{ "format": "dataset-manifest/1", "slug": "nourishment-insights", "version": 2, "seed": "nourishment-insights:1", "spec": { "format": "dataset/1", "slug": "nourishment-insights", "name": "Nourishment Insights", "tagline": "Patient demographics, body metrics, meal plans, subscriptions, reviews, and ingredients for tailored dietary support.", "description": "Health professionals design tailored meal plans that address the specific dietary needs of their patients. Each patient’s unique health conditions, preferred ingredients, and potential allergens are thoroughly assessed to create a personalized approach to nutrition. As these healthcare practitioners navigate the diverse requirements, they gather critical data points from various sources, including patient demographics, body metrics, and feedback on meal plans. This detailed process ensures that each meal plan is not only nutritious but also aligned with the patient’s lifestyle and health objectives.\n\nThe dataset consists of several interconnected tables: patients, body_metrics, meal_plans, subscriptions, reviews, and ingredients. A single row in the patients table represents an individual, encapsulating their demographics and health conditions. The body_metrics table contains physical measurements tied to each patient, facilitating a better understanding of their nutritional needs. Meal plans are crafted based on these insights and are linked to subscriptions that provide ongoing support. Reviews offer invaluable feedback on the meal plans and ingredients, highlighting patient experiences and satisfaction levels. The ingredients table lists the various components that make up each meal plan, forming a critical link between dietary preferences and the nutritional goals set for patients.\n\nThis dataset is excellent for exploring questions related to dietary trends, patient satisfaction, and the effectiveness of personalized meal planning. It allows developers to practice queries that uncover patterns in dietary needs, analyze the relationships between patient metrics and meal plans, and assess the impact of different ingredients on patient outcomes. Given that the data within this resource is synthetic, it enables users to experiment without the complexities of real-world privacy concerns.", "domain": "healthcare", "shape": "transactional", "useCases": [ "sql-practice", "orm-seeding" ], "keywords": [ "dietary needs", "meal plans", "health data", "patient reviews", "nutrition", "dietitian", "health conditions" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "patients": { "description": "This table contains patient records with personal details, including demographics and health conditions. Each patient can have multiple dietary preferences and subscriptions.", "rows": 200, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each patient." }, "first_name": { "kind": "firstName", "description": "Patient's first name." }, "last_name": { "kind": "lastName", "description": "Patient's last name." }, "age": { "kind": "int", "min": 18, "max": 80, "description": "Patient's age." }, "gender": { "kind": "enum", "values": [ "male", "female", "other" ], "description": "Patient's gender.", "weights": [ 6, 4, 1 ] }, "health_condition": { "kind": "enum", "values": [ "diabetes", "hypertension", "none" ], "weights": [ 0.5, 0.3, 0.2 ], "description": "Health conditions affecting dietary needs." } } }, "body_metrics": { "description": "This table tracks body metrics for each patient, providing insights into their health status and dietary requirements.", "rows": 200, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each body metric record." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient." }, "height_cm": { "kind": "decimal", "min": 140, "max": 200, "scale": 1, "description": "Height of the patient in centimeters." }, "weight_kg": { "kind": "decimal", "min": 40, "max": 150, "scale": 1, "description": "Weight of the patient in kilograms." }, "bmi": { "kind": "decimal", "min": 15, "max": 40, "scale": 1, "description": "Body Mass Index calculated from height and weight." } } }, "meal_plans": { "description": "This table outlines meal plans tailored to individual patients, accommodating their dietary restrictions and preferences.", "rows": 300, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each meal plan." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient for whom the meal plan is created." }, "meal_type": { "kind": "enum", "values": [ "breakfast", "lunch", "dinner", "snack" ], "description": "Type of meal included in the plan.", "weights": [ 5, 3, 2, 1 ] }, "ingredients": { "kind": "words", "max": 5, "description": "List of ingredients used in the meal.", "pool": [ "chicken", "broccoli", "quinoa", "spinach", "carrot", "tofu", "salmon", "rice", "avocado", "tomato", "pepper", "onion", "garlic", "zucchini", "kale", "sweetpotato", "cucumber", "lettuce", "eggplant", "mushroom", "beet", "cauliflower", "asparagus", "pumpkin", "lentil", "walnut", "almond", "coconut", "berry", "banana", "apple", "grape", "orange", "peach", "pear", "fig", "date", "chickpea", "pasta", "squash", "sardine", "tuna", "olive", "honey" ], "min": 1 }, "allergens": { "kind": "words", "max": 3, "description": "Known allergens associated with the meal.", "pool": [ "peanut", "tree", "milk", "egg", "wheat", "soy", "fish", "shellfish", "sesame", "mustard", "celery", "sulphite", "lupin", "gluten", "corn", "soybean", "casein", "barley", "rye", "oat", "coconut" ], "min": 1 } } }, "subscriptions": { "description": "This table manages subscription plans for patients seeking ongoing dietary support and meal planning services.", "rows": 150, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each subscription." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient with the subscription." }, "plan_type": { "kind": "enum", "values": [ "monthly", "quarterly", "yearly" ], "description": "Type of subscription plan." }, "start_date": { "kind": "date", "from": "2022-01-01", "to": "2023-12-31", "description": "Start date of the subscription.", "trend": "growing" }, "renewal_date": { "kind": "date", "after": "start_date", "withinDays": 30, "description": "Next renewal date for the subscription." } } }, "reviews": { "description": "This table contains patient reviews regarding their meal plans and overall satisfaction with dietary services.", "rows": 250, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each review." }, "patient_id": { "kind": "fk", "ref": "patients.id", "description": "Reference to the patient who wrote the review." }, "rating": { "kind": "int", "min": 1, "max": 5, "description": "Rating given by the patient for the meal plan.", "distribution": "high" }, "comments": { "kind": "sentence", "max": 300, "description": "Patient's comments or feedback on their meal experience." }, "review_date": { "kind": "date", "from": "2022-01-01", "to": "2023-12-31", "description": "Date the review was submitted.", "trend": "seasonal" } } }, "ingredients": { "description": "This table lists various ingredients available for meal planning, including nutritional information.", "rows": 100, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each ingredient." }, "name": { "kind": "words", "max": 2, "description": "Name of the ingredient.", "pool": [ "oliveoil", "chicken", "quinoa", "tofu", "salmon", "broccoli", "spinach", "carrot", "rice", "avocado", "tomato", "pepper", "onion", "garlic", "zucchini", "kale", "sweetpotato", "cucumber", "lettuce", "eggplant", "mushroom", "beet", "cauliflower", "asparagus", "pumpkin", "lentil", "walnut", "almond", "coconut", "berry", "banana", "apple", "grape", "orange", "peach", "pear", "fig", "date", "chickpea", "pasta", "squash", "sardine", "tuna" ], "min": 1 }, "calories": { "kind": "int", "min": 0, "max": 1000, "description": "Caloric value per serving of the ingredient.", "distribution": "long-tail" }, "protein": { "kind": "decimal", "min": 0, "max": 100, "scale": 1, "description": "Protein content per serving (in grams)." }, "fat": { "kind": "decimal", "min": 0, "max": 100, "scale": 1, "description": "Fat content per serving (in grams)." }, "carbohydrates": { "kind": "decimal", "min": 0, "max": 100, "scale": 1, "description": "Carbohydrate content per serving (in grams)." } } } }, "structure": "multi-table", "traits": [ "categorical", "timestamped", "nullable" ], "analysisQuestions": [ "What are the most common health conditions among patients?", "How do patient demographics impact dietary preferences?", "Which meal types receive the highest ratings from patients?", "What is the average Body Mass Index (BMI) among patients?", "How do subscription types correlate with patient satisfaction?" ], "suggestedQueries": [ { "title": "Retrieve all meal plans for a specific patient", "sql": "SELECT * FROM meal_plans WHERE patient_id = :patient_id", "dialect": "postgres" }, { "title": "Count the number of patients with specific health conditions", "sql": "SELECT health_condition, COUNT(*) FROM patients GROUP BY health_condition", "dialect": "postgres" }, { "title": "Average rating of meal plans by type", "sql": "SELECT meal_type, AVG(rating) FROM reviews JOIN meal_plans ON reviews.patient_id = meal_plans.patient_id GROUP BY meal_type", "dialect": "postgres" }, { "title": "Find patients with subscriptions expiring soon", "sql": "SELECT * FROM subscriptions WHERE renewal_date <= CURRENT_DATE + INTERVAL '30 days'", "dialect": "postgres" }, { "title": "Aggregate calories in meal plans for each patient", "sql": "SELECT meal_plans.patient_id, SUM(ingredients.calories) FROM meal_plans JOIN ingredients ON meal_plans.ingredients ILIKE '%' || ingredients.name || '%' GROUP BY meal_plans.patient_id", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Dietitians analyzing meal plan effectiveness", "body": "Dietitians can utilize the Nourishment Insights dataset to analyze the effectiveness of meal plans tailored to various health conditions. By querying patient reviews and ratings, they can better understand which ingredients and meal types lead to higher satisfaction and improved health outcomes." }, { "title": "Healthcare applications for dietary recommendations", "body": "Developers can use this dataset to build healthcare applications that provide dietary recommendations based on patient demographics and health conditions. By leveraging the structured data on ingredients and meal plans, applications can suggest personalized meal options for users." }, { "title": "Research on dietary trends", "body": "Researchers examining dietary trends can analyze the data to identify common health concerns and preferences among patients. This can help in understanding shifts in dietary needs and how they relate to broader health trends. Insights gleaned from the dataset can inform future public health initiatives." }, { "title": "Education tools for aspiring dietitians", "body": "Educational institutions can use this dataset to teach aspiring dietitians about meal planning and patient management. By engaging with real-world scenarios modeled in the dataset, students can practice developing meal plans and understanding patient needs in a practical context." } ] }, "counts": { "ingredients": 100, "patients": 200, "body_metrics": 200, "meal_plans": 300, "reviews": 250, "subscriptions": 150 }, "stats": { "patients": { "rows": 200, "columns": { "id": { "nulls": 0, "distinct": 200, "min": 1, "max": 200 }, "first_name": { "nulls": 0, "distinct": 77, "min": "Alice", "max": "Zoe" }, "last_name": { "nulls": 0, "distinct": 74, "min": "Adams", "max": "Zhang" }, "age": { "nulls": 0, "distinct": 62, "min": 18, "max": 80 }, "gender": { "nulls": 0, "distinct": 3, "min": "female", "max": "other" }, "health_condition": { "nulls": 0, "distinct": 3, "min": "diabetes", "max": "none" } } }, "body_metrics": { "rows": 200, "columns": { "id": { "nulls": 0, "distinct": 200, "min": 1, "max": 200 }, "patient_id": { "nulls": 0, "distinct": 125, "min": 1, "max": 199 }, "height_cm": { "nulls": 0, "distinct": 168, "min": 140.4, "max": 199.5 }, "weight_kg": { "nulls": 0, "distinct": 181, "min": 41, "max": 149.9 }, "bmi": { "nulls": 0, "distinct": 135, "min": 15.2, "max": 39.8 } } }, "meal_plans": { "rows": 300, "columns": { "id": { "nulls": 0, "distinct": 300, "min": 1, "max": 300 }, "patient_id": { "nulls": 0, "distinct": 158, "min": 2, "max": 200 }, "meal_type": { "nulls": 0, "distinct": 4, "min": "breakfast", "max": "snack" }, "ingredients": { "nulls": 0, "distinct": 273 }, "allergens": { "nulls": 0, "distinct": 214 } } }, "subscriptions": { "rows": 150, "columns": { "id": { "nulls": 0, "distinct": 150, "min": 1, "max": 150 }, "patient_id": { "nulls": 0, "distinct": 106, "min": 1, "max": 199 }, "plan_type": { "nulls": 0, "distinct": 3, "min": "monthly", "max": "yearly" }, "start_date": { "nulls": 0, "distinct": 137, "min": "2022-01-05", "max": "2023-12-31" }, "renewal_date": { "nulls": 0, "distinct": 135, "min": "2022-01-06", "max": "2024-01-25" } } }, "reviews": { "rows": 250, "columns": { "id": { "nulls": 0, "distinct": 250, "min": 1, "max": 250 }, "patient_id": { "nulls": 0, "distinct": 137, "min": 3, "max": 200 }, "rating": { "nulls": 0, "distinct": 5, "min": 1, "max": 5 }, "comments": { "nulls": 0, "distinct": 250 }, "review_date": { "nulls": 0, "distinct": 201, "min": "2022-01-02", "max": "2023-12-31" } } }, "ingredients": { "rows": 100, "columns": { "id": { "nulls": 0, "distinct": 100, "min": 1, "max": 100 }, "name": { "nulls": 0, "distinct": 82 }, "calories": { "nulls": 0, "distinct": 89, "min": 0, "max": 974 }, "protein": { "nulls": 0, "distinct": 95, "min": 1.7, "max": 99.2 }, "fat": { "nulls": 0, "distinct": 95, "min": 0.1, "max": 99.4 }, "carbohydrates": { "nulls": 0, "distinct": 97, "min": 3.3, "max": 99.6 } } } }, "artifacts": [ { "file": "csv/ingredients.csv", "format": "csv", "table": "ingredients", "key": "datasets/nourishment-insights/v2/csv/ingredients.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/csv/ingredients.csv", "bytes": 3382, "sha256": "29f971d832aeecd895da11bf3fa199ddf62ea6fc9d7d8fb919e728b27e6aedb1", "rows": 100, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/patients.csv", "format": "csv", "table": "patients", "key": "datasets/nourishment-insights/v2/csv/patients.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/csv/patients.csv", "bytes": 7003, "sha256": "aa5777720f6faa830db8ee6facea7dd5b20f35fa4acd4140a4a41fc1bc08de86", "rows": 200, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/body_metrics.csv", "format": "csv", "table": "body_metrics", "key": "datasets/nourishment-insights/v2/csv/body_metrics.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/csv/body_metrics.csv", "bytes": 4936, "sha256": "aeea47b38db544975759a4da75d2accf5404646e1ea4aead0471947bd916107a", "rows": 200, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/meal_plans.csv", "format": "csv", "table": "meal_plans", "key": "datasets/nourishment-insights/v2/csv/meal_plans.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/csv/meal_plans.csv", "bytes": 15277, "sha256": "a0094c3d294918e54a698043d25e5a83a22e2c7e7c78fd92e0433b5087dbd970", "rows": 300, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/reviews.csv", "format": "csv", "table": "reviews", "key": "datasets/nourishment-insights/v2/csv/reviews.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/csv/reviews.csv", "bytes": 19570, "sha256": "b8385d6176c3914617eb712b45c5bbefd43b48077dfe1a0f1d45e7c7d355a0c7", "rows": 250, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/subscriptions.csv", "format": "csv", "table": "subscriptions", "key": "datasets/nourishment-insights/v2/csv/subscriptions.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/csv/subscriptions.csv", "bytes": 5743, "sha256": "f7bc4fa31e5ae3241c3aa2a4b7d0a6795edcf8b34a545bbcd8f578550ec48626", "rows": 150, "contentType": "text/csv; charset=utf-8" }, { "file": "nourishment-insights.json", "format": "json", "table": null, "key": "datasets/nourishment-insights/v2/nourishment-insights.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/nourishment-insights.json", "bytes": 190970, "sha256": "6805364274fe18f0ca0eedc0fbb1259fb35f9d286c4e2d57020fa355577c8f22", "rows": null, "contentType": "application/json; charset=utf-8" }, { "file": "sql/postgres.sql", "format": "sql-postgres", "table": null, "key": "datasets/nourishment-insights/v2/sql/postgres.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/sql/postgres.sql", "bytes": 76958, "sha256": "3b7fc9fbc865f626c31f36a97176df232dfac46a9e86f617854d97e03a800a87", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mysql.sql", "format": "sql-mysql", "table": null, "key": "datasets/nourishment-insights/v2/sql/mysql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/sql/mysql.sql", "bytes": 76265, "sha256": "525f8a53f53aa9ce02f2a82dd3f9c908dab9cbef74bf9bce7498ff3732a81735", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mssql.sql", "format": "sql-mssql", "table": null, "key": "datasets/nourishment-insights/v2/sql/mssql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/sql/mssql.sql", "bytes": 79425, "sha256": "c334f6f7c53ddba0d9b37b2c29b5e6633ac16599a307b1dc30a331a3795bd392", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/sqlite.sql", "format": "sql-sqlite", "table": null, "key": "datasets/nourishment-insights/v2/sql/sqlite.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/sql/sqlite.sql", "bytes": 76049, "sha256": "4702b5f006f3ffdf3b4f73514fd48afa42e5038581d609c8130c0d3171e89c46", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "prisma/seed.ts", "format": "prisma-seed", "table": null, "key": "datasets/nourishment-insights/v2/prisma/seed.ts", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/prisma/seed.ts", "bytes": 148348, "sha256": "b741decfaa44bed111add3f2e6f6a960b67d83d3df8f8b5cde9a352743f61dc9", "rows": null, "contentType": "application/typescript; charset=utf-8" }, { "file": "django/fixtures.json", "format": "django-fixture", "table": null, "key": "datasets/nourishment-insights/v2/django/fixtures.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/nourishment-insights/v2/django/fixtures.json", "bytes": 244633, "sha256": "0d9422cc322611cf8fe4fb43b47099c8a355fa01670d5413ab91534aeb63c826", "rows": null, "contentType": "application/json; charset=utf-8" } ], "publishedAt": 1788767267218, "updatedAt": 1788785120626 }