Tecnológico de Monterrey's Data Hub
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Data set for paper: “Single-Track Geometry Prediction of Direct-Energy-Deposition Process of Stainless-Steel-316L with low power and slow velocity, via Mathematic Modelling with Experimental Validation”
No full textData Set for paper work, titled: “Single-Track Geometry Prediction of Direct-Energy-Deposition Process of Stainless-Steel-316L with low power and slow velocity, via Mathematic Modelling with Experimental Validation
EC-PAG01 25A: A dataset of Neurological and Physiological Data in Strategic Thinking Class
No full textThis dataset was collected during the course titled Strategic Thinking, aimed at undergraduate students in Strategy and Business Transformation (LAET). Two data collection sessions were conducted (Pre: February 21, Post: March 11), using two biometric devices in both sessions: an electroencephalogram (EEG) and the Embrace Plus device. The purpose of the data collection is to identify changes in the physiological and neurological states of students during different phases of pedagogical activities aimed at developing strategic thinking skills.
Database Structure
General Identifiers :
●User: Unique numeric identifier for each participant.
● TimeStamp: Timestamp in the format 'yyyy/mm/dd HH:MM'.
●Time: Elapsed time during each task, recorded in the format 'HH:MM:SS'.
●Section: Phase of the class during which data were recorded.
EEG Data
●EEG Sensors: Includes raw signals from four locations: TP9, AF7, AF8, TP10.
●Brainwave Bands: Mean values and standard deviation for delta, theta, alpha, beta, and gamma bands for each sensor.
Embrace Plus Data
●Electrodermal Activity (EDA): Electrodermal activity measured in μSiemens.
●Blood Volume Pulse (BVP): Blood volume pulse, the primary parameter from the device.
●Body Temperature: Measured in degrees Celsius.</p
NPFC-Test 23A: A dataset for assessing neuronal, physiological, and facial coding attributes in a human-computer interaction learning scenario
No full textThis dataset was collected through the NPFC-Test experiment, focused on capturing neural, physiological, and facial coding modalities for assessing concentration and motivation in students. The experiment involved the use of advanced biometric sensors to monitor brainwaves, biomarkers, facial gestures, and self-reported engagement levels. Using biometric devices (Muse 2, Empatica EmbracePlus, Azure Kinect), data were obtained during various tasks, including audiovisual content interactions, comprehension exercises, and self-evaluations.
This database is pioneering in providing publicly accessible data that correlates facial gestures, physiological signals, and self-reported metrics during educational tasks. It serves as a valuable resource for research at the intersection of neuroeducation and student engagement, supporting interdisciplinary collaborations to further understand cognitive and emotional responses in learning environments.
The database contains the following variables collected during the NPFC-Test experiment:
- Timestamp variable records the unique date and time identifier in the format "yyyy/mm/dd HH:MM".
- Subject_ID is a unique text identifier for each user, formatted as "IFE-EC-NPFC-T003-NN", where NN can range from 00 to 49.
- Test_Time shows the elapsed time in the test in the format "HH:MM".
- Task_Num identifies the task with specific values, such as 1.1 for demographic information or 8.1 for final meditation.
- Task_Time indicates the time spent on each task.
- Task_Type classifies the task as 0 (Self-evaluation), 1 (Focus), or 2 (Emotions).
- Frame and Task_Frame variables are used to identify each analyzed video frame, every 30 frames.
- Self-report responses are found in Selfreport_valence, Selfreport_arousal, and Selfreport_focus, with numeric values indicating the level of alertness, enjoyment, and demand for focus, respectively.
- Face_Detection shows whether the user’s face was detected (0 = No, 1 = Yes).
- Emotions such as resmasknet_anger, resmasknet_disgust, and resmasknet_fear, among others, were analyzed with probabilities ranging from 0 to 1. The same emotions were detected using an SVM model with variables like svm_anger and svm_happiness, among others, where 0 indicates the emotion was not detected, and 1 indicates it was detected.
- Physiological parameters include Temperature in degrees Celsius, EDA in microseconds, and BVP in nanowatts.
- The HeadBandOn variable indicates whether the Muse 2 headband was correctly used.
- Brainwave data are measured in absolute power bands for delta, theta, alpha, beta, and gamma waves at locations TP9, AF7, AF8, and TP10, with values ranging from -1 to 1.75 Bels. Finally, the RAW_TP9, RAW_AF7, RAW_AF8, and RAW_TP10 variables represent raw EEG values in microvolts detected at each brain location.</p
Exams collection
No full textThis collection allows access to anonymized information related to the different exams taken by undergraduate, master's, and doctoral students. Among the categories of data available in this collection are:
-Different types of exams (admission, languages).
-Characteristics of the exam application (date,place).
-Characteristics of the exam (type, version, general score).<br
Student-Courses Collection
No full textThis collection collects data from students about their academic and training activities at Tecnologico de Monterrey, from 1996 to date.
It consists of 61 variables that are represented in different categories, such as:
- Subject administration (school, division, academic area).
-Subject information (level, subject types, credits, units, schedules).
-Grades (partial and final).
-Group attributes(online,extracurricular activities,period). <br
Student dropout dataset
No full textThe dataset includes anonymized information related to undergraduate students who have enrolled and attended at least one semester at Tecnologico de Monterrey in Mexico from 2014 to 2020. Among the information categories available in this dataset are:
-Sociodemographic information (age, gender, place of origin).
-Enrollment information (program/school, region).
-Academic information related to the student (previous level average, current average, periods completed).
-Information associated with scores on admission tests (PAA, TOEFL, other initial evaluations).
-Academic history (type of school, region, national/international, Tec system).
-Student life (participation in sports, cultural, entrepreneurial activities).
-Financial information (type of scholarship, percentage of scholarship)
Professor-Courses
No full textThis collection contains curated research educational data generated by the Living Lab & Data Hub from the Institute for the Future of Education. This data is available, on-demand, for researchers from Tecnologico de Monterrey and for researchers from other institutions that collaborate with our researchers.
To access this collection it is necessary to follow the process described here
This collection gathers data from Professors about their teaching activities at Tecnológico de Monterrey, from 1996 to date. It consists of 48 variables that are represented in different categories, such as:
-Subject administration (school, department, academic area).
-Subject information (subject attributes, subject types, schedule).
-Grades(partial and final).
-Indicators of extracurricular subjects (LiFE, virtual, support)
3D audio scenes and anxiety dataset
No full textThis dataset includes anonymized information related to 60 Mexican individuals who participated in an experiment studying the effects of 3D audio scenes on anxiety, measuring psychological and electrophysiological responses. The information available in the dataset are:
DataSpecs: This file includes participants identification number, sex, age, age class, university level, assigned audio group, state and trait anxiety scores from the State Trait Anxiety Inventory (scores and deciles), emotional dimensions scores from the Self-Assessment Manikin test (valence, arousal, and dominance), Pure Tone Audiometry levels, and audio files collected from repositories to create the 3D audio scenes.
RawData: This ZIP folder has subfolders for each individual with the electrocardiographic and electrodermal signals in EDF adn CSV formats of participants in resting state, while observing aversive images, and listening to one of five auditory scenes.
PreprocessedData: This folder has subfolders for each individual with the preprocessed version of the electrocardiographic and electrodermal signals of participants in SET/FDT and CSV formats from the RawData folder.
AudioFiles: This folder has subfolders with the five audio scenes used during experimentation for loudspeaker and headphone playback.
IAPSimages: this file has the identification number of the aversive images from the International Affective Picture System used during experimentation.
VideoGenerator: this m file refers to the Matlab code used to generate a video presenting the aversive images for six seconds.
PreprocFunctions: these m files refers to the Matlab codes used to preprocess electrophysiological data
Dictionary: This file explains variables contained in the files described above in terms of concept, data type, description, units, value range, and the file in which each variable can be found.
README: These files describe the structure of the dataset and provide guidance on how to preprocess the electrophysiological recordings and play the audio files.</li
EC-CEI02 25A: Neurophysiological and Physiological Dataset from MBA Business Pitch Presentations
No full textThis dataset was collected as part of an applied educational research study focused on analyzing neurophysiological and physiological responses during entrepreneurial pitch presentations delivered by graduate-level students. The data collection was conducted within the context of business education at EGADE Business School, involving students enrolled in an MBA program presenting their business ideas.
The data were collected on July 2, 2025, during a single experimental session structured into two consecutive presentation turns. The session design allowed for the observation of multiple teams under comparable contextual conditions while managing logistical and temporal constraints.
Session Structure: Single session with two presentation turns
- Turn 1: Four student teams participated.
- Turn 2: Three student teams participated.
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.
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.</p
Multi-label Classification Dataset for Student Evaluation of Teaching (SET)
No full textThis dataset is a collection of student evaluations of instructors, annotated with various aspects of teaching quality.
This dataset provides structured insights into positive and negative aspects of teaching, student–teacher interactions, and classroom experiences.
The dataset consists of textual comments from students about their instructors, along with categorized annotations.
Each comment is analyzed for both positive and negative aspects of teaching, as well as associated opinions.
In the Files section, there is a Readme file that explains this dataset in more detail