501 to 510 of 2,499 Results
Feb 25, 2026 - AG_IntBioSys
Kühn, Theresa; Tomalka, André; Siebert, Tobias; Heymann, Michael, 2026, "Data for: Two-photon 3D-printed BSA hydrogel fibers resemble native muscle contraction dynamics", https://doi.org/10.18419/DARUS-5704, DaRUS, V1
(1) Bovine serum albumin (BSA) hydrogel spheres (diameters 10-50µm) were imaged at pH 7 and pH 4 to identify evidence of anisotropic shrinkage behavior. 2D image stacks were acquired using multiphoton fluorescence microscopy. The corresponding image files were named: BSA_sphere_[10-50]um_pH7.tif BSA_sphere_[10-50]um_pH4.tif Spheres were printed fro... |
Feb 24, 2026 - Holm group
Reinauer, Alexander; Kondrat, Svyatoslav; Holm, Christian, 2026, "Replication Data and Scripts for: Asymmetric Ionic Liquids for Enhanced Performance of Nanoporous Electrical Double Layer Capacitors", https://doi.org/10.18419/DARUS-5537, DaRUS, V1, UNF:6:QYhsar9LPd25HIHyXs7iGg== [fileUNF]
Simulation data and scripts accompanying the paper "Asymmetric Ionic Liquids for Enhanced Performance of Nanoporous Electrical Double Layer Capacitors". This dataset contains videos, plot_data and the scripts from MD-Simulations of asymmetric ionic liquid models in a plate-capacitor. For details about the files, please refer to the README. |
Feb 24, 2026 - Materials Design
Ikeda, Yuji; Forslund, Axel; Kumar, Pranav; Ou, Yongliang; Jung, Jong Hyun; Koehn, Andreas; Grabowski, Blazej, 2026, "Data for: Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions", https://doi.org/10.18419/DARUS-5272, DaRUS, V1
Data for reproducing the results in the manuscript "Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions". Find further details in README.md. |
Feb 24, 2026 - Collaborative Artificial Intelligence
Sood, Ekta; Kögel, Fabian; Bulling, Andreas, 2024, "VQA-MHUG", https://doi.org/10.18419/DARUS-4428, DaRUS, V3
We present VQA-MHUG - a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA), collected using a high-speed eye tracker. To the best of our knowledge, this is the first resource containing multimodal human gaze data over a textual question and the corresponding image. Our corpus en... |
Feb 24, 2026 - Materials Design
Glazyrin, Konstantin; Spektor, Kristina; Bykov, Maxim; Brant Carvalho, Paulo Henrique; Dong, Weiwei; Körmann, Fritz; Sano-Furukawa, Asami; Hattori, Takanori; Beyer, Doreen; Sahlberg, Martin; Ikeda, Yuji; Yu, Ji-Hun; Yang, Sangsun; Lee, Jai-sung; Bhat, Shrikant; Michael, Hanfland; Grabowski, Blazej; Divinski, Sergiy; Yusenko, Kirill, 2026, "Replication Data for: Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions", https://doi.org/10.18419/DARUS-5698, DaRUS, V2
Data for reproducing DFT simulations in the manuscript "Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions". The data contain the optimized atomic positions of the following systems in the VASP POSCAR/CONTCAR format. fcc_reference: 5 SQS models x 9 volumes in FCC, without H fcc_all_octa: 5 SQS models x 16... |
Feb 23, 2026 - Modeling Strategies for Gas migration in Subsurface
Banerjee, Ishani; Guthke (geb. Schöniger), Anneli; Nowak, Wolfgang, 2026, "Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data", https://doi.org/10.18419/DARUS-3592, DaRUS, V1
This dataset contains the codes for the invasion percolation models and comparison method used in the manuscript: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data |
Feb 23, 2026 - SOFIA Astronomy Data
SOFIA Data Center, 2026, "Precipitable Water Vapor Tables from ECMWF ERA5 Re-Analysis for SOFIA FIFI-LS Flights", https://doi.org/10.18419/DARUS-5728, DaRUS, V1
SOFIA Precipitable Water Vapor (PWV) Products for all flights with FIFI-LS between the year 2014 and 2022. Each FITS file contains a HDU "DATA", which is a binary table extension with columns for Precipitable Water Vapor (PWV), Longitude, Latitude and Atmospheric Pressure as a function of in-flight time. The source for positional data is the primar... |
Feb 20, 2026 - EXC IntCDC Associated Project 47 'Optimisation and Machine Learning for Climate-Friendly Design'
Zorn, Max Benjamin; Paule, Robin; Akbar, Zuardin; Wortmann, Thomas, 2026, "Opossum", https://doi.org/10.18419/DARUS-5685, DaRUS, V1
Opossum Opossum is an optimization plug-in for Grasshopper (for Rhino) that implements model-based and evolutionary optimization algorithms for single- and multi-objective problems. The plugin integrates GUI components into Grasshopper to configure runs, visualize results, and persist optimizer state inside Grasshopper documents. This repository co... |
Feb 20, 2026 - SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"
Dennig, Frederik L.; Keim, Daniel, 2026, "Replication Data for: "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy"", https://doi.org/10.18419/DARUS-5258, DaRUS, V1
This is the replication data for our paper "DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy", allowing additional analysis and experiments. The files, per file, below. It includes the experiment data and source code. For details on the usage, please refer to the README.md... |
Feb 20, 2026 - Materials Design
Kumar, Pranav; Körmann, Fritz; Edalati, Kaveh; Grabowski, Blazej; Ikeda, Yuji, 2026, "Data for: Hydrogen diffusion in TiCr2Hx Laves phases: A combined ab initio and machine-learning-potential study", https://doi.org/10.18419/DARUS-5544, DaRUS, V1
This dataset supports the development and validation of machine learning interatomic potentials (MLIPs) for modeling hydrogen diffusion in C14 (hexagonal) and C15 (cubic) TiCr₂-based Laves phases. It includes fitted moment tensor potentials (Level 16) and the corresponding training dataset. The data is organized into two directories: Training_db/,... |
