Resultados de investigación

This dataset contains the data provided by ArcelorMittal España (commercial names, quality and elemental composition of the studied metal scraps and raw materials) in the context of the 'IRIDISCENTE' research project, and the analysis, results and conclusions for each of the 11 scenarios considered in the work 'A five-indicator methodology for early-stage sustainable selection of metal scraps and raw materials: application in the steel industry', This work was funded by the Spanish Agencia Estatal de Investigación (AEI) through the ‘Artificial Intelligence for the sustainable design of efficient alloys and processes (IRIDISCENTE)’ research project (PLEC2023-010190)

This study presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment. The dataset captures network communication generated by hardware-based IoT devices during normal operation, including MQTT messaging, database synchronization, and web-based monitoring, as well as during the execution of predefined cyber-attack scenarios within an isolated experimental network. Network traffic was recorded at the packet level using passive network monitoring and stored in PCAP format. The packet captures were subsequently processed into bidirectional network flows using a flow-based traffic extraction pipeline, producing flow records with statistical and temporal attributes derived from the observed packet exchanges. Cyberattack-related flows were identified based on predefined attack execution time intervals obtained from experimental metadata. Network flows observed outside these intervals were labelled as benign and correspond to regular device communication. The dataset is distributed through a structured repository that includes raw packet captures, processed flow-level datasets in tabular format, and metadata files describing the experimental setup, attack scenarios, and labelling criteria. The data support flow-based analysis of IoT network traffic and the evaluation of cyberattack detection methods., This publication is part of the AI4SECIoT project (“Artificial Intelligence for Securing IoT Devices”), funded by the National Cybersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative was carried out within the framework of the Recovery, Transformation, and Resilience Plan funds, financed by the European Union (Next Generation).

This dataset comprises annual panel data for 52 Spanish provinces (NUTS-3) from 2015 to 2024, supporting the analysis in the manuscript "The Impact of Banking Deserts on Tourism Development: Evidence from Spanish Provinces." It includes 13 key variables on banking consolidation (e.g., HHI concentration, offices, firms_bank, workers_bank), provincial controls (gdp_pc, int_rate, unemploy, density), and tourism outcomes (credit_tour, firms_tour, workers_tour, overnight, travellers)., The authors acknowledge financial support from the Spanish State Research Agency (Ministry of Science and Innovation of the Government of Spain) and FEDER Funds (Knowledge Generation Projects) through the project “Methodologies for solving problems with economic, social and environmental criteria. Application to healthcare resource management” (ECOSOEN-HEALTH, grant ref. PID2022-139543OB-C44). In addition to financial support from the Spanish Ministry of Universities under the University Teacher Training Program (FPU/00606-2021).

The dataset contains all raw data of the work "Naked-eye detection of Listeria monocytogenes using smart chromogenic polymers with tuneable surface morphologies", We gratefully acknowledge the financial support provided by all the funders. The financial support provided by Fondo Europeo de Desarrollo Regional-European Regional Development Fund (FEDER, ERDF) and Regional Government of Castilla y León -Consejería de Educación, Junta de Castilla y León- (BU025P23) is gratefully acknowledged. Author S. Vallejos received Grant PID2023-147301OB-I00 and Grant 3101166576-166576-29-325 funded by MICIU/AEI/10.13039/501100011033 and FEDER, EU. This work was supported by the Regional Government of Castilla y León (Junta de Castilla y León) and by the Ministry of Science and Innovation MICIN and the European Union NextGenerationEU 597 PRTR. Author Saul Vallejos received grant BG22/00086 funded by Spanish Ministerio de Universidades.

The dataset is a set of txt files with the fictitious instaces used in the paper "Iterative Stepped Search algorithm for the clique partitioning problem" The dataset is a text file containing the solutions obtained in the article “Iterative Stepped Search Algorithm for the Clique Partitioning Problem” for a set of instances from the DIMACS library., The authors are grateful for the following entities, projects, and grants: The Spanish Research Agency and European Union (Project PID2022-139543OB-C44) and Regional Government of Castilla y León and FEDER founds (BU021G24).

This dataset is designed to support the training and evaluation of reinforcement learning models in the context of network traffic analysis. It is derived from an existing IoT network traffic dataset, from which packet capture (pcap) files were selected and processed following a custom methodology explained in [Methodological Information](methodological-information). The resulting data representation is based on a windowing approach, where network traffic is segmented into fixed-size temporal windows. Each window aggregates traffic instances and is labeled according to its composition as benign, attack, or mixed (containing both benign and malicious activity). The final datasets are generated through random combinations of these windows, enabling the creation of diverse traffic patterns that better reflect dynamic and random network conditions. This structure facilitates the use of the dataset in reinforcement learning scenarios, where agents must learn to identify, classify, or respond to varying traffic behaviors over time. Additionally, the evaluation datasets are generated following the same methodology as the training datasets, but are kept separate and are not used during the training process, allowing for an independent evaluation of model performance., This publication is part of the AI4SECIoT project ("Artificial Intelligence for Securing IoT Devices"), funded by the National Cybersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.

This dataset represents a higher-dimensional extension of the NF-UNSW-NB15 v3 dataset [Luay et al., 2025; Luay et al., 2025], in which the correlations among variables are explicitly considered and used to organize them spatially as pixels., This publication is part of the AI4SECIoT project (‘Artificial Intelligence for Securing IoT Devices’), funded by the National Cybersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.

The dataset contains time-series data collected from an industrial cyber-physical system (CPS) based on a PLC-controlled part marking station using Siemens S7-1200 and S7-1500 devices. Data acquisition follows an event-based logging approach, where changes in system variables are recorded together with their associated duration (Δt), enabling precise temporal characterization while reducing redundancy. In addition to temporal information, the dataset explicitly represents scan-level execution by incorporating identifiers of PLC scan cycles (scan_id) and the relative order of events within each scan (event_order). This allows accurate representation of multiple events occurring within the same control cycle and preserves the logical execution order of the system. The dataset includes both normal operation and fault conditions generated through controlled fault injection, specifically targeting sensors and actuators (e.g., solenoid valves). Ground truth labels are derived from the experimental configuration provided to the control system and embedded during data acquisition, ensuring consistency between system behavior and annotation. Data are organized into independent experimental batches, each corresponding to a specific operating condition. Each batch includes processed event-based data (CSV), raw network traffic captures in PCAPNG format, including industrial PROFINET communication traffic, and supporting documentation, enabling traceability and reproducibility. The dataset is designed to support the development, training, and evaluation of machine learning models for anomaly detection, fault classification, and industrial cybersecurity applications, while also enabling detailed temporal and logical analysis of discrete-event industrial processes., The funding for this work was provided by AI4SECIoT project ("Artificial Intelligence for Securing IoT Devices" - C032.23), funded by the National Cibersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.

The dataset contained the raw data from the study entitled 'Physical-chemical and microbiological characterisation of three powdered products obtained from blueberry pomace: skins and seeds'. The analysed parameters included proximate composition, mineral content, total phenolic content, total anthocyanin content, antioxidant activity (ABTS), and individual anthocyanins and phenolic compounds. The microorganisms studied were aerobic mesophilic bacteria, Enterobacteriaceae, lactic acid bacteria, Bacillus spp. and Clostridium spp. The data also included the results of a heat treatment at 90 °C for 30, 60, 90 and 120 minutes., The authors acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities T with the help of European Funds through the project PID2021-125400OB-I00.

The data set contained the raw data of the project “Use of by-products from blackberry and blueberry: charaterization, effect on health and development of safer and healthier meat products” (FUNCBERRY)., The authors acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities T with the help of European Funds through the project PID2021-125400OB-I00.