Resultados de investigación

Several studies have posited that authentic leadership (AL) and transformational leadership (TFL) imply ethical behaviour that can mitigate tendencies towards low engagement at work. However, there is a lack of studies analysing, for the same sample, the effect of both styles as a job resource and their effects on employees’ engagement as a means of facilitating their work goals and reducing their job demands. This study addresses this shortcoming by analysing the relations of both leadership styles to vigor, an affective construct, and engagement at work, a motivational outcome. Moreover, the possible mediation effect of vigor at work on the relationship between both leadership styles and engagement is considered. Finally, we explore the differential contributions of both styles to employees’ resources. A sample of Spanish employees (N = 215; 48.8% female) under the supervision of a direct leader responded concerning the TFL and AL of their closest supervisor and their own vigor at work and engagement. Our results show that vigor increases the effect of both leadership styles on engagement. Moreover, TFL, to a greater extent than AL, relates to higher engagement. Thus, vigor as an affective dimension mediates the effect of positive leadership on engagement. This study considers, for the same sample, the effects of two related, albeit different, leadership styles. The results indicate that AL and TFL are positively perceived by employees as creating a climate of energy that acts as a resource (both organizational and personal). Practitioners could enhance employees’ vigor at work and engagement by promoting these two leadership styles., This research was supported by theSpanish Ministry of Science and Innovation MCIN/AEI/10.13039/ 501100011033 project number PID2020-116521RB-I00 and by a predoctoral fellowship (FPU18/00302)

This spreadsheet includes the final databases obtained in the process described in the article "Optimizing rule weights to improve FRBS clustering in wireless sensor networks". The optimized weights for each of the analyzed scenarios can be found in the file., Wireless sensor networks (WSNs) are usually composed of tenths or hundredths of nodes powered by batteries that need efficient resource management to achieve the WSN goals. One of the techniques used to manage WSN resources is clustering, thus nodes are grouped into clusters around a cluster head (CH) which must be chosen carefully. In this article, it is presented a new centralized clustering algorithm based on a Type-1 fuzzy logic controller that infers the probability for each node of becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded after that point until the last node runs out of power. These three KBs have been obtained from the original KB designed by the authors after an optimization process. It is based on a Particle Swarm Optimization algorithm that maximizes the lifetime of the WSN in those three periods by adjusting each rule in those KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make it unaffordable. The results of the present optimized approach improve significantly upon those from other authors with similar methods. Finally, the paper presents an analysis on why some rule weights change more than others in order to design more suitable controllers in the future.

Data collected in the study on the use of the ‘Primum non cadere’ card game to enhance nursing students’ knowledge of fall prevention in older people.

We sampled the diversity of pollen dispersed by solitary bees by using bee trap nests in 12 paired olive farms located in 6 localities from Andalusia in 2020. On the one hand, each paired olive farm is constituted by an olive farm with low-intensive herb cover management and an olive farm with intensive herb cover management. The low-intensive management consists of the maintenance of herb cover during most of the year being only removed in late spring by mechanic mowing or cattle. Conversely, intensive management consists of removing the herb cover permanently by using pre-emergence and/ or post-emergence herbicides sometimes in combination with ploughing the ground several times a year. On the other hand, each paired farm in each locality is surrounded by the same landscape complexity. We classified each paired farm in three categories of landscape complexity (simple, intermediate, and complex) according to the proportion of semi-natural area estimated within a 2 km radius buffer around the centroid of each pair of farms. We use 144 bee trap nests in total, using two different materials and four cavity diameters (40× ca. 9 mm bamboo, 20× ca. 12 mm reed internodes, 20× ca. 15 mm reed internodes and 4× ca. 20 mm reed internodes), providing a total of 84 nesting cavities per nest. On each farm, six nests were set in March, matching the period of activity in the phenology of these solitary bees and the flowering plants they pollinate. Bee trap nests were set in different microhabitats (i.e. olive orchard matrix and non-productive areas). Trap nest colonization was monitored monthly, from April to September. For every cavity colonized, samples of three different pollen packs were extracted and dyed using fuchsine. Samples were identified under microscope to species.

This project was designed as a Multi-Sample Daily Diary Study primarily framed within the Job Demands-Resources (JD-R) model. The core objective was to analyze the complex relationships between job demands (e.g., emotional demands, workload), resources (e.g., leadership support, autonomy), and worker well-being/health (vigor, engagement, burnout, physical activity) at both the daily (within-person) and general (between-person) levels across diverse professional sectors, including education, healthcare, and services. The multi-level nature of the data collection allows for a fine-grained understanding of dynamic processes in the workplace., Data Management Plan and Datasets of the "Leadership and vigor at work: Benefits in physical and psychological health from a multi-method approach" Project, MCIN/AEI/10.13039/501100011033 (Spanish Ministry of Science and Innovation / AEI)

The job demands-resources theory considers an open group of personal and job demands and resources. Thus, it allows us to include personal resources not yet covered (i.e., vigor at work) or less explored (i.e., emotional abilities), as well as personal demands not yet explored (i.e., overcommitment). Additionally, from this theory, it is proposed that leaders may influence employee wellbeing. Therefore, of particular interest is to analyze positive leadership styles, such as authentic leadership (AL). This study addresses three research objectives: 1) to identify profiles of employees from a person-centered approach, combining personal resources (self-perception of emotional abilities, vigor at work and self-efficacy) and personal demands (overcommitment) with job resources and demands; 2) to analyze the relation of the identified profiles with indicators of work-related subjective well-being; and 3) to acknowledge whether the AL style determines the pertaining to a profile probability. A large heterogeneous sample of Spanish employees (N = 968) responded to a questionnaire. Data were analyzed by adopting a person-centered approach using latent profile analysis. The results revealed five patterns of job and individual characteristics: Profile 5 (very low personal resources, and low job resources and demands); Profile 4 (low resources and high demands); Profile 3 (mid-level personal resources, high job resources and low demands); Profile 2 (high personal resources, mid-level job resources and high demands); and Profile 1 (high resources and low demands). Analyses showed that workers differed significantly in well-being depending on their profile membership, with Profile 1 having the highest well-being. Profiles that yielded the worst outcomes were Profile 4 and Profile 5, especially the latter. Finally, the results indicated that AL increased the probability that a profile would show a high well-being level., This research was supported by the Spanish Ministry of Science and Innovation MCIN/ AEI /https:// doi.org/ 10. 13039/ 50110 00110 33; project number PID2020-116521RB-I00 and by a predoctoral fellowship (FPU18/00302).

We have developed a retrospective database of the solar PV plants and wind energy farms in Spain called SOWISP (SOlar and Wind Installed Spanish Power). SOWISP provides the installed wind and solar PV installed capacity in each municipality in Spain, with a monthly temporal resolution, from 2015 to 2020. SOWISP has been developed and validated based on a careful and exhaustive compilation of different public databases. Among other data, SOWISP offers the installed power and location of 59.386 solar PV plants and 1.205 wind farms in Spain (further details are provided in https://doi.org/10.1016/j.solener.2023.03.009). In order to manage the new database, a Python library, named as SOWISP_lib, was developed. This library is composed of three functions, which allow: (1) reading the SOWISP database, (2) extracting information at different spatial aggregations (towns, NUTS 2 and NUTS 3), and (3) selecting information for specific time periods or specific dates. Python script (SOWISP_lib) to use it, is here attached and are publicly available at: https://github.com/matrasujaen/SOWISP When quoting this dataset, please refer to following publication: Jiménez-Garrote A, G Sánchez-Hernández, M López-Cuesta and David Pozo-Vázquez, 2023. SOWISP - A retrospective high spatial and temporal resolution database of the installed wind and solar PV power in Spain, Solar Energy, 256, 44-54, https://doi.org/10.1016/j.solener.2023.03.009., We have developed a retrospective database of the solar PV plants and wind energy farms in Spain called SOWISP (SOlar and Wind Installed Spanish Power). SOWISP provides the installed wind and solar PV installed capacity in each municipality in Spain, with a monthly temporal resolution, from 2015 to 2020. SOWISP has been developed and validated based on a careful and exhaustive compilation of different public databases. Among other data, SOWISP offers the installed power and location of 59.386 solar PV plants and 1.205 wind farms in Spain (further details are provided in https://doi.org/10.1016/j.solener.2023.03.009). In order to manage the new database, a Python library, named as SOWISP_lib, was developed. This library is composed of three functions, which allow: (1) reading the SOWISP database, (2) extracting information at different spatial aggregations (towns, NUTS 2 and NUTS 3), and (3) selecting information for specific time periods or specific dates. Python script (SOWISP_lib) to use it, are publicly available at: https://github.com/matrasujaen/SOWISP When quoting this dataset, please refer to following publication: Jiménez-Garrote A, G Sánchez-Hernández, M López-Cuesta and David Pozo-Vázquez, 2023. SOWISP - A retrospective high spatial and temporal resolution database of the installed wind and solar PV power in Spain, Solar Energy, 256, 44-54, https://doi.org/10.1016/j.solener.2023.03.009., This work has been made possible by the project funded by Agencia Estatal de Investigación, Spain (PID2019-107455RB-C21/AEI/ 92 10.13039/501100011033 and PID2019-107455RB-C22/AEI/10.13039/ 501100011033). Authors are supported also by the Junta de Andalucía (Research group TEP-220), FEDER funds and UE-Junta de Andalucía co-funded postdoctoral fellowship PAIDI2020-DOC_01110.

In the absence of a Spanish version of the Leadership Virtues Questionnaire, this study aims to propose a short form of the scale by conducting a cultural adaptation and analyzing its factor structure. In Study 1, the procedure following ITC guidelines and qualitative analysis is performed, leading to the reformulation of several items. In Study 2, 550 workers (Samples 1 and 2) responded to this Spanish version, and the fit of the measurement models proposed for the original proposal of the instrument was tested. Since no model fit adequately, in Study 3 an EFA was carried out with Sample 1, and two items per factor were extracted. In Study 4, the fit of the measurement models to the data was tested through CFAs with Sample 2, resulting in an optimal second-order factor model. In addition, other evidence related to the construct validity of the instrument was proven., This research was supported by the Spanish Ministry of Science and Innovation through the project PID2020-116521RB-I00, funded by MCIN/AEI/10.13039/501100011033, and by a predoctoral fellowship (FPU18/00302).

Enhanced solar PV Capacity Factors database (NUTS 3, hourly, 1990–2020) for Spain., Enhanced solar PV Capacity Factors database (NUTS 3, hourly, 1990–2020) for Spain, resulting from this work: Guadalupe Sánchez-Hernández, Antonio Jiménez-Garrote, Miguel López-Cuesta, Inés M. Galván, Ricardo Aler, David Pozo-Vázquez, A novel method for modeling renewable power production using ERA5: Spanish solar PV energy, Renewable Energy, Volume 240, 2025, 122120, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2024.122120. When quoting this dataset, please refer this publication., This study was conducted in the framework the MET4LOWCAR research project (reference PID2019-107455RB-C21/AEI/10.13039/501100011033 and PID2019-107455RB-C22/AEI/10.13039/501100011033), financed by the Spanish Research Agency, Spain. Guadalupe Sánchez-Hernández also thanks the support from UE-Junta de Andalucía, Spain co-funded postdoctoral fellowship PAIDI2020-DOC_01110. The authors acknowledge the Spanish TSO Red Eléctrica for yielding actual wind and solar PV generation data.