1167 research outputs found
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DFG Sachbeihilfe 517342666 - Ähnlichkeitsanalyse schwingender Platten mit Dämpfung (Adams, Cardellino)
Full text linkProjektdaten aus der Sachbeihilfe 517342666 |
Rückfragen bitte an Christian Adams richten: https://orcid.org/0000-0002-7307-87441.
Interactive Survey Feedback and Heaping
No full textProcessed data set from a field-experimental study embedded in a web survey of the general internet population in Germany. The aim was to test the effect of survey feedback on the reporting of round numbers in open-ended numeric questions, controlling for device type and potential confounding variables. To guarantee anonymization, only recoded variables based on the original open-ended responses are included
Fluids Journal Demonstrator Corpus
No full textA demonstrator corpus containing 2077 articles from the journal Fluids
(Publisher MDPI, ISSN 2311-5521) in XML-TEI format.
The corpus was created as part of the NFDI4ING S7 project that collects, for
text and data mining, literature relevant for engineering sciences. This
collection was performed using the infrastructure und software stack build in
the "Workflow Digitale Medien" project at the University and State Library
Darmstadt. JATS-XML files provided by the publisher were automatically
converted to the TEI-XML files that are based on an application profile of the
XML specification of the Text Encoding Initiative.
The XML-TEI files can also be retrieved via the following REST API endpoints
of the eXist wdb+ system.
1\. This query provides an overview of all volumes of the journal Fluids in
eXist wdb+: https://exist.ulb.tu-
darmstadt.de/2/r/edoc/collection/jz000014.json
2\. Using the IDs of the individual volumes, the IDs of the articles contained
in these volumes can be obtained with the following query. The query is an
example for volume 7 with the ID jz000102: https://exist.ulb.tu-
darmstadt.de/2/r/edoc/collection/jz000102.json
3\. The articles can be downloaded in XML-TEI format using their IDs from the
response above. The following query can be used to download the article with
the ID jz000102-0010: https://exist.ulb.tu-darmstadt.de/2/g/jz000102-001
Passive scalar DNS data of a turbulent round jet flow at Re=3500
No full textThis item contains the passive scalar moments up to the 10th order, velocity-scalar moments up to the 6th order and the probability density function of a passive scalar of a DNS of a spatially evolving turbulent round jet flow at a Reynolds number of 3500 based on the orifice diameter and the bulk velocity at the orifice
Ergänzendes Material: Expertensysteme zur Identifikation und Bewertung von Energieeffizienzpotenzialen in der Fertigung
No full textErgänzendes Material zur Dissertation „Ioshchikhes, Borys: Expertensysteme zur Identifikation und Bewertung von Energieeffizienzpotenzialen in der Fertigung“. Datensätze, Software, Abbildungen
Dataset for automated material flow characterization of shredded WEEE: RGB-camera-based object images and mass data
No full textThree datasets containing data from particles of shredded WEEE, including ferrous metals, non-ferrous metals, plastics and printed circuit boards in two particle size ranges of 12.5 mm - 25 mm and 25 mm - 50 mm, split into image data and mass data.
- Dataset 1 contains images that were used to train and test convolutional neural networks to identify the four material types through image classification, object detection, and instance segmentation. Additionaly, the total mass per material type and particle size range is included.
- Dataset 2 contains images and corresponding particle masses that were used in the training and testing of regression models for particle mass prediction.
- Dataset 3 contains images of particles from three predefined mixed samples, together with the total mass per material type and particle size range in each sample.
The images were recorded with an industry-sized sensor-based sorting machine (Sesotec Varisort Compact [Schoenberg, Germany]) at the pilot-scale sorting plant at Fraunhofer IWKS in Alzenau. Computer vision was used to extract individual particles from the recorded image stream.
The masses were recorded using a precision balance (KERN & SOHN EWJ 3000-2 [Balingen, Germany]) at the output conveyor
Hotend Force Data and Acquisition Code for "Force-Based Process Monitoring in Extrusion-Based Additive Manufacturing"
No full textThis dataset contains synchronized force measurements recorded at the hotend during extrusion-based additive manufacturing (FFF 3D printing), along with the Python code used for data acquisition. The data were collected using specially designed test specimens to investigate the influence of misconfigured print parameters—such as extrusion multiplier, print speed, nozzle temperature, line width, and layer height—on the acting hotend forces. The dataset supports the study "Force-Based Process Monitoring in Extrusion-Based Additive Manufacturing" and provides a foundation for further research into process monitoring and control based on real-time force feedback
M-Stance: A Multi-Target, Multilingual and Multi-Cultural Stance Detection Dataset towards EU Refugee Crisis
No full textM-STANCE is a multilingual, multi-target and multi-cultural stance detection (SD) dataset. It covers social media posts from 2014 and 2019 related to the migration crisis in EU and covers four languages: English, German, Italian and Polish. In this dataset, we first propose a list of broad targets, which are further subcategorized into fine-grained targets. The fine-grained targets cover the fine-grained aspects of each broad targets. For instance, "refugees" is the fine-grained target of broad target "migrants". The targets of the dataset is annotated by LLM and the stance is annotated by humans.
We also conducted cross-cultural annotation between polish and English-German on both directions. In cross-cultural annotation, the posts in the source language are translated to the target language and annotated by speakers of the target language. The resulting cross-culturally annotated dataset can be found under the directory *x-culture*.v1.0.
Crosskonkordanz zwischen DFG-Fächern 2024 und DNB-Sachgruppen
No full textDie Crosskonkordanz bildet die Fächer der Fachsystematik der Deutschen
Forschungsgemeinschaft (DFG) (Stand 2024-2028) auf die DDC-Sachgruppen (Stand
2004) ab, wie sie in der Deutschen Nationalbibliografie (DNB) verwendet
werden.
Diese Konkordanz ist gedacht für die automatisierte Zuordnung von Ressourcen,
die nach dem einen System erschlossen sind, zum jeweils anderen System. Diese
Zuordnungen sind nicht eins-zu-eins möglich, insbesondere da die DNB-
Sachgruppen meist weniger differenziert sind als die DFG-Fächer.
Nützlich ist die Crosskonkordanz vor allem für eine Zuordnung von nach DFG-
Fächern klassifizierten Ressourcen zu den DNB-Sachgruppen, wie sie z.B. vom
[DINI-Zertifikat](https://dini.de/dienste-projekte/dini-zertifikat/) gefordert
wird.
Die Crosskonkordanz liegt in den Dateiformaten .csv (beide Richtungen) und
.xlsx sowie .json (von DFG auf DNB) vor. Ein Vorschlag zur Überführung von
Fächern aus der DFG-Klassifikation 2020-2024 in die Klassifikation 2024-2028
ist enthalten
2025_Bruns_MaterDes_CrackSuppression
No full textResearch and raw data to publication: https://doi.org/10.1016/j.matdes.2025.11372