VDS

## Overview

VDS is a dataset for object detection tasks - it contains V annotations for 8,116 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The idea of virtual and online design studio classrooms for interior design raises several issues, focusing on the communicative and practical components of learning. The COVID-19 global lockdown has significantly impacted the practical, communicative, and team-building scenarios that are part of interior design studio classes. This study aims to understand the crucial changes and measures that virtual design studios will have to adapt to due to the current COVID-19 pandemic in interior design and architecture education.
This pilot study used a blended learning environment that combines a face-to-face studio environment with an online studio environment for design studio courses. The learning environment includes INTD 212, INTD 216, INTD 311, and INTD 404 of interior design students from different levels, faculty members, and interior and architecture professionals, along with appropriate journals and literature assessments over five months.

VDS_V1

## Overview

VDS_V1 is a dataset for object detection tasks - it contains Ambulance Car annotations for 253 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The ckanext-vds extension is designed to enhance CKAN, providing functionality related to datasets, as implied by the name, and potentially visual data solutions. Considering the limited information in the provided readme, it is likely an implementation to manage and possibly visualize complex datasets within the CKAN ecosystem. Further details would be needed for a comprehensive understanding of its features. Key Features: There is no information about key features available in the documentation. Therefore, a reliable list cannot be provided. Use Cases: Due to the lack of sufficient content about use cases, it is not possible to provide concrete use cases. Technical Integration: Without any documentation, it's impossible to determine how this extension integrates with CKAN's core functionalities. Presuming standard design practices, the extension likely uses CKAN's plugin architecture. Benefits & Impact: Without specific functionality outlined, it is impossible to detail the benefits and impact of this extension. It's presumably built to enhance how CKAN handles dataset visibility.

In order to create compact models for circuit simulations in LTspice, a characterization protocol was established, as shown in the Characterization_flowchart. This procedure was applied to two different transistors, named TLN2 and GS65030. This folder is divided into three parts: 01) A flowchart for the procedure of HEMT transistor characterization using a B1505A curve tracer. 02) A dataset containing static (I–V) and dynamic (C–V) measurements for both HEMT transistors TLN2 and GS65030 03) A set of scripts designed to: - Process measurement data (Id–Vgs, Id–Vds, Id–Vsd) - Fit mathematical models to the different I–V functions - Process measurement data (Crss–Vds, Coss–Vds, Ciss–Vds) - Fit mathematical models to the different C–V functions - Generate a “.lib” file that allows the component to be reused in LTspice A file named "README_HEMT_Modeling.txt" is available for further details on the various functions. All datasets and scripts are fully reusable and are provided to support transparency, reproducibility, and further research. They accompany a journal paper that is currently under preparation.

Korea AI Dataset

## Overview

Korea AI Dataset is a dataset for object detection tasks - it contains Vehicles annotations for 8,654 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Mean yield under stress and drought tolerance indices for vegetative drought stress (VDS) and flowering drought stress (FDS) for cowpea genotypes.

Dataset

This dataset was created by HoangNH

Contents

Mean values of yield under non-stress (YP), stress (YS1) and fifteen drought tolerance indices for the three clusters revealed by cluster analysis on eighty-eight cowpea genotypes for VDS.

Comparison of the visual Deauville scores (vDS) of reader 1 (R1) and reader 2 (R2).

Mean of VDs driven by high KE nodes in different valid layers.