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Knowledge Base and RAG Evaluation Dataset
No full textThis dataset consists of two tabs:
1. Knowledge Base: This tab contains key terms (hypernyms), their associated skills (hyponyms), definitions and KSA-O classifications. It follows the structure of the dataset
"Automotive Industry Skills Taxonomy" .
2. Testing Set: This tab is used to evaluate a RAG (Retrieval-Augmented Generation) model that has access to the knowledge base from the first tab.
Structure of the Testing Set (Tab 2):
* Text: A hyponym (variation) of a skill.
* Label: Categorizes the text into one of three unique values based on its presence in the knowledge base:
-New hypernym: A key term that does not exist in the knowledge base.
-Existing hyponym : A variation of a skill that already exists in the knowledge base.
-New hyponym: A new variation that can be mapped into an existing hypernym.
* Original_key: Original hypernym associated with the hyponyms in the "Text" column. The terms in this column correspond to existing pairings in the taxonomy from "Automotive Industry Skills Taxonomy".</p
Corruption Survey
No full textThis dataset contains responses to a corruption survey by university students in Mexico and the United States, collected from September 22, 2021 through April 21, 2022. The survey used for data collection was approved by IRB at the University of Texas at Dallas. The survey is anonymous and voluntary, collecting no sensitive information, and participants were informed of its purpose before responding
Replication Data for: Factors to improve online education: A study on the impact of COVID-19 on Delhi students
No full textThis dataset was adapted from: Chaturvedi, K., Vishwakarma, D., Singh, N., 2020a. Covid-19 and its impact on education, social life and mental health of students: A survey. Children and Youth Services Review 121. doi: 10.1016/j.childyouth.2020.105866
The global gap between the skills of technical-vocational school graduates and the manufacturing industry's needs: A 2020-2025 scoping literature systematic review
No full textThis dataset corresponds to the results of a literature search on The global gap between the skills of technical-vocational school graduates and the manufacturing industry's needs between the years 2020 and 2025. The .bibTeX files are included, as well as the Excel files that indicate the quality analysis of the selected articles and the information extracted from the articles included in the review
The global need for paediatric palliative care: the evolution of serious health-related suffering in children aged 0–19 years from 1990 to 2023: a modelling study
No full textBackground: The majority of children needing palliative care globally reside in low-income and middle-income countries (LMICs) with limited or no access to such care, resulting in an excess burden of suffering. We aimed to estimate the global burden of serious health-related suffering (SHS) among children aged 0–19 years from 1990 to 2023, providing a measurement tool essential to respond to the need for more effective palliative care policies and services for children.
Methods: In this modelling study, we generated refined estimates of palliative care need for children aged 0–19 years for a 30-year time series, spanning 1990 to 2023, by extending and applying the SHS methodology originally introduced by The Lancet Commission on Global Access to Palliative Care and Pain Relief and subsequently updated in 2024. The updated methodology included convening an expert paediatric palliative care panel. First, the panel identified the health conditions specific to children and related parameters for estimation of the total SHS burden in children using epidemiological mortality and prevalence data within the Global Burden of Disease study 2023 dataset. Second, to estimate the SHS burden among decedents (those who died within the past year) and non-decedents (those who survived but experienced persistent, chronic, or progressive suffering) and quantify the condition-specific palliative care need, for each health condition the panel determined the percentage of deaths or survivors who experienced SHS and thus need palliative care or the ratio of the number of survivors with SHS to the number of deaths.
Findings: In 2023, about 10·6 million children aged 0–19 years experienced SHS worldwide, with 96% of these children residing in LMICs. The three health conditions accounting for most of the global SHS burden in children were endocrine, metabolic, blood, and immune disorders (51% of SHS in children), premature birth and birth trauma (18%), and injury, poisoning, and external causes (7%). The annual number of children experiencing SHS changed little between 1990 and 2023, but the SHS burden shifted from primarily decedents toward non-decedents, with nondecedents accounting for 59% of the total burden of SHS in children in 1990 to 81% in 2023.
Interpretation: Our findings underscore the crucial need to expand access to high-quality palliative care services for children and adolescents, particularly in LMICs. Our results also highlight the shift from decedent to non-decedent care needs associated with the substantial morbidity experienced by those living with their disease. Specific healthsystem policies to respond to the need for increased and higher-quality paediatric palliative care, especially interventions and medicines essential to address the unique palliative care needs of children, must be adequately funded to effectively reduce the avoidable burden of SHS among children
EC-CEI01 25A: Neurological and Physiological Data from Simulated and Authentic Presentation Scenarios in Innovation and Entrepreneurship Education
No full textThis dataset was collected as part of a quasi-experimental study designed to compare neurophysiological responses in two instructional conditions:
(a) a simulated practice environment where students rehearsed and received instructor feedback, and
(b) a real presentation environment involving an external industry partner (socio formador).
The study was developed within the context of Educational Innovation Management at the School of Humanities and Business, incorporating entrepreneurial learning elements and authentic evaluation scenarios. Two data collection sessions were conducted:
Pre-phase (2025-06-03): Simulated Scenario
Students participated in a simulated practice session in which they refined their pitch, rehearsed their presentation structure, and received formative feedback from their instructor. During this session, the environment was controlled, low-stakes, and focused on preparation, iterative improvement, and guided practice.
Post-phase (2025-06-10): Real Presentation Scenario
Students presented their opportunity proposal to an external industry partner (socio formador). This session represented a high-stakes, authentic evaluation environment in which students were expected to demonstrate professional communication, clarity of value proposition, and persuasive argumentation.
Both sessions replicated the same presentation tasks; however, the emotional, cognitive, and contextual demands differed significantly due to the presence of a real external evaluator in the post-phase.
Biometric Devices Used
Muse 2 EEG Headband : Neurophysiological activity was recorded using the Muse 2 EEG headband, which captures brain electrical signals at high temporal resolution.
● Sampling rate: 256 Hz
●Electrode channels: TP9, AF7, AF8, TP10
● Frequencies captured: delta, theta, alpha, beta, and gamma bands.
These signals allow the characterization of cognitive states such as attention, alertness, mental fatigue, and cognitive workload throughout both the simulated and real presentation phases.
Embrace Plus Wristband (Empatica): Physiological activity was recorded using the Embrace Plus, a multimodal wearable device designed for continuous monitoring of autonomic nervous system responses. The device captures several biomarkers relevant to cognitive and emotional states.
Biomarkers collected:
●Electrodermal Activity (EDA): Records phasic and tonic skin conductance responses associated with arousal, stress, engagement, and emotional activation.
Sampling frequency: approximately 4 Hz.
●Blood Volume Pulse (BVP): Measures peripheral blood flow through photoplethysmography (PPG), supporting the calculation of:
●Heart Rate (HR), Heart Rate Variability (HRV), Indicators associated with stress regulation, workload, and autonomic balance.
●Skin Temperature: Captures peripheral thermoregulation changes related to stress, cognitive effort, and affective responses.Sampling frequency: ~1 Hz.
These multimodal signals support the examination of students affective and physiological responses during both practice and real evaluation conditions, enabling analyses of stress, engagement, attentional changes, and autonomic activation
Smartphone addiction in Honduran university students
No full textThis dataset includes anonymized information from 530 students at the Francisco Morazán National Pedagogical University in Honduras, selected through convenience sampling. Participants ranged in age from 17 to 64 years (M = 26.16, SD = 8.33), and the majority were female.
The dataset provides valuable insights into students’ mental health and digital behavior across different stages of their academic training. It consists of two main categories of information. The first includes sociodemographic variables such as age, biological sex, and average number of hours of daily smartphone use.
The second category includes responses to three validated instruments: the Smartphone Addiction Scale–Short Version (SAS-SV), which assesses problematic smartphone use; the Internet Addiction Test (IAT), which measures problematic internet use behaviors; and the Depression, Anxiety, and Stress Scale–21 (DASS-21), which evaluates psychological distress
PM-KIMEN 24A: A dataset for analyzing EEG-based concentration and performance in gamified learning environments
No full textThis dataset was collected during the EEG-Gamified Simulator Pilot experiment, which explores the relationship between concentration, socio-emotional skills, and executive functions in educational simulation environments. The dataset combines neurocognitive, biometric, and task performance data to evaluate how concentration impacts skill acquisition. Participants engaged in gamified simulation tasks while their cognitive and emotional states were monitored using EEG devices and advanced educational tools.
The study involved undergraduate students from diverse academic disciplines interacting with a gamified simulator designed to foster decision-making. Data were collected over two sessions (S2, S3), integrating EEG metrics and performance indicators. This dataset is valuable for understanding the dynamic interplay between cognitive engagement and task performance in simulated learning environments. It supports interdisciplinary research in neuroeducation, gamification, and educational technology.
The database includes the following variables:
General Identifiers:
● User: Unique numerical identifier for each participant, ranging from 1 to 14.
● Session: Unique text identifier for each session in the format "SN" (e.g., S1, S2, S3).
● Timestamp: Date and time of data collection in the format "yyyy/mm/dd HH:MM".
● Time: Elapsed time during each task, recorded in "HH:MM:SS".
EEG and Brainwave Data:
● RAW_TP9, RAW_AF7, RAW_AF8, RAW_TP10: Raw EEG data in microvolts recorded at specific sensor locations (TP9, AF7, AF8, TP10).
● RazonTP9(B/T), RazonAF7(B/T), RazonAF8(B/T), RazonTP10(B/T): Ratios of beta to theta brainwave bands for each sensor location, reflecting concentration levels.
● X_Delta, X_Theta, X_Alpha, X_Beta, X_Gamma, X_Razon(B/T): Mean values of delta, theta, alpha, beta, gamma bands, and beta-to-theta ratio for each session.
● S_Delta, S_Theta, S_Alpha, S_Beta, S_Gamma, S_Razon(B/T): Standard deviations for the respective brainwave bands and ratios, providing variability measures.
Concentration Analysis:
● Peaks_Concentration: Detected outliers where concentration values exceed two standard deviations, indicating significant concentration spikes.
● Total_Peaks: Count of concentration peaks identified during the session.
● Predicts_Concentration: Concentration levels calculated using the predictive model developed by Pérez-Gómez et al. (2025), which estimates concentration through a low-cost EEG device.
● Total_Predicts: Count of predicted concentration peaks using the authors' model.
Simulator Performance Metrics (KPIs):
● KPI Plazo: Measures time management efficiency within tasks.
● KPI Costo: Evaluates resource management during simulations.
● KPI Satisfacción: Represents satisfaction levels of stakeholders involved in the simulation.
● KPI Total: Aggregates all performance metrics to provide an overall performance score.</p
Replication Data for: Empirical Analysis of Collaboration Patterns and Performance in Small Samples of iGEM Synthetic Biology Competition Teams
No full textThis dataset originates from a streamlined pipeline that systematically collects, processes, and curates information on iGEM team collaborations using GitLab digital traces. Additionally, this information is freely available for download from iGEM wikis on the internet. Users can leverage this dataset with R's igraph and bipartite libraries to generate network visualizations and compute collaboration metrics. These metrics can then be analyzed concerning team performance in the iGEM competition (also publicly available online), allowing researchers to explore correlations between collaboration patterns, achievements, and the characteristics of small team samples.In this case, the information downloaded from GitLab went through process of anonymizing sensitive information, so the data provided in this set is anonymous