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Charge collection distance measurements for a diamond detector using a strontium-90 source
No full textThis dataset contains charge-collection-distance (CCD) measurement data for a planar polycrystalline CVD diamond detector measured with a strontium-90 beta source. The purpose of the dataset is to document the experimental CCD response of the detector and to support the analysis presented in the associated manuscript, “Charge-carrier transport simulations in diamond detectors with electric-field-dependent mobility and charge-collection-distance-based trapping”. The data are organized by bias polarity (positive, negative), detector state (pumped, unpumped), and applied bias voltage (100 V to 500 V). The dataset includes voltage-resolved measurement files together with the Python analysis script used to process the data, extract charge spectra and most probable values, and derive CCD and CCE summary quantities. These files support reproducibility of the reported measurements and their interpretation in the context of charge transport and trapping in diamond detectors
Replication Data for: Coupled mode effects in the stationary and transient behavior of squeezed channel field-effect transistors
No full textWhile nanoscale multi-gate field-effect transistors (FETs) can mitigate unwanted short-channel effects, the quantum confinement of charge
carriers significantly influences the device behavior, leading to issues, such as increased gain compression in RF amplifier applications.
Simulating the quantum transport in these devices remains challenging, particularly in the transient case, so that the so-called mode-space approximation is often used. Even still, for devices where these modes couple with each other, no adequate time-resolved simulation methods exist. This is due to the fact that non-equilibrium Green’s function methods are virtually restricted to the steady-state analysis for these devices, while in transient density matrix approaches, the coupling between the modes is difficult to take into account and has been neglected in the past. We resolve this issue by applying the coupled mode-space approximation to a tight-binding Hamiltonian and inserting into a Heisenberg equation of motion for the density matrix. The resulting equation is solved in real space without applying a Fourier transform, eliminating the need for restrictive discretization patterns. On the contrary, the discretization pattern of our proposed method directly follows from the Hamiltonian that is used. We validate the approach against reference results obtained by real-space and mode-space non-equilibrium Green’s function methods. We demonstrate accurate modeling of mode coupling, even in devices with abruptly changing channel geometries. Finally, the effects of a channel constriction of gate-all-around FETs in amplifier operation are studied, where, even though the current densities differ, similar amplifier behavior for the devices with and without constriction is seen
CHIPMUNK
No full text
CHIPMUNK is a library containing 95 million molecules derived from in silico reactions.
A common issue during drug design and development is the discovery of novel scaffolds for protein targets. On the one hand the chemical space of purchasable compounds is rather limited; on the other hand artificially generated molecules suffer from a grave lack of accessibility in practice. Therefore, we generated a novel virtual library of small molecules which are synthesizable from purchasable educts, called CHIPMUNK (CHemically feasible In silico Public Molecular UNiverse Knowledge base). Altogether, CHIPMUNK covers over 95 million compounds and encompasses regions of the chemical space that are not covered by existing databases. The coverage of CHIPMUNK exceeds the chemical space spanned by the Lipinski rule of five to foster the exploration of novel and difficult target classes. The analysis of the generated property space reveals that CHIPMUNK is well suited for the design of protein–protein interaction inhibitors (PPIIs). Furthermore, a recently developed structural clustering algorithm (StruClus) for big data was used to partition the sub-libraries into meaningful subsets and assist scientists to process the large amount of data. These clustered subsets also contain the target space based on ChEMBL data which was included during clustering.
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Data for: Activation of a Secondary-Messenger Receptor via Allosteric Modulation of a Dynamic Conformational Ensemble
No full textBacterial signaling cascades have recently become of great relevance in the context of bacterial antibiotics resistance. Cyclic diadenylate monophosphate (c-di-AMP) is a key bacterial secondary messenger involved in growth, biofilm formation, virulence gene expression and others. The activation mechanisms of c-di-AMP receptors like the trimeric PII-like proteins upon messenger binding have, however, remained elusive due the pivotal role of highly flexible protein regions. Here, using solution NMR spectroscopy to elucidate the interplay between the ordered and disordered structural elements of the apo and messenger-bound forms of the 44 kDa homotrimeric PII-like signal transduction protein A (PstA), we reveal a sensitive modulation of the conformational ensemble of those extended loops thought to bind the downstream interaction partners by messenger association at the receptor core. The orchestration of the spatial properties of the loops, despite their retained internal dynamics, reveals the importance of allosteric effects even for disordered structural elements, whose steerable ensemble properties have long escaped the classical structural-biology understanding
Raw Data for "A purely σ-Aromatic, Planar {Ge₄}2+-Ring"
No full textPROJECT DESCRIPTION:
We report the synthesis and isolation of a germanium analog of cyclobutadiene Y2Ge4 (Y = Ph2P(NMes)-C-PPh3), featuring two imino-tethered ylide substituents. The compound is obtained through the reduction of either chlorogermylene YGeCl or digermylene YGe-GeY. The Ge4 complex exhibits a planar Ge4 core with the ylide groups coordinating in a perpendicular fashion. Detailed computational analysis revealed an aromatic character, as evidenced by several aromaticity descriptors. This aromaticity arises exclusively from the delocalization of the σ-electrons, while all other electrons in the Ge4 core are localized in lone pairs. This electronic structure closely resembles that of all-metal clusters, thereby extending the concept of all-metal aromaticity to lighter main-group elements.
DATASET DESCRIPTION:
Single folders were created for all separate compounds, named with the corresponding number in the manuscript.
The folders contain subfolders with NMR, IR, and Elemental analysis data respectively. An additional folder labeled "Geometries" contains all optimized geometries from the computational analysis as xzy-files. These files are labeled with the corresponding number of the compound in the manuscript/supporting information.
Programs that can be used to open the data:
IR: The data can be opened with LabSolutions software from Rigaku (an open source alternative would be the software "OpenChrom"). Additionally, the data are provided as text-files and pdf-files.
NMR: Can be opened with MestReNova. Additionally, for compound 6, stacked NMR spectra are provided in the attached .pptx file. These spectras can be open directly in MestReNova by double-clicking on them within the presentation.
Elemental Analysis (EA): Can be opened with Adobe Acrobat Reader.
XPS: The raw data can be opened with MS-Excel. The analysis of the data can be opened with Origin software. Also, the documented analysis can be opened with the Adobe Acrobat Reader.
UV: The raw data can be opened with MS-Excel. The UV specturm in different concentrations can be opened with the Adobe Acrobat Reader.
xyz-files: standard text programs (word, editor etc.
Dataset for 'Structural evolution of gamma-antimonene nanoribbons on Ag(110)'
No full textDataset for the corresponding paper. We report on the structural and chemical evolution of antimony (Sb) monolayer on Ag(110). Used Methods: Low-Energy Electron Diffraction (LEED) and LEED-IV; angle-resolved X-ray Photoelectron Spectroscopy (XPS); Scanning Tunneling Microscopy (STM
Love Data Week 2026
No full textDieser Datensatz enthält die Vortrags-Folien des Forschungsdatenservice der TU Dortmund im Rahmen der Love Data Week 2026 vom 09. bis 13. Februar 2026.
09.02.2026: FDM-Curriculum der UA Ruhr
In diesem kurzen Vortrag wird das FDM-Curriculum der Universitätsallianz Ruhr vorgestellt. Außerdem bieten wir einen Überblick über die Enstehung und Kooperation in der standortübergreifenden Initiative.
Das Forschungsdatenmanagement-Curriculum der Universitätsallianz Ruhr bietet Ihnen ein breites Angebot an regelmäßig stattfindenden Grundlagenschulungen für verschiedene Fachausrichtungen und Zielgruppen sowie Vertiefungsveranstaltungen in englischer und deutscher Sprache. Unabhängig davon, ob Sie an der Ruhr-Universität Bochum, der Universität Duisburg-Essen oder der Technischen Universität Dortmund forschen - alle Schulungen sind für Sie geöffnet. Für die Teilnahme an einer Grundlagenschulung und zwei Veranstaltungen aus dem Vertiefungsmodul erhalten Sie das FDM-Badge.
10.02.206: TUDOnotes: TU Dortmund University's electronic notebook
In diesem kurzen Vortrag wird das Elektronische Notizbuch der TU Dortmund - TUDOnotes vorgestellt.
Elektronische Notizbücher und Laborbücher können einen transparenten und nachvollziehbaren Forschungsprozess durch eine digitale Dokumentation von Planung, Durchführung und Auswertung von Experimenten oder anderen Forschungsvorgängen unterstützen. In dieser Veranstaltung erfahren Sie, wie Sie TUDOnotes für Ihre Forschung nutzen können und haben die Möglichkeit, mit dem Forschungsdatenservice ins Gespräch zu kommen.
11.02.206: RDMO Consent Generator: Einwilligungserklärungen einfach, sicher und effizient
In diesem kurzen Vortrag wird der automatische Generator für Einwilligungserklärungen - ConsentGen vorgestellt.
Die Erstellung datenschutzkonformer Einwilligungserklärungen muss nicht kompliziert sein. Der neue Consent-Generator in RDMO unterstützt Forschende Schritt für Schritt bei der Erstellung – von den Projektinformationen bis zu den Datenschutz-Hinweisen.
Am Ende entsteht automatisch eine individuell angepasste Vorlage für Ihr Forschungsprojekt.
12.02.2026: Langzeitverfügbarkeit
Die Langzeitverfügbarkeit von Forschungsdaten widmet sich der Herausforderungen der dauerhaften Aufbewahrung von Forschungsdaten über einen Zeitraum von mehr als 25 Jahren. Dafür ist eine spezielle Aufbereitung sowie Aufbewahrung der Daten notwendig.
Der Forschungsdatenservice der TU Dortmund bietet den Dienst TUDOlzv an, der Forschende dabei unterstützt, ihre Daten so aufzubereiten, dass sie den Anforderungen der Lanzeitverfügbarkeit entsprechen. Dabei werden Unterstützung bei der Metdaten‑Dokumentation, geeigneten Formatwahl sowie der Einlieferung in TUDOlzv.
13.02.2026: Das Datenrepositorium TUDOdata
In diesem kurzen Vortrag wird das Forschungsdaten-Repositorium der TU Dortmund - TUDOdata - vorgestellt. Mit TUDOdata können Forschende der TU Dortmund ihre Daten speichern, austauschen, archivieren und veröffentlichen. In TUDOdata erhalten Datensätze eine DOI, werden durch Metadaten beschrieben und können mit einer Lizenz Open Access veröffentlicht werden.
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Supplementary Materials: Variable selection in linear mixed model meta-regression with suspected interaction effects - How can tree-based methods help?
No full text
Supplementary Material to the Manuscript
"Variable selection in linear mixed model meta-regression with suspected interaction effects - How can tree-based methods help?"
The files provided here constitute the Supplementary Material for the manuscript containing:
A PDF file containing all supporting tables and figures.
The code and results of the re-analysis of the meta-analysis by Kimmoun et al. (Section 3 of the manuscript)
Simulation study materials (Section 4)
All relevant files for the simulation study, in particular the simulated data and the R code on which the simulations are based (Section 4 of the manuscript).
The folder structure for the code and output files is explained in the Readme.txt file.
Authors:
Jan-Bernd Igelmann, ORCiD: 0009-0001-6994-4945a
Paula Lorenz, ORCiD: 0009-0001-1949-400Xa
Markus Pauly, ORCiD: 0000-0002-0976-7190a,b
a Department of Statistics, TU Dortmund University, Germany
b Research Center Trustworthy Data Science and Security, UA Ruhr, Germany
Contact:
Jan-Bernd Igelmann: [email protected]
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Electric energy usage measurements in offices during field trials of GEMS - Gamified Energy Monitoring as a Service
No full textOverview
This dataset contains three 4-week recordings of electrical power (watts) and energy consumption (watt-hours) from individual office workstations, consisting of about 15 million data points stored in .csv.gz files with a resolution of at least 30 seconds.
Background
This data was collected as part of the project "GEMS - Gamified Energy Monitoring as a Service" in the Master Computer Science at the TU Dortmund. We built a game-system that encouraged participants to conserve electric energy by making them aware of their energy usage in a simple to use interface. Additionally the system featured a competition to conserve energy, i.e. individual participants earned game points proportional to the energy they managed to save. The system was designed to be used by a group of people for 4 weeks. This dataset contains data of three of such groups.
To measure energy use, participants received one or more smart home power outlets (plugs), which they installed and connected to their appliances. Setups ranged from a whole desk (monitors and computer) on a single plug to more granular arrangements, such as a single monitor per plug. Some plugs were shared by all players - these “team plugs” were typically installed at shared printers, coffee machines, or electric kettles. Users were instructed to behave as usual in the first 7 days. From day 8 on, they were told to conserve power.
The game offered an opt-in to donate recorded energy data, which led to the creation of this dataset, released under the CC-BY 4.0 License.
Files
This dataset contains the following files:
measurements1.csv.gz - 3,962,675 rows. Recordings of one user with two smart plugs and two team smart plugs. Gathered from 2024-10-14 to 2024-11-11.
measurements2.csv.gz - 7,612,637 rows. Recordings of 11 users with 23 smart plugs total. Gathered from 2024-11-19 to 2024-12-17.
measurements3.csv.gz - 3,490,260 rows. Recordings of 7 users with 12 smart plugs total. Gathered from 2025-01-29 to 2025-02-26.
Note that there is a 2-hour data gap on 12 December 2024 (Measurement 2) due to a partial power outage.
File format
Each file is gzip-compressed CSV, using commas as separators. These are some of the first lines of measurements1.csv.gz:
time,value,unit,user,plug,category
2024-10-14T12:53:00.151409Z,1.9,W,team,plug1,Printer
2024-10-14T12:53:02.987258Z,12.3,W,user1,plug4,Computer
2024-10-14T12:53:02.987681Z,0.9400000000000005,Wh,user1,plug4,Computer
2024-10-14T12:53:04.38592Z,4.9,W,team,plug2,Screen
Each row contains either the measured power at that timestamp or the consumed energy from the beginning up to the timestamp.
time - Timestamp in ISO 8601 format at UTC+0.
value - The numeric value, either in watts or watt-hours.
unit - The type of recording, either power (unit = W) or energy usage (unit = Wh).
user - Anonymised user ID, i.e. "team" or "user1".
plug - Anonymised plug ID.
category - User-chosen category of the connected appliances.
Please note that while the timestamp is at UTC+0, the actual recording took place in the timezone Europe/Berlin. The anonymised IDs are only valid inside their specific datasets, i.e. "user1" in the first data set is not necessarily the same person as "user1" in dataset two. You can use the following code snippets, which we provide under the CC0 License, to read the data.
R with lubridate package:
df
# With UTC+0 timezone
dftime
# or alternatively with the original timezone:
# dftime <- lubridate::as_datetime(df$time, tz = "Europe/Berlin")
Python with pandas:
import pandas as pd
df = pd.read_csv(
"measurements1.csv.gz",
header = 0,
parse_dates = ["time"],
date_format = "ISO8601"
)
# Optional
df["time"] = df["time"].dt.tz_convert('Europe/Berlin')
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Replication Data for: Spectroscopic Characterization and Electrocyclic Ring Opening of Parent 2-Azetine
No full textPROJECT DESCRIPTION:
(N-Boc)-protected 2-azetine serves as a suitable precursor for the generation of parent 2-azetine. High-vacuum flash pyrolysis experiments at 200 °C induce the deprotection of the (N-Boc)-protected 2-azetine resulting in the release of carbon dioxide and isobutene allowing the trapping of 2-azetine in a solid argon matrix at 3 K and its spectroscopic characterization by infrared spectroscopy. The assignment is supported by isotopic labeling experiments and excellent agreement with anharmonic vibrational frequencies calculated at the B2PLYP-D3/def2-TZVPP level of theory. At elevated pyrolysis temperatures, 2-azetine undergoes selective ring opening to yield 1-azabutadiene, for which both s-trans-(E) and s-trans-(Z) conformers are observed. Computed reaction pathways and free-energy profiles rationalize the temperature-dependent product distribution and the observed torquoselectivity. These results provide fundamental insight into the structure, stability, and reactivity of 2-azetine and establish matrix isolation as a powerful approach for accessing highly strained nitrogen heterocycles.
DATASET DESCRIPTION:
This dataset contains experimental IR and NMR spectra and optimized geometries published in the main manuscript and Supporting Information (SI).
IR labeled as 'Figure...' are presented in the appropriate figures in the main text or the SI. All captions follow the structure: Figure → compound name_number→ Matrix → (process). All NMR Spectra are labeled as 'Figure...' and the caption follows the structure: '1H/13C_NMR_compound-name'.
Optimized geometries including electronic energies and thermal corrections are provided in the Coordinates_Energies.xyz file