BUSCADOR RECOLECTA

Computational 3D models of human atria and torso with encoded anatomical and functional heterogeneity. The finite element model of the atria is a multi-layer mesh with a homogeneous wall thickness between 600 and 900 μm (754.893 nodes and 515.010 elements), built with linear hexahedral elements and with regular spatial resolution of 300 µm. The model consists of: 21 regions with 53 sub-divisions representing the detailed description of fibre orientation; electrophysiological heterogeneity represented by 8 cellular models with differences in the action potential properties; and conduction velocities and anisotropy ratios tuned to each atrial region. The torso was modelled using a dataset obtained from the online open repository at the Centre for Integrative Biomedical Computing (CBIC) from University of Utah. The torso model has 190.804 nodes and 1.149.531 tetrahedral elements with a spatial resolution of 0.6 mm. It has 6 different regions: lungs, bones, liver, ventricle, blood pools, and flesh.

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.

This dataset contains the raw data of the measurements presented in the paper: "A Computational Efficient Nyquist Shaping Approach for Short-Reach Optical Communications" (https://doi.org/10.1109/JLT.2019.2961506). - Medidas_BER_EVM.xls: contains BER and EVM measurements made for different types of modulations for both the HC-SCM modulator and the OVS-SCM, varying the following parameters: oversampling factor, transmitter filter span, rolloff factor, receiver filter span, receiver equalizer order, pre-emphasis filter (in transmission) - Medidas_Potencia.xls: contains BER and EVM values varying the received power for HC-SCM and OVS-SCM modulations; for all QAM modulations under study.

[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.

The present dataset is the database and image database used to train and test the Convolutional Neural Network models of the scientific paper: Deep Learning algorithm, based on convolutional neural networks, for electrical equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy

[EN] Buildings have become a key source of greenhouse gas (GHG) emissions due to the consumption of primary energy, especially when used to achieve thermal comfort conditions. In addition, buildings play a key role for adapting societies to climate change by achieving more energy efficiency. Therefore, buildings have become a key sector to tackle climate change at the local level. However, public decision-makers do not have tools with enough spatial resolution to prioritise and focus the available resources and efforts in an efficient manner. The objective of the research is to develop an innovative methodology based on a geographic information system (GIS) for mapping primary energy consumption and GHG emissions in buildings in cities according to energy efficiency certificates

Mesenchymal stem cells (MSCs) osteogenic commitment before injection enhances bone regen-eration therapy results. Piezoelectric stimulation may be an effective cue to promote MSC pre-differentiation, and poly(vinylidene) fluoride (PVDF) cell culture supports, when combined with CoFe2O4 (CFO), offer a wireless in vitro stimulation strategy. Under an external magnetic field, CFO shift and magnetostriction deform the polymer matrix varying the polymer surface charge due to the piezoelectric effect. To test the effect of piezoelectric stimulation on MSCs, our approach is based on a gelatin hydrogel with embedded MSCs and PVDF-CFO electroactive mi-crospheres. Microspheres were produced by electrospray technique, favouring CFO incorpora-tion, crystallisation in -phase (85 %) and a crystallinity degree of around 55 %. The absence of cytotoxicity of the 3D construct was confirmed 24 hours after cell encapsulation. Cells were via-ble, evenly distributed in the hydrogel matrix and surrounded by microspheres, allowing local stimulation. Hydrogels were stimulated using a magnetic bioreactor, and no significant changes were observed in MSCs proliferation in short or long term. Nevertheless, piezoelectric stimula-tion upregulated RUNX2 expression after 7 days, indicating the activation of the osteogenic dif-ferentiation pathway. These results open the door for optimising a stimulation protocol allow-ing the application of the magnetically activated 3D electroactive cell culture support for MSCs pre-differentiation before transplantation.

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.

[EN] The integration of autonomous vehicles (AVs) into road transport requires robust ex-perimental tools to analyze human–machine interaction, particularly under conditions of system disengagement. This study presents the development and validation of the EVACH autonomous driving simulator, designed to reproduce SAE Level 2 and Level 3 driving modes in rural road scenarios. The simulator was customized through hardware and software developments, including a dedicated data acquisition system to ensure accurate detection of braking, steering, and other critical control inputs. Calibration tests demonstrated high reliability, with minor errors in brake and steering control meas-urements, consistent with values observed in production vehicles. To validate the virtual driving rural environment, comparative experiments were conducted between natu-ralistic road tests and simulator-based autonomous driving. Results showed that average speeds in simulation closely matched those recorded on real roads, with differences of less than 1 km/h and significantly lower variability. These findings confirm that the EVACH simulator provides a stable and faithful reproduction of autonomous driving conditions. The platform represents a validated and versatile tool for evaluating driver workload, takeover performance, and human–machine interaction, offering valuable support for current and future research on the safe deployment of automated vehicles.

This dataset includes supplementary material (code, raw data) created and collected to support a study on the visibility of Spanish Institutional Repositories on Google Search results.