BUSCADOR RECOLECTA

Base de datos de los museos marítimos españoles con datos sobre fondos recibidos, This research was funded by SPANISH ECONOMY AND COMPETITIVENESS MINISTRY, grant number PID2019-105497 GB-I00, European Fisheries Funds, opportunities for the fisheries sector through diversification, and the FLAGs management (DivPesc).

[EN] The H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments" (GROW GREEN, Grant Agreement: 730283), developed green infrastructure pilots in: Manchester, Valencia and Wroclaw. The monitoring framework supported the pilot analysis and its impact assessment through the development of core Key Performance Indicators (KPIs) through all pilots. The historical evolution of these core KPIs are available on the Grow-Green Open Data platform sharing the software architecture for the smart city platform of Valencia City. It is an implementation of Telefónica’s Thinking Cities platform, which is based on the FIWARE standards and interfaces. All monitoring data are included on this dataset grouped on the core KPIs structure., This research was co-funded by the European Commission through the H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments (GROW GREEN)” Grant Agreement: 730283.

[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).

This repository contains the code used to generate the male and female population of models and simulate the different pharmacokinetic scenarios in Llopis-Lorente et al. (2023). Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity

Sustainability website indicators after scraping the websites of textile companies in the Comunitat Valencia region.

Quality of life indicators include measures of both objective and subjective well-being to broaden the traditional income-focused view towards a multidimensional view of welfare. In this paper, we develop a single reliable composite index that considers the effect of uncertainty and imprecision related to the specific nature of the phenomenon being measured. Additionally, we suggest a procedure for incorporating this new way of assigning priorities through multicriteria techniques. To this end, we propose a new multicriteria approach that introduces the recent notion of picture fuzzy sets to deal with uncertainty and imprecision in the assessment of objective and subjective dimensions of quality-of-life performance. To verify the effectiveness of the whole approach and the credibility of the results, we show its application for monitoring the quality of life in the European Union. Our results show that fuzzy numbers can be a useful tool when combined with multicriteria techniques in the prioritization phase of the criteria and that they increase the quality and reliability of the overall aggregate indicator. Moreover, we find that in a picture environment, the results are consistent regardless of the multicriteria approach used to provide a final ranking.

This dataset includes the raw data used to carry out a study related to the analysis of the DORA Declaration on Twitter. The dataset includes the tweets collected from the Twitter Academic API (comprising three collections: tweets published by DORA, tweets mentioning DORA, and tweets including a DORA-related hashtag), supplementary material (including extra tables and figures), and the script used to collect data from Twitch API.

The data were use to study the degradation of Methylparaben using WO3 Nanostructures. WO3 nanostructures were synthesized with different complexing agents (0.05 M H2O2 and 0.1 M citric acid) and annealing conditions (400 _C, 500 _C and 600 _C) to obtain optimal WO3 nanostructures to use them as a photoanode in the photoelectrochemical (PEC) degradation of an endocrine disruptor chemical. X-ray photoelectron spectroscopy was performed to provide information of the electronic states of the nanostructures. The crystallinity of the samples was observed by a confocal Raman laser microscope and X-ray diffraction. Furthermore, photoelectrochemical measurements (photostability, photoelectrochemical impedance spectroscopy, Mott–Schottky and water-splitting test) were also performed using a solar simulator with AM 1.5 conditions at 100 mW_cm-2. Once the optimal nanostructure was obtained, the PEC degradation of methylparaben was carried out. It was followed by ultra-high-performance liquid chromatography and mass spectrometry, which allowed to obtain the concentration of the contaminant during degradation and the identification of degradation intermediates.

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.

The numerical information in these datasets consists of two files. The first file (Dataset_SRI.xlsx) contains the asset data of monthly returns and ESG risk scores for three fund managers from January 2011 to December 2019. The second file (Pareto_Fronts.xlsx) contains the mean-variance-ESG Pareto fronts and the Pareto sets of non-dominated solutions for each manager obtained using the ev-MOGA algorithm. To get these Pareto fronts, we have considered an in-sample period of 72 monthly returns over six years from January 2011 to December 2016. To implement the ev-MOGA algorithm, we use an initial population of 50000 individuals and an auxiliary population of 500. The number of boxes defining the space of each function is 100.