विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| एपीआई-आधारित डेटा संग्रह× | सेंसर डेटा संग्रह× | |
|---|---|---|
| क्षेत्र | सर्वेक्षण पद्धति | सर्वेक्षण पद्धति |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2000s–2010s (formalized as a research method) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| प्रवर्तक≠ | Emerged from computational social science and web 2.0 platform practices | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| प्रकार≠ | Digital data collection technique | Quantitative / mixed data collection technique |
| मौलिक स्रोत≠ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| उपनाम | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| संबंधित | 5 | 5 |
| सारांश≠ | 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. | 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. |
| ScholarGateडेटासेट ↗ |
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