Comparer des méthodes

	Collecte de données par capteurs mobiles	Collecte de données via API
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	Mid-2000s (smartphone-era formalization ~2006–2010)	2000s–2010s (formalized as a research method)
Auteur d'origine	Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing	Emerged from computational social science and web 2.0 platform practices
Type	Passive and active quantitative data collection technique	Digital data collection technique
Source fondatrice	Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648
Alias	mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection	API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection
Apparentées	4	5
Résumé	Mobile sensor data collection uses the built-in sensors of smartphones, tablets, or wearable devices to capture behavioral, physiological, and environmental data in real-world settings. Sensors such as accelerometers, GPS, heart rate monitors, ambient light detectors, and microphones record data passively or on demand, enabling researchers to study human behavior with high temporal resolution outside the laboratory.	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.