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FORMINDEX Overview

FORMINDEX FORMIS INtegrated Database EXploration
Daniel Friedman 0000-0001-6232-9096 daniel@activeinference.institute

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Phase 1: Bibliographic Analysis

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Phase 1a: Bibliographic Metadata Analysis

Timeline: First analyses in late 2024, with fuller versioning/publication in 2025.
Objective: Conduct comprehensive metadata analysis of FORMIS database, ensuring fair use compliance.
Key Components:
1. Metadata-Centric Analysis: Analyze titles, authors, dates, journals, and keywords.
2. Fair Use Compliance: Enforce strict data usage protocols.
3. LLM Integration: Deploy fine-tuned models for bibliographic analysis.
4. Dynamic Visualization: Create interactive publication trend visualizations.
5. Longitudinal Analysis: Perform time-series analysis of research patterns.
6. AI-Driven Synthesis: Develop algorithms for automated research landscape reports.

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Phase 1b: Selective Full-Text Integration

Timeline: Starting after Phase 1a milestone.
Objective: Incorporate full-text analysis where legally permissible.
Key Components:
1. Legal Assessment: Establish protocols for permissible full-text access.
2. Precision Integration: Selectively incorporate legally accessible full texts.
3. RAG-Enhanced Analysis: Apply advanced techniques for content insights.
4. Cross-Reference System: Develop AI-driven metadata and full-text integration.
5. Privacy Preservation: Implement differential privacy and federated learning.
6. Ethical Framework: Establish comprehensive guidelines for full-text handling.

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Phase 2: Multi-Omic Phenotypic Integration

Timeline: Starting after Phase 1b milestone.
Objective: Create unified resource integrating FORMIS with diverse biological databases (previously active as
METAINFORMANT
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).
Key Components:
1. Multi-Omic Integration: Combine ecological, behavioral, phenotypic, and genomic data.
2. Data Harmonization: Standardize formats and ontologies across datasets.
3. Comparative Analysis Framework: Enable multi-dimensional studies linking various biological factors.
4. Open-Source Platform: Develop user-friendly interface for data access and analysis.
5. AI-Driven Analytics: Implement ML algorithms for complex multi-database queries.
6. Data Governance: Establish rigorous ethical guidelines for data sharing.
7. Dynamic Ecosystem: Create system for continuous updates and community contributions.
8. Researcher Support: Provide comprehensive training on multi-omic approaches.
This streamlined approach progresses from metadata analysis to full-text integration and multi-omic data synthesis, prioritizing legal compliance, ethical standards, and scientific innovation throughout.

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Team and Background

Daniel Friedman and possible collaborators bring expertise in entomological research, literature meta-analysis, and cognitive security to this open science project. Recent work on gene expression meta-analysis in honey bees demonstrates proficiency in entomological data pipeline development and meta-analysis techniques [1]. In a systematic literature analysis on the Free Energy Principle and Active Inference we explored synthesizing complex bibliographic and ontological information [2]. The team's involvement with the Cognitive Security and Education Forum (COGSEC) underscores their commitment to vigilance and responsibility, essential properties for maintaining integrity throughout the research process [3].
1
https://zenodo.org/records/10400745
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2
https://zenodo.org/record/7449368
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3
https://www.cogsec.org/
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