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The Dataset related to horse gait, sex, age, locomotion and kinematic measurements using inertial motion sensors, and AI gait classification. It consists of an Excel spreadsheet containing 217 rows.

Supplemental Figures for Images in JAAD; Journal of the American Academy of Dermatology.

El objetivo del presente trabajo fue identificar los factores determinantes del emprendimiento en estudiantes universitarios en Tamaulipas, México. Se analizan las actitudes emprendedoras, en particular de tres facultades de la Universidad Autónoma de Tamaulipas (México), así como su impresión sobre el rol jugado, por las diferentes instituciones académicas y empresas, en el fomento del espíritu empresarial. Adicionalmente, se estudia la percepción de las empresas sobre su capacidad de influir en el interés emprendedor de los universitarios, así como su opinión sobre el papel de la facultad en el fomento empresarial. Sexo: 1 "Hombres", 2 "Mujeres") Edad <= 3, "<= 22 años", > 3, "> 22 años") Estudios: 1 "Contador Público", 2 "Lic. Administración, 3 "Lic. Tecnologías de la información", 4 "Ing. Comercial, 5 "Lic. en Economía", 6 "Técnico superior universitario" Experiencia: 1 "Con experiencia", 2 "Sin experiencia" Especifico: 1 "Con conocimientos", 2 "Sin conocimientos" Capital social: 1 "Sí cuento con familiar o amigo", 2 "No cuento con familiar o amigo") Deseabilidad: 1 "Nada deseable", 2 "Levemente deseable, 3 "Moderadamente deseable, 4 "Sustancialmente deseable, , "Muy deseable" Viabilidad: 1 "Muy difícil", 2 "Sustancialmente fácil", 3 "Moderadamente fácil", 4 "Levemente fácil", 5 "Muy fácil" Intencion: 1 "No nunca", 2 "No, pero pienso integrarme en una empresa de mi familia", 3 "Sí, vagamente", 4 "Sí, seriamente", 5 "Sí, tengo el propósito de crear una empresa"

Supplementary Tables and Figures

Background information for the development of the Monte Carlo simulation methodology to determine the probability of flood flows in river basins

Provides code to replicate findings from "Global Banks and Natural Disasters"

The CDS-Cricket dataset is a scientific information resource. It provides images of the domestic cricket at its three developmental stages: eggs, nymphs, and adults. The images were captured in a controlled environment. This makes the dataset useful for research into deep learning models and computer vision. They are ideal for insect detection and classification tasks.

This dataset contains operational data from a grid-connected photovoltaic Energy Management System (EMS) with battery energy storage. The dataset includes variables related to photovoltaic power generation, load demand, battery power flow, grid energy exchange, and battery state of charge (SOC). Additionally, fixed battery energy storage system parameters are included to improve system reproducibility and analysis. The sampling time resolution is 1 minute. Variables Description TIME: Timestamp of data acquisition. Unit: date and time P_PV: Power generated by the photovoltaic system. Unit: kW LOAD: Load power demand of the system. Unit: kW P_BAT: Battery charge/discharge power. Positive values represent battery discharging and negative values represent charging. Unit: kW P_GRID: Power exchanged with the electrical grid. Unit: kW SOC_REAL: Real battery state of charge. Unit: % ESS_MAX: Maximum energy storage system capacity. Unit: kWh ESS_INI: Initial energy storage system capacity. Unit: kWh BDR_Kwh: Nominal battery bank capacity. Unit: kWh BDR_Min: Minimum allowed battery bank limit. Unit: kWh BDR_Max: Maximum allowed battery bank limit. Unit: kWh DOD: Battery depth of discharge. Unit: %

DESCRIPTION OF THE RESEARCH AND DATA This work presents the Madrid Traffic Dataset (MTD), a comprehensive resource for the analysis and modeling of traffic patterns in Madrid. The dataset integrates traffic sensor measurements, weather observations, labor calendar information, road infrastructure attributes, and geolocation data to support urban mobility studies and predictive modeling. In addition to the core tabular data, this release includes temporal sequences and traffic adjacency matrices, enabling time-series analysis and graph-based machine learning approaches. COMPLETE DATASET The complete version of the MTD includes data from 554 traffic sensors distributed across the Madrid region, covering 30 months, from June 2022 to November 2024. SUBSET DATASET A compact version derived from the complete dataset is also provided. It focuses on 300 traffic sensors and covers 17 months, from June 2022 to October 2023. This subset is intended for researchers who need a lighter dataset for experimentation. DATA ORGANIZATION The dataset is organized into folders identified by configuration data hashes. Each folder contains processed datasets, temporal sequences, adjacency matrices, sensor coordinate files, and configuration files. This structure supports traceability, reproducibility, and comparison between the complete and subset versions. For more details, see the associated article: Gómez, I. and Ilarri, S., Advanced Prediction of Traffic at Different Temporal Scales Using Heterogeneous Data Sources, IEEE Open Journal of Intelligent Transportation Systems, 2025. DOI: 10.1109/OJITS.2025.3637305.

The primary objective of this study was to characterize the high-frequency diel variability of the marine carbonate system and stable carbon isotopes (δ13C-DIC) in a high-advection coral reef environment. We hypothesized that despite significant physical forcing and events driven by advection, the underlying metabolic "breath" of the reef - specifically the organic carbon sources fueling nighttime respiration - could be isolated and identified using isotopic mass balance (Keeling plots). We specifically sought to test the hypothesis that certain reef systems are fueled primarily by the consumption of externally acquired organic matter (zooplankton) rather than locally produced autotrophic carbon. This dataset contains a 34-hour time-series of surface seawater carbonate chemistry collected at Turtle Crawle (TC), Jamaica, in June 2023. The data captures a full diel cycle (Day-Night-Day) featuring clear patterns of: Daytime DIC drawdown (photosynthesis) and nighttime DIC accumulation (respiration); A significant advective "reset" event occurring between 23:00 and 03:00 on Night 1, characterized by a sharp increase in salinity (+0.58) and a simultaneous drop in DIC concentration; High-precision δ13C-DIC measurements that track the integrated history of benthic metabolism, reaching an absolute minimum at dawn despite the physical reset of carbon concentrations. The data demonstrates that physical advection (likely brine-enriched groundwater or offshore water) can mask biological signals in raw concentration data (DIC and TA), leading to poor stoichiometric correlations. Despite these sources of physical "noise," the raw 1/DIC vs. δ13C-DIC relationship remained robust, yielding a stable metabolic intercept of -19.4 ± 5.4‰. The intercept indicates that the reef's nighttime respiration is fueled by a heterotrophic source (zooplankton/POM) rather than local coral-associated carbon. A key finding of this research is that traditional salinity-normalization (Friis et al., 2003) can introduce mathematical artifacts in high-advection settings where physical and chemical signals are decoupled, producing physically nonsensical results. Methods: Seawater was collected in 300 mL borosilicate BOD bottles and poisoned with saturated mercuric chloride (HgCl2) to halt biological activity. Analyses were performed using a coupled UIC MODICA and Picarro G-2131i cavity ring-down spectroscopy system. DIC (DIC; µmol/kg) and pH (total scale) were measured via phosphoric acid titration and spectrophotometric determination. δ13C-DIC was calibrated using a regional field anchor from the 2021 A22 Repeat Hydrography Cruise to reconcile the instrumental scale with the VPDB scale (constant offset of –11.1‰). Total Alkalinity (TA; µmol/kg) and in situ pH (total scale) were calculated using CO2SYS v3.0 (equilibrium constants from Lueker et al., 2000).