SSHAFER/agency-personalities-trails

Personalities-Trails

Overview

Personalities-Trails is a bilingual literary analysis dataset for research on artificial agency, literary character modeling, retrieval-augmented generation, and role-playing evaluation.

The dataset is built from selected literary works and organized into multiple subsets for detailed trait analysis, retrieval-oriented structured summaries, and merged settings that combine both views.

This repository contains processed research data only, including structured annotations, metadata, and limited text excerpts. It does not provide complete literary works and should not be treated as a substitute for the original publications.

Dataset Summary

• Root directory: resource/
• Total JSON files: 412
• Approximate size: ~5.3 GB
• Estimated total records: 50,000+
• Languages: Chinese and English

Subsets

Subset	Files	Size	Language	Description
en-traits/	90	1.3 GB	English	Full English literary analysis
en-retrieval/	37	869 MB	English	English analysis with retrieval-oriented short summaries
en-retrieval+en-traits/	90	1.7 GB	English	Merged English subset
ch-traits/	90	664 MB	Chinese	Full Chinese literary analysis
ch-retrieval/	15	100 MB	Chinese	Chinese analysis with retrieval-oriented short summaries
ch-retrieval+ch-traits/	90	694 MB	Chinese	Merged Chinese subset

Other files currently present in the directory include dataset_comparison.xlsx, fig.pptx, and find_en.py.

Data Structure

All JSON files use a top-level array. Each element is a sample containing instructions, source text, outputs, and metadata.

Common Fields

{
  "instruction": {
    "intro": "Prompt for intro analysis",
    "personalities_trails": "Prompt for character trait analysis",
    "self_awareness": "Prompt for self-awareness analysis",
    "scene": "Prompt for scene analysis"
  },
  "text": "Source literary excerpt",
  "input": "",
  "output": {
    "intro": "Structured analysis of character/location/background/event",
    "personalities_trails": "Detailed character profile",
    "self_awareness": "Self-awareness analysis",
    "scene": "Scene analysis"
  },
  "metadata": {
    "element_id": "Unique identifier",
    "filename": "Source EPUB filename",
    "languages": "eng / zho"
  }
}

Retrieval-Specific Field

Some retrieval-related subsets contain an additional output-short field for structured summaries.

{
  "output-short": {
    "scenario": {
      "place": "Location",
      "background": "Background",
      "event": "Event"
    },
    "people": [
      {
        "character-profile": {
          "name": "Character name",
          "sketch": "Character sketch"
        },
        "literary-characterization": {
          "appearance": "Appearance",
          "language": "Language style",
          "action": "Behavior",
          "psychology": "Psychology",
          "demeanor": "Demeanor"
        },
        "psychological-analysis": {
          "perspective-on-life": "View of life"
        }
      }
    ]
  }
}

Note: the scenario field appears in English retrieval files; Chinese retrieval files may only contain the people field under output-short.

Subset Types

Type	Example Directories	Fields	Purpose
traits	en-traits/, ch-traits/	Common fields	Detailed literary analysis
retrieval	en-retrieval/, ch-retrieval/	Common fields + output-short	Analysis plus compact structured summaries
retrieval+traits	en-retrieval+en-traits/, ch-retrieval+ch-traits/	Combined content	Merged subsets for broader use

Example Statistics

• Example file: traits_1984.epub.json contains 926 records
• Naming pattern: traits_[book-title].epub.json
• Main analysis dimensions: intro, personalities_trails, self_awareness, scene

Source and Construction

The dataset is derived from EPUB-format books spanning Chinese and English literary works, including both classic and modern titles. Literary passages are processed into structured annotations with detailed outputs (output) and, in some subsets, short summaries (output-short).

Intended Use

This dataset is intended for non-commercial research use, including:

• literary character modeling
• artificial agency research
• retrieval-augmented generation experiments
• role-playing and character simulation evaluation
• analysis of trait representation and self-perception

Prohibited Use

This dataset must not be used for:

• commercial use of any kind
• commercial training or fine-tuning
• reconstructing or substituting the original books
• unlawful redistribution of excerpted text
• any use that infringes the rights of authors, translators, publishers, or other rights holders

Copyright and License Notice

This dataset contains limited excerpts derived from copyrighted literary works. Rights in the original texts remain with their respective rights holders.

Please review the full license terms in LICENSE. If you are a rights holder and believe any content should be revised or removed, please contact the maintainer.

Usage

Download the full dataset

git lfs install
git clone https://huggingface.co/datasets/your-username/personalities-trails

Download selected subsets

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="your-username/personalities-trails",
    repo_type="dataset",
    allow_patterns=[
        "en-traits/*",
        "README.md",
        "LICENSE",
    ],
)

print(local_dir)

Load a JSON file directly

import json
from pathlib import Path

path = Path("en-traits/traits_1984.epub.json")
with path.open("r", encoding="utf-8") as f:
    data = json.load(f)

print(len(data))
print(data[0]["metadata"])

Load with datasets

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files="en-traits/traits_1984.epub.json",
    split="train",
)

print(dataset[0]["text"])

Limitations

• The dataset includes only limited excerpts rather than complete literary works.
• Redistribution constraints may apply because the data is derived from copyrighted books.
• Coverage depends on the selected books and processing pipeline, and does not represent all literary traditions or styles.

Citation

If you use this dataset in research, please cite the repository or the associated paper/project page when available.