Trade-Offs, Limitations, and Promises of Big Data in Social Science Research
Datos Bibliográficos
| ID | 4075262 |
|---|---|
| Autores | Patricia White (0000-0002-0013-3167, U.S. National Science Foundation), R Saylor Breckenridge (U.S. National Science Foundation) |
| Año | 2014 |
| Volumen | 31 |
| Número | 4 |
| Páginas | 331-338 |
| Fecha de publicación | 2014-07-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Review of Policy Research (JOURNAL) |
| Identificadores de la revista | ISSN: 1541-132X • E-ISSN: 1541-1338 |
| Editorial | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/ropr.12078 |
| OpenAlex | W1576350512 |
| Idioma | EN |
| Citas recibidas | 4 |
The promise of "big data" is a grand one. The collection and aggregation of massive datasets and the development of analytical tools by which to study these data is part of cutting-edge efforts across scientific disciplines, with social, behavioral, and economic sciences leading the way in many of these efforts. There has been a recent rise in National Science Foundation funding for "big data" research across directorates that coincides with scholarly, public, and governmental attention to the topic. Nonetheless, there are limitations and trade-offs to "big data" research, particularly as it corresponds to scientific modes of inquiry and the limited range of topics that fall under its umbrella, that must be recognized and incorporated into the general understanding of its long-term promise
Big data · Data collection · Data science · Grand Challenges · Political science · Social science · Sociology · Big Data and Business Intelligence · Big Data Technologies and Applications · Computer Science · Data Analysis with R
| Obras citantes distintas | 4 |
|---|---|
| Citas por año | 0,67 |
| Intervalo de citas | 2020 - 2026 (7) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 4 |