Upload folder using huggingface_hub

README.md

@@ -0,0 +1,70 @@
+---
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+- tabular-regression
+language:
+- en
+tags:
+- industrial-iot
+- edge-analytics
+- predictive-maintenance
+- test-data
+- iot
+- synthetic-data
+- sensor-data
+- smart-factory
+- machine-monitoring
+- anomaly-detection
+- manufacturing
+- telemetry
+- mindweave
+- industry-4-0
+- time-series
+pretty_name: IoT Sensor Telemetry (Synthetic)
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: anomaly_events
+  data_files: data/anomaly_events.csv
+  default: true
+- config_name: sensors
+  data_files: data/sensors.csv
+- config_name: telemetry_readings
+  data_files: data/telemetry_readings.csv
+---
+
+# IoT Sensor Telemetry (Synthetic)
+
+High-frequency telemetry from a simulated smart factory operating three
+CNC lines, a finishing cell, and a predictive-maintenance program over
+six months. Covers temperature, humidity, pressure, and vibration sensors
+sampled on a rolling 5-minute schedule with realistic shift patterns,
+machine assignments, maintenance alerts, and operational state changes.
+
+Includes two injected anomalies: a progressive calibration drift on sensor
+3 during month 4 and a sudden spike pattern on sensor 1 that signals an
+equipment failure event. Useful for time-series analytics, anomaly
+detection, edge telemetry pipelines, and Industry 4.0 monitoring demos.
+
+
+## Tables
+
+| Table | Rows |
+|-------|------|
+| anomaly_events | 2 |
+| sensors | 4 |
+| telemetry_readings | 50,000 |
+| **Total** | **50,006** |
+
+## Formats
+
+- CSV (in `data/` directory)
+- Parquet (available in full version)
+- SQLite (available in full version)
+
+## About
+
+Generated by [Mindweave Technologies](https://mindweave.tech) — realistic synthetic datasets for developers, QA teams, and data engineers.
+
+Browse all datasets: [mindweavetech.gumroad.com](https://mindweavetech.gumroad.com)

data/anomaly_events.csv

@@ -0,0 +1,3 @@
+event_id,sensor_id,event_type,start_time,end_time,description
+1,3,sensor_drift,2024-04-01 00:00:00,2024-04-30 00:00:00,Sensor 3 drifted upward throughout month 4 after missed calibration
+2,1,failure_spike,2024-05-21 03:00:00,2024-05-23 18:00:00,Sensor 1 vibration spiked during spindle bearing failure

data/sensors.csv

@@ -0,0 +1,5 @@
+id,name,metric_type,unit,machine_id,line_id,install_date
+1,SEN-001,vibration,mm_s,CNC-04,machining-a,2023-09-12
+2,SEN-002,temperature,celsius,FURN-02,heat-treat,2023-10-03
+3,SEN-003,pressure,bar,HYD-07,press-line,2023-08-19
+4,SEN-004,humidity,percent,PAINT-01,finishing,2023-11-01