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| Raccolta di Dati da Sensori con Pilot-Test× | Raccolta Dati da Sensori× | |
|---|---|---|
| Campo | Metodologia delle indagini | Metodologia delle indagini |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1990s–2000s (formalized with proliferation of digital sensing technologies) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| Ideatore≠ | General research methods practice; sensor pilot testing codified through IoT and environmental monitoring literature | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| Tipo≠ | Data collection procedure with pre-deployment validation phase | Quantitative / mixed data collection technique |
| Fonte seminale≠ | Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications. ISBN: 978-1506386706 | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| Alias | sensor pilot study, sensor pre-deployment testing, instrument validation with sensors, sensor calibration pilot | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Pilot-tested sensor data collection is a structured data gathering approach in which sensor instruments — hardware or software-based devices that measure physical, environmental, physiological, or behavioral signals — are deployed in a small-scale trial before the main study. The pilot phase verifies sensor accuracy, communication reliability, data format consistency, and placement adequacy, allowing researchers to identify and correct technical problems before full-scale data collection begins. | Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies. |
| ScholarGateInsieme di dati ↗ |
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