cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mrx55k5x7n
title: Business Analytics and Predictive Machine Learning Modeling in the Business Environment

description: Emerging economies face significant challenges in business forecasting due to data fragmentation, limited analytics infrastructure, and reactive decision-making processes. This study investigates the application of Artificial Intelligence of Things (AIoT) systems to enhance business forecasting capabilities across multiple sectors in Bangladesh. We present a comprehensive AIoT framework that integrates real-time IoT sensor data with advanced machine learning algorithms to enable predictive analytics and intelligent decision-making. Through three longitudinal case studies spanning manufacturing, retail, and financial services sectors, we demonstrate the transformative potential of AIoT implementation. Results indicate substantial improvements in forecasting accuracy (average increase of 19%), operational efficiency (cost reduction of 10-15%), and sustainability metrics. The textile manufacturing case achieved 93% demand forecast accuracy (up from 75%) and 70% reduction in downtime. The retail chain reduced stockouts by 75% while improving forecast accuracy to 90%. The fintech application enhanced loan default prediction accuracy to 89% while reducing non-performing loans by 42%. These findings underscore AIoT's viability as a strategic enabler of predictive intelligence in resource-constrained emerging markets, offering actionable insights for business leaders, policymakers, and researchers focused on digital transformation in developing economies.
DOI: 10.17632/mrx55k5x7n.1
title: Business Analytics and Predictive Machine Learning Modeling in the Business Environment

description: Emerging economies face significant challenges in business forecasting due to data fragmentation, limited analytics infrastructure, and reactive decision-making processes. This study investigates the application of Artificial Intelligence of Things (AIoT) systems to enhance business forecasting capabilities across multiple sectors in Bangladesh. We present a comprehensive AIoT framework that integrates real-time IoT sensor data with advanced machine learning algorithms to enable predictive analytics and intelligent decision-making. Through three longitudinal case studies spanning manufacturing, retail, and financial services sectors, we demonstrate the transformative potential of AIoT implementation. Results indicate substantial improvements in forecasting accuracy (average increase of 19%), operational efficiency (cost reduction of 10-15%), and sustainability metrics. The textile manufacturing case achieved 93% demand forecast accuracy (up from 75%) and 70% reduction in downtime. The retail chain reduced stockouts by 75% while improving forecast accuracy to 90%. The fintech application enhanced loan default prediction accuracy to 89% while reducing non-performing loans by 42%. These findings underscore AIoT's viability as a strategic enabler of predictive intelligence in resource-constrained emerging markets, offering actionable insights for business leaders, policymakers, and researchers focused on digital transformation in developing economies.
DOI: 10.17632/m4m6j2tjfj
title: TCP (Tomato-Chilli-Papaya Fruit & Leaf) Disease Dataset

description: Contributors: Dr. Neeta Nain
Research Scholars: Anand Kumar Jain
Institute: Malaviya National Institute of Technology Jaipur
Anadi Jain
Institute: Government women engineering College Ajmer ( Bikaner Technical University, Bikaner)
Domain Expert: Kota Agriculture University, Kota and Rajsthan Government Agriculture Department
Published: December 2025
Version: 1.0
Crops: Chilli, Papaya & Tomato
Chilli Diseases: Leaf Curl, Cercospora, Bacterial Spot, White Spot, Nutrition Deficiency.
Healthy., Healthy Chilli Fruit, Disease Chilli Fruit
Papaya Disease: Anthracnose, Bacterial Spot, Curl, Ring Spot.
Healthy. Healthy Papaya Fruit, Disease Papaya Fruit.
Tomato Disease: Healthy Tomato Fruit, Disease Tomato Fruit, Tomato healthy leaf, Tomato septoria leaf spot,
Tomato verticulium wilt.
Expert Ground-truth annotations, includ.ing soil health, humidity, nutrient deficiency, and pathological reports. Validated: 
 
Chilli, papaya, and tomato are economically important crops that are highly vulnerable to various diseases affecting leaves and fruits, leading to significant yield losses. Recent advances in artificial intelligence and deep learning have enabled automated plant disease detection; however, the performance of these models strongly depends on the availability of diverse and well-annotated datasets.

To address this need, a three-crop disease image dataset comprising chilli, papaya, and tomato is presented. The dataset includes healthy and diseased samples captured under real-field conditions, covering variations in illumination, background, and disease severity. This dataset aims to support the development of robust multi-crop and multi-disease classification models and contribute to precision agriculture and intelligent plant health monitoring systems.
DOI: 10.17632/m4m6j2tjfj.3
title: TCP (Tomato-Chilli-Papaya Fruit & Leaf) Disease Dataset

description: Contributors: Dr. Neeta Nain
Research Scholars: Anand Kumar Jain
Institute: Malaviya National Institute of Technology Jaipur
Anadi Jain
Institute: Government women engineering College Ajmer ( Bikaner Technical University, Bikaner)
Domain Expert: Kota Agriculture University, Kota and Rajsthan Government Agriculture Department
Published: December 2025
Version: 1.0
Crops: Chilli, Papaya & Tomato
Chilli Diseases: Leaf Curl, Cercospora, Bacterial Spot, White Spot, Nutrition Deficiency.
Healthy., Healthy Chilli Fruit, Disease Chilli Fruit
Papaya Disease: Anthracnose, Bacterial Spot, Curl, Ring Spot.
Healthy. Healthy Papaya Fruit, Disease Papaya Fruit.
Tomato Disease: Healthy Tomato Fruit, Disease Tomato Fruit, Tomato healthy leaf, Tomato septoria leaf spot,
Tomato verticulium wilt.
Expert Ground-truth annotations, includ.ing soil health, humidity, nutrient deficiency, and pathological reports. Validated: 
 
Chilli, papaya, and tomato are economically important crops that are highly vulnerable to various diseases affecting leaves and fruits, leading to significant yield losses. Recent advances in artificial intelligence and deep learning have enabled automated plant disease detection; however, the performance of these models strongly depends on the availability of diverse and well-annotated datasets.

To address this need, a three-crop disease image dataset comprising chilli, papaya, and tomato is presented. The dataset includes healthy and diseased samples captured under real-field conditions, covering variations in illumination, background, and disease severity. This dataset aims to support the development of robust multi-crop and multi-disease classification models and contribute to precision agriculture and intelligent plant health monitoring systems.
DOI: 10.17632/mrmj9gj949.1
title: Open government data and bank risk-taking

description: This dataset contains Stata code and associated data files for conducting regression analysis of "Open government data and bank risk-taking".
DOI: 10.17632/6c6spz68mw
title: manscript

description: research data and python method
DOI: 10.17632/6c6spz68mw.1
title: manscript

description: research data and python method
DOI: 10.15468/dl.gdbze5
title: Occurrence Download

description: A dataset listing the 241 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Swedish National Forest Inventory: Presence-absence vegetation data (1995-current)"
 ]
}

 The dataset's 241 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021827-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/djr8rnjtjp
title: Multi-Season Formula 1 Lap Dataset with Race-Control-Based Safety Car Labels (2022-2025)

description: Containing lap-level race data for the 2022-2025 Formula 1 seasons, the year-race-session data were collected using FastF1, public interface that provides access to timing and race information publicly available similarly on the web. Include only official race sessions! The data for each event were downloaded and then all of the events were merged into a single structured dataset. After eliminating invalid or incomplete laps and cleaning out missing values, the final dataset contains 89,980 driver lap observations. Each line represents one driver on one laps.

The dataset includes such contextual and performance variables as lap number, race progression, tire compound, tire life, driver position, team ID, environmental variables including (air temperature, track temperature, humidity, rainfall) etc. We applied certain basic feature engineering processes in order to make the data model-friendly. Safety Car (SC) and Virtual Safety Car (VSC) labels were constructed by parsing official race control messages and aligning them with lap-level records. This makes it possible to model rare events at the level of the lap. The dataset also supports forward-looking targets (for instance, detection of a Safety Car within the next few laps) for probabilistic risk forecasting. The dataset's structure allows experiments with event-level validation. This means that entire Grand Prix events can be separated during training and testing to avoid leakage between laps in the same event. The data set is intended for academic research, it is a set of processing and structured derivative of public interface data FastF1 that was obtained via timing.
DOI: 10.6084/m9.figshare.31346797
title: Data for "<b>The global footprint of species-use across space and ecology</b>"

description: Use of species underpins subsistence livelihoods and commercial economies. Utilising trade and seizure records, expert assessments, case studies, and field data, we develop a complete understanding of how and where species are used by humans. We find 6358 (58%) bird and 2426 (41%) mammal species are used for pets, food, sport, ornamental products, apparel, and medicine. Individual use types are well predicted by different aspects of species’ morphology, demography, and ecology. Automated text-based classification identifies specific uses as a direct threat for 3.1% of bird and mammal species, with pets and food the most prevalent drivers. Integrating targeted understanding of how species are used – and when this poses a threat – is critical to meeting global aspirations for the safe and sustainable use of wild species.<br>See README.txt file for a detailed description of data fields.
DOI: 10.6084/m9.figshare.31346797.v3
title: Data for "<b>The global footprint of species-use across space and ecology</b>"

description: Use of species underpins subsistence livelihoods and commercial economies. Utilising trade and seizure records, expert assessments, case studies, and field data, we develop a complete understanding of how and where species are used by humans. We find 6358 (58%) bird and 2426 (41%) mammal species are used for pets, food, sport, ornamental products, apparel, and medicine. Individual use types are well predicted by different aspects of species’ morphology, demography, and ecology. Automated text-based classification identifies specific uses as a direct threat for 3.1% of bird and mammal species, with pets and food the most prevalent drivers. Integrating targeted understanding of how species are used – and when this poses a threat – is critical to meeting global aspirations for the safe and sustainable use of wild species.<br>See README.txt file for a detailed description of data fields.
DOI: 10.17632/djr8rnjtjp.1
title: Multi-Season Formula 1 Lap Dataset with Race-Control-Based Safety Car Labels (2022-2025)

description: Containing lap-level race data for the 2022-2025 Formula 1 seasons, the year-race-session data were collected using FastF1, public interface that provides access to timing and race information publicly available similarly on the web. Include only official race sessions! The data for each event were downloaded and then all of the events were merged into a single structured dataset. After eliminating invalid or incomplete laps and cleaning out missing values, the final dataset contains 89,980 driver lap observations. Each line represents one driver on one laps.

The dataset includes such contextual and performance variables as lap number, race progression, tire compound, tire life, driver position, team ID, environmental variables including (air temperature, track temperature, humidity, rainfall) etc. We applied certain basic feature engineering processes in order to make the data model-friendly. Safety Car (SC) and Virtual Safety Car (VSC) labels were constructed by parsing official race control messages and aligning them with lap-level records. This makes it possible to model rare events at the level of the lap. The dataset also supports forward-looking targets (for instance, detection of a Safety Car within the next few laps) for probabilistic risk forecasting. The dataset's structure allows experiments with event-level validation. This means that entire Grand Prix events can be separated during training and testing to avoid leakage between laps in the same event. The data set is intended for academic research, it is a set of processing and structured derivative of public interface data FastF1 that was obtained via timing.
DOI: 10.6084/m9.figshare.31346797.v2
title: Data for "<b>The global footprint of species-use across space and ecology</b>"

description: Use of species underpins subsistence livelihoods and commercial economies. Utilising trade and seizure records, expert assessments, case studies, and field data, we develop a complete understanding of how and where species are used by humans. We find 6358 (58%) bird and 2426 (41%) mammal species are used for pets, food, sport, ornamental products, apparel, and medicine. Individual use types are well predicted by different aspects of species’ morphology, demography, and ecology. Automated text-based classification identifies specific uses as a direct threat for 3.1% of bird and mammal species, with pets and food the most prevalent drivers. Integrating targeted understanding of how species are used – and when this poses a threat – is critical to meeting global aspirations for the safe and sustainable use of wild species.<br>See README.txt file for a detailed description of data fields.
DOI: 10.17632/2d6zcfp43r
title: data for How Perceived AI Creativity Shapes Student Outcomes

description: data for How Perceived AI Creativity Shapes Student Outcomes
DOI: 10.17632/2d6zcfp43r.1
title: data for How Perceived AI Creativity Shapes Student Outcomes

description: data for How Perceived AI Creativity Shapes Student Outcomes
DOI: 10.17632/6rg4hk6728
title: Dataset on NAFLD Severity Classification with Ultrasound Liver Image & Clinical Data

description: This dateset contains three stage of liver condition along with clinical data and demographic data on the classification of Non-Alcoholic Fatty Liver Disease (NAFLD). This dataset meant to help in medical image analysis, machine learning, and deep learning for early liver disease detection and severity assessment. 
The ultrasound images are acquired with standard diagnostic ultrasound equipment in B-mode. Ultrasound imaging is a widely used, and cost-effective modality for liver assessment, making it particularly suitable for large-scale screening and early detection of NAFLD. Each sample is labelled according to NAFLD status, and disease severity grades (Normal, Benign, Malignant). These labels enable both binary and multi-classification tasks.
Along with images of liver, we include clinical and demographic data relevant to NAFLD diagnosis. The clinical data included with patient age, gender, body mass index (BMI), liver enzyme measurements such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST), waist size, glucose, cholesterol such as (LDL,HDL, Triglycerides). This combination of data (image and Tabular) makes possible research on multi-modal learning, which improve diagnostic accuracy and model robustness. 
All data on our dataset are anonymized to de-identified patient's personal information and to ensure compliance with ethical research standards and data protection guidelines. The dataset is strictly provided for research and educational purposes and does not contain any information that can be used to identify individual patients.
This dataset can be used for image preprocessing, classical machine learning classification, convolutional neural network (CNN)-based deep learning, disease severity grading, and comparative studies between image-only and multi-modal diagnostic approaches. the dataset Publicly accessible under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
DOI: 10.17632/6rg4hk6728.3
title: Dataset on NAFLD Severity Classification with Ultrasound Liver Image & Clinical Data

description: This dateset contains three stage of liver condition along with clinical data and demographic data on the classification of Non-Alcoholic Fatty Liver Disease (NAFLD). This dataset meant to help in medical image analysis, machine learning, and deep learning for early liver disease detection and severity assessment. 
The ultrasound images are acquired with standard diagnostic ultrasound equipment in B-mode. Ultrasound imaging is a widely used, and cost-effective modality for liver assessment, making it particularly suitable for large-scale screening and early detection of NAFLD. Each sample is labelled according to NAFLD status, and disease severity grades (Normal, Benign, Malignant). These labels enable both binary and multi-classification tasks.
Along with images of liver, we include clinical and demographic data relevant to NAFLD diagnosis. The clinical data included with patient age, gender, body mass index (BMI), liver enzyme measurements such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST), waist size, glucose, cholesterol such as (LDL,HDL, Triglycerides). This combination of data (image and Tabular) makes possible research on multi-modal learning, which improve diagnostic accuracy and model robustness. 
All data on our dataset are anonymized to de-identified patient's personal information and to ensure compliance with ethical research standards and data protection guidelines. The dataset is strictly provided for research and educational purposes and does not contain any information that can be used to identify individual patients.
This dataset can be used for image preprocessing, classical machine learning classification, convolutional neural network (CNN)-based deep learning, disease severity grading, and comparative studies between image-only and multi-modal diagnostic approaches. the dataset Publicly accessible under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
DOI: 10.17632/wnx6zftj88
title: Datasets for "Horseshoe bats make silence to hear prey"

description: Experimantal datasets for "Horseshoe bats make silence to hear prey"

Efficient information acquisition is essential for survival, and the adaptive adjustment of self-generated signals in active sensing species offers profound insights into how animals solve sensory challenges. Horseshoe bats lower the frequency of their echolocation calls during flight so that echoes remain stable at a reference frequency (fref) despite Doppler shifts. Here we reveal a previously unrecognized function: it not only aligns echoes with the most sensitive frequency range but also suppresses background clutter noise to enhance prey detection. Using phantom echo playbacks and on-board recordings, we show that bats compensate for the highest-frequency echoes rather than the strongest ones. This shifts clutter echoes below fref, leaving a “silent spectral window” above it. Furthermore, recordings during prey capture and noise playback experiments showed that spectral glints from fluttering moth wings appear in this window are exploited for prey detection, illustrating how sensory systems are shaped for reliable information extraction in cluttered environments.
DOI: 10.17632/wnx6zftj88.1
title: Datasets for "Horseshoe bats make silence to hear prey"

description: Experimantal datasets for "Horseshoe bats make silence to hear prey"

Efficient information acquisition is essential for survival, and the adaptive adjustment of self-generated signals in active sensing species offers profound insights into how animals solve sensory challenges. Horseshoe bats lower the frequency of their echolocation calls during flight so that echoes remain stable at a reference frequency (fref) despite Doppler shifts. Here we reveal a previously unrecognized function: it not only aligns echoes with the most sensitive frequency range but also suppresses background clutter noise to enhance prey detection. Using phantom echo playbacks and on-board recordings, we show that bats compensate for the highest-frequency echoes rather than the strongest ones. This shifts clutter echoes below fref, leaving a “silent spectral window” above it. Furthermore, recordings during prey capture and noise playback experiments showed that spectral glints from fluttering moth wings appear in this window are exploited for prey detection, illustrating how sensory systems are shaped for reliable information extraction in cluttered environments.
DOI: 10.17632/wzwpc9j5bx.3
title: Dataset Stunting and Nutritional Status of Toddler from Jeneponto Regency, South Sulawesi, Indonesia

description: This dataset provides comprehensive anthropometric and demographic data on 40,071 toddlers (aged 0–59 months) from Jeneponto Regency, South Sulawesi, Indonesia, collected between 2021 and 2024 . The primary focus is on stunting and nutritional status. Over 16,000 toddlers were classified as stunted based on WHO growth standards.

This data in excel format, dataset is divided into five files:
-2021 Data (3,412 toddlers): Measurements collected in 2021.
-2022 Data (1,111 toddlers): Measurements collected in 2022.
-2023 Data (3,808 toddlers): Measurements collected in 2023.
-2024 Data (31,740 toddlers): Measurements collected in 2024.
-Overall Dataset (40,071 toddlers): Aggregated data spanning all years.
-Preprocessed Data (40,071 toddlers): Data that has undergone a complete preprocessing and transformation to ensure ML Compatible.

Each record includes the following variables: Gender, Age (Months), Weight (kg) and Height (cm), Anthropometric Measurements: Weight-for-Age, Height-for-Age,Weight-for-Height, Z-Score for each Measurements. 

Dataset Structure:
Format: Excel (.xlsx)
Variables: 10
Geographic Specificity: Focused on Jeneponto Regency, South Sulawesi, Indonesia.
DOI: 10.17632/dmgcvm7865
title: The codes and real-word data supporting paper "A geographically weighted autoregressive model with an adaptive spatial weights matrix: Construction, estimation, and inference".

description: This repository contains the codes and real-word data used in submitted paper "A geographically weighted autoregressive model with an adaptive spatial weights matrix: Construction, estimation, and inference".
If you would like to replicate the results in this paper, please note that,
1. Code for model estimation of the simulation study: Folder "GWAR_simulation_
estimation" (generates Tables 1–6);
Code for test of the simulation study: Folder "GWAR_simulation_test" (generates Tables 7–8 and Figure 3);
Code for the application: Folder "GWAR_application" (generates Tables 9–11 and Figures 4–5).
2. Data of the application: Folder "data", in which "boston.txt" is the Boston data (also loadable via R package "spdep"); "z.txt" is the spatial locations of Boston data; "weight.txt" is the spatial link matrix of Boston data.