Сравнение методов

	Веб-скрейпинг	Сбор данных с датчиков
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	Late 1990s–2000s	1990s–2000s (widespread deployment with IoT ~2000s)
Автор метода	Early internet practitioners; systematised in research contexts from the late 1990s onward	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Тип	Automated digital data collection technique	Quantitative / mixed 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	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Другие названия	web harvesting, screen scraping, web crawling, automated data extraction	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Связанные	5	5
Сводка	Web scraping is a computational data collection technique in which software automatically retrieves and extracts structured or semi-structured content from websites. Widely used in social science, computational linguistics, economics, and information science, it enables researchers to assemble large datasets from publicly accessible web sources — such as news archives, social media platforms, government portals, and online marketplaces — that would be impractical to collect manually.	Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.