high-throughput DGEA after drug treatment is challenging due to the very high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells.\nWe introduce a novel, miniaturized method operating at the nanoliter scale for drug-induced DGEA. This innovative approach facilitates high-throughput and parallel analysis of the drug response of cells derived from patients, effectively circumventing issues related to limited samples and the laborious nature of traditional protocols.\nThe method is based on the Droplet Microarray (DMA) platform, a microscope glass slide with a pattern of hydrophilic spots separated by a superhydrophobic background, which enables the formation of droplets suitable for testing a minute number of cells with compounds. DMA allows for phenotypic analysis using microscopy, followed by obtaining cDNA from the treated cells and DGEA. The procedure involves cell lysis for mRNA isolation and cDNA conversion on DMA, followed by pooling of the droplets and subjecting them to qPCR analysis. \nIn this work, we demonstrate the protocol for drug-induced DGEA on the DMA platform using cell lines and primary patient-derived chronic lymphocytic leukemia (CLL) cells. The methodology established here is critical for performing DGEA on a limited number of cells, with potential applications in functional precision oncology. In this way, this method helps to gain insights from the molecular profiling of unique patient-derived samples after drug treatment in vitro, which is essential for understanding individual tumor response to anticancer drugs." , "The provided data are technical repeats from the research experimental details. including qPCR data and image analysis for cellular viability.";
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                "Research data, research results, and publications of the PhD thesis entitled 'Validation Framework for RDF-based Constraint Languages', submitted to the Department of Economics and Management at the Karlsruhe Institute of Technology (KIT)." , "# PhD Thesis---* Title: **[Validation Framework for RDF-based Constraint Languages](http://dx.doi.org/10.5445/IR/1000056458)*** Author: **[Thomas Hartmann](http://www.dr-thomashartmann.de/)*** Examination Date: 08.07.2016* University: [Karlsruhe Institute of Technology (KIT)](https://www.kit.edu/english/)* Chair: [Institute of Applied Informatics and Formal Description Methods](http://www.aifb.kit.edu/web/Hauptseite/en)* Department: [Department of Economics and Management](http://www.wiwi.kit.edu/english/index.php)* 1. Advisor: [Prof. Dr. York Sure-Vetter](http://www.aifb.kit.edu/web/York_Sure-Vetter/en), Karlsruhe Institute of Technology* 2. Advisor: [Prof. Dr. Kai Eckert](https://www.hdm-stuttgart.de/forschung_transfer/forschungsthemen/metadatenmanagement/team/eckert), Stuttgart Media University---***PhD Thesis Download**** [http://dx.doi.org/10.5445/IR/1000056458](http://dx.doi.org/10.5445/IR/1000056458)***Publications**** Complete set of publications: [publications](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/publications)***Research Data, Research Results, and Publications**** Link to  the KIT research data repository: [http://dx.doi.org/10.5445/BWDD/11](http://dx.doi.org/10.5445/BWDD/11)---***RDF Validation Requirements Database**** [http://purl.org/net/rdf-validation](http://purl.org/net/rdf-validation)***Validation Environment**** Demo: [http://purl.org/net/rdfval-demo](http://purl.org/net/rdfval-demo)* Source code: [software/rdf-validator](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/software/rdf-validator)---***Chapter 2: Foundations for RDF Validation**** XML validation: [chapter/chapter-2/xml-validation](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-2/xml-validation)***Chapter 3: Vocabularies for Representing Research Data and Related Metadata**** RDF vocabularies commonly used to represent different types of research data and related metadata: [chapter/chapter-3/common-vocabularies](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-3/common-vocabularies)* Complete running example in RDF: [chapter/chapter-3/common-vocabularies/running-example](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-3/common-vocabularies/running-example)***Chapter 4: RDFication of XML Enabling to use RDF Validation Technologies**** Evaluation results: [chapter/chapter-4/evaluation](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-4/evaluation) ***Chapter 6: Consistent Validation across RDF-based Constraint Languages**** Constraint languages implementations: [chapter/chapter-6/constraint-languages-implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-6/constraint-languages-implementations)***Chapter 7: Validation Framework for RDF-based Constraint Languages****  Formal specification, HTML documentation, and UML class diagram of the RDF Constraints Vocabulary (RDF-CV): [chapter/chapter-7/rdf-constraints-vocabulary](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-7/rdf-constraints-vocabulary)* Generic SPIN mappings for constraint types: [chapter/chapter-7/generic-SPIN-mappings/RDF-CV-2-SPIN.ttl](https://github.com/github-thomas-hartmann/phd-thesis/blob/master/chapter/chapter-7/generic-SPIN-mappings/RDF-CV-2-SPIN.ttl)***Chapter 8: The Role of Reasoning for RDF Validation**** Implementations for all constraint types expressible by OWL 2 QL, OWL 2 DL, and DSP as well as for major constraint types representable by ReSh and ShEx:  [chapter/chapter-8/constraint-types-implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-8/constraint-types-implementations)* Implementation of reasoning capabilities for all reasoning constraint types for which OWL 2 QL and OWL 2 DL reasoning may be performed: [chapter\\chapter-8\\reasoning-constraint-types-implementations/OWL2-Reasoning-2-SPIN.ttl](https://github.com/github-thomas-hartmann/phd-thesis/blob/master/chapter/chapter-8/reasoning-constraint-types-implementations/OWL2-Reasoning-2-SPIN.ttl)* Validation and reasoning implementations of constraint types:  [chapter/chapter-8/constraint-types-implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-8/constraint-types-implementations)***Chapter 9: Evaluating the Usability of Constraint Types for Assessing RDF Data Quality**** Implementations of all 115 constraints: [chapter/chapter-9/constraints](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-9/constraints)* Evaluation results for each QB data set grouped by SPARQL endpoint: [chapter/chapter-9/evaluation/data-sets/QB](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-9/evaluation/data-sets/QB)* Vocabulary implementations: [chapter/chapter-9/vocabularies/implementations](https://github.com/github-thomas-hartmann/phd-thesis/tree/master/chapter/chapter-9/vocabularies/implementations)***Appendix**** Link to appendix: [http://dx.doi.org/10.5445/IR/1000054062](http://dx.doi.org/10.5445/IR/1000054062)";
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                "\"Entwicklung eines Eignungstests fuer die Zulassung zum Sportstudium\" in Sportunterricht, 25, (1976), 5 , S. 148-153Verf. beschreibt einen Eignungstest, der zum Wintersemester 1975/76 erstmals bei maennlichen und weiblichen Bewerbern fuer das Sportstudium angewandt wurde. Die leistungsdiagnostische Aufgabe des Tests besteht in der Feststellung der motorischen Grundeigenschaftenin Relation zu den Anforderungen der Grundkurse. Es werden keine sportmotorischen Fertigkeiten, sondern motorische Eigenschaften wie Ausdauer, Geschicklichkeit und Schnelligkeit in Spielsituationen geprueft. Die Tests bestehen aus den Untergruppen Haro-Fitness-Test, Geraetturnen, Leichtathletik, Schwimmen, Spiele und setzen sich aus verschiedenen Einzeltests zusammen. Alle Ergebnisse werden in Testpunkte umgerechnet, von denen zum Schluss eine Mindestzahl erreicht werden muss. Gewisse Schwaechen in Einzeltests koennen in anderen wettgemacht werden. Zur Erstellung der Normen wurden zwei Probedurchlaeufe in Beziehung zu bereits gewonnenen Erfahrungswerten gebracht. Von jedem Item wurde die Standardabweichung ermittelt, die Messwerte in eine Standardnormalverteilung transformiert und die T-Werte ermittelt. Die Ergebnisse der Einzeltests werden in diesen Werten ausgedrueckt und fuer jede Untergruppe das arithmetische Mittel gebildet. Das Mittel aller fuenf Mittelwerte bildet das Gesamtergebnis.";
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                "# Degree of substitution of cellulose acetate and other esters: Raw ATR-FTIR spectra and DS data obtained from IR integration, 1H NMR and 31P NMR  \n## Authors\nFrank Rhein, Timo Sehn, Michael A.R. Meier\n## About\nThis repository contains the raw data used in *\"Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectrosopy and machine learning\"*. For detailed information please refer to the original publication upon acceptance.\n## Datasets\nThe following data sets are compiled in this repository and published together with raw ATR-FTIR spectra:\n- `Wolfs.A`: DOI https://doi.org/10.1002/pol.20230220 \n    - Raw ATR-FTIR data\n    - DS data from IR integration, 1H NMR, 31P NMR methods\n- `Wolfs.B`: DOI https://doi.org/10.1039/D1GC01508G\n    - Raw ATR-FTIR data\n    - DS data from 1H NMR, 31P NMR methods\n- `Sehn.A`, `Sehn.B`, `Sehn.C`: DOI https://doi.org/10.1021/acs.biomac.3c00762 \n    - Raw ATR-FTIR data\n    - DS data from 1H NMR method\nAn overview is given in `data_overview.xlsx`.\n## Structure\nRaw ATR-FTIR data is contained in the `IR_data_..` folders, while DS values from different methods are found in the respective `.txt` files. > Note: `IR_DS` refers to the integration based method reported in https://doi.org/10.1002/pol.20230220  \n## Methods\nFor more information on data generation and analytical setups please refer to the original publications referenced above. DS data based on 1H NMR data is obtained according to the newly reported evaluation routine.  \n## Evaluation Scripts\nThe Python scripts for data analysis are available on Github under https://github.com/pdhs-group/DS_IR_ML";
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                "The dataset was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anoma-lies in the workpiece the dataset can be applied for anomaly detection. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning." , "The dataset consists of seven folders. Each folder represents one milling run. In each milling run the depth of cut was set to 3 mm. A folder contains a maximum of three json files. The number of files depends on the time needed for each run which is a function of milling tool diameter and feed rate. Files in each folder were numerated in sequence. For example, folder “run1” contains the files “run1_1” and “run1_2” with the last number indicating the order in which the files were generated. The frequency of recording datapoints was set to 500 Hz. \nDuring each milling run the milling tool moved along the longitudinal side and then was moved back alongside the workpiece. This way machining started always on the same side of the workpiece. \nTable 1 provides an overview of the milling runs. Run 1 to 4 were performed with a HSS tool with a diameter of 10 mm. The tool in use was an end mill (HSS-E-SPM HPC 10 mm) developed by Hoffmann Group. During the first three runs with this end mill no tool breakage occurred. However, in run 4 the tool broke. Runs 5 and 6 were performed by milling with an end mill of the same tool series (HSS-E-SPM HPC 8 mm) that just differs in tool diameter. In contrast to this run 7 was performed by using a solid carbid tool (Solid carbide roughing end mill HPC 8 mm). Cutting with SC tools provides much higher productivity with the downside being higher tool price. In our case the SC end mill performed cuts with a feed rate of 1150 mm/min compared to 191 mm/min achieved by a HSS end mill of the same diameter. Tool breakages were recorded on all runs with end mills of diameter 8 mm. \nTable 1. overview of the data folders \nfolder name | number of json files | tool diameter | tool breakage | tool type\nrun 1  2 10 mm No HSS\nrun 2 2 10 mm No HSS\nrun 3 2 10 mm No HSS\nrun 4 2 10 mm Yes HSS\nrun 5 2 8 mm Yes HSS\nrun 6 3 8 mm Yes HSS\nrun 7  1 8 mm Yes SC\nEach json file consists of a header and a payload. The header lists all parameters that were recorded such as position, motor torque and motor current of each of a maximum of five axes of a milling machine. However, the machine used in our experiments is a 3-axis machining center which leaves the payload of 2 possible additional axes to be empty. In the payload the sequential data for each parameter can be found. A list of recorded signals can be found in Table 2.\nTable 2. recorded signals during milling\nSignal index in payload | Signal name | Signal Address |Type\n13-18 VelocityFeedForward VEL_FFW|1* double\n19-24 Power POWER|1* string\n25-30 CountourDeviation CONT_DEV|1* double\n38-43 TorqueFeedForward TORQUE_FFW|1* double\n44-49 Encoder1Position ENC1_POS|1* double\n56-61 Load LOAD|1* double\n68-73 Torque TORQUE|1* double\n68-91 Current CURRENT|1* double\n* 1 represents x-axis, 2 represents y-axis, 3 represents z-axis and 6 represents spindle-axis.\nNote that our milling center has 3 axis and therefore values for axes 4 and 5 are null.";
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