Datasets for Generative AI

Scientific Data Engineering for Next-Generation AI Systems

Training generative AI models requires extensive, high-quality, and well-annotated datasets. These datasets form the foundation for developing systems that can create realistic, context-sensitive, and adaptive outputs.

At IXP, we design and conduct structured data collection campaigns and transform raw multimodal data into research-grade datasets. Our approach integrates rigorous scientific methodology, advanced data processing, and strict ethical standards – enabling the development of powerful and trustworthy generative AI systems.

Multimodal Data Acquisition

Conduct controlled laboratory and real-world studies to collect audio, video, behavioral, and physiological data (e.g. EEG, ECG, EDA, PPG) from diverse participant groups.

Dataset Engineering & Annotation

Transform raw signals into structured, machine-readable datasets through cleaning, normalization, labeling, metadata enrichment, and multi-level annotation pipelines.

AI-Ready Data Curation

Prepare domain-specific datasets optimized for training, fine-tuning, and benchmarking of machine learning and generative AI models (speech, image, video, emotion modeling).

Ethical & Legal Compliance

Ensure GDPR-compliant procedures, informed consent, pseudonymization, and secure storage to guarantee ethical integrity and data protection throughout all project phases.

Example Project: Human State & Behavior Detection

Objective

The objective of this project was to create a scientifically validated dataset for training AI systems to detect complex human states and behaviors in realistic application contexts. Since relevant states such as distraction, cognitive load, stress, or specific behavioral patterns often evolve dynamically, the dataset needed to capture subtle physiological, behavioral, and contextual signals in a synchronized and scientifically annotated structure.

Approach

IXP conducted controlled studies in which the target states were systematically induced under ecologically valid conditions. The customer’s sensor system was fully integrated into the setup and complemented with contextual data sources such as vehicle CAN bus signals, cabin video, and physiological measures (e.g. EEG, ECG, EDA, PPG). All data streams were synchronized, cleaned, and annotated with high-quality scientific labels, combining automated methods with expert corrections and quality checks.