Mendeley Data Showcase

La Escala de Depresión basada en el Criterio Diagnóstico Axial para la Depresión fue desarrollada por Ramos-Brieva, Cordero y Gutiérrez-Rojas (2009) con el propósito de evaluar los componentes esenciales de la experiencia depresiva desde una perspectiva dimensional. El instrumento se fundamenta en la idea de que la depresión no debe entenderse únicamente como una categoría diagnóstica, sino como un continuo de gravedad que puede manifestarse en diferentes niveles. La escala está compuesta por siete ítems que evalúan áreas consideradas nucleares en la depresión: motivación o interés por las cosas, impulso para la actividad, capacidad de experimentar placer, percepción del trabajo cotidiano, estado de ánimo, energía corporal y cualidad subjetiva de la experiencia emocional. Cada dimensión se responde mediante una escala de 10 puntos entre dos adjetivos opuestos. Los estudios reportan adecuadas propiedades psicométricas, destacándose una sensibilidad de 0.93, especificidad entre 0.82 y 0.92, consistencia interna de α = 0.92 y una fiabilidad de κ = 0.76, lo que respalda su utilidad para la evaluación de síntomas depresivos. La puntuación total oscila entre 7 y 70 puntos. Los puntajes más altos indican una mayor intensidad de síntomas depresivos. La interpretación propuesta clasifica los resultados en tres niveles: bajo (7–28), medio (29–49) y alto (50–70). Características psicométricas: • Sensibilidad: 0.93 • Especificidad: 0.82–0.92 • Consistencia interna: α = 0.92 • Fiabilidad: κ = 0.76 Fundamento teórico: El criterio diagnóstico axial se basa en un enfoque dimensional que busca superar las limitaciones de los sistemas categóricos tradicionales como DSM y CIE, permitiendo una evaluación más precisa de la heterogeneidad clínica de la depresión.

This dataset contains pseudonymized vehicle-tracking telemetry from 100 vehicles fitted with Teltonika FMB920 tracking units and monitored across Pakistan by a commercial vehicle-tracking provider. It spans twelve consecutive months (March 2024-February 2025) and comprises 61,331,270 message-level records. Vehicle positions fall within roughly 24.2-35.9° N and 66.6-75.7° E. The dataset was compiled to provide an openly shareable, privacy-preserving record of real fleet operation for research in intelligent transportation and geospatial analysis. Typical uses include reconstructing per-vehicle trajectories and trips, analysing speed, stop and ignition patterns, and assessing GPS-fix quality. The data are also suitable for developing or benchmarking anomaly-detection and trust-scoring methods for Internet-of-Things systems. The records are supplied as twelve monthly archives named with ISO year-month dates (telemetry_2024-03.zip through telemetry_2025-02.zip); each holds one CSV file with the matching name (telemetry_2024-03.csv through telemetry_2025-02.csv). Each row is a single message from one device and has 15 fields: stable vehicle and device pseudonyms, device model, GpsTime, message identifier, longitude (X), latitude (Y), speed, travel direction, altitude, visible-satellite count, ignition state, main battery voltage, packet-validity flag, and Canal. GpsTime is the DateTime when the packet was created by the device. Canal indicates if the message is regular (1) or in response to server ping (0). Vehicle registration numbers and device IMEIs were removed before release and replaced with stable pseudonyms. Column definitions are given in data_dictionary.csv. A 100,000-row sample is provided in sample.csv for quick inspection without downloading the full archives. It uses the same 15 fields as the monthly files and contains 10,000 records from each of ten vehicles (vehicle_020, vehicle_037, vehicle_044, vehicle_047, vehicle_051, vehicle_075, vehicle_076, vehicle_077, vehicle_079 and vehicle_081), all drawn from the first five days of March 2024 (1-5 March 2024). The identifiers Vehicle_Id and Device_Imei_Id are stable across all months, so a vehicle or device can be followed through the whole period. Reporting cadence is not fixed: intervals between consecutive messages vary by vehicle and vehicle state, and Ignition_Status and VectorSpeed help separate moving, idling and parked periods. The data carry no event labels (attacks, faults, anomalies), so any labels needed for supervised tasks must be supplied by the user. The release also includes records_per_vehicle.csv, which gives one total record count per vehicle for all 100 vehicles and is ordered by Vehicle_Id, and number_of_vehicles_and_records_per_month.csv, which lists each monthly archive and contained CSV with vehicle counts, record counts, and a total row.

The corresponding author uploaded the raw operational monitoring dataset used in the manuscript entitled “Interpretable Machine Learning and Rolling-Origin Validation Identify Two Distinct Control and Predictability Regimes in Full-Scale Aerated Lagoon Performance.” The dataset contains daily monitoring records from the Ismailia Sewage Treatment Plant (ISTP), a full-scale aerated-lagoon wastewater treatment plant located in Serabioum, Ismailia Governorate, Egypt. The record covers the period from 2000 to 2008 and includes 8,211 complete observations representing 2,737 sampling dates across three in-series lagoon stages: aerated, facultative, and maturation lagoons. The dataset includes influent, operational, and stage-specific effluent variables, including BOD₅, TSS, fecal coliform, temperature, flow, pH, VSS, and DO. The files are provided in CSV and Excel formats, with the Excel file including a data dictionary to support reuse and interpretation.

This dataset provides an empirical and genealogical reconstruction of the Matutino principalía lineage in Anilao, Iloilo (Panay Island, Philippines). It traces the lineage's socio-political continuity and integration into the colonial bureaucracy following the Clavería decree of 1849, establishing a continuous "biological archive" from the 19th century to the present day.

The ClusterSense-CO dataset is a large-scale dataset developed to support research on operational cost optimization and energy-efficient resource management in CPU cluster infrastructures. The dataset contains detailed information on resource utilization, power consumption, operational expenses, workload distribution, energy costs, and optimization-related performance metrics. It enables researchers to investigate cost-aware scheduling strategies, energy consumption prediction, resource provisioning techniques, and sustainable computing solutions. The dataset is suitable for developing machine learning and optimization models aimed at reducing operational costs while maintaining high system performance and resource efficiency in modern cluster computing environments.

The ClusterSense-SS dataset provides a large-scale collection of job scheduling and resource allocation records from CPU cluster computing systems. The dataset includes information related to job characteristics, resource requirements, execution times, queue waiting times, priority levels, CPU and memory allocations, and scheduling outcomes. It is intended to facilitate research in intelligent workload scheduling, resource optimization, and AI-driven cluster management. This dataset can be used to develop and evaluate machine learning models for efficient task scheduling, reduced execution latency, balanced resource utilization, and improved overall system performance in high-performance computing environments.

The ClusterSense-FP dataset is a comprehensive dataset designed for predictive failure analysis in CPU cluster computing environments. The dataset contains operational and performance-related metrics collected from cluster nodes, including CPU utilization, memory usage, disk I/O, network activity, temperature, power consumption, and system health indicators. It aims to support the development of machine learning and artificial intelligence models for early failure detection, predictive maintenance, anomaly detection, and reliability assessment in distributed computing infrastructures. Researchers and practitioners can utilize this dataset to improve system availability, reduce downtime, and enhance the resilience of large-scale cluster environments.

The mitotic index data obtained are also included.

Fig.5d of Takano et al. (JGR-Atm)