{
  "format": "dataset-manifest/1",
  "slug": "audio-editing-transactions",
  "version": 1,
  "seed": "audio-editing-transactions:1",
  "spec": {
    "format": "dataset/1",
    "slug": "audio-editing-transactions",
    "name": "Audio Editing Transactions",
    "tagline": "Audio editing transactions detailing user profiles, projects, and effects used over time in sound production.",
    "description": "Users edit and transform audio files to create engaging soundscapes in a dynamic audio production environment. These audio files can consist of multiple tracks, each layered with different effects and adjustments. The complexity of projects varies significantly; while many involve straightforward edits such as trimming or volume adjustments, others showcase elaborate configurations with extensive effects chains that highlight the creative and technical prowess of audio professionals. Each edited piece serves a distinct purpose, whether for music production, sound design, or post-production work in film and multimedia. \n\nThe primary table in this data model is `audio_transactions`, which captures each instance of audio file manipulation. Each row in this table represents a unique transaction where a user has performed an action on an audio file, such as editing, applying effects, or exporting the final product. This table includes details about the user, the type of edit made, timestamps, and the specific audio file involved. By examining the relationships within this table, one can discern patterns in user behavior, common editing techniques, and project timelines, which can be invaluable for understanding trends in audio editing practices. \n\nQuestions that this data can help answer include: How often do users utilize specific effects? What types of edits are most common across various projects? Are there any noticeable trends in editing practices over time? Additionally, analyzing this information can provide insights into user preferences and the efficiency of different editing techniques. \n\nIt’s important to note that the data presented here is synthetic, designed to simulate real-world transactions within the audio editing industry and assist developers in practicing their data manipulation and analysis skills.",
    "domain": "energy",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "audio production",
      "transactions",
      "editing",
      "effects",
      "user profiles",
      "synthetic data"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "audio_transactions": {
        "description": "Each row represents a unique audio editing transaction, including user details, project complexity, and effects used.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "user_id": {
            "kind": "int",
            "min": 1,
            "max": 1000,
            "description": "Identifier for the user who created the project."
          },
          "project_name": {
            "kind": "sentence",
            "min": 5,
            "max": 20,
            "description": "The name of the audio project."
          },
          "track_count": {
            "kind": "int",
            "min": 1,
            "max": 20,
            "description": "The number of tracks in the audio project."
          },
          "effects_used": {
            "kind": "words",
            "min": 5,
            "max": 50,
            "description": "List of effects applied to the audio tracks."
          },
          "duration": {
            "kind": "decimal",
            "min": 1,
            "max": 300,
            "scale": 2,
            "description": "Total duration of the audio project in seconds."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of when the project was created.",
            "dayProfile": "waking"
          },
          "complexity_level": {
            "kind": "enum",
            "values": [
              "simple",
              "moderate",
              "complex"
            ],
            "description": "The complexity level of the project."
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "nullable",
      "timestamped"
    ],
    "analysisQuestions": [
      "What is the average duration of audio projects created by users?",
      "How do complexity levels vary among different users?",
      "Which effects are most commonly used in audio editing?",
      "What trends can be observed in project creation over time?",
      "How does the number of tracks correlate with project complexity?"
    ],
    "suggestedQueries": [
      {
        "title": "Get all projects by a specific user",
        "sql": "SELECT * FROM audio_transactions WHERE user_id = 1;",
        "dialect": "postgres"
      },
      {
        "title": "Average duration of projects grouped by complexity",
        "sql": "SELECT complexity_level, AVG(duration) FROM audio_transactions GROUP BY complexity_level;",
        "dialect": "postgres"
      },
      {
        "title": "Count of projects created per month",
        "sql": "SELECT DATE_TRUNC('month', created_at) AS month, COUNT(*) FROM audio_transactions GROUP BY month;",
        "dialect": "postgres"
      },
      {
        "title": "List effects used in complex projects",
        "sql": "SELECT effects_used FROM audio_transactions WHERE complexity_level = 'complex';",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Audio Editing Software Development",
        "body": "Developers working on audio editing software can use this dataset to simulate user transactions, testing features like project loading and effect application. By analyzing how users interact with different audio projects, developers can refine their software's user experience and performance."
      },
      {
        "title": "Data Analysis for Marketing",
        "body": "Marketing teams can leverage this dataset to understand user behavior in audio production. By examining the types of projects created and the effects used, they can tailor marketing strategies to target specific user segments, enhancing engagement and product adoption."
      },
      {
        "title": "Training Machine Learning Models",
        "body": "Data scientists can utilize this synthetic dataset to train machine learning models aimed at predicting user preferences or recommending effects based on project complexity. This could lead to the development of smarter audio editing tools that adapt to user needs."
      }
    ]
  },
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    "audio_transactions": 500
  },
  "stats": {
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      "columns": {
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          "distinct": 500,
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        "project_name": {
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        "track_count": {
          "nulls": 0,
          "distinct": 20,
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        "effects_used": {
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          "min": 1.27,
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        "created_at": {
          "nulls": 0,
          "distinct": 500,
          "min": "2020-01-15 14:48:19",
          "max": "2023-12-18 14:24:36"
        },
        "complexity_level": {
          "nulls": 0,
          "distinct": 3,
          "min": "complex",
          "max": "simple"
        }
      }
    }
  },
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