Salīdzināt metodes

	Attālinātā tīmekļa datu ieguve	Datu vākšana, izmantojot API
Nozare	Aptauju metodoloģija	Aptauju metodoloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2010s (cloud infrastructure era)	2000s–2010s (formalized as a research method)
Autors	Distributed computing and web automation communities	Emerged from computational social science and web 2.0 platform practices
Tips	Automated remote data collection technique	Digital data collection technique
Pirmavots	Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media. ISBN: 978-1491985571	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648
Citi nosaukumi	cloud web scraping, server-side scraping, remote automated data extraction, distributed web scraping	API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection
Saistītās	3	5
Kopsavilkums	Remote web scraping is a data collection approach in which automated scripts or bots harvest publicly accessible web content — text, tables, metadata, or links — running on remote servers or cloud infrastructure rather than on the researcher's local machine. This separation allows continuous, large-scale, or geographically distributed crawling that local setups cannot sustain, making it particularly suited to longitudinal or high-volume data collection tasks.	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.