cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yr29rg7kvj.2
title: Application of nonlinear least squares fitting to parameterized shapes

description: This work presents the ParamFit algorithm, which proposes a parametric function for fitting simple closed curves and a methodology for determining the initial conditions for the Levenberg-Marquardt fitting algorithm. The parametric function is evaluated in three different applications: images of seeds, diatoms, and some well-known geometric planar curves. Furthermore, it is compared with the neural network model (PointNet). The fitting validation was performed using the Jaccard metric, the coefficient of determination (R^2), the mean square error, (RMSE) and the residual error. The results obtained show that the newly proposed parametric function provides high levels of precision, particularly excelling in the classification of seeds and diatoms, with potential applications in agriculture and ecology for the selection and monitoring of species.
DOI: 10.17632/yr29rg7kvj.3
title: Application of nonlinear least squares fitting to parameterized shapes

description: This work presents the ParamFit algorithm, which proposes a parametric function for fitting simple closed curves and a methodology for determining the initial conditions for the Levenberg-Marquardt fitting algorithm. The parametric function is evaluated in three different applications: images of seeds, diatoms, and some well-known geometric planar curves. Furthermore, it is compared with the neural network model (PointNet). The fitting validation was performed using the Jaccard metric, the coefficient of determination (R^2), the mean square error, (RMSE) and the residual error. The results obtained show that the newly proposed parametric function provides high levels of precision, particularly excelling in the classification of seeds and diatoms, with potential applications in agriculture and ecology for the selection and monitoring of species.
DOI: 10.17632/4gs637chtz.1
title: Systematic Review ULBS Research

description: This dataset supports the review article titled "Advancing Methodological Rigor in Urban Landscape and Behavioral Science: A Bayesian and AI-Driven Evaluation of Modern Research Frameworks." The review examines advancements in Urban Landscape and Behavioral Science (ULBS), focusing on integrating data-driven methodologies with advanced techniques such as Agent-Based Modeling, Machine Learning, and Big Data Analytics.

The systematic review analyzes 212 pivotal articles published between 2010 and February 2024, sourced from databases including Scopus, Science Direct, and Taylor and Francis. This comprehensive analysis identifies emerging spatiotemporal trends, methodological advancements, and significant research gaps within ULBS.

Key findings from the review highlight the transformative impact of Internet of Things (IoT) devices and real-time analytics on urban landscape behavior studies. Additionally, the research introduces innovative probabilistic and optimization frameworks for evaluating methodological suitability. These frameworks enhance traditional Bayesian models by incorporating weighted priors, information gain functions, and multi-objective optimization, allowing for adaptive method selection based on varying data characteristics and research objectives.

Included in this dataset are bibliographic details of the reviewed articles, along with supplementary materials such as figures and tables that visualize the findings and highlight key insights. This dataset is intended to facilitate further research and encourage collaboration among scholars and practitioners in the fields of architecture, urban planning, and behavioral science.

You are free to share, copy, and modify this dataset under the terms of the Creative Commons Attribution (CC BY) license, as long as appropriate credit is given. You must provide a link to the CC BY license and indicate if changes were made. However, please refrain from using this dataset in a manner that suggests endorsement by the rights holder.

For any content within this dataset that is identified as belonging to a third party, further permission may be required. By utilizing this dataset, you agree to comply with these terms and to respect the rights of the original authors and content creators.
DOI: 10.17632/4gs637chtz.2
title: Systematic Review ULBS Research

description: This dataset supports the review article titled "Advancing Methodological Rigor in Urban Landscape and Behavioral Science: A Bayesian and AI-Driven Evaluation of Modern Research Frameworks." The review examines advancements in Urban Landscape and Behavioral Science (ULBS), focusing on integrating data-driven methodologies with advanced techniques such as Agent-Based Modeling, Machine Learning, and Big Data Analytics.

The systematic review analyzes 212 pivotal articles published between 2010 and February 2024, sourced from databases including Scopus, Science Direct, and Taylor and Francis. This comprehensive analysis identifies emerging spatiotemporal trends, methodological advancements, and significant research gaps within ULBS.

Key findings from the review highlight the transformative impact of Internet of Things (IoT) devices and real-time analytics on urban landscape behavior studies. Additionally, the research introduces innovative probabilistic and optimization frameworks for evaluating methodological suitability. These frameworks enhance traditional Bayesian models by incorporating weighted priors, information gain functions, and multi-objective optimization, allowing for adaptive method selection based on varying data characteristics and research objectives.

Included in this dataset are bibliographic details of the reviewed articles, along with supplementary materials such as figures and tables that visualize the findings and highlight key insights. This dataset is intended to facilitate further research and encourage collaboration among scholars and practitioners in the fields of architecture, urban planning, and behavioral science.

You are free to share, copy, and modify this dataset under the terms of the Creative Commons Attribution (CC BY) license, as long as appropriate credit is given. You must provide a link to the CC BY license and indicate if changes were made. However, please refrain from using this dataset in a manner that suggests endorsement by the rights holder.

For any content within this dataset that is identified as belonging to a third party, further permission may be required. By utilizing this dataset, you agree to comply with these terms and to respect the rights of the original authors and content creators.
DOI: 10.17632/4gs637chtz.3
title: Systematic Review ULBS Research

description: This dataset supports the review article titled "Advancing Methodological Rigor in Urban Landscape and Behavioral Science: A Bayesian and AI-Driven Evaluation of Modern Research Frameworks." The review examines advancements in Urban Landscape and Behavioral Science (ULBS), focusing on integrating data-driven methodologies with advanced techniques such as Agent-Based Modeling, Machine Learning, and Big Data Analytics.

The systematic review analyzes 212 pivotal articles published between 2010 and February 2024, sourced from databases including Scopus, Science Direct, and Taylor and Francis. This comprehensive analysis identifies emerging spatiotemporal trends, methodological advancements, and significant research gaps within ULBS.

Key findings from the review highlight the transformative impact of Internet of Things (IoT) devices and real-time analytics on urban landscape behavior studies. Additionally, the research introduces innovative probabilistic and optimization frameworks for evaluating methodological suitability. These frameworks enhance traditional Bayesian models by incorporating weighted priors, information gain functions, and multi-objective optimization, allowing for adaptive method selection based on varying data characteristics and research objectives.

Included in this dataset are bibliographic details of the reviewed articles, along with supplementary materials such as figures and tables that visualize the findings and highlight key insights. This dataset is intended to facilitate further research and encourage collaboration among scholars and practitioners in the fields of architecture, urban planning, and behavioral science.

You are free to share, copy, and modify this dataset under the terms of the Creative Commons Attribution (CC BY) license, as long as appropriate credit is given. You must provide a link to the CC BY license and indicate if changes were made. However, please refrain from using this dataset in a manner that suggests endorsement by the rights holder.

For any content within this dataset that is identified as belonging to a third party, further permission may be required. By utilizing this dataset, you agree to comply with these terms and to respect the rights of the original authors and content creators.
DOI: 10.17632/pdsjz2wjw7.1
title: Sensor-Based Environmental Monitoring Dataset: Temperature & Humidity

description: Dataset Description: Environmental Sensor Readings from Mars Rover Prototype

Research Hypothesis
A scaled-down Mars Rover prototype can effectively collect temperature and humidity data, demonstrating how real-time environmental monitoring can be used for autonomous navigation, climate analysis, and anomaly detection. 

By analyzing the collected data, we aim to identify trends, evaluate sensor accuracy, and explore potential improvements in robotic exploration. This includes assessing response time, consistency, and anomalies caused by external factors like human interference or sudden environmental changes. 

What the Data Shows
This dataset contains timestamped temperature and humidity readings collected at regular time intervals by the rover’s onboard DHT22 sensor. The data highlights: 
- Gradual fluctuations in environmental conditions. 
- Notable temperature spikes (~10°C) introduced using a lighter to test sensor response. 
- Stable humidity levels with minor deviations due to air circulation or sensor drift. 

Notable Findings
- Controlled Temperature Spikes: Short bursts of heat resulted in clear temperature increases (~10°C), demonstrating the sensor's ability to detect and log transient changes. 
- Humidity Stability: Humidity levels remained within a narrow range, confirming minimal impact from applied temperature fluctuations. 
- Gradual Environmental Variations: Small temperature and humidity shifts were observed, likely due to ambient conditions and ventilation effects. 

How the Data Was Gathered
- Sensor Used: DHT22 (for temperature & humidity). 
- Data Collection Frequency: Logged every few seconds. 
- Controlled Testing: Heat spikes added using a lighter to simulate external interference. 
- Data Transmission: Logged in real-time via wireless communication to a laptop. 

How to Interpret and Use the Data 
- Identify Trends: Observe temperature and humidity variations over time. 
- Detect Anomalies: Locate sharp temperature spikes (~10°C increases) caused by external heating. 
- Compare Sensor Performance: Evaluate how quickly temperature normalizes after a spike. 
- Develop Predictive Models: Train machine learning models to predict environmental changes. 

Potential Applications
- Autonomous Environment Monitoring: Detecting and responding to environmental anomalies. 
- Sensor Calibration & Validation: Testing DHT22 sensor accuracy under different conditions. 
- Climate Simulation & Research: Indoor climate modeling & environmental trend analysis. 
- Robotics & AI: Training AI for automated responses to climate fluctuations.
DOI: 10.17632/pdsjz2wjw7.2
title: Sensor-Based Environmental Monitoring Dataset: Temperature & Humidity

description: Dataset Description: Environmental Sensor Readings from Mars Rover Prototype

Research Hypothesis
A scaled-down Mars Rover prototype can effectively collect temperature and humidity data, demonstrating how real-time environmental monitoring can be used for autonomous navigation, climate analysis, and anomaly detection. 

By analyzing the collected data, we aim to identify trends, evaluate sensor accuracy, and explore potential improvements in robotic exploration. This includes assessing response time, consistency, and anomalies caused by external factors like human interference or sudden environmental changes. 

What the Data Shows
This dataset contains timestamped temperature and humidity readings collected at regular time intervals by the rover’s onboard DHT22 sensor. The data highlights: 
- Gradual fluctuations in environmental conditions. 
- Notable temperature spikes (~10°C) introduced using a lighter to test sensor response. 
- Stable humidity levels with minor deviations due to air circulation or sensor drift. 

Notable Findings
- Controlled Temperature Spikes: Short bursts of heat resulted in clear temperature increases (~10°C), demonstrating the sensor's ability to detect and log transient changes. 
- Humidity Stability: Humidity levels remained within a narrow range, confirming minimal impact from applied temperature fluctuations. 
- Gradual Environmental Variations: Small temperature and humidity shifts were observed, likely due to ambient conditions and ventilation effects. 

How the Data Was Gathered
- Sensor Used: DHT22 (for temperature & humidity). 
- Data Collection Frequency: Logged every few seconds. 
- Controlled Testing: Heat spikes added using a lighter to simulate external interference. 
- Data Transmission: Logged in real-time via wireless communication to a laptop. 

How to Interpret and Use the Data 
- Identify Trends: Observe temperature and humidity variations over time. 
- Detect Anomalies: Locate sharp temperature spikes (~10°C increases) caused by external heating. 
- Compare Sensor Performance: Evaluate how quickly temperature normalizes after a spike. 
- Develop Predictive Models: Train machine learning models to predict environmental changes. 

Potential Applications
- Autonomous Environment Monitoring: Detecting and responding to environmental anomalies. 
- Sensor Calibration & Validation: Testing DHT22 sensor accuracy under different conditions. 
- Climate Simulation & Research: Indoor climate modeling & environmental trend analysis. 
- Robotics & AI: Training AI for automated responses to climate fluctuations.
DOI: 10.17632/6dgkv6kpr2.1
title: Student grade Prediction

description: This dataset contains semester-wise academic performance data of BTech students from GIET University. It includes the grades of students from their 1st to 4th semesters, along with their corresponding 5th-semester grades. The dataset is intended for use in educational data mining and machine learning applications, specifically for predicting the 5th-semester grades of students based on their past performance.The dataset consists of 379 student records, with each record containing the following attributes:

SEM 1: Grade obtained in the 1st semester.

SEM 2: Grade obtained in the 2nd semester.

SEM 3: Grade obtained in the 3rd semester.

SEM 4: Grade obtained in the 4th semester.

SEM 5: Grade obtained in the 5th semester (target variable for prediction).The grades are represented on a scale of 0 to 10, where 10 is the highest achievable grade. This dataset can be used to develop predictive models for academic performance, identify trends in student performance, and support decision-making in educational institutions.

Keywords:
Grade Prediction, Student Performance, Educational Data Mining, Academic Analytics, Machine Learning, GIET University

Potential Applications:

Predicting student performance in future semesters.

Identifying at-risk students for early intervention.

Analyzing trends in academic performance over time.
DOI: 10.17632/j8j9jcr8rf.1
title: Datasets of the Delphi study on development of an expert consensus-based checklist for appraising educational resources on first aid for snakebites

description: The data supporting the study's findings, including tables summarizing anonymized expert ratings and comments, calculated quantitative expert-group results, checklist modifications, and blank questionnaire forms.
DOI: 10.17632/9k3rc2pgwv.1
title: Core Articles

description: This repository contains the core articles utilized in my thesis research, along with the dataset analyzed using the Web of Science platform.
DOI: 10.15468/dl.fy9jwh
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((101.58953553048924 23.35003209121425,104.39611656118593 23.35003209121425,104.39611656118593 26.156613121910947,101.58953553048924 26.156613121910947,101.58953553048924 23.35003209121425))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cynops chenggongensis Kou & Xing, 1983"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024345-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cygws7y2wh.1
title: Process Model of Shared Leadership Development: Antecedents, socially constructed interactions and derived practices

description: Datafile MAXQDA, focus groups and evaluation of codes
DOI: 10.17632/zgtryg9wvg.1
title: EEG MI-BCI Mouse Cursor Dataset

description: This dataset contains EEG recordings collected for research on brain-computer interface (BCI) applications, specifically for controlling a mouse cursor using motor imagery (MI). The data was recorded using an Emotiv Epoc+ 14 channel headset. Volounteers performed 1) left-hand movement, 2) right-hand movement and 3) break tasks. The data were recorded in Biomedical Instrumentation and Signal Processing Labpratory (BISPL) at Independent University, Bangladesh.
DOI: 10.17632/zgtryg9wvg.2
title: EEG MI-BCI Mouse Cursor Dataset

description: This dataset contains EEG recordings collected for research on brain-computer interface (BCI) applications, specifically for controlling a mouse cursor using motor imagery (MI). The data was recorded using an Emotiv Epoc+ 14 channel headset. Volounteers performed 1) left-hand movement, 2) right-hand movement and 3) break tasks. The data were recorded in Biomedical Instrumentation and Signal Processing Laboratory (BISPL) at Independent University, Bangladesh.
DOI: 10.17632/9x3cmtty6x.1
title: GTA

description: GTA by businesses in ASIA
DOI: 10.17632/dhdt6xrhrm.1
title: DEFIANCE: Decentralized Evidentiary Forensics for Immutable Auditability, Networked Chain-of-Custody, and Enforcement (A Blockchain-Based Forensic Firearm Tracking System and Evidentiary Dataset)

description: This dataset supports the thesis "A Cryptographic Architecture for Decentralized Firearm Forensics" and contains the complete codebase, verification artifacts, deployment scripts, audit records, forensic output samples, and evidentiary templates that comprise the DEFIANCE system — the first decentralized blockchain-based firearm forensic infrastructure.

DEFIANCE enables smart contract–governed NFT firearm identity, biometric transfer validation, multisignature warrant-based access control, and zero-knowledge-compatible forensic event logging. This dataset includes all modules necessary to deploy, test, audit, and reproduce the system in compliance with U.S. evidentiary law (Federal Rules of Evidence 902(13) and 902(14)) and academic reproducibility guidelines.
DOI: 10.17632/mkhn7kxjvy.1
title: Hand Gesture Accelerometer and Gyroscope Dataset (HGAG-DATA)

description: This dataset includes information from 43 healthy participants (26 males and 17 females). The participants encompass an extensive age range of 18 to 69 years and are classified by dominant hand, comprising 34 right-handed and 9 left-handed individuals. Furthermore, they are categorized according to physical activity levels, comprising 28 non-athletic individuals and 15 athletic individuals. All participants were equipped, trained, and directed to perform 11 essential gestures relevant in many circumstances and closely linked to daily life requirements. The motions encompass clapping, coin flipping, finger snapping, fist making, horizontal wrist extension, index finger flicking, index thumb tapping, shooting, thumbs up, wrist extension, and wrist flexion. The dataset comprises 23,650 six-dimensional gesture samples, captured at a rate of 550 samples per subject following 50 repetitions of each of the eleven motions (11x50x43 = 23,650). Each gesture sample consisted of six directional time series signals, incorporating two signals for each of the x, y, and z axes from the accelerometer and gyroscope sensors. This dataset comprises a total of 141,900 signals (23,650 x 6 = 141,900). Consequently, this can serve as a significant resource for handling extensive data sets, such as those utilized in the development of machine learning and deep learning models, and as a reference dataset, it facilitates model benchmarking. The data will be especially helpful for societies that work with biomedical signals when they are trying to recognize and classify hand gestures for use in human-computer interaction (HCI) tasks. The dataset has been uploaded and is accessible online in two distinct hierarchical configurations. The initial structure categorizes the data according to the gesture name, but the subsequent structure arranges it by subject number. This configuration enhances potential advantages and facilitates management, permitting the reassembly of data in many formats as required.
DOI: 10.17632/mkhn7kxjvy.2
title: Hand Gesture Accelerometer and Gyroscope Dataset (HGAG-DATA)

description: This dataset includes information from 43 healthy participants (26 males and 17 females). The participants encompass an extensive age range of 18 to 69 years and are classified by dominant hand, comprising 34 right-handed and 9 left-handed individuals. Furthermore, they are categorized according to physical activity levels, comprising 28 non-athletic individuals and 15 athletic individuals. All participants were equipped, trained, and directed to perform 11 essential gestures relevant in many circumstances and closely linked to daily life requirements. The motions encompass clapping, coin flipping, finger snapping, fist making, horizontal wrist extension, index finger flicking, index thumb tapping, shooting, thumbs up, wrist extension, and wrist flexion. The dataset comprises 23,650 six-dimensional gesture samples, captured at a rate of 550 samples per subject following 50 repetitions of each of the eleven motions (11x50x43 = 23,650). Each gesture sample consisted of six directional time series signals, incorporating two signals for each of the x, y, and z axes from the accelerometer and gyroscope sensors. This dataset comprises a total of 141,900 signals (23,650 x 6 = 141,900). Consequently, this can serve as a significant resource for handling extensive data sets, such as those utilized in the development of machine learning and deep learning models, and as a reference dataset, it facilitates model benchmarking. The data will be especially helpful for societies that work with biomedical signals when they are trying to recognize and classify hand gestures for use in human-computer interaction (HCI) tasks. The dataset has been uploaded and is accessible online in two distinct hierarchical configurations. The initial structure categorizes the data according to the gesture name, but the subsequent structure arranges it by subject number. This configuration enhances potential advantages and facilitates management, permitting the reassembly of data in many formats as required.