University of La Laguna Showcase

Base de datos resultante de los cuestionarios realizados durante los talleres de ODS. Se encuentran agrupados los cuestionarios ex ante y ex post

Experimental and simulated light-off curves

Article data Characterisation of fear of public speaking and social cognition. A machine learning study.

The study goal was to identify the cultural model that stakeholder groups use to determine whether their cultural views of property rights (use and access) and management techniques differed among the surveyed island regions: El Hierro (Canary Islands, Spain); Mahé, Praslin and La Diegue Islands (Seychelles) and Dunde (Solomon Islands). The dataset contains results from an agreement questionnaire based on the Cultural Consensus Analysis (Bernard, 2000). Within every community (zone) on each island region, a random stratified sample of households was chosen. Each interview was conducted with a chosen household head in the local language. Interviewers were also indigenous from each region and had previous experience working on every zone, employing qualitative research tools. A total of 400 surveys were conducted at the sampled locations within the island groups. The research methods and activities were approved by Rhodes University Human Research Ethics Committee (RU-HREC), approval number 2023-5096-8105. Ecosdyn (Eco-social Dynamics Consulting) and the University of La Laguna were also involved in the study and dataset development.

New benchmark instances for the Cross-Dock Door Assignment and Scheduling Problem (CDSP) and capacities at the inbound/outbound doors, 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, the number of pallets to be moved from a supplier to a customer is randomly generated 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 equals 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 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%).

EEG brain state-dependent design that leverages real-time variations in alpha power to modulate the loudness (volume) of pre-recorded audio files during an experimental task akin to the traditional Digit Span test. The study assesses whether the perceptual intensity of speech signals can modulate speech tracking and short-term memory.

This dataset referred contains 15,000 synthetic signatory records, each corresponding to a different simulated individual. Each record includes an ID (a unique personal identifier following the Spanish Home Office format, consisting of eight numeric digits followed by a single alphabetic character) and a signature associated with that individual. The dataset is used to evaluate the performance of a transformer-based OCR system for handwritten signature verification for lawmaking petitions submitted by citizens.

This benchmark, based on the Solomon instances, has been generated for solving a Vehicle Routing Problem with Synchronization for Waste Collection. The Solomon instances include information on a single depot and customer locations, demand, time windows, vehicle capacity, and fleet size. To adapt these instances to our multi-synchronization problem, the following modifications were made: up to three waste types are considered simultaneously, with demands assigned using a non-negative discrete uniform distribution bounded above by the original demand values; synchronization time windows are introduced by doubling and halving the original durations; each customer may submit three different bids, with the highest bid assigned to the shortest time window and the lowest to the widest, thereby reflecting a preference for more restrictive time intervals; finally, a fleet of 20 vehicles with homogeneous capacity of 200 units per waste type is assumed. These bid values are computed mathematically based on a baseline corresponding to the cost of serving a customer via the trivial route (depot–customer–depot), to which a small random factor is applied. In cases where transportation costs vary depending on the type of waste, that is, when a function to weigh each type of waste is considered, the same procedure is applied, taking into account the specific waste type generated by each customer. The files storing the instances are denoted as "f", "f2", "ftc", and "f2tc", wherein "tc" indicates an instance created with weighted wastes taken into account and "2" indicates an instance for two types of waste; otherwise, the instances were created for either non-weighted wastes or three types of wastes. Each instance file presents the following variables: — customer: unique ID identifier associated with each customer; 0 is reserved for the depot (numeric). — xCoord: the value of the x coordinate in Euclidean space (numeric). — Coord: the value of the y coordinate in Euclidean space (numeric). — demandWasteType1: non-negative demand value for the first type of waste (numeric). — demandWasteType2: non-negative demand value for the second type of waste (numeric). — demandWasteType3: non-negative demand value for the third type of waste (numeric). — readyTime: value in minutes at which a customer's time window starts; start of the planning horizon if depot (numeric). — dueDateNarrow: value in minutes at which a customer's shortest time window ends (numeric). — dueDate: value in minutes at which a customer's base time window ends (numeric). — dueDateWide: value in minutes at which a customer's widest time window ends (numeric). — service: value in minutes of vehicle's service time at the customer (numeric). — bidNarrow: value associated with the customer's shortest time window (numeric). — bid: value associated with the customer's base time window (numeric). — bidWide: value associated with the customer's widest time window (numeric).

Long term database of morphometric measurements of Blainville’s beaked whales off El Hierro, 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 dataset comprises high-definition video recordings documenting tongue-nibbling behaviour in killer whales (Orcinus orca) observed both under human care at Loro Parque (Orca Ocean, Tenerife, Spain) and in a wild population off the coast of northern Norway. Tongue-nibbling is a rare and understudied tactile interaction with potential affiliative and social bonding functions. The dataset includes: One video file recorded in 2024 in Tverrfjorden (Norway), showing a detailed instance of tongue-nibbling between two wild individuals. Footage captured by Allison Kelly Estevez, and Michael Estevez One video recorded during a ethological study in Loro Parque, illustrating repeated occurrences of the same behaviour in 2013.