uploading and testing stuffs

Resource Title: Description of the resource. Resource Title: Brief explanation of the data or functionality provided. Resource Title: Summary of the content and its purpose.

This data package contains fieldwork imagery and video from biomass research activities, stored in the biomass_field work pictures/ directory. It includes 1 video file (DSC_1420.mp4) and 12 image files: IMG_20180419_151549.jpg, IMG_20181205_084903.jpg, IMG_2923.JPG, IMG_3234.JPG, P1110913.jpg, P1110921.jpg, P1110922.jpg, _DSC1414.jpg, _DSC1488.jpg, _DSC1548.jpg, _DSC1549.jpg, and _DSC1554.jpg. This description was AI-generated on 13 May 2025 13:18

This data package contains Go source code and documentation for a CKAN API integration tool. Key components include: go.mod and go.sum: Go module configuration and dependencies test.md: Test documentation or scripts pkg/ckanapi/: Core CKAN API implementation with packages for organization, resource, user, and package management pkg/ckanbot/: Bot logic implementation pkg/ckanbot_descriptor/: Descriptor tool components including AppHubAI API integration and utility functions cmd/ckanbot_descriptor/: Command-line executable for the descriptor tool The archive also includes an AI-generated image description depicting a computer case with labeled hardware components like GPUs, CPU, power supply, and water cooling system. This image appears to be unrelated to the Go codebase but is included as part of the package contents. |----ckanbot | go.mod | test.md | go.sum |----pkg | |----ckanap` | | | |organization.go | | | |ckanapi.go | | | |resource.go | | | |user.go | | | |package.go | | |----ckanbot | | | |ckanbot.go | | |----ckanbot_descriptor | | | |----apphubai_api | | | | |apphubai.go | | | |descriptor.go | |----cmd | | |----ckanbot_descriptor | | | |ckanbot_descriptor | | | |main.go

Jaeger (yet another phage identifier) is a deep-learning-based method for discovering bacteriophages. This repository will serve as the main distribution point for model weights. The project repository is https://github.com/Yasas1994/Jaeger.git

msl datasets for https://github.com/Yasas1994/vcat

[Retrieved Data from the Wetscapes 1 project. Everything here is from the original author and not form me]

This dataset includes multiple resources of images that can be used to generate nerfs or gaussian splats.

Teaching materials designed as part of the project ‘Viking gold - treasure troves as a translocal heritage.’

[Retrieved Data from the Wetscapes 1 project. Everything here is from the original author and not form me]

Recordings of bat calls from studyareas in MV, 2024

This repository contains the dataset used in the paper "Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset" by Dr. Samaneh Zolfaghari, Teodor Stoev, and Prof. Dr. Kristina Yordanova. If you use the dataset, please cite the paper using the Bibtex below @ARTICLE{10409517, author={Zolfaghari, Samaneh and Stoev, Teodor and Yordanova, Kristina}, journal={IEEE Access}, title={Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset}, year={2024}, volume={}, number={}, pages={1-1}, doi={10.1109/ACCESS.2024.3356352}} as well as the original KTA dataset paper "Kitchen task assessment dataset for measuring errors due to cognitive impairments" by Yordanova, Kristina and Hein, Albert and Kirste, Thomas @inproceedings{yordanova2020kitchen, title={Kitchen task assessment dataset for measuring errors due to cognitive impairments}, author={Yordanova, Kristina and Hein, Albert and Kirste, Thomas}, booktitle={2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)}, pages={1--6}, year={2020}, organization={IEEE} } Description of the files All the archive files containing our data are in the folder data which contains two other folders all_actions (containing the experimental data and the labels we used for the evaluation of the classifier when trained with all action classes in the KTA dataset), and most_common_actions (containing the experimental data and labels we used to evaluate the classifier on the 6 most common actions).

Organisation name: Experimentelle Pflanzenökologie Dataset name: Pictures Mesocosms Wetscapes Source: Data collected on the WETSCAPES project. Note: Pictures from the mesocosm facility in Greifswald. Files:

The data package contains a collection of images depicting various outdoor and structured environments involving black plastic containers, plant growth systems, and scientific or agricultural setups. Key elements include large cylindrical or rectangular black plastic tubs filled with soil, water, or mixed organic matter, often containing small plants, weeds, or aquatic vegetation. Many images show grid-like arrangements of containers, suggesting controlled experiments or cultivation systems. Common features include PVC pipes, irrigation components, sensors, and blue plastic jerrycans, indicating hydroponic, hydrological, or research applications. Some scenes depict people interacting with the setups—such as tending to plants, handling equipment, or working in research environments—while others focus on the infrastructure itself, like metal frameworks, concrete surfaces, and outdoor courtyards. The contexts range from botanical gardens and hydroponic farms to industrial or academic research facilities, with varying lighting conditions and seasonal elements like fallen leaves or autumn foliage. The imagery emphasizes a blend of natural growth and engineered systems, often highlighting organized, systematic approaches to plant cultivation or environmental studies. This description was AI-generated on 13 May 2025 09:10

A set of short films accompanying the Vikinggold exhibition.

Replication data for Bussmann, Margit & Natalia Iost. Presidential popularity and international crises: an assessment of the rally-‘round-the-flag effect in Russia. Post-Soviet Affairs, Vol. 40, No. 2, 2024

population survey on the demands on the agriculturally shaped environment in Western Pomerania, 2019 and R-scripts

Emerging evidence suggests an important role of the gut microbiome in processes of brain ageing1,2. However, the corresponding role of the gut virome including the phageome is currently ill-defined. Here, metagenomic gut virome profiles from 1655 individuals in the Study of Health in Pomerania (SHIP-TREND-0)3,4 were analysed in conjunction with FreeSurfer brain age scores (FSBA)5,6 derived from magnetic resonance imaging data. Of 124 virus genera, Cequinquevirus presence was significantly associated with reduced FSBA by -2.53 years (95%-confidence interval (CI):(-4.16-0.91), p=3.42e-04, FDR=0.04) Cequinquevirus also associated with alterations in microbial composition, and co-occurring with L. Delbrueckii and L. Paracaseii. L. Delbrueckii presence was associated with lower FSBA as well on a nominally significant level. Analysis of potential mediating clinical parameters was inconclusive. Overall this study provides evidence that gut phages may serve as markers of delayed brain ageing, highlighting Cequinquevirus and its potential implications for the design of interventions aiming at promoting healthy brain ageing. Additional information:

Bilder und COLMAP Kamera-Positionen vom Karzer der Universität Greifswald. Für das Testen von 3D-Rekonstruktion geeignet.

Jungen Erwachsenen Lett*innen wurden verschiedenartigen Aussagen vorgelegt. Ihre Aufgabe war es, die populistischen von den nicht-populistischen zu unterscheiden.