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Raw Microscopy Data for: Correlative single-molecule and structured illumination microscopy of fast dynamics at the plasma membrane.
No full textTotal internal reflection fluorescence (TIRF) microscopy offers powerful means to uncover the functional organization of proteins in the plasma membrane with very high spatial and temporal resolution. Traditional TIRF illumination, however, shows a Gaussian intensity profile, which is typically deteriorated by overlaying interference fringes hampering precise quantification of intensities – an important requisite for quantitative analyses in single-molecule localization microscopy (SMLM). Here, we combine flat-field illumination by using a standard πShaper with multi-angular TIR illumination by incorporating a spatial light modulator compatible with fast super-resolution structured illumination microscopy (SIM). This unique combination enables quantitative multi-color SMLM with a highly homogenous illumination. By using a dual camera setup with optimized image splitting optics, we achieve versatile combination of SMLM and SIM with up to three channels. We deploy this setup for establishing robust detection of receptor stoichiometries based on single-molecule intensity analysis and single-molecule Förster resonance energy transfer (smFRET). Homogeneous illumination furthermore enables long-term tracking and localization microscopy (TALM) of cell surface receptors identifying spatial heterogeneity of mobility and accessibility in the plasma membrane. By combination of TALM and SIM, spatially and molecularly heterogenous diffusion properties can be correlated with nanoscale cytoskeletal organization and dynamics
Validating the Induction of Neutral Mood from Videos
No full textIn this experimental study, we aimed at validating the induction of neutral mood by means of participants rating of four 10-minute video sequences showing landscape scenes. The induction of neutral mood is part of a larger experiment (preregistration: drks.de. ID: DRKS00025780) funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)
Osnabrück — Synthetic Scalable Cube Dataset
No full textRetrieving the 3D shape of an object from a collection of images or a video is currently realized with multiple view geometry algorithms, most commonly Structure from Motion (SfM) methods. With the aim of introducing artificial neuronal networks (ANN) into the domain of image-based 3D reconstruction of unknown object categories, we developed a scalable voxel-based dataset in which one can choose different training and testing subsets. We show that image-based 3D shape reconstruction by ANNs is possible, and we evaluate the aspect of scalability by examining the correlation between the complexity of the reconstructed object and the required amount of training samples. Along with our dataset, we are introducing, in this paper, a first baseline achieved by an only five-layer ANN. For capturing life’s complexity, the ANNs trained on our dataset can be used a as pre-trained starting point and adapted for further investigation. Finally, we conclude with a discussion of open issues and further work empowering 3D reconstruction on real world images or video sequences by a CAD-model based ANN training data set.
Data Sets
3x3x3 - 100 000 cubes cf. cube3by3by3TBP.tar.bz2 for views and cubes_3x3x3_random_200000.tar.gz for 3D objects
4x4x4 - 300 000 cubes cf. cube4by4by4TBP.tar.bz2 for views and cubes_4x4x4_random_325000.tar.gz for 3D objects
8x8x8 430 000 cubes cf. cube8by8by8TBP.tar.bz2 for views and cubes_8x8x8_random_430000.tar.gz for 3D objects
Generator Tools
Python Voxelizer
This python script create voxelized objects incl. a voxel set list out of ply, off or stl 3D object files. (cf. model2VoxelCloud.py)
Cube Generator
This generator, written in Matlab, randomly generates n 3D objects. Each such object is created by taking a unit cube in R³ and subdividing it into a r x r x r sub cubes. The parameter r can be defined by the user. By ensuring the uniqueness of the cube distribution in the voxel grid, this generator is able to generate 2^(r³) different 3D objects and export them as 3D *.obj object files.
Views Generator
This generator, written in Matlab, (can be optionally used for voxelization of 3D objects and) renders w input images with a pixel resolution x by x. Where the w different viewpoints are uniformly distributed around the object by using the Fibonacci lattice.
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Measurement: Parallel Starlink Throughput
No full textThis dataset contains measurement data from throughput and ping measurements within the starlink LEO network using seven parallel links. The corresponding Article in whose context this dataset was collected is:
Zimmermann, T., Laniewski, D., Lanfer, E., Thieme, S., Wintering, N., & Aschenbruck, N. (2025). Measuring the Potential for Bundled Starlink Performance Within a Single Service Cell. In Proceedings of the 50th IEEE Conference on Local Computer Networks (LCN), Sydney, Australia, October 14–16, 2025. DOI: 10.1109/LCN65610.2025.1114630
Data from Neural Network Training in the Obstacle Tower Environment to Investigate Embodied, Weakly Supervised Learning
No full textDescription:
This repository presents data collected to investigate the role of embodiment and supervision in learning. This is done inside a simulated 3D maze world with a navigation task using mainly visual input in the form of RGB images. The main contribution of this data repository is to provide a network model trained in this environment with weak supervision and a closed loop between action and perception. Additionally, control networks are provided which were trained with varying degrees of supervision and embodiment. In the corresponding paper [1] the representations of these networks are compared based on sparsity measures and well as content of the encodings and the possibility to extract semantic labels. For the training of the control conditions several new data sets were created which are also included here. They contain a collection of images from the simulated world with corresponding semantic labels. Overall, they provide a good basis for further analysis and a more in-depth investigation of representation learning and the effect of embodiment and supervision on representations.Steps to reproduce:
Data was generated through a 3D simulation of a maze environment called Obstacle Tower. The data of interest are the trained neural network weights and the networks activations corresponding with different input frames. Three main networks were trained. A reinforcement learning agent which trained through interaction with the simulated environment, an autoencoder trained to reconstruct images collected by the agent and a classifier, trained to classify objects in the images. Exact training and testing conditions, hyperparameter and network structure are provided in the corresponding paper.
For the training of the reinforcement learning agent the Unity ml-agents toolkit PPO implementation is used with small modifications for extra data collection and control experiments. The code we used can be found here: https://github.com/vkakerbeck/ml-agents-dev . Model checkpoint files are saved for different points in training but mostly the final version of the network is analysed in the corresponding paper [1] . The autoencoder and classifier are trained using Python with TensorFlow and Keras. The corresponding code can be found here: https://github.com/vkakerbeck/Learning-World-Representations/tree/master/DataAnalysis . The data also contains activations in the hidden layer of the network corresponding to 4000 test images for all three networks. Code for this can be found in the same GitHub repository. The datasets used for training the autoencoder and classifier were created by collecting observations in the Obstacle Tower environment using the trained agent. These observations were then labelled automatically, and the labels were cross checked by hand.
A Description of the individual files is included in the data folder (Description.txt). Due to storage constraints no all model checkpoint files used to create figure 6 of the paper could be uploaded. However, feel free to contact me (vkakerbeck[at]uos.de) if you are intrested in these detailed checkpoint files of the controll runs and I will make them available to you
Siddata Study Assistant Dataset from Prototpye 3.0
No full textWe collected these data from user interaction with version 3.0 of the SIDDATA study assistant software. Students from three German universities used the software, and the dataset contains data from all three universities, only from those students who explicitly agreed to donate their data. Data aggregation occurred over the course of 5 months, from December 2021 to May 2022. The dataset consists of one .csv file with 21.890 rows representing one activity. The semantics and the context of data acquisition are described in detail in a related publication. https://doi.org/10.5220/001103880000318
Replikationsdaten für: Comparison of the diversity of prokaryotic communities between agricultural and uncultivated soils in the Osnabrück region
No full textAs part of the master's thesis "Comparison of the diversity of prokaryotic communities between agricultural and uncultivated soils in the Osnabrück region" (Oswald, 2025), a total of 60 soil samples were taken from 20 agricultural and uncultivated sites (one triplicate each). The samples were analysed to identify similarities and differences in microbial and functional composition. Nanopore sequencing from Oxford Nanopore Technologies (ONT) and subsequent bioinformatic analyses were used to characterise the microbiome at each site. Agricultural practices (soil movement, type of usage), physico-chemical parameters (e.g. C/N ratio, pH) and climatic factors (soil moisture level) were included to identify explanatory factors for differences in α- and β-diversity and functional composition between sites. The most influential factor on microbial composition was found to be pH. In addition, a change in α-diversity was observed between tilled and no-tilled soils. While the composition of the microbiome and consequently the functional composition is similar between sites, hierarchical cluster analysis in combination with α-diversity and relative abundances of individual functions gives an indication of the formation of stable microbial communities in undisturbed soils.
The dataset provided here contains the recorded metadata for the sites, the microbiome data and the R script used to process the data, perform statistical tests and create graphics. It is recommended to read the readme.pdf first
Replication Data for: A Classifier-Deduced Signal Extraction Approach for Time Difference Estimation in Acoustic Sensor Network
No full textThis dataset is provided to replicate the results from the paper titled
"A Classifier-Deduced Signal Extraction Approach for Time Difference Estimation in Acoustic Sensor Network."
The data consists of multiple parts, each packaged into a separate zip-file. To use the data, you will need to download all parts and recombine them into a single archive. The file README.yaml desribes the structure.
The final .zip file is limited to 36 GB, while the unzipped directory requires 136.7 GB of storage space.
In Ubuntu, you can reassemble the parts using the Terminal:
user@computer$ cat cdse_data_part_* > cdse_data.zip</pre
Replikationsdaten: Erwartungen von Schulleiterinnen und Schulleitern
No full textDie durchgeführte Studie stellt eine Replikation der Studie von Schmitz/Voreck (2008) zu den Erwartungen des Schulleiterungspersonals an ihre Lehrkräfte dar. Befragt wurde Schulleitungspersonal an berufsbildenden Schulen in Deutschland zwischen Juli und September 2021. An der Online-Befragung beteiligten sich 170 Personen. Der bereinigte Datensatz umfasst 103 Fälle und ist nicht repräsentativ
SUF LBS-Monitor: 2. Erhebungswelle
No full textDie Daten stammen aus einer Längsschnittstudie zur professionsbezogenen Entwicklung von Studierenden in Studiengängen für das Lehramt an berufsbildenden Schulen (LBS-Monitor). Die Studie wird durchgeführt im Rahmen des Projektes "DEIN LBS-Campus", das vom Bundesministerium für Bildung und Forschung gefördert wird (Laufzeit: 01.03.2020-31.12.2023). Über Onlinebefragungen an mehreren Standorten der hochschulischen beruflichen Lehrerbildung werden soziodemographische Merkmale der Studierenden, Angaben zum Studium und zu den Studienvoraussetzungen, Merkmale der professionsbezogenen Entwicklung, verschiedene Studien- und berufsbezogene Aspekte und ausgewählte Qualitätsaspekte sowie formale Leistungsindikatoren erfasst. Die 2. Welle wurde im Sommersemester 2022 durchgeführt. Zur Verfügung gestellt werden eine Scientific-Use-File (SUF) des Wellendatensatzes und des Gesamtdatensatzes. Beide stehen im SPSS-Format zur Verfügung. Die bereinigte SUF des Wellendatensatzes umfasst 290 Fälle und 308 Variablen. Die SUF Gesamtdatensatzes umfasst 758 Fälle und 608 Variablen. Die Daten werden über verschiedene Berichte dokumentiert. Das Skalenhandbuch enthält eine Dokumentation des Gesamtdatensatzes und wird über die Erhebungswellen fortgeschrieben. Das Codebook, der Datenbericht und der Wellenbericht beziehen sich auf die 2. Erhebungswelle. Das Codebook dokumentiert die erhobenen Variablen hinsichtlich Fragestellung und Codierung, im Datenbericht werden statistische Kennwerte berichtet. Die Erhebung und ausgewählte Analyseergebnisse sind im Wellenbericht zu finden