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Add Dataset example 4 - Sensor and time series data #141

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Add Dataset example 4
bact Apr 23, 2026
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Add sensors to SBOM
bact Apr 24, 2026
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43 changes: 43 additions & 0 deletions dataset/example04/README.md
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---
SPDX-FileType: DOCUMENTATION
SPDX-License-Identifier: CC-BY-4.0
---

# Dataset example 4 - Sensor and time series data

## Description

This example illustrates an SBOM for a dataset of sensor readings collected
from industrial equipment, used to train models that predict when maintenance
is needed.

The SBOM demonstrates Dataset-profile properties for
**sensor and time series datasets**, including collection process,
multiple dataset types, update mechanism, known bias, and access controls.

## Profile conformance

`core`, `dataset`

## SPDX files

| Version | File |
| ------- | ---- |
| SPDX 3.0 | [spdx3.0/example04.spdx3.json](./spdx3.0/example04.spdx3.json) |
| SPDX 3.1 (draft) | [spdx3.1/example04.spdx3.json-draft](./spdx3.1/example04.spdx3.json-draft) |

[![A diagram of Dataset example 4 - Sensor and time series data.](./example04.spdx3.png "A diagram of Dataset example 4 - Sensor and time series data.")](./example04.spdx3.png)

## Key properties demonstrated

| Property | Notes |
| -------- | ----- |
| `/Dataset/confidentialityLevel` | `green` - data may be shared within a defined partner community |
| `/Dataset/dataCollectionProcess` | How sensor readings were recorded and labeled |
| `/Dataset/datasetSize` | `5798205850` bytes (~5.4 GB) - deprecated in SPDX 3.1, use `/Software/artifactSize` |
| `/Dataset/datasetType` | `sensor`, `timestamp` - multiple types combined; `sensor` = physical readings, `timestamp` = time-indexed records |
| `/Dataset/datasetUpdateMechanism` | Quarterly appended snapshots |
| `/Dataset/hasSensitivePersonalInformation` | `no` |
| `/Dataset/intendedUse` | Research use cases - deprecated in SPDX 3.1, use `/Core/intendedUse` |
| `/Dataset/knownBias` | Gaps in equipment and failure type coverage documented |
| `/Dataset/sensor` | 8 sensor types as key-value pairs: sensor name → calibration and range description |
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159 changes: 159 additions & 0 deletions dataset/example04/spdx3.0/example04.spdx3.json
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{
"@context": "https://spdx.org/rdf/3.0.1/spdx-context.jsonld",
"@graph": [
{
"type": "CreationInfo",
"@id": "_:creationinfo",
"specVersion": "3.0.1",
"createdBy": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#Organization1"
],
"created": "2024-09-15T00:00:00Z"
},
{
"type": "Organization",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#Organization1",
"creationInfo": "_:creationinfo",
"name": "Industrial IoT Consortium"
},
{
"type": "SpdxDocument",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001",
"creationInfo": "_:creationinfo",
"profileConformance": [
"core",
"dataset"
],
"rootElement": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#SBOM1"
]
},
{
"type": "software_Sbom",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#SBOM1",
"creationInfo": "_:creationinfo",
"profileConformance": [
"core",
"dataset"
],
"rootElement": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#DatasetPackage1"
],
"software_sbomType": [
"analyzed"
]
},
{
"type": "dataset_DatasetPackage",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#DatasetPackage1",
"creationInfo": "_:creationinfo",
"name": "PumpHealth-Sensors",
"summary": "Time series sensor dataset from industrial centrifugal pumps for predictive maintenance model training.",
"description": "Multivariate time series data collected from 47 centrifugal pumps at two manufacturing facilities over 18 months. Each pump is instrumented with 8 sensor types sampled at 1-second intervals. The dataset includes labeled fault events (bearing wear, seal degradation, impeller imbalance, cavitation) and normal operation periods. Designed for training anomaly detection and remaining useful life (RUL) prediction models.",
"software_packageVersion": "3.1",
"software_primaryPurpose": "data",
"software_downloadLocation": "https://example.com/datasets/pumphealth-sensors",
"software_copyrightText": "Copyright 2024 Industrial IoT Consortium",
"builtTime": "2024-01-01T00:00:00Z",
"releaseTime": "2024-09-15T00:00:00Z",
"dataset_datasetType": [
"sensor",
"timestamp"
],
"dataset_sensor": [
{
"type": "DictionaryEntry",
"key": "vibration",
"value": "Triaxial accelerometer; range 0–50 mm/s RMS; calibrated every 6 months"
},
{
"type": "DictionaryEntry",
"key": "bearing_temperature",
"value": "Type-K thermocouple; range -40–200 °C; ±0.5 °C accuracy"
},
{
"type": "DictionaryEntry",
"key": "inlet_pressure",
"value": "Piezoresistive transducer; range 0–10 bar; calibrated annually"
},
{
"type": "DictionaryEntry",
"key": "outlet_pressure",
"value": "Piezoresistive transducer; range 0–25 bar; calibrated annually"
},
{
"type": "DictionaryEntry",
"key": "flow_rate",
"value": "Electromagnetic flow meter; range 0–500 m³/h; ±0.3% full scale"
},
{
"type": "DictionaryEntry",
"key": "motor_current",
"value": "Hall-effect current clamp; range 0–100 A; ±0.5% accuracy"
},
{
"type": "DictionaryEntry",
"key": "shaft_speed",
"value": "Magnetic encoder; range 0–3600 RPM; resolution 1 RPM"
},
{
"type": "DictionaryEntry",
"key": "fluid_temperature",
"value": "PT100 RTD; range -20–150 °C; ±0.1 °C accuracy"
}
],
"dataset_datasetSize": 5798205850,
"dataset_datasetAvailability": "registration",
"dataset_confidentialityLevel": "green",
"dataset_dataCollectionProcess": "Sensors installed and calibrated by certified technicians. Data logged via OPC-UA to a SCADA system, then extracted in CSV format at 1-second resolution. Fault events manually labeled by maintenance engineers from work order records and physical inspections. Normal/fault label verified by at least two engineers per event.",
"dataset_dataPreprocessing": [
"Missing values (sensor dropout <0.5% of records) filled by linear interpolation",
"Outliers beyond 5 sigma removed and flagged in a companion outlier log",
"Synchronized across sensors using nearest-timestamp alignment within 50ms window",
"Train/validation/test split: 70/15/15 by pump ID (not by time) to prevent data leakage"
],
"dataset_datasetUpdateMechanism": "Quarterly snapshots appended. Each release includes new labeled fault events from the previous quarter. Historical data unchanged; version number incremented on each release.",
"dataset_knownBias": [
"Fault events are underrepresented relative to normal operation (ratio approximately 1:23)",
"Both facilities use pumps from the same manufacturer family; generalization to other manufacturers not validated"
],
"dataset_hasSensitivePersonalInformation": "no",
"dataset_intendedUse": "Training predictive maintenance models for industrial centrifugal pumps. Benchmarking of anomaly detection and remaining useful life algorithms.",
"verifiedUsing": [
{
"type": "Hash",
"algorithm": "sha256",
"hashValue": "c9e5d04a3ef67b82248a5c69e3f0d2e1b4c7a0f2d3e4f5a6b7c8d9e0f1a2b3c4"
}
],
"comment": "SPDX 3.0→3.1 CHANGES: 'dataset_datasetSize' (5798205850 bytes) deprecated in SPDX 3.1, use 'software_artifactSize'. 'dataset_intendedUse' deprecated in SPDX 3.1, use Core 'intendedUse'. Compare with spdx3.1/example04.spdx3.json-draft."
},
{
"type": "Relationship",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#Relationship1",
"creationInfo": "_:creationinfo",
"relationshipType": "hasDeclaredLicense",
"from": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#DatasetPackage1",
"to": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#LicenseExpression1"
]
},
{
"type": "Relationship",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#Relationship2",
"creationInfo": "_:creationinfo",
"relationshipType": "hasConcludedLicense",
"from": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#DatasetPackage1",
"to": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#LicenseExpression1"
]
},
{
"type": "simplelicensing_LicenseExpression",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004001#LicenseExpression1",
"creationInfo": "_:creationinfo",
"simplelicensing_licenseExpression": "LicenseRef-PumpHealth-Research",
"comment": "Research and benchmarking use permitted. Commercial deployment of models trained on this dataset requires a separate commercial license."
}
]
}
158 changes: 158 additions & 0 deletions dataset/example04/spdx3.1/example04.spdx3.json-draft
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{
"@context": "https://spdx.org/rdf/3.1/spdx-context.jsonld",
"@graph": [
{
"type": "CreationInfo",
"@id": "_:creationinfo",
"specVersion": "3.1",
"createdBy": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#Organization1"
],
"created": "2025-01-01T00:00:00Z"
},
{
"type": "Organization",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#Organization1",
"creationInfo": "_:creationinfo",
"name": "Industrial IoT Consortium"
},
{
"type": "SpdxDocument",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002",
"creationInfo": "_:creationinfo",
"profileConformance": [
"core",
"dataset"
],
"rootElement": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#SBOM1"
]
},
{
"type": "software_Sbom",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#SBOM1",
"creationInfo": "_:creationinfo",
"profileConformance": [
"core",
"dataset"
],
"rootElement": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#DatasetPackage1"
],
"software_sbomType": [
"analyzed"
]
},
{
"type": "dataset_DatasetPackage",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#DatasetPackage1",
"creationInfo": "_:creationinfo",
"name": "PumpHealth-Sensors",
"summary": "Time series sensor dataset from industrial centrifugal pumps for predictive maintenance model training.",
"description": "Multivariate time series data collected from 47 centrifugal pumps at two manufacturing facilities over 18 months. Each pump is instrumented with 8 sensor types sampled at 1-second intervals. The dataset includes labeled fault events (bearing wear, seal degradation, impeller imbalance, cavitation) and normal operation periods. Designed for training anomaly detection and remaining useful life (RUL) prediction models.",
"software_packageVersion": "3.1",
"software_primaryPurpose": "data",
"software_downloadLocation": "https://example.com/datasets/pumphealth-sensors",
"software_copyrightText": "Copyright 2024 Industrial IoT Consortium",
"software_artifactSize": 5798205850,
"builtTime": "2024-01-01T00:00:00Z",
"releaseTime": "2024-09-15T00:00:00Z",
"intendedUse": "Training predictive maintenance models for industrial centrifugal pumps. Benchmarking of anomaly detection and remaining useful life algorithms.",
"dataset_datasetType": [
"sensor",
"timestamp"
],
"dataset_sensor": [
{
"type": "DictionaryEntry",
"key": "vibration",
"value": "Triaxial accelerometer; range 0–50 mm/s RMS; calibrated every 6 months"
},
{
"type": "DictionaryEntry",
"key": "bearing_temperature",
"value": "Type-K thermocouple; range -40–200 °C; ±0.5 °C accuracy"
},
{
"type": "DictionaryEntry",
"key": "inlet_pressure",
"value": "Piezoresistive transducer; range 0–10 bar; calibrated annually"
},
{
"type": "DictionaryEntry",
"key": "outlet_pressure",
"value": "Piezoresistive transducer; range 0–25 bar; calibrated annually"
},
{
"type": "DictionaryEntry",
"key": "flow_rate",
"value": "Electromagnetic flow meter; range 0–500 m³/h; ±0.3% full scale"
},
{
"type": "DictionaryEntry",
"key": "motor_current",
"value": "Hall-effect current clamp; range 0–100 A; ±0.5% accuracy"
},
{
"type": "DictionaryEntry",
"key": "shaft_speed",
"value": "Magnetic encoder; range 0–3600 RPM; resolution 1 RPM"
},
{
"type": "DictionaryEntry",
"key": "fluid_temperature",
"value": "PT100 RTD; range -20–150 °C; ±0.1 °C accuracy"
}
],
"dataset_datasetAvailability": "registration",
"dataset_confidentialityLevel": "green",
"dataset_dataCollectionProcess": "Sensors installed and calibrated by certified technicians. Data logged via OPC-UA to a SCADA system, then extracted in CSV format at 1-second resolution. Fault events manually labeled by maintenance engineers from work order records and physical inspections. Normal/fault label verified by at least two engineers per event.",
"dataset_dataPreprocessing": [
"Missing values (sensor dropout <0.5% of records) filled by linear interpolation",
"Outliers beyond 5 sigma removed and flagged in a companion outlier log",
"Synchronized across sensors using nearest-timestamp alignment within 50ms window",
"Train/validation/test split: 70/15/15 by pump ID (not by time) to prevent data leakage"
],
"dataset_datasetUpdateMechanism": "Quarterly snapshots appended. Each release includes new labeled fault events from the previous quarter. Historical data unchanged; version number incremented on each release.",
"dataset_knownBias": [
"Fault events are underrepresented relative to normal operation (ratio approximately 1:23)",
"Both facilities use pumps from the same manufacturer family; generalization to other manufacturers not validated"
],
"dataset_hasSensitivePersonalInformation": "no",
"verifiedUsing": [
{
"type": "Hash",
"algorithm": "sha256",
"hashValue": "c9e5d04a3ef67b82248a5c69e3f0d2e1b4c7a0f2d3e4f5a6b7c8d9e0f1a2b3c4"
}
],
"comment": "SPDX 3.1 CHANGES: (1) 'dataset_datasetSize: 5798205850' (bytes, SPDX 3.0, deprecated) renamed to 'software_artifactSize: 5798205850' (~5.4 GB) in SPDX 3.1. (2) 'dataset_intendedUse' (SPDX 3.0, deprecated) replaced by Core-level 'intendedUse'. Compare with spdx3.0/example04.spdx3.json."
},
{
"type": "Relationship",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#Relationship1",
"creationInfo": "_:creationinfo",
"relationshipType": "hasDeclaredLicense",
"from": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#DatasetPackage1",
"to": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#LicenseExpression1"
]
},
{
"type": "Relationship",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#Relationship2",
"creationInfo": "_:creationinfo",
"relationshipType": "hasConcludedLicense",
"from": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#DatasetPackage1",
"to": [
"https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#LicenseExpression1"
]
},
{
"type": "simplelicensing_LicenseExpression",
"spdxId": "https://spdx.org/spdxdocs/dataset-example04-b1b2c3d4-e5f6-7890-abcd-000000004002#LicenseExpression1",
"creationInfo": "_:creationinfo",
"simplelicensing_licenseExpression": "LicenseRef-PumpHealth-Research"
}
]
}
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