পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| API-ভিত্তিক ডেটা সংগ্রহ× | সেন্সর ডেটা সংগ্রহ× | |
|---|---|---|
| ক্ষেত্র | জরিপ পদ্ধতি | জরিপ পদ্ধতি |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 2000s–2010s (formalized as a research method) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| প্রবর্তক≠ | Emerged from computational social science and web 2.0 platform practices | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| ধরন≠ | Digital data collection technique | Quantitative / mixed data collection technique |
| মৌলিক উৎস≠ | 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 ↗ |
| অপর নাম | 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 |
| সম্পর্কিত | 5 | 5 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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