Resultados totales (Incluyendo duplicados): 33870
Encontrada(s) 3387 página(s)
Encontrada(s) 3387 página(s)
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
Dataset. 2022
GROW-GREEN CORE KPIS
GROW-GREEN PILOTS MONITORING
- Orozco Messana, Javier|||0000-0001-8611-8816
- Calabuig Moreno, Raimon|||0000-0003-0810-881X
- Vallés Planells, María Concepción|||0000-0002-5932-0485
- Galiana Galán, Francisco|||0000-0001-7897-6538
- Tudorie, Carla Ana-Maria|||0000-0002-3060-6199
- Alfonso Solar, David|||0000-0003-0141-075X
- Peñalvo López, Elisa|||0000-0002-3143-822X
- Andrés Doménech, Ignacio|||0000-0003-4237-4863
[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.
Proyecto: European Commission/H2020/730283
DOI: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
HANDLE: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
PMID: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
Ver en: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191820
Dataset. 2023
UNCERTAINTY ASSESSMENT OF PROARRHYTHMIA PREDICTIONS DERIVED FROM MULTI-LEVEL IN SILICO MODELS
- Kopanska, Karolina
- Rodríguez-Belenguer, Pablo
- Llopis-Lorente, Jordi|||0000-0002-3958-8062
- Trénor, Beatriz|||0000-0001-9166-6112
- Saiz, Javier|||0000-0002-9850-0825
- Pastor, Manuel
[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).
DOI: Dataset/10251/191820" target="_blank">http://hdl.handle.net/10251/191820, https://dx.doi.org/10.4995/Dataset/10251/191820
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191820
HANDLE: Dataset/10251/191820" target="_blank">http://hdl.handle.net/10251/191820, https://dx.doi.org/10.4995/Dataset/10251/191820
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191820
PMID: Dataset/10251/191820" target="_blank">http://hdl.handle.net/10251/191820, https://dx.doi.org/10.4995/Dataset/10251/191820
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191820
Ver en: Dataset/10251/191820" target="_blank">http://hdl.handle.net/10251/191820, https://dx.doi.org/10.4995/Dataset/10251/191820
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191820
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193255
Dataset. 2023
COMBINING PHARMACOKINETIC AND ELECTROPHYSIOLOGICAL MODELS FOR EARLY PREDICTION OF DRUG-INDUCED ARRHYTHMOGENICITY
- Llopis-Lorente, Jordi|||0000-0002-3958-8062
- Baroudi, Samuel
- Koloskoff, Kévin
- Romero Pérez, Lucia|||0000-0003-4605-8630
- Mora-Fenoll, María Teresa|||0000-0002-8069-2486
- Basset, Matthieu
- Benito, Sylvain
- Dayan, Frederic
- Saiz Rodríguez, Francisco Javier|||0000-0002-9850-0825
- Trénor Gomis, Beatriz Ana|||0000-0001-9166-6112
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
DOI: Dataset/10251/193255" target="_blank">http://hdl.handle.net/10251/193255, https://dx.doi.org/10.4995/Dataset/10251/193255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193255
HANDLE: Dataset/10251/193255" target="_blank">http://hdl.handle.net/10251/193255, https://dx.doi.org/10.4995/Dataset/10251/193255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193255
PMID: Dataset/10251/193255" target="_blank">http://hdl.handle.net/10251/193255, https://dx.doi.org/10.4995/Dataset/10251/193255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193255
Ver en: Dataset/10251/193255" target="_blank">http://hdl.handle.net/10251/193255, https://dx.doi.org/10.4995/Dataset/10251/193255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193552
Dataset. 2023
WEBSITE INDICATORS ON TEXTILE COMPANIES IN THE COMUNITAT VALENCIANA REGION
- Doménech i de Soria, Josep|||0000-0002-7302-5810
- García-Bernabeu, Ana|||0000-0003-3181-7745
- Díaz García, Pablo|||0000-0002-7093-6061
Sustainability website indicators after scraping the websites of textile companies in the Comunitat Valencia region.
DOI: Dataset/10251/193552" target="_blank">http://hdl.handle.net/10251/193552, https://dx.doi.org/10.4995/Dataset/10251/193552
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193552
HANDLE: Dataset/10251/193552" target="_blank">http://hdl.handle.net/10251/193552, https://dx.doi.org/10.4995/Dataset/10251/193552
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193552
PMID: Dataset/10251/193552" target="_blank">http://hdl.handle.net/10251/193552, https://dx.doi.org/10.4995/Dataset/10251/193552
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193552
Ver en: Dataset/10251/193552" target="_blank">http://hdl.handle.net/10251/193552, https://dx.doi.org/10.4995/Dataset/10251/193552
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/193552
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/194651
Dataset. 2023
MEASURING QUALITY OF LIFE IN EUROPE: A NEW MULTICRITERIA APPROACH
MEASURING QUALITY OF LIFE IN EUROPE: A NEW MULTICRITERIA APPROACH
- García Bernabeu, Ana María|||0000-0003-3181-7745
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.
Proyecto: //
DOI: Dataset/10251/194651" target="_blank">http://hdl.handle.net/10251/194651, https://dx.doi.org/10.4995/Dataset/10251/194651
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/194651
HANDLE: Dataset/10251/194651" target="_blank">http://hdl.handle.net/10251/194651, https://dx.doi.org/10.4995/Dataset/10251/194651
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/194651
PMID: Dataset/10251/194651" target="_blank">http://hdl.handle.net/10251/194651, https://dx.doi.org/10.4995/Dataset/10251/194651
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/194651
Ver en: Dataset/10251/194651" target="_blank">http://hdl.handle.net/10251/194651, https://dx.doi.org/10.4995/Dataset/10251/194651
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/194651
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/199150
Dataset. 2023
DORA DECLARATION TWEET COLLECTION
- Orduña Malea, Enrique|||0000-0002-1989-8477
- Bautista Puig, Núria
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.
Proyecto: Generalitat Valenciana//GV/2021/141
DOI: Dataset/10251/199150" target="_blank">http://hdl.handle.net/10251/199150, https://dx.doi.org/10.4995/Dataset/10251/199150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/199150
HANDLE: Dataset/10251/199150" target="_blank">http://hdl.handle.net/10251/199150, https://dx.doi.org/10.4995/Dataset/10251/199150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/199150
PMID: Dataset/10251/199150" target="_blank">http://hdl.handle.net/10251/199150, https://dx.doi.org/10.4995/Dataset/10251/199150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/199150
Ver en: Dataset/10251/199150" target="_blank">http://hdl.handle.net/10251/199150, https://dx.doi.org/10.4995/Dataset/10251/199150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/199150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
Dataset. 2023
CHARACTERIZATION OF WO3 NANOSTRUCTURES USED IN THE DEGRADATION OF METHYLPARABEN
- García Antón, José|||0000-0002-0289-1324
- Cifre Herrando, Mireia|||0000-0002-8800-3585
- Roselló Márquez, Gemma
- García García, Dionisio Miguel|||0000-0001-8951-4558
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.
DOI: Dataset/10251/200232" target="_blank">http://hdl.handle.net/10251/200232, https://dx.doi.org/10.4995/Dataset/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
HANDLE: Dataset/10251/200232" target="_blank">http://hdl.handle.net/10251/200232, https://dx.doi.org/10.4995/Dataset/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
PMID: Dataset/10251/200232" target="_blank">http://hdl.handle.net/10251/200232, https://dx.doi.org/10.4995/Dataset/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
Ver en: Dataset/10251/200232" target="_blank">http://hdl.handle.net/10251/200232, https://dx.doi.org/10.4995/Dataset/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200255
Dataset. 2023
PREPARATION OF ELECTRODES BASED ON WO3 NANOSTRUCTURES FOR LI-ION BATTERIES
- García Antón, José|||0000-0002-0289-1324
- Roselló Márquez, Gemma
- García García, Dionisio Miguel|||0000-0001-8951-4558
- Cifre Herrando, Mireia|||0000-0002-8800-3585
- Blasco Tamarit, María Encarnación|||0000-0001-7314-082X
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.
DOI: Dataset/10251/200255" target="_blank">http://hdl.handle.net/10251/200255, https://dx.doi.org/10.4995/Dataset/10251/200255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200255
HANDLE: Dataset/10251/200255" target="_blank">http://hdl.handle.net/10251/200255, https://dx.doi.org/10.4995/Dataset/10251/200255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200255
PMID: Dataset/10251/200255" target="_blank">http://hdl.handle.net/10251/200255, https://dx.doi.org/10.4995/Dataset/10251/200255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200255
Ver en: Dataset/10251/200255" target="_blank">http://hdl.handle.net/10251/200255, https://dx.doi.org/10.4995/Dataset/10251/200255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200255
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200260
Dataset. 2023
REAL-WORLD DATASETS FOR SUSTAINABLE PORTFOLIO SELECTION
- García Bernabeu, Ana María|||0000-0003-3181-7745
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.
Proyecto: //
DOI: Dataset/10251/200260" target="_blank">http://hdl.handle.net/10251/200260, https://dx.doi.org/10.4995/Dataset/10251/200260
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200260
HANDLE: Dataset/10251/200260" target="_blank">http://hdl.handle.net/10251/200260, https://dx.doi.org/10.4995/Dataset/10251/200260
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200260
PMID: Dataset/10251/200260" target="_blank">http://hdl.handle.net/10251/200260, https://dx.doi.org/10.4995/Dataset/10251/200260
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200260
Ver en: Dataset/10251/200260" target="_blank">http://hdl.handle.net/10251/200260, https://dx.doi.org/10.4995/Dataset/10251/200260
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200260
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/201071
Dataset. 2023
GEOREFERENCED DATASET OF ABANDONED CITRUS CROPS (COMUNITAT VALENCIANA, SPAIN)
- Morell Monzó, Sergio|||0000-0001-8883-2618
- Sebastiá Frasquet, María Teresa|||0000-0002-8042-5628
- Estornell Cremades, Javier|||0000-0003-0854-5358
This dataset contains georeferenced agricultural citrus parcels with information on crop status (non-productive, productive or abandoned) for the years 2019, 2020 and 2021 in GeoJSON format. The dataset is divided into 7 subsets (OLV_19, LSF_20, LSF_21, PV_21, TV_21, BP_21, and NL_21) in different locations of the Comunitat Valenciana (Spain). However, most of the data are located in the region of La Safor. Each of these subsets has its own characteristics in terms of sampling/classification method, date of acquisition and number of parcels. In total there are 1676 agricultural parcels labeled. The boundaries of the parcels are defined by the Geographic Information System of Agricultural Parcels (SIGPAC) database, which corresponds to the cadastral limits (“parcela” in SIGPAC nomenclature). Each parcel may contain one or more subplots ("recinto" in SIGPAC nomenclature) but with only one crop status. Data are derived from: Morell Monzó, S. (2023). “Desarrollo de procedimientos para la detección del abandono de cultivos de cítricos utilizando técnicas de teledetección” [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/193058
Proyecto: Generalitat Valenciana//AICO/2020/246
DOI: Dataset/10251/201071" target="_blank">http://hdl.handle.net/10251/201071, https://dx.doi.org/10.4995/Dataset/10251/201071
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/201071
HANDLE: Dataset/10251/201071" target="_blank">http://hdl.handle.net/10251/201071, https://dx.doi.org/10.4995/Dataset/10251/201071
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/201071
PMID: Dataset/10251/201071" target="_blank">http://hdl.handle.net/10251/201071, https://dx.doi.org/10.4995/Dataset/10251/201071
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/201071
Ver en: Dataset/10251/201071" target="_blank">http://hdl.handle.net/10251/201071, https://dx.doi.org/10.4995/Dataset/10251/201071
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/201071
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