{
  "format": "dataset-manifest/1",
  "slug": "generative-design-software",
  "version": 2,
  "seed": "generative-design-software:1",
  "spec": {
    "format": "dataset/1",
    "slug": "generative-design-software",
    "name": "Generative Design Insights",
    "tagline": "Design iterations, materials, user interactions, and simulation results from a generative design software platform.",
    "description": "A design firm utilizes generative design software to enhance product development by optimizing material selection and ensuring structural integrity. The operation involves multiple users who initiate numerous design iterations, testing various combinations of materials and objectives to achieve successful outcomes that meet strict performance standards. Each design iteration represents a unique attempt to solve a design challenge, with varying parameters set by users, who are also tracked through their interactions with the software. The firm analyzes the results from these iterations to refine their approach and improve future designs.\n\nThe primary tables in this dataset include design_iterations, which contains the various attempts made by users to meet their design objectives; materials, detailing the properties of different materials used in the designs; users, capturing information about those who engage with the software; simulation_results, illustrating the outcomes of each design iteration's performance tests; cost_estimates, providing a financial perspective on the materials and processes involved; and design_objectives, outlining the goals set for each design iteration.\n\nEach row in the design_iterations table links to a user in the users table, signifying who initiated the iteration, while the materials table connects to indicate the properties and types of materials used in each attempt. Simulation_results are associated with design_iterations, showcasing how well each iteration performed against its set objectives, and cost_estimates link to different materials and design iterations to provide insights into the financial implications of the design choices. This interconnected data allows for a comprehensive analysis of design performance, costs, and user engagement.\n\nPractitioners can explore questions such as which materials lead to the most successful outcomes, how user interactions affect design iterations, and what common objectives drive successful designs. The dataset is synthetic, providing a rich environment for data practitioners to hone their skills in data analysis, performance metrics, and optimization strategies in the realm of generative design.",
    "domain": "saas",
    "shape": "multi-tenant",
    "useCases": [
      "orm-seeding",
      "api-mocking"
    ],
    "keywords": [
      "generative design",
      "material optimization",
      "design iterations",
      "simulation outcomes",
      "cost estimates",
      "user interactions"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "design_iterations": {
        "description": "This table records individual design iterations initiated by users, linking to the materials and objectives used. Each row represents a unique iteration with relevant attributes.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each design iteration."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Reference to the user who initiated the design iteration.",
            "nullable": true
          },
          "material_id": {
            "kind": "fk",
            "ref": "materials.id",
            "description": "Reference to the material used in the design iteration.",
            "nullable": true
          },
          "objective": {
            "kind": "enum",
            "values": [
              "strength",
              "weight",
              "cost"
            ],
            "description": "The primary objective of the design iteration."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp when the design iteration was created.",
            "nullable": true,
            "dayProfile": "business"
          },
          "status": {
            "kind": "enum",
            "values": [
              "successful",
              "failed",
              "in_progress"
            ],
            "description": "Current status of the design iteration.",
            "nullable": true,
            "when": {
              "column": "objective",
              "in": [
                "strength",
                "weight",
                "cost"
              ]
            }
          }
        }
      },
      "materials": {
        "description": "This table lists various materials available for design iterations, detailing their properties and cost estimates.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each material."
          },
          "name": {
            "kind": "fullName",
            "description": "Name of the material."
          },
          "density": {
            "kind": "decimal",
            "min": 0.1,
            "max": 10,
            "scale": 2,
            "description": "Density of the material in g/cm³."
          },
          "cost_per_kg": {
            "kind": "decimal",
            "min": 1,
            "max": 100,
            "scale": 2,
            "description": "Cost of the material per kilogram.",
            "distribution": "long-tail"
          },
          "strength": {
            "kind": "decimal",
            "min": 10,
            "max": 200,
            "scale": 2,
            "description": "Tensile strength of the material in MPa."
          }
        }
      },
      "users": {
        "description": "This table contains information about users who interact with the generative design software.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "full_name": {
            "kind": "fullName",
            "description": "Full name of the user."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address of the user."
          },
          "signup_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the user signed up.",
            "dayProfile": "waking",
            "trend": "growing"
          }
        }
      },
      "simulation_results": {
        "description": "This table captures the outcomes of simulations run for each design iteration, linking back to the iteration.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each simulation result."
          },
          "design_iteration_id": {
            "kind": "fk",
            "ref": "design_iterations.id",
            "description": "Reference to the design iteration for which the simulation was run."
          },
          "result": {
            "kind": "enum",
            "values": [
              "pass",
              "fail"
            ],
            "description": "Outcome of the simulation."
          },
          "simulation_time": {
            "kind": "decimal",
            "min": 0.1,
            "max": 10,
            "scale": 2,
            "description": "Time taken to run the simulation in seconds.",
            "nullable": true
          },
          "analysis_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the simulation result was analyzed.",
            "nullable": true,
            "dayProfile": "business"
          }
        }
      },
      "cost_estimates": {
        "description": "This table provides cost estimates associated with each design iteration, considering materials and simulation results.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each cost estimate."
          },
          "design_iteration_id": {
            "kind": "fk",
            "ref": "design_iterations.id",
            "description": "Reference to the design iteration associated with this cost estimate."
          },
          "estimated_cost": {
            "kind": "decimal",
            "min": 10,
            "max": 1000,
            "scale": 2,
            "description": "Estimated total cost for the design iteration.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "actual_cost": {
            "kind": "decimal",
            "min": 10,
            "max": 1000,
            "scale": 2,
            "description": "Actual cost incurred for the design iteration.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "cost_analysis_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the cost analysis was performed.",
            "nullable": true,
            "dayProfile": "business"
          }
        }
      },
      "design_objectives": {
        "description": "This table outlines various design objectives that users may focus on during their design iterations.",
        "rows": 30,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each design objective."
          },
          "objective": {
            "kind": "enum",
            "values": [
              "strength",
              "weight",
              "cost",
              "aesthetics"
            ],
            "description": "Description of the design objective."
          },
          "priority": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "description": "Priority level of the design objective, where 1 is the highest.",
            "nullable": true,
            "when": {
              "column": "objective",
              "in": [
                "strength",
                "weight",
                "cost",
                "aesthetics"
              ]
            }
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What materials are most frequently used in successful design iterations?",
      "How does the objective influence the success rate of design iterations?",
      "What is the average cost difference between successful and failed design iterations?",
      "Which design objectives are prioritized by most users?",
      "How do simulation times vary for different design objectives?"
    ],
    "suggestedQueries": [
      {
        "title": "Join design iterations with materials",
        "sql": "SELECT di.id, di.objective, m.name FROM design_iterations di JOIN materials m ON di.material_id = m.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count successful vs failed design iterations",
        "sql": "SELECT status, COUNT(*) FROM design_iterations GROUP BY status;",
        "dialect": "postgres"
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      {
        "title": "Average cost per simulation result",
        "sql": "SELECT sr.result, AVG(ce.estimated_cost) FROM simulation_results sr JOIN cost_estimates ce ON sr.design_iteration_id = ce.design_iteration_id GROUP BY sr.result;",
        "dialect": "postgres"
      },
      {
        "title": "Design objectives and their priorities",
        "sql": "SELECT objective, AVG(priority) FROM design_objectives GROUP BY objective;",
        "dialect": "postgres"
      },
      {
        "title": "Simulation times for each design iteration",
        "sql": "SELECT di.id, sr.simulation_time FROM design_iterations di JOIN simulation_results sr ON di.id = sr.design_iteration_id;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "ORM Seeding for Development",
        "body": "Developers can use this dataset to seed their databases when building applications focused on generative design. By utilizing the various tables, they can create a realistic environment that mimics real-world user interactions and design processes."
      },
      {
        "title": "API Mocking for Testing",
        "body": "This dataset serves as a valuable resource for creating mock APIs that simulate interactions with generative design software. Developers can showcase how their applications would handle user requests, material properties, and simulation results, allowing for effective testing of user interfaces and backend logic."
      },
      {
        "title": "Data Analysis for Design Optimization",
        "body": "Data analysts can leverage this dataset to identify trends in material usage and design objectives. By querying the dataset, they can uncover insights that help improve design outcomes and guide future iterations, ultimately leading to more optimized product development."
      },
      {
        "title": "Educational Purposes in Design Courses",
        "body": "Instructors teaching design and engineering courses can utilize this dataset to provide students with hands-on experience in data analysis and interpretation. The dataset offers a realistic context for students to explore concepts related to generative design and material science."
      }
    ]
  },
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    "materials": 50,
    "users": 200,
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    "cost_estimates": 500,
    "simulation_results": 400
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