Сравнение на методи

	Дистанционно уеб скрапиране	Събиране на данни чрез API
Област	Методология на проучванията	Методология на проучванията
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2000s–2010s (cloud infrastructure era)	2000s–2010s (formalized as a research method)
Създател	Distributed computing and web automation communities	Emerged from computational social science and web 2.0 platform practices
Тип	Automated remote data collection technique	Digital data collection technique
Основополагащ източник	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
Други названия	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
Свързани	3	5
Резюме	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.