{
  "format": "dataset-manifest/1",
  "slug": "fitness-network-connections",
  "version": 3,
  "seed": "fitness-network-connections:1",
  "spec": {
    "format": "dataset/1",
    "slug": "fitness-network-connections",
    "name": "Fitness Network Connections",
    "tagline": "User interactions, workouts, meal logs, and friendships in a health and wellness community.",
    "description": "Users engage in a vibrant fitness community, participating in diverse workouts and tracking their meals to enhance their health and wellness. Each individual records their daily water intake, logs their meals, and shares their progress, creating a dynamic environment where personal journeys are intertwined. The social aspect is emphasized through friendships formed within the network and various challenges that motivate users to push their limits and achieve their fitness goals.\n\nThe 'users' table captures essential information about each participant, including personal details, fitness goals, and preferences. The 'workouts' table logs the various exercise sessions individuals undertake, detailing workout types, durations, and intensity levels. 'meal_logs' track users' nutritional habits by documenting their meals and caloric intake. Friendships are represented in the 'friendships' table, showing how individuals connect and support each other on their fitness journeys. Lastly, 'challenges' offer a framework for users to participate in competitions or group goals, fostering engagement and accountability.\n\nA single row in the 'users' table represents an individual member of the community, while the 'workouts' table illustrates the activities they have completed. Each entry in 'meal_logs' corresponds to a user’s food intake for a specific day. The 'friendships' table connects users, allowing analysis of social interactions, while the 'challenges' table reveals the competitive spirit that drives motivation. By joining these tables, you can explore how personal data, social connections, and challenges impact overall health outcomes.\n\nThis dataset is particularly useful for answering questions related to user engagement, meal tracking behavior, workout preferences, and the influence of social interactions on fitness goals. It provides an excellent practice opportunity for developers looking to analyze relational data and understand community dynamics in a health and wellness context. Please note that all data in this dataset is synthetic, designed for practice and experimentation.",
    "domain": "restaurants",
    "shape": "graph-edges",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "fitness",
      "network",
      "workout",
      "meals",
      "social",
      "community",
      "health",
      "wellness"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "This table holds user profiles containing personal information and health metrics. Each user has a unique ID, and their data includes body measurements and workout preferences.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the user."
          },
          "age": {
            "kind": "int",
            "min": 18,
            "max": 65,
            "description": "Age of the user.",
            "distribution": "normal"
          },
          "weight": {
            "kind": "decimal",
            "min": 40,
            "max": 150,
            "scale": 1,
            "description": "Weight of the user in kilograms.",
            "distribution": "normal"
          },
          "height": {
            "kind": "decimal",
            "min": 150,
            "max": 200,
            "scale": 1,
            "description": "Height of the user in centimeters.",
            "distribution": "normal"
          },
          "workout_preference": {
            "kind": "enum",
            "values": [
              "Cardio",
              "Strength",
              "Yoga",
              "Pilates",
              "CrossFit"
            ],
            "description": "Preferred type of workout.",
            "weights": [
              5,
              3,
              2,
              1,
              1
            ]
          }
        }
      },
      "workouts": {
        "description": "This table logs workouts performed by users, linking to the user profiles. Each workout entry captures details about the type of workout, duration, and calories burned.",
        "rows": 600,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each workout entry."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "ID of the user who performed the workout."
          },
          "workout_type": {
            "kind": "enum",
            "values": [
              "Running",
              "Swimming",
              "Cycling",
              "Weightlifting"
            ],
            "description": "Type of workout performed.",
            "weights": [
              6,
              4,
              3,
              2
            ]
          },
          "duration_minutes": {
            "kind": "int",
            "min": 5,
            "max": 180,
            "description": "Duration of the workout in minutes.",
            "distribution": "normal"
          },
          "calories_burned": {
            "kind": "decimal",
            "min": 50,
            "max": 1000,
            "scale": 1,
            "description": "Calories burned during the workout.",
            "distribution": "long-tail"
          },
          "workout_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the workout was performed.",
            "trend": "growing"
          }
        }
      },
      "meal_logs": {
        "description": "This table captures meals logged by users, with details on the meal type and nutrition information. Each log links to the user who recorded the meal.",
        "rows": 800,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each meal log entry."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "ID of the user who logged the meal."
          },
          "meal_type": {
            "kind": "enum",
            "values": [
              "Breakfast",
              "Lunch",
              "Dinner",
              "Snack"
            ],
            "description": "Type of meal.",
            "weights": [
              4,
              5,
              6,
              3
            ]
          },
          "calories_consumed": {
            "kind": "decimal",
            "min": 100,
            "max": 2000,
            "scale": 1,
            "description": "Calories consumed in the meal.",
            "distribution": "long-tail"
          },
          "meal_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the meal was logged.",
            "trend": "seasonal"
          }
        }
      },
      "friendships": {
        "description": "This table maintains friendships between users, indicating social connections within the community. Each entry links two users.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each friendship entry."
          },
          "user_id_1": {
            "kind": "fk",
            "ref": "users.id",
            "description": "ID of the first user in the friendship."
          },
          "user_id_2": {
            "kind": "fk",
            "ref": "users.id",
            "description": "ID of the second user in the friendship."
          },
          "friendship_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the friendship was formed.",
            "trend": "growing"
          }
        }
      },
      "challenges": {
        "description": "This table logs challenges that users participate in, tracking their involvement and achievements within the community. Each entry links to a user and a challenge.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each challenge entry."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "ID of the user participating in the challenge."
          },
          "challenge_name": {
            "kind": "words",
            "min": 5,
            "max": 50,
            "description": "Name of the fitness challenge."
          },
          "status": {
            "kind": "enum",
            "values": [
              "Ongoing",
              "Completed",
              "Failed"
            ],
            "description": "Current status of the challenge.",
            "weights": [
              7,
              2,
              1
            ]
          },
          "start_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Start date of the challenge.",
            "trend": "growing"
          },
          "end_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "End date of the challenge.",
            "trend": "growing"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "geospatial",
      "timestamped",
      "categorical"
    ],
    "analysisQuestions": [
      "What are the most popular workout types among users?",
      "How does meal logging frequency impact workout performance?",
      "Which users have the most friendships and how does that relate to their fitness achievements?",
      "What challenges are most commonly completed versus failed?",
      "How do user demographics influence their workout preferences?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Users and Workouts",
        "sql": "SELECT users.full_name, workouts.workout_type FROM users JOIN workouts ON users.id = workouts.user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Calories Burned per Workout Type",
        "sql": "SELECT workout_type, AVG(calories_burned) FROM workouts GROUP BY workout_type;",
        "dialect": "postgres"
      },
      {
        "title": "Count of Users by Meal Type",
        "sql": "SELECT meal_type, COUNT(DISTINCT user_id) FROM meal_logs GROUP BY meal_type;",
        "dialect": "postgres"
      },
      {
        "title": "Total Calories Consumed Over Time",
        "sql": "SELECT meal_date, SUM(calories_consumed) FROM meal_logs GROUP BY meal_date ORDER BY meal_date;",
        "dialect": "postgres"
      },
      {
        "title": "Friendship Network Analysis",
        "sql": "SELECT u1.full_name AS user1, u2.full_name AS user2 FROM friendships JOIN users u1 ON friendships.user_id_1 = u1.id JOIN users u2 ON friendships.user_id_2 = u2.id;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Fitness Trainer Analytics",
        "body": "Fitness trainers can utilize this dataset to analyze workout preferences and meal tracking habits among their clients. By understanding trends in user engagement and community interaction, trainers can tailor their programs to better meet the needs of their clients and enhance their fitness journeys."
      },
      {
        "title": "Health App Development",
        "body": "Developers of health and fitness applications can use this dataset for testing and improving their products. By simulating real-world user interactions and behaviors, developers can ensure their apps effectively track workouts, meals, and social connections, leading to a more engaging user experience."
      },
      {
        "title": "Academic Research",
        "body": "Researchers studying social behaviors in fitness communities can leverage this dataset to explore connections between social interactions and fitness outcomes. By analyzing the data, they can gain insights into how friendships and challenges impact individual health journeys."
      },
      {
        "title": "Nutritionist Insights",
        "body": "Nutritionists can analyze meal logs and user profiles to identify common dietary patterns and nutritional habits within the community. This information can help inform dietary recommendations and interventions tailored to specific user groups or demographics."
      }
    ]
  },
  "counts": {
    "users": 200,
    "challenges": 400,
    "friendships": 300,
    "meal_logs": 800,
    "workouts": 600
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