Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Удаленный веб-скрейпинг× | Сбор данных через API× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2000s–2010s (cloud infrastructure era) | 2000s–2010s (formalized as a research method) |
| Автор метода≠ | 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 |
| Основополагающий источник≠ | 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 |
| Другие 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 |
| Связанные≠ | 3 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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