University of La Laguna Showcase

Theme_Identifiability_Ad_hoc_Spanish.xls consists of five sheets. The first sheet, "Theme identifiability", contains the raw data. For each participant, it presents the 70 critical words along with their respective lists of 10 ad hoc associates. The first column shows the participant number, the second column identifies the list, the third column specifies the type of relationship of the lists (ad hoc), and the fourth column indicates the type of word (studied vs. critical). The fifth column contains the words themselves, while the sixth column provides the English translations of the critical and studied words. Adjacent columns include all responses provided by participants in the first, second, and third positions, as well as the confidence judgements related to the first response. Additionally, intrusions are noted—i.e., words from a study list that a participant mistakenly identified as the theme of that list, and therefore are not considered valid responses. The second sheet, “First word”, contains the total count of responses given in the first position for each list. It includes all the words that participants generated as the theme in the first position, associated with their respective critical word and list number. Additionally, it provides the English translation of the critical word, the total number of participants who responded to each list, the number of participants who identified a word as the theme, the percentage of participants who did so, and the mean confidence judgement for each first word. Intrusions are also noted, including the quantity and the incorrectly identified word. The third and fourth sheets, “Second word” and “Third word”, respectively, are dedicated to the count of responses given in the second and third positions. The layout is identical to the previous sheets, except that the column for the mean confidence judgement is not included. The fifth sheet, "Summary," presents the final summary, showing the most frequently identified theme for the first, second, and third positions for each list.

Dataset of Computer Aided Drug Design (CADD) and Molecular Dynamics (MD) operations for the article, titled "NEW DYNAMIC SCORING METHOD FOR DEEP EVALUATION OF NALOXEGOL AS Β-TUBULIN BINDING INHIBITOR"

Raw results from cell and animal experiments

There are new benchmark instances for the Cross-Dock Door Assignment and Scheduling Problem (CDSP), with instances from eight origins and destinations, four inbound and outbound doors, up to 50 origins and destinations, and 30 inbound and outbound doors. The instances have been obtained following the criteria of the paper by Nassief et al. (2016), with some modifications. First of all, the number of pallets to be moved from a supplier to a customer is randomly generated by using a uniform distribution U[1,5] until density values are set to 25, 35, 50, and 75%. Each inbound truck sends pallets from at least one outgoing truck, and each outgoing truck receives pallets from at least one incoming truck. The distance matrix is generated with numbers from the interval [1, 1 + |I|- 1], meaning that a direct distance between two doors is equal to 1, and then an increment of 1 unit is added for the next indirect door. The number of considered incoming/outgoing trucks is 8, 9, 10, 11, 12, 15, 20, and 50. The number of considered inbound/outbound doors is 4, 5, 6, 7, 10, and 30. The door capacities are equal for each instance and calculated by dividing the total flow coming from all origins by the total number of inbound doors and then adding a capacity slackness of 5, 10, 15, 20, and 30%. To generate this set of instances, we have considered the combinations of values reported in the paper by Sayed (2020). The instances are referred to as 00×00×00x00 (number of the incoming and outgoing trucks x number of inbound and outbound doors x capacity slackness associated with the inbound/outbound doors - 5, 10, 15, 20, and 30% - x density of the flow matrix - 25, 35, 50, and 75%).

A collection of images created by Midjourney using various promtps to understand the AI's biases in character concept art.

Research hypothesis: grazing intensity and stage of secondary succession after deforestation affect richness, diversity, plant composition of herbaceous and tree - shrub layer and forest structural traits (density, biomass, basal area an others) of tropical dry forest. Main findings: Grazing intensity, succession stage and interaction between both showed significant effects on most of variables. Grazing intensity did not disrupt natural succession process.

Chromosomal analysis of products of conception using targeted direct embryo biopsy by operative hysteroscopy

BICI−ULL (Bioclimatic Indicators in the Canary Islands by the ULL−GOTA) includes 53 biovariables derived from monthly temperature, precipitation, radiation, humidity, and wind speed averages over different periods calculated through dynamic regionalisation models. This project aims to improve our understanding of climate and its impacts on small, isolated regions with diverse ecosystems, providing valuable insights for the scientific community. The dataset can be found in the ‘Bioclimatic indicators’ folder. It consists of 795 files in NetCDF4 format with a grid cell resolution of 3×3 km, covering the region of the Canary Islands. The files represent 53 bioclimatic indicators calculated in 30-year intervals for the periods: 1980−2009, 2030−2059, and 2070−2099, under the Representative Concentration Pathway (RCP) scenarios RCPs 4.5 and 8.5, for future. The non-hydrostatic WRF model, with boundary conditions from three global models (GFDL−ESM2M, IPSL−CM5A−MR, and MIROC−ESM), was used to perform the simulations. The grid dimensions are (186×77), with a total of 14322 cell points. It covers a longitude range of 18.3◦W to 13.3◦W and a latitude range of 27.5◦N to 29.6◦N, although the biovariables are only computed for land cells. The files comply with the CF conventions for climate and forecast metadata designed to promote the processing and exchange of files created with the netCDF4 Application Programmer Interface. The names associated with each file follow the format: BIOx_model_rcp_yini_yend.nc The raw data from which the biovariables are generated are also available in this repository, in the ‘Raw Data’ folder. These include the standard variables: Mean, maximum, and minimum temperature (TG, TX, TN), precipitation (PR), shortwave downwelling flux (SR), relative humidity (RH), and wind speed (WS). Each file contains 12 timesteps, one for each month (January, February, …). The content of the variables is the mean over the 30-year periods. A specific bias adjustment technique known as the Scaled Distribution Mapping (SDM) algorithm, designed to preserve trends, was used (Switanek et al., 2017). File names follow the format: model_yini_yend_rcp_variable.nc x represents the number of the climatic indicator model makes reference to the driving Global Circulation Model used for the regionalsation simulations. It can be GFDL, IPSL, or MIROC. rcp indicates the forcing scenario, RCP 4.5 or RCP 8.5, or it will be defined as ‘HIST’ in the case of the historical period. yind and yend represent the years of the period, with ini identifying the first year of the period and end identifying the last year. variable refers to the standard variable, which can be TG, TX, TN, PR, SR, RH, WS The main folders contain, in turn, subfolders corresponding to each driving global model, and to the respective period and scenario. The definition of the 53 BICI-ULL bioindicators have been also summarised in the BICI-ULL_bioindicators_definitions.pdf file.

Dataset of Computer Aided Drug Design (CADD) and Molecular Dynamics (MD) operations for article, titled "NEW DYNAMIC SCORING METHOD FOR DEEP EVALUATION OF NALOXEGOL AS Β-TUBULIN BINDING INHIBITOR"

This repository contains the database used for the development of the study "Evolution and Trends in Research on Family Digital Leisure: A Bibliometric Analysis" followed the PRISMA 2020 Protocol. This study aims to analyze the evolution of the field of research on family digital leisure from 2012 to 2022. The analysis, conducted through a bibliometric approach, seeks to identify trends, changes, and emerging patterns in scientific literature. To achieve this purpose, two specific objectives were established. First, the temporal evolution of the field was explored, evaluating the variation in scientific production and academic attention over the last decade. Second, emerging themes and thematic groups that have gained relevance in scientific literature were identified. On January 31, 2023, a search was conducted in the topic section (abstract, title, and keywords) of the Web of Science (WoS), Clarivate Analytics, and Scopus Main Collection. The descriptors used were: (leisure OR game OR entertainment) AND (digital OR internet OR online OR cyber OR technolg* OR ICT OR ICTs) AND (family). This search was limited to the years 2012-2022. The initial search generated 2953 results, which, after removing duplicates, were reduced to 1843 documents. These were subjected to the following inclusion criteria: (1) being articles or reviews and (2) having been published between 2012 and 2022. Consequently, chapters of books, conference papers, and other formats were excluded, as well as scientific production accepted for publication in 2023 (early or advance access). Finally, 1381 documents were obtained to carry out the bibliometric study.