Mendeley Data Showcase

The raw data of a study that investigate the bactericidal activity of an anticancer molecule (Pan-Ras-In-1) against Staphylococcus aureus.

This study investigates the sources of dawn-side O⁺ density enhancements in the Martian ionosphere by combining observations from NASA's MAVEN spacecraft with photochemical model simulations. The accompanying text files contain the datasets used to produce each figure in the paper.

This is the original data of the article：Epitranscriptomic control of insect diapause: m6A RNA methylation couples hormonal signals to developmental fate.

La adicción a las redes sociales se define como un patrón de uso excesivo, compulsivo y persistente de plataformas digitales que genera consecuencias negativas en la salud mental, física, social y académica. Las personas presentan dificultades para controlar el tiempo de uso, síntomas de abstinencia emocional cuando no pueden acceder a las redes y una tendencia a utilizarlas como mecanismo para afrontar el estrés o emociones negativas. La evidencia científica muestra asociaciones con ansiedad, depresión, estrés, alteraciones del sueño, baja autoestima, aislamiento social y disminución del rendimiento académico y laboral. Entre los factores explicativos destacan el miedo a perderse experiencias importantes (FOMO), la búsqueda constante de aprobación social mediante comentarios y 'likes', dificultades en la regulación emocional y déficits en el control inhibitorio. Los adolescentes y jóvenes adultos constituyen la población más vulnerable debido a características propias de su desarrollo neuropsicológico. Las investigaciones señalan que las intervenciones más eficaces son la Terapia Cognitivo-Conductual (TCC) y los programas basados en mindfulness, los cuales favorecen el control de impulsos, la regulación emocional y la reducción del uso problemático de las redes sociales.

The raw datasets supporting the spatial pattern analysis of benthic macroinvertebrate diversity and its environmental drivers in the middle and lower Yangtze River are publicly available in the repository.

This dataset provides a directed graph of semantic proximity between intellectual concepts (philosophers, scientists, artists, educational institutions, religions, ideologies, and fields of knowledge), built from the mappingbased-objects dump (version lang=en) of DBpedia. The graph was produced as part of the project for the course MC859 - Project in Theoretical Computer Science (University of Campinas, 2025), which applies centrality algorithms (PageRank and Personalized PageRank) and community detection methods to open ontologies. Starting from the 22,791,171 semantic triples in the original dump, a subnetwork was extracted based on 11 thematic predicates selected for their relevance to the analysis of intellectual prestige flow: dbo:influencedBy, dbo:influenced, dbo:doctoralAdvisor, dbo:doctoralStudent, dbo:academicAdvisor, dbo:almaMater, dbo:knownFor, dbo:notableWork, dbo:field, dbo:religion, and dbo:ideology. Inverse relations (influencedBy, doctoralAdvisor, academicAdvisor) were semantically reoriented so that all edges point from the influencer to the influenced entity, aligning the graph with the canonical interpretation of PageRank as a measure of prestige. After reorientation and deduplication, the final graph contains 326,270 vertices and 426,617 directed edges, with an average degree of 2.615. The graph is highly fragmented in terms of strongly connected components (approximately 325,000 SCCs, of which 99.97% are singletons), but structurally cohesive when weakly connected components are considered — a typical characteristic of directed ontologies that encode chronological relations of influence. The degree distribution follows a power law (scale-free network), with prominent hubs at educational institutions (Harvard, Cambridge, Yale), religions (Christianity, Islam, Catholicism), and disciplines (Painting, Philosophy, Law).

Abstract: This dataset encompasses the cross-platform source code, algorithmic tracking logic, and interface architecture for an automated computer vision framework engineered to evaluate motor skills and frequency metrics in table tennis through the standardized wall-pass test. Developed using a decoupled dual-stack layout—utilizing a high-performance Python back-end powered by OpenCV and a responsive web-based HTML5/JavaScript dashboard—this system automates the diagnostic evaluation of athletic coordinates without laboratory-bound sensors. The computational core leverages real-time color segmentation within the Hue-Saturation-Value (HSV) color space combined with morphological noise filtration (erosion/dilation) to isolate ball dynamics based on high-circularity contour parameters. Concurrently, a localized pixel threshold matrix defines the dynamic "wall target zone." The tracking engine utilizes a bounded deque memory buffer to reconstruct fading 2D spatial trajectories while applying mathematical cross-over logic to count successful passes as the ball centroid bisects the calibrated target quadrants. This data package includes complete backend multi-color tracking configurations, configuration scripts, and styling templates, offering a foundational toolkit for sports scientists, performance analysts, and table tennis coaches looking to digitize reactive training paradigms. Keywords: Computer Vision in Sports, HSV Color Segmentation, Object Tracking, Table Tennis Analytics, Wall Pass Test

This dataset contains high‑resolution measurements of solar irradiance and ambient temperature collected on roof (horizontal plane, I1/T1) and façade (vertical plane, I2/T2) surfaces of an urban university building in Medellín, Colombia. The monitoring system was implemented using an edge computing device that enabled local acquisition, validation, and transmission of sensor data to the cloud in real time. The dataset is organized as time series in both CSV and Parquet formats, ensuring accessibility and reproducibility. It provides continuous records from January 30, 2026 (14:30 local time) to June 12, 2026 (12:39 local time), with an average sampling interval of approximately 1.71 seconds. Measurements on dual orientations (roof vs. façade) offer valuable insight into the variability of solar exposure in building‑integrated photovoltaic (BIPV) contexts, highlighting the influence of surface inclination and surrounding urban elements. The dataset underwent a structured processing workflow to ensure quality and consistency. Initially, the consolidated CSV file was loaded into the edge computing environment, and only relevant columns were selected (sensor ID, measurement value, and units). Timestamps were converted from UTC to local time in Medellín (UTC‑5). Subsequently, null records were removed, and out‑of‑range values were excluded, specifically irradiance and temperature values below zero. Files referring to irradiance measurements are denoted with the prefix irr_, while files referring to temperature measurements are denoted with the prefix temp_. The suffix 1 corresponds to sensors installed on the roof (horizontal plane), and the suffix 2 corresponds to sensors installed on the façade (vertical plane). Parque i ├── Horizontal measurement (I1, T1) - roof │ ├── irr_1.csv │ ├── irr_1.parquet │ ├── temp_1.csv │ └── temp_1.parquet └── Vertical measurement (I2, T2) - façade ├── irr_2.csv ├── irr_2.parquet ├── temp_2.csv └── temp_2.parquet

To investigate the influence of air injection configurations on the flow behavior and lifting performance of airlift systems, computational fluid dynamics (CFD) simulations were conducted for gas-liquid two-phase flow in a riser. A system with a riser diameter of 213 mm, length of 9.95 m, and submergence ratio of 0.9 was analyzed. Two injection strategies were considered: an injection-chamber-assisted configuration and a direct-injection configuration. The chamber-assisted design included uniformly distributed circular orifices, transverse slots, and longitudinal slots, while the direct-injection case examined the effects of orifice diameter and number, and injection angle. Performance was evaluated using liquid lifting rate, lifting efficiency, gas volume fraction, and flow field characteristics. Results indicate that the injection chamber significantly enhances gas distribution uniformity and inlet flow conditioning, leading to improved lifting performance and operational stability compared with direct injection. Among chamber configurations, uniformly distributed circular orifices provide the best performance due to more homogeneous gas dispersion and stronger gas-liquid interaction, followed by transverse slots, while longitudinal slots promote gas accumulation and reduce efficiency. For direct injection, a clear coupling between orifice diameter and number is observed: moderate enlargement of orifices reduces flow resistance and improves axial transport, whereas excessive enlargement weakens radial mixing. The effect of injection angle is limited, with 45° inclination providing only slight improvement over 90° injection. Transient results further show that gas gradually migrates upward under jet momentum and buoyancy, resulting in a dynamically evolving gas supply region and time-dependent flow behavior.

Supplemental material for ‘Characterizing dairy cows based on their daily behavior deviation patterns relative to the herd using a clustering approach’.