Standards Research Data

This dataset provides an in-depth exploration of risk management practices within the software startup ecosystem, capturing the perspectives of 89 startups through a comprehensive survey of 72 questions. It investigates the prevalence and management of risks related to team dynamics, methodologies, and product development across various stages of startup growth. Key areas of focus include the identification, analysis, and mitigation of risks, alongside an examination of how these practices evolve as startups transition from early to later stages.

The dataset encompasses a diverse array of electrical signals representing Power Quality Disturbances (PQD), both in single and combined forms, meticulously generated in adherence to the IEEE 1159 guideline.  Crucially, the dataset includes both raw data and corresponding labels, facilitating supervised learning tasks and enabling the development and evaluation of classification algorithms.

This study presents a novel rapid bacterial detection method employing an organic complementary metal–oxide–semiconductor (O-CMOS) inverter fabricated with pentacene and PTCDI-C13. Various bacteria, including Escherichia coli (E. coli) O157, Staphylococcus aureus (S. aureus) ATCC25922, and Enterococcus faecalis (E. faecalis) SH-1051210, were tested on the inverters operating at an ultra-low voltage of 2 V.

We present a point cloud dataset, named Point Cloud Quality Dataset-Augmented Reality (PCQD-AR). It includs ten reference point clouds and their 90 distorted versions, which were encoded by the reference software of Video-based Point Cloud Compression (V-PCC) under different pairs of geometry and texture quantization parameters. We conduct a subjective test of point clouds in the augmented reality environment.