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| Pengumpulan Data Penderia Jauh× | Pengumpulan Data Berasaskan API× | |
|---|---|---|
| Bidang | Metodologi Tinjauan | Metodologi Tinjauan |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1990s–2000s (proliferated with wireless and IoT technologies) | 2000s–2010s (formalized as a research method) |
| Pengasas≠ | Multiple contributors; foundational wireless sensor network (WSN) survey by Akyildiz et al. | Emerged from computational social science and web 2.0 platform practices |
| Jenis≠ | Automated quantitative data collection | Digital data collection technique |
| Sumber perintis≠ | Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Alias | remote sensing data acquisition, wireless sensor data collection, distributed sensor data collection, telemetric data collection | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Berkaitan≠ | 6 | 5 |
| Ringkasan≠ | Remote sensor data collection is the systematic acquisition of measurements from geographically distributed sensing devices without requiring direct human presence at each location. Sensors continuously or periodically record physical, chemical, or biological variables — temperature, pressure, motion, light, GPS coordinates — and transmit readings wirelessly or via network to a central repository for analysis. Widely used in environmental monitoring, precision agriculture, health informatics, and smart infrastructure. | API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match. |
| ScholarGateSet data ↗ |
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