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add data-to-paper project
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## [data-to-paper](https://github.com/Technion-Kishony-lab/data-to-paper)
AI-driven research from data to human-verifiable research papers
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<br>
<img src="https://github.com/Technion-Kishony-lab/data-to-paper/assets/65530510/e33bcb52-5f4e-4fd0-8be9-ebd64607c449" width="400" align="center">
<br>
### Category
Science, Research, Multi-agent
### Description
[*data-to-paper*](https://arxiv.org/abs/2404.17605) is a framework for systematically navigating the power of AI to perform complete end-to-end
scientific research, starting from raw data and concluding with comprehensive, transparent, and human-verifiable
scientific papers.
Towards this goal, *data-to-paper* systematically guides interacting
LLM and rule-based agents through the conventional scientific path, from annotated data, through creating
research hypotheses, conducting literature search, writing and debugging data analysis code,
interpreting the results, and ultimately the step-by-step writing of a complete research paper.
The *data-to-paper* framework is created as a research project to understand the
capacities and limitations of LLM-driven scientific research, and to develop ways of harnessing LLM to accelerate
research while maintaining, and even enhancing, key scientific values, such as transparency, traceability and verifiability,
and while allowing scientist to oversee and direct the process
[see also: [living guidelines](https://www.nature.com/articles/d41586-023-03266-1)].
### Links
- [GitHub](https://github.com/Technion-Kishony-lab/data-to-paper)
- [arXiv preprint](https://arxiv.org/abs/2404.17605)
- [Demo video](https://www.youtube.com/watch?v=Nt_460MmM8k)
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## [Databerry](https://www.databerry.ai/)
(Pivoted to Chaindesk) No-code cahtbot building