Resultados de investigación

Temporal point clouds sampled from a workpiece in progress using 16 different machining tools. The datasets were created using a machining simulation in the Unit Industrial Software Applications, RISC Software GmbH, Hagenberg, Austria in the context of the EU-network GRAPES (http://grapes-network.eu)., Machine learning (Thesaurus UB) https://vocabularis.crai.ub.edu/thub/concept/thub:981058506973006706 Neural networks (Computer science) (Thesaurus UB) https://vocabularis.crai.ub.edu/thub/concept/thub:981058506729006706 Computer simulation (Thesaurus UB) https://vocabularis.crai.ub.edu/thub/concept/thub:981058505709706706, Experimental data., Peer reviewed

A user-centred identifier enables verifiable and decentralized digital identity, and lead users to control and to generate their own identifiers using systems they trust. This is how Self-Sovereign Identity works. This paper presents the case of universities, where several different agents need their own identifier and shows a digital identity mathematical model for the academic context. Moreover, the Alastria model for the samecontext is detailed for a specific case of students’ mobility., This work was partially supported by Fundación Samuel Solórzano under grant no. FS/26-2020, and by the Spanish State Research Agency (AEI) of the Ministry of Science and Innovation (MICINN), project P2QProMeTe (PID2020-112586RB-I00/AEI/ 10.13039/501100011033)., Peer reviewed

Underwater images and manual tags of different fish taxa detected from the image repository of the OBSEA Cabled Observatory from January 2013 to December 2014. OBSEA station is located 4km off-the-coast of Vilanova i la Geltrú (Catalonia, Spain) at a depth of 20 meters . Tags are rectangles marking the fish position in a picture (region of interest), identified by their x/y vertix values (in pixels).The aim of this dataset is to train Artificial Intelligence (AI) algorithms to automatically detect and or classify fish specimens from underwater pictures.Two cameras were used (Sony SNC-RZ25N from 2013-01-01 to 2014-12-11 and Axis P1346-E from 2014-12-11 to 2014-12-31). 29 fish taxa have been visually identified and classified according to their taxa name. Unclassified fish have been marked as Unknown fish.The fish taxa recognized in the dataset are: Coris julis, Chromis chromis, Diplodus vulgaris, Diplodus sargus, Oblada melanura, Spondyliosoma cantharus, Diplodus cervinus, Diplodus annularis, Spicara maena, Dentex dentex, Diplodus puntazzo, Conger conger, Symphodus mediterraneus, Atherina sp., Scorpaena sp., Pagrus pagrus, Pagellus sp., Serranus cabrilla, Sciaena umbra, Sarpa salpa, Trachurus sp., Sparus aurata, Apogon sp., Seriola dumerili, Thalassoma pavo, Epinephelus marginatus, Boops boops, Symphodus cinereus, Symphodus tinca, Peer reviewed

In 2014, the continental shelf and submarine canyon off Cap de Creus (NW Mediterranean) were included in the Site of Community Importance (SCI) “South-West Gulf of Lions canyons system” of the Natura 2000 Network of protected areas. Before the declaration of the SCI (2007-2103), 60 underwater video transects were performed in the area by means of ROVs and a manned submersible, covering more than 30 km of seafloor. Based on the annotation of the video images, we identified the main invertebrate megabenthic communities dwelling from the shelf (70 m depth) down to the bottom of the canyon head (400 m depth). The results of the multivariate analyses indicated that this marine area hosted several benthic communities dominated by suspension- and filter-feeding species, including octocoral gardens, soft-coral assemblages, crinoid fields, sponge grounds and cold-water corals. This dataset includes (1) the geographical location of the benthic communities identified in the video footage, as determined by the clustering analysis, (2) the intra-community diversity associated to the benthic communities, provided as the number of megafauna species identified in each sampling unit and the exponential of Shannon, and (3) the spatial distribution of the benthic communities mapped by means of the Random Forest classification algorithm. This dataset is related to the article “Diversity, structure and spatial distribution of megabenthic communities in Cap de Creus continental shelf and submarine canyon (NW Mediterranean)” submitted to Progress in Oceanography by Dominguez-Carrió et al., which contains further information on how the data were obtained and processed., This dataset was obtained in the framework of the European projects HERMES (Goce-CT-2005-511234-I) and Life+ INDEMARES (LIFE07/NAT/E/000732), and the Spanish projects DEEP CORAL (CTM2005-07756-C02-02/MAR) and the Acciones Complementarias (CTM2005-24174-E, CTM2006-27063-E/MAR, CTM2007-28758-E/MAR). [...] CD-C acknowledges the financial support received by the EU funded projects Life+ INDEMARES (LIFE07/NAT/E/000732) and ENPI ECOSAFIMED (ENPI CBC MED 2007-2013) and by the FCT-IP Project UIDP/05634/2020, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

This dataset contains data from a population-representative survey examining various factors relating to digital exclusion (particularly digital mobility exclusion). The survey was conducted with 423 adult participants in the Netherlands in 2020 and 2021. This dataset is part of a series of 5 datasets which used the same questionnaire (translated into different languages) in different countries in Europe.

Biomechanical data on the strength of Cerebral Bridging Veins (CBVs), a study funded by La Marató de TV3 (2020-2021). Data on maximum axial force on the tested specimens, strain rate of the test, and the relationship between the two are examined and the probability distributions for the sample are obtained. Data set used for the article "Injury metrics for assessing the risk of acute subdural hematoma in traumatic events" (2021), published in IJERPH ( ISSN: 1660-4601, Basel, Switzerland, MDPI), Project: ``Age Effects on Traumatic Brain Injuries'' funded by Fundació La Marató de TV3, Barcelona, No. Grant 289/C/2017, 201704.30.

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.

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.