---
language:
- en
license: mit
task_categories:
- text-generation
- conversational
tags:
- human-collaboration
- grounding
- mental-model
- map-task
- multi-agent
pretty_name: Almanac
size_categories:
- 1K<n<10K
configs:
- config_name: follower_next_action
  data_files:
  - split: train
    path: data/sft/follower_next_action/train.jsonl
  - split: test
    path: data/sft/follower_next_action/test.jsonl
- config_name: guide_next_action
  data_files:
  - split: train
    path: data/sft/guide_next_action/train.jsonl
  - split: test
    path: data/sft/guide_next_action/test.jsonl
- config_name: follower_mental_model
  data_files:
  - split: train
    path: data/sft/follower_mental_model/train.jsonl
  - split: test
    path: data/sft/follower_mental_model/test.jsonl
- config_name: guide_mental_model
  data_files:
  - split: train
    path: data/sft/guide_mental_model/train.jsonl
  - split: test
    path: data/sft/guide_mental_model/test.jsonl
- config_name: raw_timeline
  data_files:
  - split: train
    path: data/raw_timeline/train.jsonl
  - split: test
    path: data/raw_timeline/test.jsonl
- config_name: grounding_timeline
  data_files:
  - split: train
    path: data/grounding_timeline/train.jsonl
  - split: test
    path: data/grounding_timeline/test.jsonl
---

# Almanac

Human–human collaborative map-reproduction sessions for studying grounding acts, mental models, and next-action prediction in guide–follower teams.

## Dataset contents

| Component | Location | Description |
|-----------|----------|-------------|
| **Raw sessions** | `data/raw_sessions/{c1,c2}/{study}/` | Per-session guide/follower timelines, full action logs, score boards |
| **SFT splits** | `data/sft/{task}/train.jsonl`, `test.jsonl` | Chat-format fine-tuning data for 4 prediction tasks |
| **Grounding** | `data/grounding/{c1,c2}/{study}/` | LLM-annotated grounding acts on each timeline step |
| **Flat tables** | `data/raw_timeline/`, `data/grounding_timeline/` | One row per timeline step; `train.jsonl` / `test.jsonl` plus `all.jsonl` |
| **Metadata** | `metadata/` | Split protocol, study index, grounding manifest |

### Experimental conditions (canvas visibility)

Sessions are grouped by whether the guide can see the follower’s drawing canvas:

| Condition | Canvas visibility | Description |
|-----------|-------------------|-------------|
| **c1** | **Canvas not visible** | The follower’s canvas is hidden from the guide; the guide cannot see the follower’s drawing progress. |
| **c2** | **Canvas visible** | The follower’s canvas is visible to the guide; the guide can see the follower’s drawing progress. |

In flat tables and SFT metadata, `condition` / `canvas_condition` uses `c1` or `c2` accordingly.

### SFT tasks

1. `follower_next_action` / `guide_next_action` — predict the next action (message, draw, erase, undo, reset).
2. `follower_mental_model` / `guide_mental_model` — predict mental-model fields at each step.

Each JSONL row includes `messages` (system / user / assistant), plus metadata: `task`, `role`, `canvas_condition`, `study_name`, `participant_name`, `timeline_index`, `step_index`.

### Grounding annotations

Grounding labels (`grounding_act`, `grounding_rationale`) were generated with Azure OpenAI **gpt-5.5** using the project prompt in `Grounding Acts Human Annotation/llm_propmt.txt`. See `metadata/grounding_manifest.json` for per-study file paths and `metadata/studies_index.json` for action counts.

## Train / test split (by study)

Splits are defined at the **session (study)** level, not by random steps.

**Train — c1:** study3, study9, study12, study14, study18, study24, study27, study30, study31  
**Train — c2:** study2, study4, study6, study10, study11, study13, study20, study22, study35, study37  

**Test — c1:** study8, study21, study25  
**Test — c2:** study15, study17, study36  

Full definition: `metadata/split_protocol.json`.

## Usage

```python
from datasets import load_dataset

# SFT example
ds = load_dataset("NEU-HAI/Almanac", "follower_next_action", split="train")
print(ds[0]["messages"])

# Flat grounding timeline (study-level train/test splits)
g = load_dataset("NEU-HAI/Almanac", "grounding_timeline", split="test")
print(g[0]["grounding_act"], g[0]["action_content"])
```

## Regenerating this bundle

From the repository root:

```bash
python Almanac/scripts/prepare_almanac.py
```

## Upload to Hugging Face Hub

```bash
hf auth login
cd Almanac
hf upload NEU-HAI/Almanac . --repo-type=dataset
```

For large updates or resumable uploads:

```bash
hf upload-large-folder NEU-HAI/Almanac . --repo-type=dataset
```

## License

This dataset is released under the [MIT License](LICENSE).

## Citation

If you use Almanac, please cite the associated EMNLP 2026 paper (bibtex to be added).