Standards Research Data

This dataset is in support of my research paper 'Statistical Variability Study of RDF and LER on Nanosheet FETs at Sub 3nm Node'. In this study, we studied the influence of RDF and LER on NSFETs through NEGF method for simulation. The results are generated by simulation software, Nano-electronic Simulation Software (NESS), which was developed by the University of Glasgow’s Device Modelling Group. Our results predict the figures of merit of nanosheet devices at different channel lengths and widths with the influence of RDF and LER.

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.

CTCS-2 level train control engineering data is primarily categorized into trackside infrastructure data and line parameters, including line velocity table, line gradient table, line broken chain detail table, balise position table, main line signal data table and so on. The dataset image above is an example of a balise position table.

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 is the raw data for the article "Algorithm for Gait Parameters Estimation Based on Heel-Mounted Inertial Sensors", which includes walking data recorded by IMUs and data recorded by an optical motion capture (OMC) system, where the experimental data consisted of three parts:

Validity Experiment Data includes gait data (both IMU and OMC) of 30 participants who were instructed to walk in a straight line within the motion capture area and return upon reaching the end to ensure a sufficient step count.

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.

An experimental study was conducted on a high-voltage glass-type disc (LD-160) to investigate the effect of string arrangements on pollution and icing flashover characteristics. Two Artificial Neural Network (ANN) applications were developed to simulate and calculate the flashover voltage based on the experimental results. The test results showed that the inverted T-type arrangement can improve the pollution flashover voltage and increase the icing flashover voltage of insulator strings compared to the traditional arrangement of the I-string.

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.

This represents a comprehensive collection of data from a Automative manufacturing unit. This unit could be involved in a range of production activities, from assembly line manufacturing to more complex, multi-stage processes. The dataset is designed to capture various operational parameters that are crucial for analyzing and optimizing manufacturing processes.

Clinical/Lab Data Quantitative Continuous age (years), height (cm), weight (kg), bmi (kg/m2) - Body Mass Index, hemoglobin

(g/dL) - Hemoglobin concentration, hba1c (%) - Glycated Hemoglobin, ast (U/L) -

Aspartate Aminotransferase, alt (U/L) - Alanine Aminotransferase, creatinine (mg/dL),

nt proBnp (pg/mL) - N-terminal pro b-type Natriuretic Peptide

Categorical Nominal gender (Male/Female), smoking status (Smoker/Non-smoker), htn (Yes/No) - Hypertension,

dm (Yes/No) - Diabetes Mellitus, ckd (Yes/No) - Chronic Kidney Disease