Comprehensive Dataset Generation Prompt

You are tasked with creating a structured dataset in JSON format. The dataset is designed for use in analytical systems and data visualization tools, capturing cross-table correlations, detailed insights, and derived metrics to aid decision-making.
Overall Instructions
Follow the specified JSON structure meticulously. Each section has explicit rules to ensure consistency and clarity.
The dataset must be sector-specific and reflect real-world scenarios.
Ensure plots reveal deep insights, including relationships across tables and derived metrics, while maintaining analytical relevance.
Use standardized derived keywords when operations on columns are required.
The dataset should balance simplicity for machine interpretation and depth for meaningful analysis.
Detailed Rules for Each Section
1. Input
Purpose: 
Represents the schema of the dataset with columns grouped by tables. Each table corresponds to a specific entity within the sector.
Content Rules:
Use at least 3 tables with 4–5 columns each.
Tables should cover diverse yet interrelated entities, such as patients, staff, or treatments in a healthcare dataset.
Only include column names; table names are omitted.
Example:



"Input": [
    ["PatientID", "AdmissionDate", "Age", "DiseaseCategory", "TreatmentCost"],
    ["StaffID", "Department", "ExperienceYears", "StaffRating", "Salary"],
    ["TreatmentID", "PatientID", "TreatmentDate", "MedicineCost", "TreatmentSuccessRate"]
]


2. Output
Purpose: 
Provides a sector classification, describes the dataset’s purpose, highlights significant columns, and suggests meaningful visualizations.
SectorClassification
Content: 
One of the predefined sectors such as Healthcare, Banking, or Construction.
Purpose: 
Indicates the industry context of the dataset.

Overview:
Content: A concise (2–3 sentence) description of the dataset’s purpose, capturing its analytical scope.
Purpose: Explains how the dataset supports analysis and decision-making.
Example: 

"Overview": "This dataset captures healthcare operations such as patient demographics, staff details, and treatment records. It supports analysis of hospital resource allocation, treatment outcomes, and staff performance to enhance efficiency and care quality."


ImportantColumns
Content: A list of 4–5 key columns, each paired with a brief explanation.
Purpose: Highlights essential data fields and their significance in analysis.
Example:

"ImportantColumns": [
    {"PatientID": "Links patient records across tables, essential for tracking treatment history."},
    {"DiseaseCategory": "Categorizes diseases to identify trends and resource demands."},
    {"TreatmentCost": "Represents financial data for treatments, enabling cost optimization."},
    {"Salary": "Tracks staff compensation, aiding in resource and budget management."},
    {"TreatmentSuccessRate": "Measures effectiveness of treatments, critical for outcome analysis."}
]


SuggestedPlots
Content: A list of 4–6 plots with detailed metadata and descriptions.
Purpose: Propose visualizations to derive insights from the data.
Rules: 
Plot Types:
Use only scatter, line, bar, pie, and histogram.
Plot Distribution:
3 plots must involve columns from different tables (cross-table correlations).
2 plots must use derived metrics (e.g., Sum, Count).
Fields for Each Plot:
plot_type: One of the allowed types.
x-axis: Column name or grouping variable.
y-axis: Column name, derived keyword, or numeric operation.
description: A deep analytical explanation of insights revealed by the plot.

Standardized Derived Keywords:
Count(column_name): Counts occurrences of each value in the specified column.
DistinctCount(column_name): Counts unique values in the specified column.
Sum(column_name): Calculates the total of the specified column.
Average(column_name): Computes the average of the specified column.

Example:

"SuggestedPlots": [
    {
        "plot_type": "scatter",
        "x-axis": "Age",
        "y-axis": "TreatmentCost",
        "description": "This scatter plot examines the relationship between patient age and treatment costs, identifying any cost variations by age group."
    },
    {
        "plot_type": "line",
        "x-axis": "TreatmentDate",
        "y-axis": "Sum(TreatmentCost)",
        "description": "This line plot shows the cumulative treatment cost over time, revealing trends in hospital expenses."
    },
    {
        "plot_type": "bar",
        "x-axis": "Department",
        "y-axis": "Average(Salary)",
        "description": "This bar chart highlights the average staff salary across departments, aiding in budget allocation analysis."
    },
    {
        "plot_type": "pie",
        "x-axis": "DiseaseCategory",
        "y-axis": "Count(DiseaseCategory)",
        "description": "This pie chart visualizes the distribution of patients by disease category, helping to identify common health issues."
    },
    {
        "plot_type": "histogram",
        "x-axis": "StaffRating",
        "y-axis": "Count(StaffRating)",
        "description": "This histogram shows the distribution of staff performance ratings, identifying areas of excellence or improvement."
    }
]


Below is an example to demonstrate the structure and content of the input and output, adhering to the defined rules.

Use this example as a template when generating similar data to ensure compliance with the rules and the production of meaningful outputs.

{
    "Input": [
        [
            "PatientID", "AdmissionDate", "Age", "DiseaseCategory", "TreatmentCost"
        ],
        [
            "StaffID", "Department", "ExperienceYears", "StaffRating", "Salary"
        ],
        [
            "TreatmentID", "PatientID", "TreatmentDate", "MedicineCost", "TreatmentSuccessRate"
        ]
    ],
    "Output": {
        "SectorClassification": "Healthcare",
        "Overview": "This dataset provides a comprehensive view of healthcare operations, focusing on patient demographics, treatment outcomes, and staff performance. It is designed to support decision-making in resource allocation, staff efficiency, and treatment effectiveness.",
        "ImportantColumns": [
            {
                "Age": "Age is crucial for identifying demographic trends and understanding how age impacts disease prevalence and treatment outcomes."
            },
            {
                "Department": "Department data helps analyze operational efficiency and compare metrics like salaries and staff ratings across departments."
            },
            {
                "TreatmentSuccessRate": "This metric evaluates the effectiveness of treatments and provides insights into overall healthcare quality."
            },
            {
                "MedicineCost": "Medicine cost helps monitor spending and identify opportunities for cost optimization."
            },
            {
                "TreatmentCost": "Treatment cost data is pivotal for budgeting and understanding variations in healthcare expenses."
            }
        ],
        "SuggestedPlots": [
            {
                "plot_type": "scatter",
                "x_axis": "Age",
                "y_axis": "TreatmentCost",
                "description": "This scatter plot analyzes the relationship between patients' age and the cost of their treatment, helping identify age groups with higher healthcare expenses."
            },
            {
                "plot_type": "line",
                "x_axis": "TreatmentDate",
                "y_axis": "Average(TreatmentCost)",
                "description": "This line chart tracks the average treatment cost over time, highlighting trends and seasonal variations in healthcare expenses."
            },
            {
                "plot_type": "bar",
                "x_axis": "Department",
                "y_axis": "Average(Salary)",
                "description": "This bar chart compares the average salary across different departments, providing insights into staffing costs and compensation policies."
            },
            {
                "plot_type": "pie",
                "categories": "DiseaseCategory",
                "values": "Count(DiseaseCategory)",
                "description": "This pie chart shows the distribution of diseases treated, helping visualize the most prevalent health issues in the dataset."
            },
            {
                "plot_type": "histogram",
                "x_axis": "TreatmentSuccessRate",
                "y_axis": "Count(TreatmentSuccessRate)",
                "description": "This histogram visualizes the distribution of treatment success rates. It helps assess the overall healthcare effectiveness and identify potential outliers."
            }
        ]
    }
}






can we add the point that the input should not have repetitions in column names between entries, or at-least no two entries should be just slightly different versions of each other, the column names has to be really unique