Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Sbírání dat pomocí API× | Sběr senzorových dat× | |
|---|---|---|
| Obor | Metodologie dotazníkových šetření | Metodologie dotazníkových šetření |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 2000s–2010s (formalized as a research method) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| Tvůrce≠ | Emerged from computational social science and web 2.0 platform practices | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| Typ≠ | Digital data collection technique | Quantitative / mixed data collection technique |
| Původní zdroj≠ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| Další názvy | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. | 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. |
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