Porovnat metody

	Sběr dat z více zdrojů prostřednictvím API	Sbírání dat pomocí API
Obor	Metodologie dotazníkových šetření	Metodologie dotazníkových šetření
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2010s (accelerated with proliferation of public APIs)	2000s–2010s (formalized as a research method)
Tvůrce	Emergent practice in computational social science; formalized by Salganik, Ruths, Pfeffer, and others	Emerged from computational social science and web 2.0 platform practices
Typ	Quantitative / mixed data collection technique	Digital data collection technique
Původní zdroj	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
Další názvy	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
Příbuzné	4	5
Shrnutí	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.