Сравнение методов

	Сбор данных на основе множественных API	Сбор данных с датчиков
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	2010s (accelerated with proliferation of public APIs)	1990s–2000s (widespread deployment with IoT ~2000s)
Автор метода	Emergent practice in computational social science; formalized by Salganik, Ruths, Pfeffer, and others	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Тип	Quantitative / mixed data collection technique	Quantitative / mixed data collection technique
Основополагающий источник	Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Другие названия	multi-API data harvesting, multi-platform API collection, cross-API data aggregation, federated API data collection	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Связанные	4	5
Сводка	Multi-source API-based data collection is a systematic technique in which a researcher simultaneously or sequentially queries two or more application programming interfaces (APIs) to harvest digital data for a research project. By drawing from multiple platforms or services — such as social media APIs, government open-data portals, or scientific data repositories — researchers can build richer, more representative datasets than any single source permits. The method is especially prominent in computational social science, digital humanities, public health surveillance, and environmental monitoring.	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.