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Citizens’ Perception of Social Media in Emergencies: Insights and Trends from German Representative Samples from 2017 to 2021 [Data Set]
No full textThis repository contains the dataset and codebook of a representative german survey on citizens' attitudes and perceptions of social media use in emergencies. Please cite the original paper when using this data: "Reuter, C., Kaufhold, M. A., Biselli, T., & Pleil, H. (2023). Increasing Adoption Despite Perceived Limitations of Social Media in Emergencies: Representative Insights on German Citizens’ Perception and Trends from 2017 to 2021. International Journal of Disaster Risk Reduction, 96, 103880.
Voltage induced damage progression on the raceway surfaces of thrust ball bearings, Part II: High Speed Measurements
No full textThis set of data is obtained by André Harder during his experiments on the progression of voltage induced bearing damages. The experiments were made on the bearing test rig at the Institute of Product Development and Machine Elements of the Technical University of Darmstadt in the time between December 2021 and March 2022. Thrust ball bearings were tested to investigate the damage progression on the bearing surface without destroying the bearing. The description of the test parameters, the test procedure and the explanation of the naming convention of the data can be found in the first part of the published data (see related sources). This set of data gives three additional domains of the measured data. The high speed radial and axial acceleration measurements obtained every minute of the test run are part of the A-domain. The high speed measurements of the electric voltage applied on the bearing and the corresponding electric current are part of the E-domain. Lastly, a measurement of the electric impedance of the bearing was obtained, before applying the damaging electrical signal. These measurements are part of the I-domain
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling: Implementations of the estimates and links to image data sets
No full textThis repository contains the Python code required to reproduce the simulation part of the paper "Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling" from Walter (2023) referenced below. The Python version used is Python 3.9.7. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 449102119. The Cifar-10 image dataset consisting of the real images from which the classes "dogs" and "cats" were used can be downloaded from the link given below. In the Techincal Report "Learning Multiple Layers of Features from Tiny Images" from Alex Krizhevsky (2009) (for a link see below) this dataset of real images is described in more detail. Also for the SVHN dataset by Netzer et al. (2011), links for download and a link to the corresponding paper are given below
Figure 3 Voltage loss curves
No full textFigure 3: Voltage losses obtained for the unmodified and for the N-doped Pt/C catalysts in GDE and in MEA characterization at low and high I/C ratio compared to an I/C ratio of 0.5. The data presented in A) was obtained by subtracting the polarization curve of the respective catalyst in the respective cell type shown in Figure 2B by the one shown in Figure 2A. The data presented in B) was obtained by subtracting the polarization curve of the respective catalyst in the respective cell type shown in Figure 2B by the one shown in Figure 2C
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
No full textWe propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. In contrast to previous work, we abandon the use of computationally involved adversarial objectives, network ensembles and style transfer. Instead, we employ standard data augmentation techniques − photometric noise, flipping and scaling − and ensure consistency of the semantic predictions across these image transformations. We develop this principle in a lightweight self-supervised framework trained on co-evolving pseudo labels without the need for cumbersome extra training rounds. Simple in training from a practitioner's standpoint, our approach is remarkably effective. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios
Listing 7 & 8
No full textWorking example for listings 7 & 8 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report
Drop impact on a sticky porous surface with gas discharge - Supplementary material
No full textThis collection contains the raw data (Microsoft Excel xlsx-format) for the plots as well as the computational finite-element model (Comsol Multiphysics mph-format and model description in html) for the following publication:
Weimar, L., Hu, L., Baier, T., & Hardt, S. (2022). Drop impact on a sticky porous surface with gas discharge: transformation of drops into bubbles. Journal of Fluid Mechanics, 953, A6. https://doi.org/10.1017/jfm.2022.921
Investigation and comparison of resin materials in transparent DLP-printing for application in cell culture and organs-on-a-chip - secondary data, research data
No full textThis submission contains csv and opju data which is used to create the diagrams in the publication "Investigation and comparison of resin materials in transparent DLP-printing for application in cell culture and organs-on-a-chip", DOI 10.1039/d1bm01794b, as well as raw measurement data, sorted in folders according to the figures in the publication
Datensatz - Künstliche Intelligenz im Studium Eine quantitative Befragung von Studierenden zur Nutzung von ChatGPT & Co.
No full textDas Dokument beinhaltet einen Datensatz zu einer deutschlandweite Befragung von Studierenden, die das Nutzungsverhalten im Umgang mit KI-basierten Tools im Rahmen des Studiums und Alltags erfasst. Insgesamt haben über 6300 Studierende an der anonymen Befragung teilgenommen. Teil der Befragung war zunächst ein Choice-based Conjoint-Experiment (CBC), bei dem die Teilnehmenden acht fiktive Kaufentscheidungen unter der Auswahl von je zwei Angeboten treffen mussten. Darüber hinaus beinhaltet die Befragung das Themengebiet der Nutzung von KI-basierten Tools im Rahmen des Studiums. Die Fragen beziehen sich dabei auf die Nutzungsintensität von KI im Bereich des Studiums, aber auch im privaten und beruflichen Kontext, sowie auf konkrete Einsatzbereiche von KI-basierten Tools. Darüber hinaus wird nach konkreten KI-Tools gefragt, die von den Teilnehmenden bereits genutzt wurden. Abgeschlossen wird der Fragebogen mit studienbezogenen (Abschluss, Studienbereich, Bundesland) und soziodemografischen (Geschlecht, Alter) Items. Die digitale Umsetzung des Messinstrumentes wurde mit dem XM Paket des Umfrage- und Statistik-Tool qualtricsXM durchgeführt. Eine erste deskriptive Auswertung der Studie ist zu finden unter: https://doi.org/10.48444/h_docs-pub-39
Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals
No full textThis is the resource for the dataset and models released as a part of our EMNLP 2023 paper "Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals"v