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Replication Data for: Tuning Regioselectivity in Cyclopolymerization through Carbene Ligand Size Modulation in Molybdenum Imido Alkylidene Catalysts
No full textIn this dataset, all simulation data are listed. That includes all geometry optimizations and, where relevant, energy and/or partial charge calculations. All structures are named "*.xyz", the input files are named "*.chm", and the output files are named "out". The names of the catalysts are different in the simulations than the ones used in the paper. Catalystnames.xlsx shows how these terms translate to one another. Data from collaborating groups can be found in a seperate data set.
The calculations for all intermediates for the mechanism of Mo3 can be found in Cat3_comp, with the subdirectories being named after the investigated minimum structure. 4r-1a, 4r-1b, ... include the calculations for the intermediates that are used to gather data to form the data science model. The subdirectories are named after the investigated catalyst. NHC_Au and X_Au include the calculations about linear gold complexes that feature all NHC and X ligands from the other simulations, respectively.
ML includes all data analysis. This includes an extraction of different parameters from the structure, an overview of many different possible fit functions, and a detailed investigation of the most relevant ones featuring cross-validation and y-randomizations
Data for: Excited State Opto-Ionic Reservoir Computing in Hybrid Perovskite Electrochemically-Gated Luminescent Cells
No full textThis dataset contains measurement data related to the paper "Excited State Opto-Ionic Reservoir Computing in Hybrid Perovskite Electrochemically-Gated Luminescent Cells".
The data originates from optical and electrical experiments investigating neuromorphic reservoir computing based on opto-ionic dynamics in hybrid lead-halide perovskite devices.The measurements were performed on electrochemically gated MAPbBr₃ perovskite microcrystal films and single-crystal devices. Input signals were encoded as phase-modulated optical excitation pulses synchronized with an applied alternating voltage. The resulting system response was recorded primarily via spatially and temporally resolved photoluminescence (PL) microscopy, complemented by spectroscopic, lifetime, and X-Ray diffraction measurements.
Photoluminescence intensity and dynamics represent the internal state of the opto-ionic reservoir. Spatial pixels or defined regions of interest correspond to computational nodes, while temporal evolution encodes memory effects arising from ion migration and excited-state recombination dynamics. Variations in signal amplitude, lifetime, and spatial distribution therefore reflect nonlinear system responses used for time-series processing and classification tasks.
The dataset could be reused for - benchmarking physical or neuromorphic reservoir computing approaches - studies of opto-ionic coupling and excited-state dynamics in hybrid perovskites - image-based time-series analysis or dimensionality-reduction research.
The data is organized according to experiment type - Modulation: Photoluminescence response to optical and electronic modulation - Stability: Replicates of the modulation experiment for different devices and as a function of device lifetime - BitScan: Photoluminescence response to the 4-bit binary classification benchmark - PL lifetime imaging: Photoluminescence lifetime microscopy data of single-crystal devices - GIWAXS: grazing incidence wide angle x-ray scattering of MAPbBr3 thin films and different stages of device fabrication - UV-VIS: Transmission spectra of MAPbBr3 thin films
Atmospheric transmission models for FIFI-LS: altitude 35kft, 39deg O3
No full textThis dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with SOFIA Redux
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of 17O16O isotopologue abundance coefficients.
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
File naming convention:
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
where xx is the flight altitude in kft, yy is the zenith angle in degree, and zz the
precipitable water vapor value in micron.
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".</p
Replication Data for: "Readout of a solid state spin ensemble at the projection noise limit"
No full textCSV data sets to reproduce all plots in the main paper. The datasets are either 1D plots with the x-values in the first column and all plotted y-curves in the following columns or alternating x_i,y_i columns if the x-values are not consistent over all traces. 2D plots are given in the format: y-values: first column, x-values: title of columns. Information on how the data was acquired and what it actually shows can be found in the related manuscript
Replication Data for: Tuning Regioselectivity in Cyclopolymerization through Carbene Ligand Size Modulation in Molybdenum Imido Alkylidene Catalysts
No full textAll primary data files of measurements and processed data from the journal article mentioned under 'Related Publications' from the Buchmeiser group can be found here. The data set contains the NMR spectra of novel catalysts and polymers. The single-crystal X-ray structure of the novel catalyst is also available. Data from collaborating groups (computational data) can be found in a separate dataset
Data for: Mixed-Length Multivariate Covalent Organic Framework for Combined Near-Infrared Photodynamic Therapy and Drug Delivery
No full textAll primary data files of measurements and processed data of the journal article can be found here.
The data is structured according to analytical techniques. The material measured is described in a file name.
The dataset includes only experimental data and no simulational data.
The measurement methods are described in the SI of the journal article.
The data can be used to replicate the experiments, to evaluate and compare the materials' properties with others and to investigate the materials' structures
Data for Tau method
No full textCode and data for the publication "Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions" (2024). The folder "code_tau_method" includes the files and data to generate the plots for cases of a didactic example, one synthetic dataset and one real dataset discussed in the publication.
It includes seven 9 *.m files, 2 folders (data, mat), and 1 readMe text file. The folder "data" contains all the needed data for synthetic and real data cases. Download this folder and open the main function "main_0.m". To perform the analysis and generate the plots as shown in the publication, users can specify the "studyCase" (1,2 :toy example 3:synthetic data, 4:real data) in the beginning of "main_0.m" function and run the main function "main_0.m"
Data for "Revealing the Monomer Gradient of Polyether Copolymers Prepared Using N-Heterocyclic Olefins: Metal-Free Anionic versus Zwitterionic Lewis Pair Polymerization"
No full textThis data set contains representative catalyst precursor 1H NMR raw data related to the publication cited above (literature-known iodide salts of NHO 1 and NHO 2, respectively). Liberation of the active catalysts from such precursors was done by the cooperation partners as described in the paper
Replication Data for: Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short Term Memory Learning
No full textThis dataset contains all trainingsdata and model weights which are used within the paper "Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short Term Memory Learning".
The manipulation of Deformable Linear Objects (DLOs) such as cables poses a significant challenge for automation due to their infinite degrees of freedom and non-linear dynamics. In this paper we present a machine learning based optimal control approach for the manipulation of DLOs. This approach is divided into two main components: modeling and control. For modeling the dynamics of the DLO, we propose a learning based approach using a bidirectional Long Short-Term Memory (biLSTM) network. The biLSTM network is trained on synthetic data generated by the MuJoCo physics engine. For manipulating the DLO, a model predictive control strategy that employs Model Predictive Path Integral (MPPI) control is selected. The proposed approach is evaluated through simulation and experiments. The results demonstrate the effectiveness of the proposed method in achieving accurate and efficient manipulation of DLOs.
The dataset contains the following files:
model weights
biLSTM_bs128_hs256_lr00001_epochs50_10k.pth
biLSTM_bs128_hs256_lr00001_epochs50_20k.pth
biLSTM_bs128_hs256_lr00001_epochs50_30k.pth
rollout dataset (rollout.npz)
trainingdata
dataset_10k.npz
dataset_20k.npz
dataset_30k.npz
python file for extracting data from .npz files (getDataset.py)
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Supplemental Material for "A Large-Scale Quantitative Analysis of Avatars in VR and AR"
No full textThis repository provides the supplemental material for the paper "A Large-Scale Quantitative Analysis of Avatars in VR and AR", published in the TVCG Special Issue on the 2026 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). The supplemental material compromises the following data:
Excel with two sheets consisting the labeled avatar image data and a sheet consisting the pdf metadata
A folder with the excel sheet data in csv format
A zip filer with 14 440 avatar images
PDF document with supplementary material
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