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bonndata is the institutional, cross-disciplinary research data repository for the publication of research data for all researchers at the University of Bonn.
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311 to 320 of 5,303 Results

Krater_5806_model.zip Jan 27, 2026 - Attic Red-Figure Krater in the Museum Alte Kulturen Tübingen (Inv. 5806) ZIP Archive - 130.0 MB - MD5: 89466b08a27a7cddd76350af26b14b60
Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380) Jan 27, 2026 - Museum Alte Kulturen Günzel, Adriana, 2026, "Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380)", https://doi.org/10.60507/FK2/IIUH2Q, bonndata, V1 Attic Red-Figure Chous in the Museum Alte Kulturen (University of Tübingen, Inv. 1380) showing a youth with a chous pulling a cart with a hare. Date: -430 to -420 BC.
Chous_1380_images.zip Jan 27, 2026 - Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380) ZIP Archive - 2.7 GB - MD5: 0022e89fc87b66db12a0082584057726
Chous_1380_model.zip Jan 27, 2026 - Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380) ZIP Archive - 83.5 MB - MD5: bcb62424f6888b54d0d5a336ff52443d
GÜNZEL_Chous_1380_readme.txt Jan 27, 2026 - Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380) Plain Text - 2.4 KB - MD5: e7c47644ff4cdd9dd81e76919bf9dfa7
Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Jan 26, 2026 Gounoue, Steve, 2026, "Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"", https://doi.org/10.60507/FK2/DRYP80, bonndata, V2 In this repository, you can find the code to train and evaluate SCANNER+, a novel neighborhood-based self-enrichment approach for traffic speed prediction. SCANNER+ learns effective node representations in dynamic road traffic settings. This work extends SCANNER, which utilizes correlation-based pattern detection and a self-enrichment mechanism.
.gitignore Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Plain Text - 3.4 KB - MD5: be85f81e6cc8066629a0464226450c3c
LICENSE Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Plain Text - 1.0 KB - MD5: 9166a54d6b45565ba0e1420c2de7fdf0
README.md Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Markdown Text - 4.1 KB - MD5: e0d58f8633a84bea05fec828fc1e5f07
config.py Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Python Source Code - 4.9 KB - MD5: 0389a89900e28058294d79ee957393cf

Krater_5806_model.zip

Jan 27, 2026 - Attic Red-Figure Krater in the Museum Alte Kulturen Tübingen (Inv. 5806)

ZIP Archive - 130.0 MB -

Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380)

Jan 27, 2026 - Museum Alte Kulturen

Günzel, Adriana, 2026, "Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380)", https://doi.org/10.60507/FK2/IIUH2Q, bonndata, V1

Attic Red-Figure Chous in the Museum Alte Kulturen (University of Tübingen, Inv. 1380) showing a youth with a chous pulling a cart with a hare. Date: -430 to -420 BC.

Chous_1380_images.zip

Jan 27, 2026 - Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380)

ZIP Archive - 2.7 GB -

Chous_1380_model.zip

Jan 27, 2026 - Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380)

ZIP Archive - 83.5 MB -

GÜNZEL_Chous_1380_readme.txt

Jan 27, 2026 - Attic Red-Figure Chous in the Museum Alte Kulturen Tübingen (Inv. 1380)

Plain Text - 2.4 KB -

Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Jan 26, 2026

Gounoue, Steve, 2026, "Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"", https://doi.org/10.60507/FK2/DRYP80, bonndata, V2

In this repository, you can find the code to train and evaluate SCANNER+, a novel neighborhood-based self-enrichment approach for traffic speed prediction. SCANNER+ learns effective node representations in dynamic road traffic settings. This work extends SCANNER, which utilizes correlation-based pattern detection and a self-enrichment mechanism.

.gitignore

Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Plain Text - 3.4 KB -

LICENSE

Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Plain Text - 1.0 KB -

README.md

Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Markdown Text - 4.1 KB -

config.py

Jan 26, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Python Source Code - 4.9 KB -

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