issdandavis/scbe-life-science-research-training-demo

--- pretty_name: SCBE Research Training Package language: - en license: mit tags: - research - pubmed - instruction-tuning - citation-generation task_categories: - text-generation - question-answering size_categories: - n<1K --- # SCBE Research Training Package This package was generated from live `pubmed` pulls for the query `protein structure prediction` and is meant for lightweight Hugging Face dataset and SFT experiments. ## Files - `papers.jsonl`: normalized raw research records - `sft_train.jsonl`: train split for instruction-style tasks - `sft_validation.jsonl`: validation split - `dataset_manifest.json`: build metadata - `research_report.md`: generated paper/report artifact ## Counts - Raw papers: `5` - Train rows: `10` - Validation rows: `0` - Tasks per paper: `metadata-extraction`, `citation-generation` ## Lead Papers - [Evolutionary-scale prediction of atomic-level protein structure with a language model.](https://pubmed.ncbi.nlm.nih.gov/36927031/) (`36927031`) - [Protein Structure Prediction: Conventional and Deep Learning Perspectives.](https://pubmed.ncbi.nlm.nih.gov/34050498/) (`34050498`) - [Integrated structure-based protein interface prediction.](https://pubmed.ncbi.nlm.nih.gov/35879651/) (`35879651`) - [Protein structure prediction using the evolutionary algorithm USPEX.](https://pubmed.ncbi.nlm.nih.gov/36780132/) (`36780132`) - [The trRosetta server for fast and accurate protein structure prediction.](https://pubmed.ncbi.nlm.nih.gov/34759384/) (`34759384`) ## Schema Raw records include `id`, `title`, `authors`, `primary_category`, `categories`, `published`, `updated`, `summary`, `url`, `pdf_url`, and a `text` field suitable for indexing. SFT rows include `instruction`, `response`, `messages`, `source_id`, and `task`.