BUSCADOR RECOLECTA

Data include the active (kW) and reactive (kvar) power for the pilot area of H2020 RESOLVD project, each item in one column and with a time step of one hour and one year length.

Data include the voltage and current at the transformer terminals, also the measured current losses from applying various switching strategies. This data serves to reproduce Figure 18 and 19 in the paper.

The dataset contains the following files: (1) readme.txt, it is a text file including details about the content of the dataset; (2) Measurement in ARSD_SMS_Data.xls, it is a MS Excel file containing the data used in the analysis of the primary studies, reported in Section 4 of the paper; and, (3) Metrics in ARSD.pdf, it is pdf file containing the complete list of metrics reported in the different primary studies. The metrics are classified into the following categories: QR metrics, QMIs, and QMI metrics. In the MS Excel file, the data is organised in separated sheets: (a) RQ1 - Literature characterisation; (b) RQ2 - QR Metrics, where QR refers to Quality Requirements; (c) RQ3 - QMI, where QMI refers to Quality Management Indicators; (d) RQ3 - Tools, corresponding to the reported tools used to manage QMIs; and, (e) RQ3 - QMI Metrics., This dataset has been used to generate part of the results included in the manuscript: “Quality Measurement in Agile and Rapid Software Development: A Systematic Mapping”, submitted to the Information and Software Technology journal (IST) at the time of publishing this dataset. The paper reports the results of a systematic mapping study surveying the literature related to quality requirements management through metrics in agile and rapid software development processes. The dataset contains the data extracted from the primary studies that was used in the analysis phase of the study and reported in the results section of the paper. The data extracted from the primary studies has been complemented with some data used for specific purposes, e.g. during the analysis phase, we included the list of companies collaborating in the paper analysing the authors’ affiliations to understand the level of implication of companies in the field. Complementing the data analysed, the dataset includes some detailed results that are not included in the body of the paper, i.e., the list of metrics reported in the selected primary studies.

This file includes data on system vertical and lateral stiffness of stainless steel frames under gravity and gravity plus wind load scenarios obtained from the finite element simulations carried out on six stainless steel frames.

Numerical Model for a 19th Century Hydrometric (Case 1: 1000 L/s inlet flow). Aquests datasets són els models numèrics que corresponen als 6 casos de simulacions fetes amb el software de CFD anomenat Flow3D.

Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of ultimate load factors corresponding to different random samples of six different nominal stainless steel frames under gravity and wind load combinations. The values of the random variable assignments are given for each case. The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R.; Zhang, H. System-based reliability analysis of stainless steel frames subjected to wind loads. "Structural Safety", July 2022, vol. 97, art. No. 102211. DOI: https://doi.org/10.1016/j.strusafe.2022.102211

Data associated with the study called "Selectively targeting tumorigenicity in osteosarcoma". Each file corresponds to the raw data of one of the figures of the paper

This file includes finite element simulation data (ultimate load factors) carried out on 60 steel and stainless steel frames, including regular and irregular frame configurations with different section sizes. Ultimate load factors corresponding to different initial imperfection combinations are included.

These data are associated to the paper with the same title and are structured according to the figures in the paper. These data correspond to the research results of the WP3 of the European Project 4D-POLYSENSE., The present dataset reports the scientific results corresponding to a scientific study where a bidimensional (2D) polypropylene flexible mesh material has been converted into a fourth dimension (4D) dynamic system. The platform is composed by a fibres of isotactic polypropylene (iPP) mesh coated with thermosensitive poly(N-isopropylacrylamide-co-N,N'-methylene bis(acrylamide) (PNIPAAm-co-MBA) hydrogel, and undergoes variations in its geometry by reversible folding/unfolding behaviour. Folding and unfolding motion in aqueous solution of bilayer and monolayer systems composed by two or one layer of hydrogel, respectively, are reported in the present dataset. Furthermore, an infrared thermographic camera and an optical microscope were used to evaluate the macroscopic and microscopic structure stimulus response in air. Additionally, PP-g-PNIPAAm meshes showed an increase in the mechanical properties (bursting strength) with respect to the uncoated mesh.

We have created a representative dataset based on the collection of different traffic metrics from VANET simulations in urban scenarios. The five collected metrics that compose the dataset used to train and test our ML-based forwarding algorithm are available bandwidth, distance to destination, vehicles’ density, MAC layer losses, and vehicle’s trajectory. Notice that those metrics are gathered by the vehicles from the beacons periodically interchanged with the vehicles in their neighborhood (i.e., with vehicles within their transmission range). This way, nodes have local knowledge of the VANET, according to the decentralized nature inherent in VANETs.