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YAML Metadata Warning: The task_categories "classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, other

YAML Metadata Warning: The task_categories "goal-tracking" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, other

YAML Metadata Warning: The task_categories "emotion-detection" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, other

Dataset Card for LifeFlow Mood & Goal Tracker Dataset

This dataset card documents the LifeFlow UI dataset extracted from a goal-tracking and self-reflection application. It includes user inputs such as mood selection, journaling notes, and goal progress updates.

Dataset Details

Dataset Description

The dataset captures multiple dimensions of personal productivity and emotional status. It includes:

Daily emotion selections from a predefined set (😡, 😢, 😐, 😊, 😍)
Timeframe selection (Daily, Weekly, Monthly, etc.)
Journal notes for each day
Goal progress percentage
Curated by: Amr Naguib
Language(s) (NLP): English, Dutch
License: MIT

Dataset Sources

Repository: [Not published yet]
Demo: [UI screenshot available]

Uses

Direct Use

Monitoring personal goal progress
Analyzing emotional trends
Training models for mood classification

Out-of-Scope Use

Medical diagnosis
Behavioral prediction at individual level

Dataset Structure

Each record includes:

timeframe (string)
mood (categorical)
note (string)
goal_progress (int, 0–100)

Dataset Creation

Curation Rationale

To facilitate structured tracking of mood and productivity and provide a base for building recommender or reflection systems.

Source Data

Data Collection and Processing

Data is manually logged through UI input or button selection, stored in structured JSON.

Who are the source data producers?

Users of the LifeFlow system (single-user mock data at this stage).

Annotations

Annotation process

Mood selection is self-annotated by users.

Who are the annotators?

Dataset creators or mock testers.

Personal and Sensitive Information

Dataset does not contain identifiable personal data.

Bias, Risks, and Limitations

The dataset represents synthetic or limited-user input and is not generalizable to broader populations without additional data.

Recommendations

Use for prototyping or educational purposes only.

Citation

BibTeX:

@misc{lifeflow2025,
  title={LifeFlow Mood & Goal Tracker Dataset},
  author={Amr Naguib},
  year={2025},
  note={https://huggingface.co/datasets/amr701/lifeflow}
}

Dataset Card Contact

Contact: Amr Naguib – [email protected]

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