cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4pnwdgt7b7
title: Synthetic Network Traffic Dataset for Anomaly Detection Using Machine Learning in SDN Environments

description: This dataset contains 10,005 synthetic network flow records designed to support research in network anomaly detection, particularly in Software-Defined Networking (SDN) environments. The core hypothesis behind the dataset's creation is that statistical analysis of flow-level features—such as connection duration, packet count, byte transmission, protocol usage, and port behavior—can effectively distinguish between normal and malicious traffic patterns.
The dataset simulates realistic traffic scenarios, representing both benign network flows and various types of malicious activity, including DDoS attacks, port scanning, data exfiltration, brute-force authentication attempts, and protocol misuse. Each record includes temporal metadata, IP addresses, port numbers, protocol types, connection duration, packet counts, and byte statistics. 
The integration of machine learning methods, particularly the Random Forest algorithm, into Software-Defined Networks (SDN) enables the creation of an efficient and adaptive system for detecting DDoS attacks. Such a system ensures high accuracy in network traffic classification, timely anomaly detection, and minimizes the impact of false positives on network performance/ 
Characteristics of Normal Traffic:
Primarily TCP traffic (70%) with standard HTTP/HTTPS, SSH, and DNS communications
Connection durations typically range from 0.1 to 5.0 seconds
Packet counts range between 5–100 per connection
Balanced byte transmission patterns reflecting typical client-server interactions
Use of standard service ports (80, 443, 22, 53, etc.)
Characteristics of Anomalous Traffic:
Very short connection durations (0.01–0.1s), minimal packet counts (1–5), and low byte transmissions typical of port scanning
Extremely high packet counts (1,000–10,000) with disproportionate ratios of sent to received bytes
Extended connection durations (10–60s) and high outbound byte transmission (100KB–1MB) during DDoS or data exfiltration attacks
Repeated connections to authentication ports (SSH-22, RDP-3389), moderate duration and packet count during brute-force attacks
Use of uncommon protocols (GRE, ESP, AH) with non-standard port combinations indicating protocol anomalies
Dataset Applications:
Training supervised machine learning models for anomaly detection in network traffic
Evaluating classification algorithm performance on imbalanced network security datasets
Testing feature engineering techniques for traffic analysis
Educational use in cybersecurity and network monitoring courses
Label Interpretation:
Label 0 represents normal, benign network traffic
Label 1 represents anomalous, potentially malicious network traffic.
DOI: 10.17632/4pnwdgt7b7.1
title: Synthetic Network Traffic Dataset for Anomaly Detection Using Machine Learning in SDN Environments

description: This dataset contains 10,005 synthetic network flow records designed to support research in network anomaly detection, particularly in Software-Defined Networking (SDN) environments. The core hypothesis behind the dataset's creation is that statistical analysis of flow-level features—such as connection duration, packet count, byte transmission, protocol usage, and port behavior—can effectively distinguish between normal and malicious traffic patterns.
The dataset simulates realistic traffic scenarios, representing both benign network flows and various types of malicious activity, including DDoS attacks, port scanning, data exfiltration, brute-force authentication attempts, and protocol misuse. Each record includes temporal metadata, IP addresses, port numbers, protocol types, connection duration, packet counts, and byte statistics. 
The integration of machine learning methods, particularly the Random Forest algorithm, into Software-Defined Networks (SDN) enables the creation of an efficient and adaptive system for detecting DDoS attacks. Such a system ensures high accuracy in network traffic classification, timely anomaly detection, and minimizes the impact of false positives on network performance/ 
Characteristics of Normal Traffic:
Primarily TCP traffic (70%) with standard HTTP/HTTPS, SSH, and DNS communications
Connection durations typically range from 0.1 to 5.0 seconds
Packet counts range between 5–100 per connection
Balanced byte transmission patterns reflecting typical client-server interactions
Use of standard service ports (80, 443, 22, 53, etc.)
Characteristics of Anomalous Traffic:
Very short connection durations (0.01–0.1s), minimal packet counts (1–5), and low byte transmissions typical of port scanning
Extremely high packet counts (1,000–10,000) with disproportionate ratios of sent to received bytes
Extended connection durations (10–60s) and high outbound byte transmission (100KB–1MB) during DDoS or data exfiltration attacks
Repeated connections to authentication ports (SSH-22, RDP-3389), moderate duration and packet count during brute-force attacks
Use of uncommon protocols (GRE, ESP, AH) with non-standard port combinations indicating protocol anomalies
Dataset Applications:
Training supervised machine learning models for anomaly detection in network traffic
Evaluating classification algorithm performance on imbalanced network security datasets
Testing feature engineering techniques for traffic analysis
Educational use in cybersecurity and network monitoring courses
Label Interpretation:
Label 0 represents normal, benign network traffic
Label 1 represents anomalous, potentially malicious network traffic.
DOI: 10.17632/xz4d5zj5yw.3
title: Rule-Based SQL Injection (RbSQLi) Dataset

description: The RbSQLi dataset has been developed to support advanced research and development in the detection of SQL injection (SQLi) vulnerabilities. It contains a total of 10,304,026 structured entries, out of which 2,813,146 are labeled as malicious and 7,490,880 as benign. The malicious entries are categorized into six distinct types of SQL injection attacks: Union-based (758,600 samples), Stackqueries-based (746,480 samples), Time-based (531,580 samples), Meta-based (481,280 samples), Boolean-based (226,080 samples), and Error-based (69,126 samples).

The malicious payloads for Union-based, Time-based, and Error-based injection types were sourced directly from the widely used and reputable open-source GitHub repository "Payloads All The Things – SQL Injection Payload List" (https://github.com/payloadbox/sql-injection-payload-list). Moreover, ChatGPT was employed to generate additional payloads for Boolean-based, Stack queries-based, and Meta-based injection categories. This hybrid approach ensures that the dataset reflects both known attack patterns and intelligently simulated variants, contributing to a broader representation of SQLi techniques.

All payloads were carefully curated, anonymized, and structured during preprocessing. Sensitive data was replaced with secure placeholders, preserving semantic meaning while protecting data integrity and privacy. The dataset also underwent a thorough sanitization process to ensure consistency and usability. To support scalability and reproducibility, a rule-based classification algorithm was used to automate the labeling and organization of each payload by type. This methodology promotes standardization and ensures that the dataset is ready for use in machine learning pipelines, anomaly detection models, and intrusion detection systems. In addition to being comprehensive, the dataset provides a substantial volume of clean (benign) data, making it well-suited for supervised learning, comparative experiments, and robustness testing in cybersecurity research.

This dataset is intended to facilitate progress in the development of more accurate and generalizable SQL injection detection systems and to serve as a reliable benchmark for the broader security and machine learning communities.
DOI: 10.17632/25r6txd45n.2
title: Provenance Modelling of Fossil Bones Using Geochemistry and Machine Learning: Source Data

description: The data presented here support the research paper “Geochemistry-Based Provenance Modelling in Fossil Bones: A Machine Learning Study from the Upper Cretaceous Gobi Localities”, intended for a submission to "Cretaceous Research".

The dataset contains trace elements concentrations from fossil dinosaur bones from the Upper Cretaceous Nemegt and Djadokhta formations. 
For the analytical purposes, the dataset was divided into two subsets: the first one consisting of long bones (tibiae, femora, radii and humeri) and the other including trabecular bones (ribs and vertebrae) and metatarsals. Locality labels were used to train and evaluate several machine learning classifiers (logistic regression, random forest, AdaBoost, XGBoost) to assess the potential of bone geochemistry for provenance prediction. Feature selection was conducted on the best-performing models to identify the elements contributing the most to the model performance. These results were compared with those obtained using Linear Discriminant Analysis. 

The data are provided in CSV format in the “Data” folder. The folder “Figures” contains the figures used in the manuscript. The folder “Supplementary files” contains interactive HTML plot ("Element profiles.html") showing the all the concentration profiles across each analysed sample, including the ones measured along several profiles, together with the concentration profiles presented in a PDF file ("All profiles.pdf").
DOI: 10.15468/dl.keh6sy
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Specimen, Fossil, Living Specimen, Occurrence evidence)",
 "Geometry POLYGON((4.23756 43.73705,4.44999 43.73705,4.44999 43.90184,4.23756 43.90184,4.23756 43.73705))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chiroptera",
 "Year 2020-2025"
 ]
}

 The dataset includes 35 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042512-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12152165
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17632/w5cj2yjttz
title: Data and Codes for: Markup Polarization and Efficacy of Monetary Policy

description: Data and Code for "Markup Polarization and Efficacy of Monetary Policy: A Tale of Two Firms"
Eunmi Ko
June 2025
MATLAB version used: R2024b
DOI: 10.17632/w5cj2yjttz.1
title: Data and Codes for: Markup Polarization and Efficacy of Monetary Policy

description: Data and Code for "Markup Polarization and Efficacy of Monetary Policy: A Tale of Two Firms"
Eunmi Ko
June 2025
MATLAB version used: R2024b
DOI: 10.5517/cctmvmw
title: CCDC 763859: Experimental Crystal Structure Determination

description: Related Article: L.R.Falvello, E.F.Vazquez, R.Granero, M.Tomas|2010|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/94hx6tg4gp.1
title: LSTM Raw Data for Irrigation Forecasting in Mango (Mangifera indica L.), Chimichagua, Colombia

description: This dataset contains daily time-series of averaged soil matric potential and calculated reference evapotranspiration from a commercial mango (Mangifera indica L.) plantation in Chimichagua, Cesar, Colombia, for the period of December 21, 2022, to April 10, 2023. Although original sensor data were collected at 15-minute intervals, the provided dataset consists of daily values processed for validating predictive models based on LSTM neural networks for efficient irrigation management. The file includes both field-derived observed values and LSTM model predictions, enabling applications in precision agriculture, water resource management, and agricultural sustainability in tropical climates.
DOI: 10.5517/ccdc.csd.cc2gtwkr
title: CCDC 2288995: Experimental Crystal Structure Determination

description: Related Article: N. Graw, P.N. Ruth, T. Ernemann, R. Herbst-Irmer, D. Stalke|2023|J.Appl.Crystallogr.|56|1315|doi:10.1107/S1600576723007203
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.2hj872
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lemna gibba L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.529 -24.0715,20.509 -24.0455,20.509 -24.0715,20.529 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042513-250525065834625/datasets/export for details.
DOI: 10.15468/dl.4p66sc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Closterium Nitzsch ex Ralfs, 1848",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042514-250525065834625/datasets/export for details.
DOI: 10.17632/59t4k3cdt2
title: Time Use, Energy Expenditure and Food Intakes in Agricultural Households: Case Study from Telangana India

description: This data set was collected through a study was conducted jointly by the University of Reading, UK and the International Crop Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India under a grant from the Global Challenges Research Fund (GCRF) for a project entitled “Through the Looking Glass: Applying a Gender Lens to Agricultural Transformation, Labour Intensification and Nutrition Outcomes in LMICs''. The data collection for this project was undertaken by Dr Padmaja Ravula, Dr Kavitha Kasala and their team at ICRISAT, India.
DOI: 10.17632/59t4k3cdt2.1
title: Time Use, Energy Expenditure and Food Intakes in Agricultural Households: Case Study from Telangana India

description: This data set was collected through a study was conducted jointly by the University of Reading, UK and the International Crop Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India under a grant from the Global Challenges Research Fund (GCRF) for a project entitled “Through the Looking Glass: Applying a Gender Lens to Agricultural Transformation, Labour Intensification and Nutrition Outcomes in LMICs''. The data collection for this project was undertaken by Dr Padmaja Ravula, Dr Kavitha Kasala and their team at ICRISAT, India.
DOI: 10.17632/4mwsjvzmbm
title: Association between acneiform eruption grade and survival outcomes in metastatic colorectal cancer patients treated with epidermal growth factor receptor inhibitors

description: Supplemental figures for Ong et al. "Association between acneiform eruption grade and survival outcomes in metastatic colorectal cancer patients treated with epidermal growth factor receptor inhibitors"
DOI: 10.17632/4mwsjvzmbm.1
title: Association between acneiform eruption grade and survival outcomes in metastatic colorectal cancer patients treated with epidermal growth factor receptor inhibitors

description: Supplemental figures for Ong et al. "Association between acneiform eruption grade and survival outcomes in metastatic colorectal cancer patients treated with epidermal growth factor receptor inhibitors"
DOI: 10.15468/dl.vgexgz
title: Occurrence Download

description: A dataset containing 253 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "OccurrenceStatus is Absent",
 "TaxonKey is Puffinus puffinus (Brunnich, 1764)"
 ]
}

 The dataset includes 253 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042515-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/x47h2kz4n3
title: Integration of AI Chatbots in MSMEs: Factors Affecting Adoption and Their Effects on Organizational Performance

description: INTEGRATION OF AI CHATBOTS IN MSMES: FACTORS AFFECTING ADOPTION AND THEIR EFFECTS ON ORGANIZATIONAL PERFORMANCE

The research objects focus on the following construct:
1. Organizational Readiness
2. Competitive Pressure
3. AI CHatbot Adoption
4. Organization Performance

This study uses a quantitative survey design to identify the factors that influence the adoption of AI chatbots in the MSME sector and their impact on organizational performance. This approach was chosen to measure the relationship between variables and provide a clear explanatory framework for understanding the impact of these factors on tourism innovation. The primary tool used for data collection is a 5-point Likert scale questionnaire (1 = strongly disagree, 5 = strongly agree). The questionnaire covers various indicators related to organizational readiness (cultural readiness, strategic readiness, IT readiness, innovation valence, cognitive readiness, and partnership readiness), competitive pressure (product substitutability, market size, and entry costs), AI chatbot adoption (perceived usefulness, perceived ease of use, subjective norms, compatibility, facilitating conditions, trust), and organization performance (financial performance, personal satisfaction, stakeholder perspective, and personal growth). A pilot test was conducted to ensure the validity and reliability of the instrument, involving a small sample of respondents similar to the study population. 

The unit of analysis in this study includes approximately 250 MSME owners or managers in Bandung City who have adopted chatbots. Data collected from the survey were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This method was chosen because it can effectively handle small to medium-sized samples. The analysis involved two main stages: the measurement model and the structural model. The measurement model evaluated the reliability and validity of the constructs, while the structural model tested the hypothesized relationships between organizational readiness, competitive pressure, AI chatbot adoption, and organization performance. The PLS-SEM approach provided a comprehensive understanding of the factors driving AI chatbot adoption by SMEs and their impact on organization performance.
DOI: 10.17632/x47h2kz4n3.1
title: Integration of AI Chatbots in MSMEs: Factors Affecting Adoption and Their Effects on Organizational Performance

description: INTEGRATION OF AI CHATBOTS IN MSMES: FACTORS AFFECTING ADOPTION AND THEIR EFFECTS ON ORGANIZATIONAL PERFORMANCE

The research objects focus on the following construct:
1. Organizational Readiness
2. Competitive Pressure
3. AI CHatbot Adoption
4. Organization Performance

This study uses a quantitative survey design to identify the factors that influence the adoption of AI chatbots in the MSME sector and their impact on organizational performance. This approach was chosen to measure the relationship between variables and provide a clear explanatory framework for understanding the impact of these factors on tourism innovation. The primary tool used for data collection is a 5-point Likert scale questionnaire (1 = strongly disagree, 5 = strongly agree). The questionnaire covers various indicators related to organizational readiness (cultural readiness, strategic readiness, IT readiness, innovation valence, cognitive readiness, and partnership readiness), competitive pressure (product substitutability, market size, and entry costs), AI chatbot adoption (perceived usefulness, perceived ease of use, subjective norms, compatibility, facilitating conditions, trust), and organization performance (financial performance, personal satisfaction, stakeholder perspective, and personal growth). A pilot test was conducted to ensure the validity and reliability of the instrument, involving a small sample of respondents similar to the study population. 

The unit of analysis in this study includes approximately 250 MSME owners or managers in Bandung City who have adopted chatbots. Data collected from the survey were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This method was chosen because it can effectively handle small to medium-sized samples. The analysis involved two main stages: the measurement model and the structural model. The measurement model evaluated the reliability and validity of the constructs, while the structural model tested the hypothesized relationships between organizational readiness, competitive pressure, AI chatbot adoption, and organization performance. The PLS-SEM approach provided a comprehensive understanding of the factors driving AI chatbot adoption by SMEs and their impact on organization performance.
DOI: 10.15468/dl.9fy3vb
title: Occurrence Download

description: A dataset containing 326 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-14.81773 41.10290,3.94461 41.10290,3.94461 50.36688,-14.81773 50.36688,-14.81773 41.10290))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Physeter Linnaeus, 1758",
 "Year 1995-2025"
 ]
}

 The dataset includes 326 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042517-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zq46yf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lemna gibba L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -23.2724,25.1306 -24.0715,25.5395 -24.0715,25.5395 -23.2724))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042518-250525065834625/datasets/export for details.