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