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Research Tracking System
A system to track questions, experiments, hypotheses, findings, and decisions throughout the research process.
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Why This Matters
For Researchers: Systematically organize research processes, track decision paths, and maintain clear connections between ideas and outcomes.
For Science: Reduce duplicated efforts, make research sharing more efficient, and enable better collaboration across teams and institutions.
For AI: Help AI systems understand open-ended discovery processes and learn from human research patterns and decision-making.
Core Belief
Open-ended discovery is a core aspect of research
Yet there isn't much data to understand how humans organize their research process. By tracking these chains of thought, we can better understand and enhance the creative process of scientific discovery.
Example: Markov ICL Research
Below is a real research process tracking the development of understanding in-context learning through Markov chains, showing how questions evolve into experiments, findings lead to pivots, and insights build upon each other.
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
C.F. Park, E.S. Lubana, I. Pres, H. Tanaka
ICLR 2025 Spotlight· 2025
Node Types
Question
Hypothesis
Experiment
Observation
Thought
Work
Pivot
Idea
Finding
Literature Review
Analysis
Eureka