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Data for "Mesoionic N-Heterocyclic Olefins as Initiators for the Lewis Pair Polymerization of Epoxides"
No full textThis dataset is structured according to Tables (and specifc table entries) and Figures in the research article and the Supporting Information. It contains NMR raw data as well as relevant GPC raw data (the latter in the form of exported .txt files). Also, NMR raw data for the mNHO initiators/catalysts is provided (Hansmann Group)
Replication data for: "Efficient synthesis of well-defined ordered mesoporous aluminosilicates with tailorable acidity"
No full text
This dataset contains all primary and/or processed data underlying the publication "Efficient synthesis of well-defined ordered mesoporous aluminosilicates with tailorable acidity". The publication reports the synthesis and characterization of ordered mesoporous aluminosilicates by direct liquid crystal templating.
Take a look at the publication
Data files are grouped by measurement method, including (but not limited to):
27Al solid state NMR
Total scattering and pair distribution function (PDF)
Nitrogen physisorption
Pyridine-loaded FTIR spectroscopy (Py-FTIR)
Small-angle X-ray scattering (SAXS)
Sample names follow the denomination:
OMAS_x/1_Tyy — where OMAS = ordered mesoporous aluminosilicate; x/1 = molar Si/Al ratio; Tyy = polycondensation temperature in °C.
OMS_Tyy — where OMS = ordered mesoporous silica; Tyy = polycondensation temperature in °C.
These datasets support the analysis of how aluminum content and polycondensation temperature influence structural order, pore size, and surface acidity in OMAS.
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Replication Data for: Dynamic Optimisation Of Façade-integrated Solar Cooling Elements: Adsorption Cooling Versus Photovoltaic Scenarios
No full textThis dataset contains:
Python and Modelica code to reproduce the system models, dynamic optimization problems, to fit the fluid and working pair properties as well as to generate the result data and figures
the raw and result data
the figures within the publication
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Replication Data for: A general method for identifying patch-bound radiance distributions from multi-channel irradiance measurement devices
No full textThis dataset contains:
- code: The Python code to analyze the given Honeybee result data, the incidence operator training data, integrated incidence operator data (F_tot) with interactive plotly figures is given. Additionally, the code contains the functions for identifying radiance values, applying radiance values to different scenes, optimizing half-angle vectors as well as reproducing the result data and figures.
- data: The result data, mainly as numpy array. The data are structured according to the subcaptions within the manuscript within which the procedure of data generation is introduced, subcaption 2.4 and 2.5. The folders subcap_3_1 and subcap_3_2 contain the raw data behind the figures 3-5, denoted with x and y data. Additionally, the weather data are given in csv and epw file format.
- figures: The figures within the publication. They are named according to the figure number in the manuscript.
- scenes: The Rhino3D-Grasshopper files of the identification and validation scenes. Use these files to analyze the geometries, material definitions of the surfaces and sensor placements. Additionally, you can reproduce the measurement data with the "AnnualIrradiance" Honeybee component. The basis for the incidence operator training data generation is the component Honeybee "ModelToRad"
Code for: Validating a Numerical Model for Calco-Carbonic Reactive Flow in a Laboratory Scale Fracture Analog
No full textThis dataset contains the DuMux code for the simulations in paper "Validating a Numerical Model for Calco-Carbonic Reactive Flow in a Laboratory Scale Fracture Analog"
To run the simulations at your own computer, please conduct the following steps:
Install docker for (example in ubuntu)[https://docs.docker.com/engine/install/ubuntu/]
Make sure you can run docker without sudo (see: https://docs.docker.com/engine/install/linux-postinstall/).
Download the file docker.tar.gz, unzip it and enter into the directory.
In it you will find a convenience script `docker_wendel2026a.sh` that you can execute as follows:
./docker_wendel2026a.sh open
To exit the container type:
exit
To reenter the container type:
docker restart [container-i]
docker exec -it [container-id] bash.
Inside the running container you can execute a convenience script that compiles the executables and performs the simulations:
cd wendel2026a/
./runAll.sh
For more detailed instructions we refer to the README.md on our git repository:
wendel2026a : https://git.iws.uni-stuttgart.de/dumux-pub/wendel2026a</a
Data set for Publication "Revealing the Pseudocapacitance Charge Storage Mechanism of Sulfur-doped Carbon Supercapacitors in Non-Aqueous Electrolyte through in situ EPR"
No full textThis dataset contains the raw data used in the manuscript "Revealing the Pseudocapacitance Charge Storage Mechanism of Sulfur-doped Carbon Supercapacitors in Non-Aqueous Electrolyte through in situ EPR".
It contains the recorded Raman and EPR data, the data of the electrochemical characterization of the material and the TEM data files
Sequencing data related to Gutekunst et al. "Nucleosome linker DNA methylation by DNMT3A/DNMT3B3 is controlled by nucleosome binding and multimerization of DNMT3 complexes on DNA"
No full textMethods
DNA libraries for Illumina NGS were prepared with a two-step PCR approach. For this, the Nucleosome 1 - Linker - Nucleosome 2 sequence was split into two amplicons, one comprising the Nucleosome 1 - Linker and the second comprising Linker - Nucleosome 2. For library preparation, 1 μL of bisulfite-converted DNA was amplified in a first PCR reaction using barcoded primers and HotStartTaq DNA Polymerase (QIAGEN). In the second PCR reaction, 1 µL of PCR1 product was amplified using i5 and i7 indexing primers and Q5 polymerase (New England Biolabs). Successful amplification was verified by agarose gel electrophoresis. Samples were pooled in equimolar amounts, purified with NucleoSpin® Gel and PCR Clean-up kit and used for Illumina paired end 2×250 bp sequencing conducted at Novogene. Bioinformatic analysis of NGS data was conducted using a local instance of a Galaxy server. Obtained sequence reads were trimmed with the Trim Galore! Tool, discarding tails with a quality score below 20. Afterwards reads were paired using PEAR. Reads were filtered according to the expected DNA length using the Galaxy Filter FASTQ tool. The de-multiplexing was done by the selection of the reads with specific combinations of barcodes and Illumina adapters which were then then mapped against a corresponding reference sequence using bwameth (github.com/brentp/bwa-meth). Finally, the DNA methylation of individual CpG sites was computed using MethylDackel (github.com/dpryan79/MethylDackel). Here, the final analysed sequences are provided.
DNA sequencenes
Dinucleosome Linker-70
CGAGGTCGACGGTATCGATAAGCTTCTGGAGAATCCCCCAGCCGAGGCCGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCAGGTGTCAGATATATACATCCTGTAACGCTATCCGCGCCACGTCTACGCTNNNTACGAGAACGCCGAGACGTGCGAGCAGCGAAAGCGGCCGaCCTGGAGAATCCAGGTGCTGAGGCAGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCACCACTCAGATATATACATCCTGTAAGGGCGAATTCCACATTG
Dinucleosome Linker-58(1)
CGAGGTCGACGGTATCGATAAGCTTCTGGAGAATCCCCCAGCCGAGGCCGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCAGGTGTCAGATATATACATCCTGTGCCACGTCTACGCTNNNTACGAGAACGCCGAGACGTGCGAGCAGCGAAAGCGGCCGaCCTGGAGAATCCAGGTGCTGAGGCAGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCACCACTCAGATATATACATCCTGTAAGGGCGAATTCCACATTG
Dinucleosome Linker-58(2)
CGAGGTCGACGGTATCGATAAGCTTCTGGAGAATCCCCCAGCCGAGGCCGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCAGGTGTCAGATATATACATCCTGTAACGCTATCCGCGCCACGTCTACGCTNNNGAGACGTGCGAGCAGCGAAAGCGGCCGaCCTGGAGAATCCAGGTGCTGAGGCAGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCACCACTCAGATATATACATCCTGTAAGGGCGAATTCCACATTG
CpG rich DNA
CTATGGAAACCCCTGTGGAGCTTCAGGGGCACGAGTGAGGCGGGCGCTGGCGGGCCAAGGTGACGAAGGCGCCTCCGGCTCTTGGGCCAGCGGACTGAGCGGTGGAGCAGAACTTGGGTGCCTCGGGGACCGCCAAAAAGTGGCCTTGTCCACTTCTCTGAG
Further information
The primers are provided in "NGS_Primers"
A compilation of the sequencing reads provided here is given in "NGS_Table"
References
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic acids research 2022, 50, W345-W351, doi: 10.1093/nar/gkac247
Albrecht, C., Bashtrykov, P., and Jeltsch, A. (2024) Amplicon-Based Bisulfite Conversion-NGS DNA Methylation Analysis Protocol. Methods Mol Biol 2842, 405-418, doi: 10.1007/978-1-0716-4051-7_21
For all details regarding furhter analysis and interpretation of the data, refer to the corresponding manuscript (Gutekunst et al., 2026), which is connected with this data entry
Data for: Hydrogen diffusion in TiCr2Hx Laves phases: A combined ab initio and machine-learning-potential study
No full textThis 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/, which contains DFT-calculated energies, forces, and stresses for training configurations, and Trained_MTPs/, which contains the final fitted MLIPs for both the C14 (C14.mtp) and C15 (C15.mtp) phases. The dataset is fully referenced in the accompanying manuscript and supplementary materials
Digital Twin task logs for Collective Robotic Construction (CRC) — ROB|ARCH 2024
No full text
Digital Twin task logs for Collective Robotic Construction (CRC) — ROB|ARCH 2024
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Digital Twin task logs for Collective Robotic Construction (CRC) — ROB|ARCH 2024
Overview
This repository contains a dataset of Digital Twin (DT) task logs from a
Collective Robotic Construction (CRC) workshop held at ROB|ARCH 2024.
Over three days, 14 participants programmed eight low-cost mobile robots (RADr) to develop
and test decentralised construction behaviours, while a Vicon motion tracking system
provided global state feedback.
The dataset captures the DT’s task-level interaction with:
8 RADr robots (mobile, magnetic gripper, onboard sensors)
Vicon (external tracking of robot + material poses)
Digital material modules (passive tracked objects; labelled DM0…)
Each experimental run is recorded as a JSON array of task records (e.g., Move,
Grip, Read) with timestamps, task parameters, and the corresponding responses
from the physical actors.
Case study context (CRC)
Workspace: ~4.6 × 5.8 m floor divided into a 4 × 5 grid.
Tracking: Vicon motion capture (overhead coverage).
Robots: 8 × RADr (2-wheel drive, magnetic gripper, onboard proximity/boundary sensors).
Materials: passive “digital material” modules with retroreflective markers (tracked by Vicon).
Execution mode: Adaptive multi-actor execution (robots act in parallel; robots can be
inserted/removed/reprogrammed during a run; DT maintains shared situational awareness).
The DT architecture instantiated two principal task families:
RADr tasks: Move, Grip
Vicon tasks: Read (a.k.a. ReadState snapshots)
Repository structure
.
├── Data/
│ ├── Run-1.json
│ ├── Run-2.json
│ ├── ...
│ └── Run-12.json
├── Figures/
│ ├── Tasks and durations per CRC run.png
│ ├── Average task rate per RADr robot.png
│ └── Per RADr control loop times.png
├── Analysis.py
└── Replay_Run.gh
What each top-level item is
Data/Run-*.json: Primary dataset. One JSON file per CRC run (job).
Figures/: Example plots generated from Analysis.py (included for convenience).
Analysis.py: Reference analysis script that loads the runs and reproduces the included plots.
Replay_Run.gh: Grasshopper definition (Rhino/Grasshopper) for replaying/visualising a run
(see “Replay” section below).
Runs included
The dataset contains 12 runs (Run-1 … Run-12).
Each run is a task log containing a mixture of:
Vicon Read snapshots (typically ~1 Hz)
RADr Move tasks (motor commands)
RADr Grip tasks (motor commands + gripper toggle)
Overall totals (all runs combined):
Total task records: 22,571
Vicon Read: 12,688
RADr Move: 8,080
RADr Grip: 1,234
Mean run duration: 18.43 min (range: 8.98–40.72 min)
Data format
Each Data/Run-*.json file is a JSON array:
[
{ "task_id": "...", "task_type": "Read", "main_actor": "Vicon", ... },
{ "task_id": "...", "task_type": "Move", "main_actor": "RADr_3", ... },
...
]
Task record schema (common fields)
Task Data Schema that includes the following fields:
task_id: A unique identifier for the task record (UUID).
name: A human-readable task name (e.g., "Wander", "Pick", "Vicon_ReadValues").
task_type: The task category/type (one of Read, Move, Grip; plus one Capture placeholder).
main_actor: The physical actor that executed the task (e.g., Vicon, RADr_0…RADr_7).
description: Optional description text for the task.
message: A log message associated with the task.
element_id: Optional list of related design element references (empty in these runs).
job: The run identifier associated with this record (e.g., "Run-4").
level: Process hierarchy level (mostly 0 in these logs).
task_index: Task order/index value (null in these logs).
actors_data: Task input parameters grouped per actor (nested details omitted).
start_time: Task start timestamp (ISO 8601 string) or null.
end_time: Task end timestamp (ISO 8601 string) or null.
progress: Task state indicator (observed values: 0, 1, 2).
response: Task output/response payload for the actor (may be null; nested details omitted).
project: Project identifier (e.g., "col_robots").
Task types and their data structures
1) Vicon — Read
Purpose: request a global environment snapshot from Vicon.
Typical fields:
main_actor: "Vicon"
task_type: "Read"
actors_data: {"Vicon": {"ReadData": "true"}}
response: {"Vicon": "<JSON string>"}
Vicon response payload structure
After json.loads(record["response"]["Vicon"]), you obtain a dictionary:
{
"RADr_0": [ [ [x,y,z], flag ], [ [qx,qy,qz,qw], flag ] ],
"RADr_1": [ ... ],
...
"DM0": [ [ [x,y,z], flag ], [ [qx,qy,qz,qw], flag ] ],
...
}
Where:
x,y,z are positions in the Vicon world frame (values suggest millimetres, z ~ 60–130 mm for floor objects).
qx,qy,qz,qw are a quaternion orientation.
flag is a Boolean that indicate measurement validity/occlusion (in these logs it is typically false).
Entity keys include:
Robots: RADr_0 … RADr_7
Digital materials: DM0 … DM24
2) RADr — Move and Grip
RADr tasks control an individual two-wheeled robot (RADr_i). In this dataset there are two closely
related task types:
Move: send a wheel-motor command sequence.
Grip: send a wheel-motor command sequence and toggle the magnetic gripper.
Typical fields:
main_actor: "RADr_6" (or any RADr_i)
task_type: "Move" or "Grip"
actors_data["RADr_i"]["move_vectors"]: a string representing a list of 2D wheel-force vectors, one per control step
actors_data["RADr_i"]["gripper"] (only for Grip): "True" / "False" (stored as a string)
Example actors_data snippets:
// Move
"actors_data": {
"RADr_6": {
"move_vectors": "[[-0.1, 0.0], [0.0, 0.65], [0.0, 0.8], [0.0, 1.0]]"
}
}
// Grip
"actors_data": {
"RADr_6": {
"gripper": "True",
"move_vectors": "[[0.0, 0.5], [0.0, 1.0], [0.0, 1.0]]"
}
}
RADr response payload (common to Move and Grip)
response typically contains a per-robot entry whose value is a JSON string that must be parsed
with json.loads(...). After parsing, the response provides an updated local state from onboard sensors:
KeyTypeMeaning
Material_SensorboolWhether a material module is detected nearby.
GripperboolCurrent magnetic gripper state.
Robot_SensorintRobot proximity sensor indicator (values observed: -1, 0, …).
Boundary_SensorboolBoundary detection (e.g., leaving the work area).
CounterintA robot-side counter / tick value (useful for debugging timing).
Derived metrics (as used in Analysis.py)
The included analysis script derives two useful “DT performance” proxies from the task logs:
Task rate per robot
For each robot and run:
task_rate = (# Move + Grip tasks) / (robot active time)
Active time is computed from the robot’s first task start to its last task end within a run.
Average task-rate summary (mean ± std across runs):
Overall mean across robots (mean of robot means): 8.44 tasks/min.
Approximate per-robot control-loop time
For each robot, an “ABM-like loop time” is approximated as:
loop_time_s = start_time(next task) - end_time(current task)
This captures the combined effects of:
participant logic / ABM compute time,
DT engine scheduling,
communication latency,
and any short pauses between tasks.
Overall mean (after outlier removal): 0.66 s.
Usage
1) Load the dataset in Python
import json
import pandas as pd
with open("Data/Run-1.json", "r") as f:
tasks = json.load(f)
df = pd.DataFrame(tasks)
2) Reproduce the included figures
Install dependencies:
pip install numpy pandas matplotlib
Run:
python Analysis.py
The script reads all files in Data/ and writes plots to Figures/.
3) Replay in Grasshopper (Rhino)
Replay_Run.gh is a Grasshopper definition intended to visualise a run by reading Vicon snapshots
from the task logs and animating robot/material poses. Open it in Rhino/Grasshopper and ensure the
Data/ folder path is correctly set inside the definition.
(Exact inputs/parameters depend on your Grasshopper environment; this repository does not include a
*.ghx parameter manifest.)
Notes and known quirks
Timestamps: start_time/end_time are ISO 8601 strings; a small fraction of records may contain null.
Units: Vicon positions are presented in millimetres in a fixed world frame (based on magnitude vs. the physical workspace size).
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Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
No full textThis dataset contains all relevant data to reproduce the results of the paper titled "Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations".
It contains the exact time series data of the two-particle reduced density matrix (2RDM) for each parameter configuration of the parameter scan, with some additional quantities discussed in the paper as well. Additionally, the test metrics of the neural ODE models for the whole parameter scan as well as the hyper-parameter optimized constraint models are provided, which contain the predicted and target trajectory of the reduced representation of the 2RDM as well as some additional metrics.
The exact time series of the Fermi-Hubbard model contains six files per parameter configuration:
'cumu_blocks.dat' contains the absolute values of the spin up-up and up-down block of the two-particle cumulant as well as the absolute value of the kernel component and the whole three-particle cumulant of the spin up-up-down block over time
'energy_mcscf.dat' contains the correlation energy and a column called energy, where the first value of this column is required to calculate the initial potential energy
'energyspectrum_groundstate.dat' where the first value is used to calculate the initial potential energy
'Hubbard_all.den2ab' contains the spin up-down block of the 2RDM over time
'occ_numbers.doublon2' contains the doublon occupation numbers for each site over time
'occ_numbers.rho1' contains the occupation numbers for each site over time
The neural ODE test metrics for each parameter configuration are provided as well. Each file contains a short and long prediction of the normalized and reduced representation of the 2RDM data as well as the corresponding target values. Additionally, the Pearson correlation and some other metrics are given for the short prediction of 1000 time steps.
Similar test metrics files are provided for the hyper-parameter optimized constraint and non-constraint models