data/DATASETS.md

DADOES Dataset Survey

DADOES detects mood from English daily reports written by agents in a simulated world. Public emotion corpora are useful for a foundation model, but they do not fully cover the DADOES taxonomy. The critical gap is direct supervision for lonely, bored, and tired.

Last checked: 2026-07-01.

Coverage Summary

Mood area	Public coverage	Recommendation
Broad positive and negative moods	Good coverage from GoEmotions and related emotion corpora.	Keep GoEmotions as the public baseline.
lonely	Usable public datasets exist, mostly Reddit-derived.	Add a separate loneliness adapter/evaluation path after license review.
bored	No clean English text emotion dataset found with reliable boredom labels.	Build DADOES-owned labels from agent reports.
tired	Public fatigue datasets are mostly medical, driving, physiological, or non-emotion tasks.	Build DADOES-owned labels from agent reports.
Agent daily-report style	Public corpora are mostly Reddit, Twitter, or dialogue.	Collect and label in-domain reports.

Candidate Datasets

Dataset	License / status	Useful labels	DADOES decision
google-research-datasets/go_emotions	Apache-2.0	Broad fine-grained emotions plus neutral. No direct lonely, bored, or tired.	Already used as the baseline training and evaluation source.
facebook/empathetic_dialogues	CC-BY-NC-4.0	Dialogue situations include lonely and several useful adjacent moods.	Parser is wired for local exported CSV, but trainer only reads it when DADOES_INCLUDE_NON_COMMERCIAL=1. Do not ship public weights trained on it as AGPL-only artifacts.
FIG-Loneliness/FIG-Loneliness	CC-BY-NC-4.0	Binary loneliness plus fine-grained loneliness context fields.	Parser is wired for local exported JSONL, but trainer only reads it when DADOES_INCLUDE_NON_COMMERCIAL=1.
yael-katsman/Loneliness-Causes-and-Intensity	CC-BY-4.0	Loneliness causes and intensity from 1 to 5. Small dataset.	Public optional source. The trainer maps it to partial lonely supervision.
smit18/text_emotion	Apache-2.0, sparse dataset card	Twitter-style labels: happiness, love, sadness, worry, hate.	Public optional source. It is auxiliary only and does not cover lonely, bored, or tired as labels.
PrajwalNayaka/Text-Emotion	Apache-2.0, sparse dataset card	neutral, happiness, love, empty, hate, anger, enthusiasm, relief, fun, sadness, surprise.	Public optional source with sparse provenance metadata. empty maps to sad, not lonely, to avoid false direct supervision.
Um1neko/text_emotion	Apache-2.0 metadata	Plutchik-like labels with intensity: anger, anticipation, disgust, fear, joy, sadness, surprise, trust, neutral.	Public optional source for broad mood coverage. No direct missing dimensions.
DailyDialog, EmotionLines, MELD, EmoWOZ	Varies by source	Conversation emotions, usually basic emotions or task-dialogue sentiment.	Useful for conversation robustness, not for bored or tired supervision.
DADOES agent reports	Project-owned if collected from our simulation.	All DADOES labels, especially lonely, bored, and tired.	Required for production-quality coverage.

Required DADOES Domain Set

Public data should not be treated as enough for the missing dimensions. The first production data set should be project-owned and should include:

• At least 100 held-out reviewed examples each for lonely, bored, and tired.
• At least 50 held-out reviewed examples for every other mood.
• Multi-label records, because a report can be both lonely and tired, or bored and frustrated.
• Negative examples for each missing dimension. For example, a report that says "I was alone but focused and calm" should not automatically be lonely.
• Source metadata outside the model input: world day, agent id, location, and major events. This lets us audit bias without leaking metadata into the text-only classifier.

Recommended JSONL schema:

{"text":"I patrolled the empty road for hours and nobody answered my calls.","labels":["lonely","tired"],"source":"agent_report","reviewed":true}

Label Rules For Missing Dimensions

Use these rules when labeling agent reports:

• lonely: social isolation, missing companionship, rejection, abandonment, or inability to reach others. Being physically alone is not enough.
• bored: under-stimulation, repetitive routine, lack of meaningful activity, waiting without engagement, or explicit boredom. Calm is not boredom.
• tired: physical or mental fatigue, exhaustion, sleepiness, depletion, or inability to continue from low energy. Sadness or frustration is not enough.

Training Plan

1. Keep the current GoEmotions checkpoint as the public baseline.
2. Use cargo run --release --features dataset-download --bin prepare_external_datasets to prepare public raw-file sources under data/raw/external.
3. Keep non-commercial sources behind DADOES_INCLUDE_NON_COMMERCIAL=1 and in a separate model card. Provide their local CSV/JSONL exports explicitly.
4. Build domain_eval.jsonl before using synthetic or weakly labeled domain data for training.
5. Use synthetic and LLM-labeled reports only for training expansion, not for final benchmark claims unless they are human-reviewed.