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Research Platform

Open science publishing for emotional regulation research

Methodology

How we conduct research at TEG-Blue. Our commitment to open science, rigorous validation, and ethical standards.

Open Science Principles
All TEG-Blue research follows open science principles: • Pre-registration of studies before data collection • Open data sharing (anonymized) via Zenodo • Open access publication of all findings • Transparent reporting of methodology and results • Reproducible analysis pipelines documented in public repositories We believe that emotional regulation research should be accessible to everyone—researchers, practitioners, and individuals seeking self-understanding.
Validation Framework
Our validation approach uses multiple methods: 1. **Inter-rater Reliability**: Independent raters assess the same samples to ensure consistent identification of regulatory states. 2. **Convergent Validity**: New measures are compared against established instruments (e.g., DERS, AAQ-II) to verify they capture related constructs. 3. **Discriminant Validity**: We test that our measures differentiate between distinct regulatory states, not just general distress. 4. **Ecological Validity**: Studies use naturalistic language samples and real-world contexts, not just laboratory settings.
Ethical Standards
All research involving human participants follows ethical guidelines: • Informed consent obtained before participation • Right to withdraw at any time without consequence • Data anonymization before analysis and sharing • No deception in study design • Debriefing provided after participation • Mental health resources offered to all participants We take particular care with vulnerable populations and ensure appropriate support structures are in place.
AI-Readable Research
All publications are designed for both human and AI consumption: • Structured JSON-LD metadata on every page • Semantic HTML with proper heading hierarchy • Dublin Core and Schema.org annotations • Native HTML expandable sections (not JavaScript accordions) • Clear, consistent terminology throughout This ensures that AI systems can accurately understand and cite our research, while maintaining human readability.