Rango001/MindScale_data

MindScale Sample Data

Questionnaire-based mental health screening and symptom assessment

Overview · Contents · Data Format · Using with MindScale · Usage Notes

📋 Overview

This directory contains 100 questionnaire-format sample records for inspecting MindScale's data structure and preparing input demonstrations. Each JSON file represents one sample.

• Screening: 50 samples covering anxiety, depression, life satisfaction, and life stress.
• Assessment: 50 samples covering five questionnaire-derived symptom domains, with target categories defined below.
• Scope: These examples are a small sample collection, not the full study cohort. They are not sufficient to reproduce the paper's population-scale performance estimates.

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📂 Contents

open_data/
|-- README.md
|-- At_risk_samples/       50 screening JSON files
`-- Assessment_samples/    50 assessment JSON files

Both folders include primary- and secondary-school questionnaire formats. The number and names of questionnaire items therefore vary by school stage. Sample manifests and sampling-weight files are not included in this directory.

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🧾 Data Format

All sample files use UTF-8 JSON encoding.

Field	Contents
input	Observed background and environmental questionnaire responses as key-value pairs
mask	Item response records, each containing value and an ordered candidate list
label	Four screening targets in At_risk_samples
re_label	Five assessment targets in Assessment_samples; some screening files also contain ancillary assessment results here
anxiety_depression	Auxiliary anxiety and depression outcomes in assessment samples

Item Response Records

The following is an illustrative entry within mask:

{
  "Self-strength": {
    "value": "Basically agree",
    "candidate": [
      "Strongly disagree",
      "Not quite agree",
      "Basically agree",
      "Strongly agree"
    ]
  }
}

• Meaning of mask: This field contains response values, not only missing items. Pretraining predicts these responses, while downstream input builders incorporate their values as questionnaire features.
• Missing responses: Some assessment item records use "-" for an unavailable response. Preserve this marker and the original candidate list.
• Auxiliary outcomes: label and re_label are stage-specific fields, not interchangeable aliases. Ancillary outcomes should be kept separate from the selected prediction targets.

Screening Targets

Read the screening target mapping from label.

Target key	Candidate labels, in model order
anxiety	No anxiety, Mild anxiety, Severe anxiety
depression	No depression, Mild depression, Severe depression
satisfaction	Low life satisfaction, High life satisfaction
stress	No life stress, Low life stress, High life stress

Assessment Targets

Read the assessment target mapping from re_label.

Target key	Candidate labels, in model order
re_suicidal	Mild suicidal ideation, Severe suicidal ideation
re_obsessive	Mild obsessive symptoms, Severe obsessive symptoms
re_psychotic	Mild psychotic symptoms, Severe psychotic symptoms
re_mania	Mild mania, Severe mania
re_ADHD	Mild ADHD, Severe ADHD

These strings are the model's questionnaire-derived categories, not clinical diagnoses. Preserve their spelling and capitalization when using released adapters; the corresponding mappings are defined in the code's dataset_info.py.

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🚀 Using with MindScale

• Select the stage

  Use At_risk_samples for --task_type Screen and Assessment_samples for --task_type Assessment. The screening samples' input and mask fields can also supply pretraining inputs; screening outcomes are not pretraining targets.

• Prepare the target mapping for the current input builder

  The comparative builder reads targets from label, whereas the non-comparative builder reads them from re_label. Prepare working copies with the selected stage's target mapping in the required field:

Stage	Source target field	Comparative mode	Non-comparative mode
Screen	label	Keep screening targets in label	Copy screening targets to re_label in a working copy
Assessment	re_label	Copy assessment targets to label in a working copy	Keep assessment targets in re_label

Preserve any original auxiliary outcomes separately when preparing these copies.

• Prepare manifests

  For downstream runs, create the following UTF-8 text files. Sample paths must be relative to --data_path, with one entry per line.

train.txt          Reference/training sample paths
grade_train.txt    School-stage label for each reference/training sample
mean_IP_train.txt  Template-sampling weight for each reference/training sample
test.txt           Test sample paths
grade_test.txt     School-stage label for each test sample

The three training files must be line-aligned with one another; the two test files must be line-aligned with one another. Use separate reference and test samples, and calculate sampling weights from reference/training labels only.

• Map school stages

  The grade manifests use the following labels, derived from input["School-status"]:

Sample value	Grade-manifest label
Schoolchild	Primary school
Middle school student	Middle school
High school student	Middle school

With two templates, each school-stage group used during training needs at least three reference samples; evaluation needs at least two reference samples. Templates must be drawn from the reference/training set.

• Load model weights

  Follow the accompanying MindScale code README for installation, checkpoint loading, and prediction export. Three-stage LoRA adapters are available on Hugging Face.

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📝 Usage Notes

• Research scope: Use these samples for authorized research, schema inspection, and integration checks. They are not a clinical assessment service.
• Privacy: Questionnaire records contain sensitive background and mental health information. Protect participant privacy and do not attempt to re-identify individuals or link records to identifiable people.
• Release permissions: Availability in this directory does not by itself establish permission for unrestricted reuse or redistribution. Follow the data provider's applicable authorization and data-use terms.

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