University of La Laguna Showcase

Dataset of Computer Aided Drug Design (CADD) and Molecular Dynamics (MD) operations for the article, titled "AN EXTENSIVE STUDY ON RACEMIC TRIGLYCERIDES AS AMIDOHYDROLASE ENZYME INHIBITORS"

This dataset includes responses from 935 university students (M = 21.23; SD = 4.23; 66.09% female) who completed a battery of self-report measures assessing personality traits, emotional dysregulation, decision-making styles, and executive control, as well as indicators of Problematic Social Media Use (PSMU) and Internet Gaming Disorder (IGD). The data were collected as part of a cross-sectional study conducted within the framework of the I-PACE model (Brand et al., 2019), aiming to identify shared and distinct psychosocial predictors of PSMU and IGD in young adults. Variables include raw scores for all scales, demographic information (age, gender), and group classifications based on vulnerability to PSMU, IGD, or co-occurrence of both. The dataset can be used to explore behavioral addiction profiles in youth, the interaction between cognitive-emotional variables, and the development of tailored prevention strategies.

Long term database of morphometric measurements of short-finned pilot whales off the Canary Islands (Eastern North Atlantic population) from aerial photographs. Includes absolute body length measurements of the whales and proportional measurements of their body at 5% increments from rostrum. For further information please contact arranz@ull.edu.es. University of La Laguna, Tenerife, Spain.

This study examines the alignment between whale-watching (WW) experiences and tourist expectations in three North Atlantic destinations. As marine wildlife tourism, above all whale watching, gains popularity, it is vital to strike the right balance between tourist satisfaction and ecological conservation. Whale watching is now a global tourist trend, with locations like the Canary Islands and the Azores rapidly becoming prime spots for these activities. These locations attract a significant number of tourists with varying recreational interests and diverse perceptions of each destination and its natural resources. While often marketed as sustainable tourism, the ecological impacts of whale watching are a matter of concern, especially in the region of Macaronesia, where cetaceans are under increased exposure to commercial and recreational vessels. These factors contribute to changing dynamics in stakeholder perceptions and in the management of marine resources. The findings here highlight how perceptions and images of whale watching evolve and how these changes influence stakeholder behavior and preferences. Evolving whale-watching practices may reinforce or diminish conservation and environmental education images. By addressing the balance between tourist satisfaction, operational guidelines and conservation efforts, this research aims to promote more sustainable practices in marine wildlife tourism, ensuring ecological integrity and enriching experiences for visitors.

Raw results from cell and animal experiments

FASTA files containing fragments of the mitochondrial gene Cytochrome C Oxidase subunit I (COI) obtained with Sanger sequencing, for two sea urchin species, Arbacia lixula and Paracentrotus lividus. These samples were collected from individuals inhabiting along natural CO2 gradient in the Canary Islands, Spain.

This dataset builds upon the publicly available work by Taranvee, who collected household energy consumption data at one-minute resolution from a smart meter monitoring multiple appliances (e.g., dishwasher, home office, fridge, kitchen). The original release also includes regional weather information (humidity, temperature, atmospheric pressure, etc.), providing a rich contextual layer for understanding how environmental factors influence residential power usage. In this augmented version, we introduce additional consumption columns representing distinct IoT devices, Car charger,Water heater,Air conditioning,Home Theater,Outdoor lights,microwave,Laundry,Pool Pump Each of these new columns tracks an appliance's energy usage in kilowatts ([kW]), effectively broadening the dataset’s scope for modeling complex, multi-device scenarios within a single smart home Original work: https://www.kaggle.com/datasets/taranvee/smart-home-dataset-with-weather-information

Dataset of Computer Aided Drug Design (CADD) and Molecular Dynamics (MD) operations for the article, titled "DEEP DYNAMIC DRUG SCORING METHOD: CASE STUDY OF TAXANE β-TUBULIN DISASSEMBLY INHIBITORS"

This dataset was collected to investigate the hypothesis that individuals with borderline personality disorder (BPD) traits exhibit implicit emotional biases and heightened neural reactivity to negative stimuli compared to healthy controls. The data demonstrates that participants with BPD traits responded faster to negative words in an Implicit Association Test (IAT) and showed distinct neural activity patterns in Event-Related Potentials (ERPs), particularly in the P2, N400, and Late Positive Potential (LPP) components. The dataset includes behavioral measures (reaction times and accuracy) from the IAT, as well as electrophysiological recordings obtained via EEG. Participants were classified into two groups (BPD-vulnerable and healthy controls) based on clinical assessments including the SCID-II, BSL-23, and self-reported history of self-harm. EEG data were collected using a 32-electrode cap and processed to extract ERP amplitudes for congruent and incongruent trials with both positive and negative emotional valence words. The findings can be interpreted to suggest that individuals with BPD traits have an implicit negativity bias and altered cognitive-emotional processing mechanisms. This dataset can be used to explore the relationship between implicit biases, emotional processing, and neural activity, offering potential applications for further research in psychopathology and cognitive neuroscience.

MultilingualTweetEval2024 The MultilingualTweetEval2024 dataset consists of two subsets: 1.General Purpose: Includes anonymized tweets in multiple languages (Chinese, English, Spanish, French, and German) containing only the text. 2.English Labeled: Contains English tweets that have been auto-labeled using a custom model for hate speech detection. Both datasets are fully anonymized and designed to facilitate research in multilingual tweet analysis and hate speech detection in Twitter content.