1167 research outputs found
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Empirische Analyse der Anforderungen und Gestaltungselemente der traceability-gestützten CO2-Bilanzierung
No full textDer Datensatz enthält die Ergebnisse einer strukturierten Online-Befragung, die zur Bewertung der Praxisrelevanz der Anforderungen und Gestaltungselemente für das Gestaltungsmodell zur traceability-gestützten CO2-Bilanzierung durchgeführt wurde. Insgesamt wurden 40 Personen befragt. Die Datei enthält mehrere Tabellenblätter. Das Tabellenblatt Rohdaten beinhaltet die unbearbeiteten Antworten aus der Online-Befragung. Im Tabellenblatt Stichprobe wird eine Auswertung zur Charakterisierung der Teilnehmenden durchgeführt. Zwei weitere Tabellenblätter zeigen Auswertungen zur Vorerfahrung der Teilnehmenden in den Bereichen Digitalisierung und CO2-Bilanzierung. Das Tabellenblatt Gestaltungsdimensionen zeigt die Auswertung zur Praxisrelevanz auf Basis der Bewertung durch die Teilnehmenden. Die Ergebnisse werden im Tabellenblatt Visualisierung für die Darstellung in der zugehörigen Forschungsarbeit aufbereitet
AFM - One-line measurements - Treated Data (Igor files)
No full textThe first Igor Pro file contains the post-processed data from stitched AFM images originally acquired in PeakForce Tapping mode. The images were imported and analyzed using Gwyddion, where cross-sectional analysis was performed on the height, adhesion, dissipation, and Young’s modulus channels. Each cross-section was averaged with 128 adjacent profiles to improve signal quality. The height profile was further normalized to its initial value of 486 nm, corresponding to the reference condition at 5.7% relative humidity (RH). This file contains both the raw channel data and the processed, normalized profiles used for further analysis.
The second contains force–distance data extracted from PeakForce Tapping mode measurements on the polymer edge, acquired during one-line mode scans at controlled humidity conditions. Specifically, it includes two representative force–distance curves recorded at 70% and 90% relative humidity (RH). The file provides the raw and processed force data used to illustrate humidity-dependent changes in surface mechanical response, such as adhesion and stiffness. These curves were selected for their clarity and typical behavior and were plotted for direct comparison to visualize the impact of RH on the tip–sample interaction
Unterstützungsevaluation des TAS
No full textDie vorliegende Datei zeigt die erhobenen Daten im Zuge der Unterstütungsevaluation auf. Befragt wurden Experten aus der Industrie hinsichtlich Nutzbarkeit und Zufriedenheit mit dem TAS
Podoportation - dataset
No full textData recorded during the experiment. For additional information see the readme file within
Mapping der DFG-Fachsystematik (2024-2028) auf die Destatis Personal- und Stellenstatistik (2023)
No full textDas Mapping bildet die Fächer der Fachsystematik der Deutschen
Forschungsgemeinschaft (DFG) (Stand 2024-2028) auf die Klassifikation der
Destatis Personal und Stellenstatistik an Hochschulen (Stand 2023) ab.
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, da sich die Differenzierung der
beiden Klassifikationen unterscheidet.
Ein möglicher Anwendungsfall ist die Überführung von DFG Fächern im Rahmen
eines Forschungsinformationssystem (FIS) in die entsprechenden Destatis
Stellen.
Die Crosskonkordanz liegt in den Dateiformaten .csv und .xlsx vor
Simulated Raman libraries of gaseous CO, H2, N2, O2, CO2, and H2O for high-temperature diagnostics
No full textThis dataset accompanies the journal article “Simulated Raman libraries of gaseous CO, H₂, N₂, O₂, CO₂, and H₂O for high-temperature diagnostics”. It provides simulated Raman line lists for key isotopologues relevant to reactive flow applications: ¹²C¹⁶O, ¹³C¹⁶O, H₂, HD, D₂, ¹⁴N₂, ¹⁴N¹⁵N, ¹⁶O₂, ¹⁶O¹⁸O, ¹²C¹⁶O₂, ¹³C¹⁶O₂, ¹⁶O¹²C¹⁷O, ¹⁶O¹²C¹⁸O, H₂¹⁶O, HD¹⁶O, and D₂¹⁶O. Additionally, line lists for mixtures reflecting natural isotopic abundances are included. All data are provided as zipped .csv files in 1 K increments from 250 K to 2500 K. Each file contains transition wavenumbers, associated photon counts in both [XX] and [XY] polarization configurations, and other relevant quantum numbers where applicable
Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
No full textUnsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global semantic categories within an image corpus without any form of annotation. Building upon recent advances in self-supervised representation learning, we focus on how to leverage these large pre-trained models for the downstream task of unsupervised segmentation. We present PriMaPs – Principal Mask Proposals – decomposing images into semantically meaningful masks based on their feature representation. This allows us to realize unsupervised semantic segmentation by fitting class prototypes to PriMaPs with a stochastic expectation-maximization algorithm, PriMaPs-EM. Despite its conceptual simplicity, PriMaPs-EM leads to competitive results across various pre-trained backbone models, including DINO and DINOv2, and across different datasets, such as Cityscapes, COCO-Stuff, and Potsdam-3. Importantly, PriMaPs-EM is able to boost results when applied orthogonally to current state-of-the-art unsupervised semantic segmentation pipelines
Real-world misleading visualizations QA dataset
No full textThe real-world misleading visualization QA dataset accompanies the paper "'Protecting multimodal large language models againts misleading visualizations". The dataset contains 42 multiple-choice QA pairs, and the URLs to the 42 corresponding chart images used to answer the questions. The dataset is made available under a CC-BY-SA-4.0 license. Please cite our paper if you find this dataset useful to your work.1.
Missci: Reconstructing Fallacies in Misrepresented Science
No full textDataset, fact-checking articles and human analysis for the publication "Missci: Reconstructing Fallacies in Misrepresented Science" (ACL 2024
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding
No full textDespite LiDAR (Light Detection and Ranging) being an effective privacy-preserving alternative to RGB cameras to perceive human activities, it remains largely underexplored in the context of multi-modal contrastive pre-training for human activity understanding (e.g., human activity recognition (HAR), retrieval, or person re-identification (RE-ID)). To close this gap, our work explores learning the correspondence between LiDAR point clouds, human skeleton poses, IMU data, and text in a joint embedding space. More specifically, we present DeSPITE, a Deep Skeleton-Pointcloud-IMU-Text Embedding model, which effectively learns a joint embedding space across these four modalities. At the heart of our empirical exploration, we have combined the existing LIPD and Babel datasets, which enabled us to synchronize data of all four modalities, allowing us to explore the learning of a new joint embedding space. Our experiments demonstrate novel human activity understanding tasks for point cloud sequences enabled through DeSPITE, including SkeletonPointcloudIMU matching, retrieval, and temporal moment retrieval. Furthermore, we show that DeSPITE is an effective pre-training strategy for point cloud HAR through experiments in MSR-Action3D and HMPEAR.Models, Experiments, Dat