Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- medical
+- biomedicine
+- clinical-trials
+- large-language-models
+- supervised-finetuning
+- evidence-based-medicine
+- trial-design
+- systematic-review
+
+config_names:
+- study_search
+- study_screening
+- evidence_summarization
+- trial_design
+- sample_size_estimation
+- trial_completion_assessment
+
+configs:
+- config_name: study_search
+  data_files:
+  - split: train
+    path: sft_study_search_data_cleaned.parquet
+
+- config_name: study_screening
+  data_files:
+  - split: train
+    path: sft_study_screening_data.parquet
+
+- config_name: evidence_summarization
+  data_files:
+  - split: train
+    path: sft_evidence_summarization_data.parquet
+
+- config_name: trial_design
+  data_files:
+  - split: train
+    path: sft_design_data.parquet
+
+- config_name: sample_size_estimation
+  data_files:
+  - split: train
+    path: sft_sample_size_data.parquet
+
+- config_name: trial_completion_assessment
+  data_files:
+  - split: train
+    path: sft_trial_completion_assessment_data.parquet
+---
+# TrialPanorama: Supervised Fine-Tuning Data for Clinical Research LLMs
+
+## Dataset Summary
+
+**TrialPanorama SFT** is a large-scale, task-oriented **supervised fine-tuning (SFT) dataset** designed to train large language models for **end-to-end clinical research and trial development workflows**.
+
+The dataset is derived from **TrialPanorama**, a structured clinical research resource aggregating **1.6M+ clinical trial records** across global registries and linking them with biomedical ontologies and supporting literature. It focuses on transforming raw clinical trial data and curated evidence into **instruction–response pairs** that reflect realistic, expert-level research tasks.
+
+The dataset supports training LLMs to operate as **clinical research assistants** capable of systematic literature review, trial design reasoning, and evidence-based decision making.
+
+---
+
+## Supported Training Tasks
+
+Each task is released as a separate dataset configuration.
+
+| Task (config) | Description |
+|---|---|
+| `study_search` | Given a clinical research question, retrieve and justify relevant studies from large trial and literature corpora. |
+| `study_screening` | Perform inclusion/exclusion decisions for candidate studies based on eligibility criteria and study metadata. |
+| `evidence_summarization` | Synthesize structured and unstructured trial evidence into concise, faithful summaries. |
+| `trial_design` | Generate or refine clinical trial designs, including arms, interventions, and eligibility criteria. |
+| `sample_size_estimation` | Estimate appropriate sample sizes under specified statistical and design assumptions. |
+| `trial_completion_assessment` | Assess trial completion likelihood and rationalize risks using trial design and historical evidence. |
+
+---
+
+## Data Characteristics
+
+- **Instruction-following format** suitable for SFT
+- Grounded in **real clinical trial records**
+- Emphasizes **clinical reasoning**, not surface text generation
+- Covers both **systematic review** and **trial design & optimization** tasks
+- Designed to support **generalist and agentic LLM training**
+
+All files are provided in **Apache Parquet** format.
+
+---
+
+## Typical Fields
+
+Each record may include:
+- Task-specific **instruction or prompt**
+- Structured **context** (trial metadata, eligibility criteria, outcomes, phase)
+- **Model response targets** written or validated by domain experts
+- Task and difficulty metadata
+
+Exact schemas vary by task.
+
+---
+
+## Intended Use
+
+This dataset is intended for:
+- **Supervised fine-tuning of LLMs** for clinical research tasks
+- Training **research-oriented AI agents** for trial planning and evidence synthesis
+- Building domain-adapted models for **systematic review automation**
+- Academic benchmarking of clinical reasoning capabilities
+
+### Not Intended For
+
+- Model evaluation (see DeepEvidence benchmarks for evaluation)
+- Clinical decision making
+- Direct medical or regulatory use
+
+---
+
+## How to Load
+
+Load a specific training task via its configuration name:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("zifeng-ai/TrialPanorama-SFT", "study_screening")
+ds["train"]