# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
A Data Trust Office based at TU Dresden for the Saxon scientific landscape.
Website: https://dd-trust.zih.tu-dresden.de

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
A Data Trust Office based at TU Dresden for the Saxon scientific landscape

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Data publications from research of Dresden University of Applied Sciences.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Data publications from research of Dresden University of Applied Sciences

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Publish source code or a piece of software

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Publish source code or a piece of software

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Publish source code or a piece of software

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Publish source code or a piece of software

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Publish source code or a piece of software

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
TU Bergakademie Freiberg Data Publications

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Data publications from research of Freiberg University of Mining and Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Data publications from research of Freiberg University of Mining and Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
TU Bergakademie Freiberg Data Publications

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Data publications from research of Dresden University of Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Data publications from research of Dresden University of Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Data publications from research of Leipzig University.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Data publications from research of Leipzig University

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Hochschule für Technik und Wirtschaft Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Dresden University of Applied Sciences.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Dresden University of Applied Sciences

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Hochschule für Technik und Wirtschaft Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Freiberg University of Mining and Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Freiberg University of Mining and Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Dresden University of Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Dresden University of Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Leipzig University.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Leipzig University

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
ListDB RepThree: Stationary camera videos and trajectories at a signalized 3-way intersection taken on different Wednesdays over a 3-year period (2019, 2020 with Covid-19 lockdown, 2022)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TableOfContents[].value
- Sample video data
- Sample trajectory data 
- Video data 
- Trajectory data 
- ListDBApp to collect metadata
- ListDB codebook to specify the metadata

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TechnicalInfo[].value
Videos containing traffic observation (.mp4)
Mined trajectories (.csv; .tlgx)
ListDBApp to collect metadata (.apk)
Text files containing metadata (.txt; .csv)
Codebook specifying the metadata (.pdf)

The videos were collected using consumer cameras (GoPro Hero Session 4; DJI Action 2). The metadata was collected using the ListDBApp (see .apk). The source trajectory data was extracted from video using DataFromSky TrafficSurvey (https://ai.datafromsky.com/aerial) - an AI video analytics-based service for gathering advanced traffic data for researchers and traffic engineers.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
ListDB: Representative datasets of video-based traffic observations
Representative datasets of video-based traffic observations according to the ListDB specification (w3id.org/listdb/). 
We collected the videos mainly at different intersections in Germany following a fixed sampling plan to ensure representativity.
We describe the sampling plan and the collection procedure in the following paper: 
Maximilian Bäumler, Matthias Lehmann, Günther Prokop (2023). "Generating representative test scenarios: The "Fuse for Representativity" (FUSE4Rep) process model to collect and analyse traffic observation data". In: 27th ESV Conference. Yokohama. Paper NO.23-0122-O

The paper is available here: https://index.mirasmart.com/27esv/PDFfiles/27ESV-000122.pdf

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The dataset can be used to study road traffic before, during, and after Covid-19. Moreover, the dataset can help to derive test scenarios for scenario-based testing, for example. 

The dataset includes seven days of drone videos and their corresponding trajectories recorded in Dresden, Germany, at a signalized 3-way intersection. Data collection in 2020 also took place on days with Covid-19 conditional lockdown, including curfew. The data collection followed a fixed sampling plan covering four different time slots daily (weekdays: 07.30-09.00 / 10.00-11.30 / 13.00-14.30 / 15.30-17.00) within 2019-2022. Moreover, extensive metadata is provided following the ListDB specification (w3id.org/listdb). Source trajectory data was extracted from video using DataFromSky TrafficSurvey (https://ai.datafromsky.com/aerial) - an AI video analytics-based service for gathering advanced traffic data for researchers and traffic engineers. Main facts: 1056 minutes of video (2019: 160 minutes / 2020: 560 minutes / 2022: 336 minutes). The analysed trajectories are available for each video.
.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
ListDB RepThree: Stationary camera videos and trajectories at a signalized 3-way intersection taken on different Wednesdays over a 3-year period (2019, 2020 with Covid-19 lockdown, 2022)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title.alternative[].value
Dresden (Germany): Sidonienstrasse / St. Petersburger Strasse

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionObject.Other[].value
Traffic observation videos of an intersection

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The ListDB provides a uniform standard for collecting and describing video-based traffic observations, e.g. using drones. 
This project provides selected data sets, i.e. recorded videos including metadata.   

|| (German) Publication: https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-833252
|| ListDB Gitlab: w3id.org/listdb

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.fundingAcknowledgement[].value
We sincerely thank DataFromSky for supporting the analysis. The video-based traffic observation was conducted within the MOTUS project (19FS2015A; motus-project.com) and supported by the mFUND grant by Germany’s Federal Ministry for Digital and Transport

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
ListDB - Leverage information on street traffic

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Data set to the publication Vertical partitioning of CO2 production in a forest soil (SUBSOM)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.relatedItem.IsSupplementTo[].value
Wordell-Dietrich, P., Don, A., Wotte, A., Rethemeyer, J., Bachmann, J., Helfrich, M., Kirfel, K., & Leuschner, C. (2019). Vertical partitioning of CO2 production in a Dystric Cambisol. Biogeosciences Discussions, April, 1–27. https://doi.org/10.5194/bg-2019-143

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
The dataset contains:
1) measurements of volumetric water content, soil temperature, carbon dioxide in three soil profiles in a beech forest in Germany
2) isotopic data of carbon dioxide in the soil atmosphere
3) diffusion parameters

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The archive contains 2 years monitoring data of volumetric water content, soil temperature and carbon dioxide concentrations of three soil profiles.
It contains data on volumetric water content, soil temperature and carbon dioxide concentration from three subsoil observatories. It also contains data on soil respiration measurements, isotopic data of the soil atmosphere and diffusion parameters for CO2 flux modelling. The file column description contains information on the headings and units for each data file

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Data set to the publication Vertical partitioning of CO2 production in a forest soil (SUBSOM)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
temperature and moisture sensor - UMP-1, Umwelt-Geräte-Technik GmbH, Germany

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
PTFE suction cups - ecoTech Umwelt-Meßsysteme GmbH, Germany

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
solid-state infrared gas sensors -GMP221, Vaisala Oyi, Finland

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
gas chromatograph - Agilent  7890A, Agilent Technologies, USA

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
isotope ratio mass spectrometer - Delta Plus with GP interfaces and GC-Box, Thermo Fisher Scientific, Germany

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
respiration chamber - EGM-3 SRC-1. PP-Systems, USA

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
respiration chamber - LI-6400-09, LI-COR Inc., USA

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Raw data of the cylinder tests for determining compressive strength and modulus of elasticity

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
This collection contains the raw data of the tests on cylindrical specimens for determining the compressive strength and the modulus of elasticity of each concrete batch.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
This dataset contains the raw data of the tests on concrete cylinders to determine the compressive strength and the modulus of elasticity of the different batches. Two concrete mixes were investigated (C30 and C80). The batch is referenced by a roman number (I to VI). Three cylinder specimens were tested from each batch. The first was loaded with continously increasing force until failure. The ultimate load was used to determine the load levels of the tests for the modulus of elasticity according to DIN EN 12390-13, method B, for the tests on the other two specimens. After carrying out these two tests, both specimens were also loaded with increasing force until failure. Following channels are used in each file:
Zeit (Standardmessrate) [time (standard measuring rate)] in s, Zeit (langsame Messrate) [time (slow measuring rate)] in s, Kraft [force] in kN, Maschinenweg [machine displacement] in mm, Laser bei Umfang [displacement measured by laser sensor at circumference] 0° in mm, Laser bei Umfang [displacement measured by laser sensor at circumference] 120° in mm, Laser bei Umfang [displacement measured by laser sensor at circumference] 240° in mm, Lufttemperatur [relative humidity, related to the slow measuring rate] in °C, DD1_1 und DD1_2 (LVDT, placed at middle specimen height at 0° and 180°, measuring length 150 mm) in mm

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Raw data of the cylinder tests for determining compressive strength and modulus of elasticity

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Beton weist unter zyklischen Lasten ein komplexes Verformungsverhalten auf. Dieses beeinflusst die Spannungsverteilung auf der Querschnittsebene von Betonbauteilen und wirkt sich positiv auf deren Lebensdauer aus. Das normative Ermüdungsnachweiskonzept prognostiziert die Lebensdauer von Betonbauteilen auf der Grundlage von Wöhlerlinien. Die Verformungsentwicklung des Betons und die Spannungsumlagerungen auf der Querschnittsebene finden nahezu keine Berücksichtigung, wodurch ermüdungsbeanspruchte Betonbauwerke gemäß dem derzeitigen Nachweiskonzept nicht wirtschaftlich bemessen werden.Das Ziel des Forschungsvorhabens ist das Verformungsverhalten und im speziellen die Spannungsumlagerungen von ermüdungsbeanspruchten Betonbauteilen zu untersuchen. In diesem Rahmen werden sowohl FE-Simulationen als auch experimentelle Versuche durchgeführt. Für die Simulationen wird ein am Institut für Massivbau der Leibniz Universität Hannover entwickeltes Dehnungsmodell für ermüdungsbeanspruchten Beton zugrunde gelegt. Die experimentellen Untersuchungen werden auf dem Resonanzversuchstand des Instituts durchgeführt. Dieser weltweit einmalige Versuchsstand ermöglicht die Untersuchung von Betonbalken im Very High Cycle Fatigue-Bereich, also bis in einen Lastwechselbereich von mehr als 1•10^7 Lastwechseln. Dies ist für die Versuchsdurchführung von wesentlicher Bedeutung, da die Spannungsumlagerungen sehr hohe, ertragbare Lastwechselzahlen bei den Betonbalken bedingen.Die numerischen Simulationen werden anhand der experimentellen Untersuchungen verifiziert. Auf dieser Grundlage können allgemeingültige Aussagen zu dem Verformungsverhalten und den Spannungsumlagerungen von ermüdungsbeanspruchten Betonbauteilen getroffen werden sowie neue Bemessungsvorschläge formuliert werden. Die Erkenntnisse des Vorhabens dienen der Bemessung dynamisch beanspruchter Bauwerke wie Windenergieanlagen oder Eisenbahnbrücken des Hochgeschwindigkeitsverkehrs. Sie ermöglichen den Schritt in Richtung einer realitätsnäheren Bemessung und damit schlankeren, wirtschaftlicheren und kostengünstigeren Ausführung von ermüdungsbeanspruchten Betonbauwerken.

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Strain curves measured with different DFOS types and varying spatial resolution by an ODiSI 6108

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
A tsv file with associated metadata is stored for each of the individual combinations of displacement step (1-14 mm), gage pitch (0.65 mm, 1.30 mm, 2.60 mm, 5.20 mm) and DFOS type (ACR, ES, ORM, V9). This makes a total of 14 x 4 x 4 = 224 files. The files were processed using Python.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Based on the principle of coherent optical frequency domain reflectometery (c-OFDR), cracks in concrete structures can be precisely localized and their widths determined due to the high spatial resolution in the sub-millimeter range. However, with longer distributed fiber optic sensors (DFOS) lengths, as is common in structural health monitoring, the spatial resolution is reduced. Experimental studies were carried out to investigate the influence of the spatial resolution on the measurement quality in the area of large strain gradients. Four different DFOS, two robust and two filigree DFOS types, were subsequently installed on a 4 m long reinforced concrete beam. In a displacement controlled 4-point bending test, crack widths of up to 0.5 mm were measured with four different gage pitch (gp).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Strain curves measured with different DFOS types and varying spatial resolution by an ODiSI 6108

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The goal of the IDA-KI research project is to develop methods for automated analysis and evaluation of monitoring data. The validation is carried out using real measurement data acquired on a 45 m long research bridge (openLAB). First, the reference condition of the bridge is measured with and without service loads over a period of one year. Subsequently, targeted load tests will be performed up to the severe damage state. At the same time, redundant sensors will be damaged or manipulated to create a real database with specific signal characteristics for metrological and structural anomalies. This will provide an important basis for the development of evaluation methods (also based on machine learning), so that measurement faults in monitoring data can be corrected in the future and differentiated from structural damage.

Further information can be found in: https://onlinelibrary.wiley.com/doi/10.1002/best.202300094

Project title: IDA-KI (Automated assessment of monitoring data for infrastructure constructions using AI and IoT)
Funding: Federal Ministry for Digital and Transport, Germany, within the innovation program mFUND
Funding reference: 19FS2013A

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Ultrafast switching of trions in 2D materials by terahertz photons

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
External control of optical excitations is key for manipulating light–matter coupling and is highly desirable for photonic technologies. Excitons in monolayer semiconductors emerged as a unique nanoscale platform in this context, offering strong light–matter coupling, spin–valley locking and exceptional tunability. Crucially, they allow electrical switching of their optical response due to efficient interactions of excitonic emitters with free charge carriers, forming new quasiparticles known as trions and Fermi polarons. However, there are major limitations to how fast the light emission of these states can be tuned, restricting the majority of applications to an essentially static regime. Here we demonstrate switching of excitonic light emitters in monolayer semiconductors on ultrafast picosecond time scales by applying short pulses in the terahertz spectral range following optical injection. The process is based on a rapid conversion of trions to excitons by absorption of terahertz photons inducing photodetachment. Monitoring time-resolved emission dynamics in optical-pump/terahertz-push experiments, we achieve the required resonance conditions as well as demonstrate tunability of the process with delay time and terahertz pulse power. Our results introduce a versatile experimental tool for fundamental research of light-emitting excitations of composite Bose–Fermi mixtures and open up pathways towards technological developments of new types of nanophotonic device based on atomically thin materials.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Ultrafast switching of trions in 2D materials by terahertz photons

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Updated structural equation model (SEM) used in the manuscript "Prokaryotic community composition and extracellular polymeric substances affect soil microaggregation in carbonate containing semiarid grasslands"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TableOfContents[].value
Text file containing R script (SEM_model.txt) and text file containing dataset (Dataset_SEM.txt)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TechnicalInfo[].value
Data Processing: Data to run the script is given in the file Dataset_SEM.txt. Additional details on the data set is given in the excel file in the same collection (see tab Dataset_SEM).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
Text file containing R script (SEM_model.txt) and text file containing dataset (Dataset_SEM.txt).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Structural equation model used in the manuscript "Prokaryotic community composition and extracellular polymeric substances affect soil microaggregation in carbonate containing semiarid grasslands" (Accepted and published in Frontiers in Environmental science - Soil Processes). Details on the data and its individual parameters are provided in the excel file in the same collection.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Updated structural equation model (SEM) used in the manuscript "Prokaryotic community composition and extracellular polymeric substances affect soil microaggregation in carbonate containing semiarid grasslands"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Data / results obtained from the first sampling campaign on the selected sites in Rambla Honda and near the town of Alboloduy, Almeria Spain.