Compară metode

	Colectarea de date multi-sursă bazată pe API	Colectarea de date bazată pe API
Domeniu	Metodologia anchetelor	Metodologia anchetelor
Familie	Process / pipeline	Process / pipeline
Anul apariției	2010s (accelerated with proliferation of public APIs)	2000s–2010s (formalized as a research method)
Autorul original	Emergent practice in computational social science; formalized by Salganik, Ruths, Pfeffer, and others	Emerged from computational social science and web 2.0 platform practices
Tip	Quantitative / mixed data collection technique	Digital data collection technique
Sursa seminală	Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648
Denumiri alternative	multi-API data harvesting, multi-platform API collection, cross-API data aggregation, federated API data collection	API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection
Înrudite	4	5
Rezumat	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.	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.