{
  "format": "dataset-manifest/1",
  "slug": "social-connection-insights",
  "version": 2,
  "seed": "social-connection-insights:1",
  "spec": {
    "format": "dataset/1",
    "slug": "social-connection-insights",
    "name": "Social Connection Insights",
    "tagline": "User interactions, profiles, polls, and tags from an online community fostering social connections.",
    "description": "Users actively engage within a vibrant online community, sharing thoughts and experiences through various forms of interaction. This dynamic environment fosters social connections as members participate in polls, utilize tags to express interests, and organize events. Each interaction helps to build a complex web of relationships, where the frequency and type of engagement can highlight trends and shifts in community dynamics.\n\nThe primary table, user_interactions, captures distinct events in which users participate. Each row represents a unique interaction, detailing user profiles, the polls they take part in, the tags they employ, and the events they attend. This table allows for insightful analysis by linking users to the specific polls and tags they engage with, thus enabling developers to explore how social connections evolve and flourish.\n\nPracticing with this data opens up a wide array of analytical possibilities. Developers can investigate trends in user engagement, identify popular polls and tags, and examine how niche interactions contribute to the overall community dynamics. Questions such as which tags foster the most interaction, how event participation varies over time, and what influences users' choice of polls can all be explored using these insights. Additionally, understanding the relationships among different interactions can help evaluate the effectiveness of community-building strategies and the overall health of the social network.\n\nIt’s important to note that the data is synthetic, crafted to simulate real user interactions while ensuring privacy and confidentiality. This makes it an ideal playground for testing analytical techniques and developing data-driven insights in the realm of social interactions.",
    "domain": "social",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "user interactions",
      "poll participation",
      "tags",
      "social connections",
      "events",
      "community",
      "data analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "user_interactions": {
        "description": "Each row represents a unique interaction by a user, including their profile information, poll participation, and tag usage. This table captures various dimensions of user engagement within the community, linking user activities to specific polls and tags.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each interaction."
          },
          "user_id": {
            "kind": "int",
            "min": 1,
            "max": 1000,
            "description": "Identifier for the user engaging in the interaction."
          },
          "poll_id": {
            "kind": "int",
            "min": 1,
            "max": 200,
            "description": "Identifier for the poll in which the user participated.",
            "nullable": true,
            "when": {
              "column": "interaction_type",
              "in": [
                "comment",
                "share"
              ]
            }
          },
          "tag": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Tags used by the user to express their interests.",
            "nullable": true,
            "pool": [
              "travel",
              "food",
              "music",
              "fitness",
              "art",
              "technology",
              "books",
              "nature",
              "photography",
              "fashion",
              "gaming",
              "movies",
              "sports",
              "wellness",
              "culture",
              "science",
              "history",
              "education",
              "social",
              "community",
              "events",
              "lifestyle",
              "health",
              "relationships",
              "design",
              "innovation",
              "entrepreneurship",
              "volunteering",
              "mindfulness",
              "pets",
              "hobbies",
              "crafts",
              "parenting",
              "outdoors",
              "adventure",
              "selfcare",
              "cooking",
              "writing",
              "journaling",
              "podcasts",
              "theater",
              "dance",
              "spirituality",
              "mentalhealth",
              "traveling",
              "exploration",
              "sustainability",
              "environment",
              "local",
              "networking",
              "inspiration",
              "creativity",
              "charity",
              "activism",
              "philanthropy"
            ]
          },
          "interaction_type": {
            "kind": "enum",
            "values": [
              "vote",
              "comment",
              "share",
              "invite"
            ],
            "description": "Type of interaction performed by the user.",
            "weights": [
              6,
              2,
              1,
              1
            ]
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the interaction occurred.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What types of interactions are most common among users?",
      "Which polls generate the highest levels of engagement?",
      "How do users utilize tags to express their interests?",
      "What is the distribution of interaction types over time?",
      "Are there any notable trends in user activity during specific periods?"
    ],
    "suggestedQueries": [
      {
        "title": "Count interactions by type",
        "sql": "SELECT interaction_type, COUNT(*) FROM user_interactions GROUP BY interaction_type;",
        "dialect": "postgres"
      },
      {
        "title": "Poll engagement analysis",
        "sql": "SELECT poll_id, COUNT(*) as engagement_count FROM user_interactions GROUP BY poll_id ORDER BY engagement_count DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Tag usage frequency",
        "sql": "SELECT tag, COUNT(*) as usage_count FROM user_interactions GROUP BY tag ORDER BY usage_count DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Interactions over time",
        "sql": "SELECT DATE(timestamp) as interaction_date, COUNT(*) FROM user_interactions GROUP BY interaction_date ORDER BY interaction_date;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Social Media Analytics",
        "body": "Data analysts in a social media company can use this dataset to identify trends in user engagement, understand which polls spark the most interest, and analyze how tags influence user interaction patterns. This information can help improve community features and enhance user experience."
      },
      {
        "title": "Community Engagement Strategies",
        "body": "Community managers can leverage the insights from this dataset to develop targeted strategies for increasing user interaction. By identifying popular polls and frequently used tags, they can create content and events that resonate with users, fostering a more engaged community."
      },
      {
        "title": "ORM Development Practice",
        "body": "Developers working on an ORM can utilize this dataset as a practice tool for generating models and database migrations. They can create relationships between user interactions, polls, and tags, allowing for a better understanding of how to structure social application data."
      }
    ]
  },
  "counts": {
    "user_interactions": 500
  },
  "stats": {
    "user_interactions": {
      "rows": 500,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 500,
          "min": 1,
          "max": 500
        },
        "user_id": {
          "nulls": 0,
          "distinct": 402,
          "min": 4,
          "max": 1000
        },
        "poll_id": {
          "nulls": 344,
          "distinct": 108,
          "min": 2,
          "max": 196
        },
        "tag": {
          "nulls": 48,
          "distinct": 354
        },
        "interaction_type": {
          "nulls": 0,
          "distinct": 4,
          "min": "comment",
          "max": "vote"
        },
        "timestamp": {
          "nulls": 0,
          "distinct": 500,
          "min": "2023-01-02 01:59:59",
          "max": "2023-12-31 17:51:50"
        }
      }
    }
  },
  "artifacts": [
    {
      "file": "csv/user_interactions.csv",
      "format": "csv",
      "table": "user_interactions",
      "key": "datasets/social-connection-insights/v2/csv/user_interactions.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/csv/user_interactions.csv",
      "bytes": 26832,
      "sha256": "3993fc777dba7a179d5a5dcdfc45ba33ecbab9b99a693b7dea73c16004bd402c",
      "rows": 500,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "social-connection-insights.json",
      "format": "json",
      "table": null,
      "key": "datasets/social-connection-insights/v2/social-connection-insights.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/social-connection-insights.json",
      "bytes": 91785,
      "sha256": "7c1927fe5ef995aa4dd5fa6700a95c7e93df9601910ffa0b0fb9a19d6b251946",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    },
    {
      "file": "sql/postgres.sql",
      "format": "sql-postgres",
      "table": null,
      "key": "datasets/social-connection-insights/v2/sql/postgres.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/sql/postgres.sql",
      "bytes": 37751,
      "sha256": "7ed5a93ef39438f82f2f1f3668133e0bb711b8f1d871114d6afbba7a5bf5acad",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mysql.sql",
      "format": "sql-mysql",
      "table": null,
      "key": "datasets/social-connection-insights/v2/sql/mysql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/sql/mysql.sql",
      "bytes": 37625,
      "sha256": "173c3964c794b67c1ed1c0b5d699fee3ad316f9aff1cee37adb88b85e3d07b9e",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mssql.sql",
      "format": "sql-mssql",
      "table": null,
      "key": "datasets/social-connection-insights/v2/sql/mssql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/sql/mssql.sql",
      "bytes": 39159,
      "sha256": "1c1633066735e50f1ac12e78e7d461aa44336a784fae7c61bf1ce00a08ce3ff3",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/sqlite.sql",
      "format": "sql-sqlite",
      "table": null,
      "key": "datasets/social-connection-insights/v2/sql/sqlite.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/sql/sqlite.sql",
      "bytes": 37599,
      "sha256": "7732539b865ee9a0d5b5cd924dddae86df048f8612cd41909f2ced1592d51ff7",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "prisma/seed.ts",
      "format": "prisma-seed",
      "table": null,
      "key": "datasets/social-connection-insights/v2/prisma/seed.ts",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/prisma/seed.ts",
      "bytes": 76015,
      "sha256": "a1acaf9df8f5fa36efe28641abb22412e84a361802430e7ebd502ce8b19c3cac",
      "rows": null,
      "contentType": "application/typescript; charset=utf-8"
    },
    {
      "file": "django/fixtures.json",
      "format": "django-fixture",
      "table": null,
      "key": "datasets/social-connection-insights/v2/django/fixtures.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/social-connection-insights/v2/django/fixtures.json",
      "bytes": 118256,
      "sha256": "aa8d4705ad7fd68c417398f6b20e23e87f9155f54b41aac9f800e0d21b67444f",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    }
  ],
  "publishedAt": 1788767601215,
  "updatedAt": 1788786119547
}