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Supplemental Materials for STEP: Sequence of Time-Aligned Edge Plots
No full textSupplemental materials for STEP: Sequence of Time-Aligned Edge Plots
submitted to the Information Visualization Journal's special issue on Graph & Network Visualization and Beyond. The structure of the folder is as follows:
.
│
├── case_study
│ └── Contains the graph ensembles data used in the case study
│
├── param_study
│ └── The generated graphs [G1 -- G6], used in the parameter study
│
├── Stockholm_International_Peace_Research_Institute_Arms_Transfers_Database
│ └── the arms transfers network dataset used in the usecase example
│
└── wgcobertura
│ └── the software call graph dataset used in the usecase example
│
└── code
└── R implementation of the data generative model used in the parameter study.
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Data Supplement (PhD Thesis Cicac-Hudi, Mario)
No full textThe document contains the Data supplement to the PhD Thesis of Cicac-Hudi, Mario. The document contains representations of processed spectroscopic data (NMR spectra, FTIR spectra, mass spectra, UV-VIS spectra) and results of computational studies (atomic coordinates, calculated energies)
Getting the right clones in an automated manner: an alternative to sophisticated colony-picking robotics
No full textIn recent years, the Design-Build-Test-Learn (DBTL) cycle has become a key concept in strain engineering. Modern biofoundries enable automated DBTL cycling using robotic devices. However, both highly automated facilities and semi-automated facilities encounter bottlenecks in clone selection and screening. While fully automated biofoundries can take advantage of expensive commercially available colony picker, semi-automated facilities have to fall back on affordable alternatives. Therefore, our clone selection method is particularly well-suited for academic settings, requiring only basic infrastructure of a biofoundry.
The automated liquid clone selection method (ALCS) represents a straightforward approach for clone selection. Similar to sophisticated colony picking robots, the ALCS approach aims to achieve high selectivity. Investigating the time analogue of five generations, the model-based set-up reached a selectivity of 98 ± 0.2 % for correctly transformed cells. Moreover, the method is robust to variations in cell numbers at the start of ALCS. Beside Escherichia coli, promising chassis organisms, such as Pseudomonas putida and Corynebacterium glutamicum, were successfully applied. In all cases, ALCS enables the immediate use of the selected strains in follow-up applications. In essence, our ALCS approach provides a ‘low-tech’ method to be implemented in biofoundry settings without requiring additional devices
Data for: Experimental Analysis of Lifelines in a 15,000 L Bioreactor by Means of Lagrangian Sensor Particles
No full textThe DaRUS repository entails the raw files, result files and the MATLAB codes for the publication "Experimental Analysis of Lifelines in a 15,000 L Bioreactor by Means of Lagrangian Sensor Particles" in the journal "Chemical Engineering Research and Design".
This study employs Lagrangian Sensor Particles (LSPs) with a diameter of 40 mm equipped with a pressure sensor to investigate cell lifelines in a 15,000 L stirred tank reactor (STR) with three Elephant Ear impellers. The Stokes number of the LSPs is approx. 0.004 on a macro-scale. The vertical probability of presence, axial velocity profiles, circulation time distributions, and residence time distributions are quantified to analyze single-phase mixing heterogeneities, detect hydrodynamic compartments and conduct a Lagrangian regime analysis. Results reveal a similarly distributed probability of presence in the vertical reactor center but emphasize the LSP's sensitivity to fluctuating densities. Axial velocity distributions illustrate characteristic impeller-induced flow patterns, and circulation time distributions identify three compartments with comparatively shorter times in the axial center. Residence time distributions exhibit a similar compartmentalized profile. Moreover, the study estimates a potential oxygen deprivation zone for CHO cells in the upper compartment and demonstrates the LSP's efficacy in characterizing impeller systems. Contrary to literature, the ratio of examined global mixing times to circulation times is 1.0, highlighting macro-scale mixing. The research underscores that LSPs offer crucial insights into industrial-scale STRs, specifically for determining hydrodynamic compartments without having optical access
Entwickelte Materialien zur Dissertation "On the Development of Activating Teaching Materials in Theoretical Physics"
No full textThis Dataset lists the developed materials and their background files of the dissertation "On the Development of Activating Teaching Materials in Theoretical Physics" by Philipp Scheiger.
The material is provided in PDF form, but their generation code and the graphics used are also available. In most cases this is latex code, but in some cases it is also vector graphics.
The material can be used or adapted by other lecturers for their own courses
Replication data of B3 group for: “Chirality Transfer of Stereogenic Boron Centers Enabled by a SN2 Type Mechanism”
No full textIn this dataset all HPLC (high performance liquid chromatography) chromatograms and NMR spectra of O,N- and C,N-chelates, intermediates, ate-complexes and borates of the SN2-type addition can be found. Data from collaborating groups can be found in a seperate data set. The NMR spectra is structured according to the chapters of the research article in which they appear. The HPLC data is structured according to the molecule abbreviations used in the Supporting Information
Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 3 + 1 Data Points
No full textThis data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 3 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8 km x 12.8 km x 5 m with 2560 x 2560 x 1 cells. The 4th data point is intended for scalability test with the same parameters except for the size of 25.6 km x 25.6 km x 5 m with 5120 x 5120 x 1 cells. The varying parameters of the data sets are the positions of the heat pumps and a heterogeneous permeability field (Perlin noise, fixed min/max value). Other parameters that define the data sets, such as porosity and hydraulic pressure gradient are chosen to be as close as possible to reality.
Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122. (2025-05-19)
Generated with scripts from Dataset generation with Pflotran (commit 8549bbd9e22d2) with arguments given in inputs/args.yaml
Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics (Software and Data)
No full textHere, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics".
Abstract
Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible.
VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html or below.<br
Supplementary Data for 'Vastly different energy landscapes of the membrane insertions of monomeric gasdermin D and A3'
No full textSimulation files, molecular structures and trajectories, and jupyter notebooks used for analysis underlaying our publication on membrane insertion of monomeric gasdermin D to E. coli polar lipid extract (PLE).
In detail:
charmm36-july2017.ff simulation directory for GROMACS 202X
used and useful gromacs files (topologies and mdp files)
jupyter notebooks for analysis
snapshots and trajectories of monomeric gasdermin D in E. coli PLE, including umbrella sampling simulations
snapshots and trajectories of monomeric gasdermin D in POPC/30%cholesterol
snapshots and trajectories of arcs of seven gasdermin D subunits in E. coli PLE.
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Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing
No full textA classic, central Service-Oriented Computing (SOC) challenge is the service composition problem. It concerns solving a user-defined task by selecting a suitable set of services, possibly found at runtime, determining an invocation order, and handling request and response parameters. The solutions proposed in the past two decades mostly resort to additional formal modeling of the services, leading to extra effort, scalability issues, and overall brittleness. With the rise of Large Language Models (LLMs), it has become feasible to process semistructured information like state-of-practice OpenAPI documentation containing formal parts like endpoints and free-form elements like descriptions. We propose Compositio Prompto to generate service compositions based on those semi-structured documents. Compositio Prompto acts as an encapsulation of the prompt creation and the model invocation such that the user only has to provide the service specifications, the task, and which input and output format they expect, eliminating any manual and laborious annotation or modeling task by relying on already existing documentation. To validate our approach, we implement a fully operational prototype, which operates on a set of OpenAPIs, a plain text task, and an input and output JSON schema as input and returns the generated service composition as executable Python code. We measure the effectiveness of our approach on a parking spot booking case study. Our experiments show that models can solve several tasks, especially those above 70B parameters, but none can fulfill all tasks. Furthermore, compared with manually created sample solutions, the ones generated by LLMs appear to be close approximations.
Methodology (summarized): We perform an automated service composition for parking spot booking using LLMs for the study. There are six parking services and two payment services. The six parking services are duplicated with different distances and prices to create distinct sets 1 and 2. We define eight prompts and perform the composition using 14 different LLMs. We use a best-of-three-shot approach to reduce the influence of randomness. Finally, we assess functionality manually and apply code similarity metrics to a manually crafted sample solution. All experiments are described in detail in the full paper.
Content:
code/*:Code to perform the experiments. For details, see "code/README.md".
code/evaluation/prompt_generation.py:Services and prompt generation.
code/evaluation/sample_solution/*:Sample solution and similarity evaluation.
results/*:Results for the runs with the LLMs. Filename structure: "results/{model_name}-{run}/prompt_{prompt_number}_set_{set_number}_{artifact}". The artifact can be "code_0.py" for the generated code, "code_1.py" if tasked to improve the code, or "prompt.txt" for the used prompt. For the best run, the code metrics are in "comparison.json".
prompt_template.txt:Pseudo code for the prompt template. Implemented in code/evaluation/prompt_generation.py.
Note: Please use the tree view to access the files.
License: License for the "code/*": MIT. License for the "results/*": CC BY 4.0.</p