Resultados de investigación

Typical organisational goals promoted by knowledge management (KM) are being complemented with resilience-related objectives in many organisations worldwide. The need for resilience goes beyond companies, reaching their supply chains in a concept known as enterprise and supply chain resilience (ESCR). In complex times where organisations must learn to deal with disruptive events (DEs), ranging from internal events like inventory stockouts to external events, such as pandemics and wars, knowledge sharing becomes critical. Presenting a teaching case (TC) developed using instructional design principles, which aims to teach ESCR in real-world scenarios, is the main objective of this work. The proposed TC has clear goals, objectives, and learning outcomes, it intends to engage the students in situations that require problem-solving and decision-making. It is an open problem case with multiple perspectives where solutions can vary since DEs are difficult to predict and manage. The case includes detailed context information for the reader to understand and diagnose the situation and, after that, develop and create an action plan and a logic model to solve the problems related to the critical elements of the case. The TC presents a real-life situation, through the present dataset, where an organisation and its supply chain are facing a critical incident, an impactful DE, which is causing severe problems to the flow of products between customers and suppliers. This case has no unique answer, and the solutions require critical thinking., The authors would like to acknowledge the support of the researchers participating in the project “Business Continuity Managers Training Platform” (CONTINUITY) (business-continuity-project.eu).

[EN] The dataset contains the data that have been obtained during the synthesis and the characterization (chemical and photoelectrochemical) of TiO2/ZnO2 hybrid nanosponges used as photoanodes for photoelectrochemical applications. TiO2/ZnO2 hybrid nanosponges were obtained by electrodeposition of ZnO on TiO2 nanosponges with different times (15 min, 30 min, and 60 min), temperatures (25ºC, 65 ºC, and 75 ºC) and Zn(NO3)2 concentrations (0.5-10 mM). A chemical characterization was performed using X-Ray Photoelectron Spectroscopy (XPS) and Grazing Incidence X-Ray Diffraction (GIXRD). Electrochemical characterization of nanosponges was performed by Photoelectrochemical Water Splitting (PWS).

The collated data correspond to the parameters to estimate the resources consumption and the emissions to the environment from the main farming activities (resources consumption and emissions from infrastructure building and management, fertiliser and pesticide production, on-field fertiliser and pesticide emissions, fuel consumption from machinery use, and irrigation), together with the environmental impacts of the unit processes (e.g. electricity mix, production of agricultural inputs) required to assess the environmental impacts from a panel of reference holdings at the main NUTS 2 in Spain., The authors gratefully acknowledge both the Universitat Politècnica de València for providing the funds for N.K. Sinisterra-Solís’s research contract through Subprogram 1 (PAID-01-18).

The data are related with the preparation of electrodes based on WO3 nanostructures used as anode materials for Li-ion batteries. The nanostructured WO3 thin film was effectively synthesized by an electrochemical procedure. Then, an annealing treatment at 600◦C in air environment for 4 h was carried out. In the second electrode synthesized, a carbon layer was uniformly deposited on WO3 nanostructures to obtain a WO3/C electrode. Finally, WO3/WS2 electrodes were prepared by means of in situ sulfurization of WO3 one-step solid-state synthesis using tungsten trioxide (WO3) and thiourea as precursor material. By using X-ray photoelectron spectroscopy, X-ray diffraction analysis and Raman spectra, the three electrodes have been morphologically characterized. Electrochemical properties were analysed by cyclic voltammogram, galvanostatic charge/discharge cycling, and electrochemical impedance spectroscopy.

This dataset includes the raw data collected from two Open Access megapublisher websites (mdpi.com and frontiersin.org) across several SEO tools (Ahrefs, Semrush, SERPstats, Ubersuggest, Sistrix). From each domain name, SEO-level metrics (e.g., organic keywords, global traffic, traffic by country) were collected and structured.

[EN] This dataset includes, as supplementary material, the detailed calculations and expert questionnaires required to carry out the prioritization of Ecosystem Services in three wetlands in Spain, aggregating expert opinions and using the Analytic Network Process (ANP) method.

Twitter dataset for a research on the 2015 Spanish General Election. The tweets were obtained through the Twitter API's Search function since November 2 to December 21, 2015. Three criteria for filtering tweets were established: two general terms linked to the elections ("#20D", "20-D"); the name and usernames of the four main parties (PP, PSOE, Podemos and Ciudadanos), and the name and usernames of the four candidates from those parties. The research assessed the tweet's issue according to Patterson (1980)'s typology for evaluating political mediatisation. The dataset contains three files: the unlabelled collection of the 15.8 million tweet ids obtained through the API, the annotated sample of the 3,117 tweet ids for assessing the issue via machine learning, and the readme.txt file. It is recommended to open the file from a text editor or through a script. The id_str must be handled as a string. If the file is open from Excel, the id_str will be interpreted as a number, and then converted into a float, losing information in this transformation

This dataset contains the experimental data generated during the evaluation of commercial wallpapers composed of different layers of polymeric and cellulosic materials as potential radon barriers for indoor spaces. It includes the raw radon concentration measurements for a single layer, double layer and double layer with aluminium configuration recorded in the source and receiver chambers during 48-hour diffusion tests, the main specifications of the experimental set-up used to perform the measurements, and the thickness of the laminated waste samples used in the study. The experimental data were used to calculate the radon concentration reduction and the radon diffusion coefficient, following a modified version of the ISO/TS 11665–13:2017 standard, as reported in the associated publication. The experimental data were generated using a two-chamber radon diffusion test following a modified version of ISO/TS 11665-13:2017. The experimental set-up consisted of two sealed AISI 316L stainless steel chambers separated by the test sample. A sealed pitchblende stone (1.469 kBq) was placed inside the source chamber, while the receiver chamber collected the radon that permeated through the sample. Radon concentration in both chambers was continuously monitored using two Durridge RAD7 detectors connected in closed-loop circuits. Temperature and relative humidity were monitored throughout the experiments. Each test lasted 48 hours, during which radon concentrations in the source and receiver chambers were recorded at 30-minute intervals. These raw experimental data were subsequently used to calculate the radon concentration reduction and the radon diffusion coefficient following the procedure described in ISO/TS 11665-13:2017. The dataset also includes the main specifications of the experimental set-up, such as chamber characteristics, radon source activity and test area. The thickness of each wallpaper sample was determined from eight measurements taken at different locations on the sample, from which the mean thickness and the standard deviation were calculated. The radon concentration data recorded by the RAD7 detectors at 30-minute intervals were exported. For each experiment, the dataset includes the test date and the radon concentration values measured in the source and receiver chambers throughout the 48-hour test. No additional processing, filtering or normalization was applied to the concentration data before their inclusion in the dataset. The experimental set-up specifications were compiled from the laboratory protocol used during the diffusion tests. For the thickness data, the reported mean thickness and standard deviation were calculated from eight thickness measurements taken at different locations on each laminated waste sample.

Cocoa roasting triggers coupled thermal reactions that shape bioactive composition and promote neoformed contaminants (NFCs), but frameworks linking precursor dynamics and bioactivity remain limited. Here, multivariate relationships among Maillard precursors, physicochemical variables, bioactive descriptors and NFCs were evaluated during cocoa roasting at 110–130º C across six roast progression (RP) stages. RP was the main driver of chemical change: sucrose decreased by >95%, while glucose and fructose peaked at RP1-RP2 (up to ~2500 mg/ g) before declining, alongside a decrease in asparagine to ~0.1–0.2 mg/g. NFCs peaked at intermediate stages and then decreased with advanced roasting (40–95%), as did phenols, antioxidant activity (60–85%) and methylxanthines (~90%). Pareto-PCA revealed a safety-bioactivity trade-off, identifying RP3-RP4 as relevant window. Partial Least Squares Regression showed that furfuryl alcohol (R (R 2 2 2 =0.83) and 5-hydroxymethylfurfural = 0.82) were well explained by information on precursors, physicochemical properties and bioactive compounds, while acrylamide (R = 0.34) remained poorly predictable.

[EN] These datasets were generated to train ("PopulationDrugsTrainingKrNaLCaL.xlsx") and test ("PopulationDrugsTestKrCaLNaL.xlsx") the uncertainty assessment models developed in the paper "Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models" by Kopanska, Rodríguez-Belenguer, et al., The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365, supported from European Union's Horizon 2020 and the EFPIA. We also received funding from the SimCardioTest supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016496. J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the “Formacion de Profesorado Universitario” (Grant Reference: FPU18/01659). The work was also partially support by the Dirección General de Política Científica de la Generalitat Valenciana (PROMETEO/ 2020/043).