Decoding stimulus-specific regulation of promoter activity of p53 target genes - Data and analysis code

All measurements derived from smFISH experiments as well as additional output from Bayesian inference are publicly via the institutional repository of Technical University Darmstadt. This includes separate plots of the parameters (f, µ, δ) for each gene and condition, histogram fits of the polymerase occupancy, histogram fits of the RNA counts, plot of MCMC convergence, posterior distribution of the parameters inference and fitting of TS quantification. The corresponding analysis code is available as well.1.

Scene-Centric Unsupervised Panoptic Segmentation

Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised panoptic scene understanding, we eliminate the need for object-centric training data, enabling the unsupervised understanding of complex scenes. To that end, we present the first unsupervised panoptic method that directly trains on scene-centric imagery. In particular, we propose an approach to obtain high-resolution panoptic pseudo labels on complex scene-centric data combining visual representations, depth, and motion cues. Utilizing both pseudo-label training and a panoptic self-training strategy yields a novel approach that accurately predicts panoptic segmentation of complex scenes without requiring any human annotations. Our approach significantly improves panoptic quality, e.g., surpassing the recent state of the art in unsupervised panoptic segmentation on Cityscapes by 9.4% points in PQ. Acknowledgments: This project was partially supported by the European Research Council (ERC) Advanced Grant SIMULACRON, DFG project CR 250/26-1 "4D-YouTube", and GNI Project ``AICC''. This project has also received funding from the ERC under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 866008). Additionally, this work has further been co-funded by the LOEWE initiative (Hesse, Germany) within the emergenCITY center [LOEWE/1/12/519/03/05.001(0016)/72] and by the State of Hesse through the cluster project ``The Adaptive Mind (TAM)''. Christoph Reich is supported by the Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA) through the DAAD programme Konrad Zuse Schools of Excellence in Artificial Intelligence, sponsored by the Federal Ministry of Education and Research. License: Code, predictions, and checkpoints are released under the Apache-2.0 license, except for the ResNet-50 DINO backbone (dino_RN50_pretrain_d2_format.pkl), which is adapted from CutLER and published under the CC BY-NC-SA 4.0 license

LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews

We release the dataset associated with our paper "LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews".This is the version 1 of the datase

Decoupling of water and ion dynamics in nanophase-segrated mixtures of an ionic liquid and water studied by NMR experiments and MD simulations

Datasets of the figures shown in the article with the same title as this submission. Original manuscript submitted to Journal of Physics D: Applied Physics in March 2025. Revised version submitted to Journal of Physics D: Applied Physics on 2025-05-30

AFM - Topographic & Mechanical, dry & swollen polymer - Raw data

This dataset contains the raw NanoScope data acquired using PeakForce Tapping mode over a 20 µm² scan area, with a resolution of 256 samples/line. The measurements were conducted at a tip velocity of 20 µm/s, a peak force of 25 nN, and an excitation frequency of 2 kHz. The data includes unprocessed topography and mechanical properties such as adhesion, dissipation, and Young’s modulus, which were subsequently processed using NanoScope Analysis 1.9

Podoportation - dataset

Data recorded during the experiment. For additional information see the readme file within

Superresolution-Compatible DNA Labeling Technique with Silicon Rhodamine -Linked Nucleotide Reveals Chromatin Mobility and Organization Changes During Neuronal Differentiation

Chromatin dynamics play crucial roles in cellular differentiation, yet tools for studying global chromatin mobility in living cells remain limited. Here, we developed a new STED-compatible silicon rhodamine-linked SiR-dCTP nucleotide combined with SNTT1 to label chromatin in live cells and track chromatin mobility during neural differentiation. Using correlative microscopy, we quantified the labeled chromatin domain sizes using STED super resolution and confocal and we have labeled domains of the sizes closer to chromatin loop domains. Additionally, using this approach we demonstrate that chromatin mobility progressively decreases during the transition from human induced pluripotent stem cells (iPSCs) to neural stem cells (NSCs) and ultimately to neurons. This reduction in mobility correlates with differentiation state, suggesting a potential role for chromatin dynamics in cellular plasticity. Mechanistic insights into this phenomenon using global MNase assay reveals that the accessibility of chromatin decreases during neuronal differentiation suggesting that chromatin gets more restricted with implications for understanding chromatin regulation during development

Stadtwald-Daten 2024

Datensatz zum ersten Stadtwald-Paper, enthält Umweltparameter, Mikroklima-Daten und die Daten der Waldzustandserhebung

Supporting Videos "Capillary-Wave-Driven Jumping Droplets on Superhydrophobic Colloidal Rafts"

Drop nucleation, growth, coalescence induced jumping and capillary wave driven jumping on superhydrophobic colloidal raf

Publication data analysis for TUDa, RWTH and KIT, 2017-2024, based on OpenAlex

Publication data is queried and retrieved from the OpenAlex catalog of scholarly records, using python. A simple script is used (the original script and data can be found here: https://doi.org/10.48328/tudatalib-1391.2), utilizing the OpenAlex API via the pyAlex library. Retrieved record data is dumped in json format to facilitate recurring or iterative analysis. A basic analysis regarding Open-Access is performed by this same script, list comprehension is used to filter records based on type and Open-Access status / features. Lastly, the analysis results are provided via simple printout. The script can be run as is to provide an analysis of peer-reviewed scientific journal articles published by members of Technical University of Darmstadt (TUDa), RWTH Aachen University (RWTH) and Karlsruhe Institute of Technology (KIT) during 2016-2023. As a basic analysis regarding Open-Access, total number of publications is counted per institution per year, as well as number of Open-Access publications and number of "gold" "Open-Access" publications (i.e. primary location of publication is an Open-Access Journal). This repository entry provides updated data dump from the years between 2017 and 2024