Consciousness-Is-Measurable

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Consciousness-Is-Measurable

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Consciousness treated as an engineering problem. Curated datasets and benchmarks for measuring awareness, self-models, and memory across humans, animals, and machines.

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# Consciousness Is Measurable
Open-Source Project to solve one of the main mysteries.

This repository approaches consciousness as an engineering problem rather than a mystical concept. Our current objective is to provide Python scripts that enable easy access to available brain datasets, facilitating practical analysis, experimentation, and reproducible research.    
  
If consciousness exists, it leaves data traces. If it leaves data traces, it can be measured, benchmarked, and compared.  
This project exists to collect, structure, and benchmark those traces.

### What This Repo Actually Contains
1. Curated Datasets (Human, Animal, Machine)

We try to organize the dataset here and visualization, not all datasets inthe  public domain, and some of them need approval. In this case, we provide a script and details on how to receive the dataset    
Human / Biological  
- EEG datasets (awake, asleep, anesthetized)
- fMRI awareness and masking experiments
- Connectome-level structural data
Cognitive / Behavioral Proxies
- Reaction time and attention-switching tasks
- Metacognition confidence datasets
- Error-awareness experiments
Machine / Synthetic
- LLM self-reflection traces
- Long-horizon agent memory logs
- Identity consistency across context resets
- Tool-use self-modeling outputs
Each dataset includes:

Why it relates to consciousness
- What proxy it measures
- Known limitations
- Our task to create the next format     
  
consciousness/  
├── datasets/  
│ ├── human/mouse/insect    
│ │ ├── eeg/  
│ │ ├── fmri/  
│ │ └── anesthesia_sleep/  
│ ├── cognitive/  
│ │ ├── attention/  
│ │ ├── metacognition/  
│ │ └── reaction_time/  
│ └── machine/  
│ ├── llm_reflection/  
│ ├── agent_memory/  
│ └── identity_consistency/  
│
├── benchmarks/  
│ ├── self_model/  
│ ├── memory_continuity/  
│ ├── perturbation_tests/  
│ └── integration_metrics/  
│
├── theory/  
│ └── minimal_assumptions.md  
│
├── papers/  
│ └── references.md  
│
└── README.md

Contact  
If you have an interest in contributing to the project    
ildarr2016@gmail.com

Join the team  
Join Discord via the next [link](https://discord.gg/pevTsW5ZrE)   
LinkedIn [Group](https://www.linkedin.com/company/brain-news/)

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