image: Immense quantities of data and new computational methods are encouraging scientists to reimagine the meaning of "good" science. view more
Credit: By Camelia.boban - Own work, CC BY-SA 3.0, <a href="https://commons.wikimedia.org/w/index.php?curid=33124418" target="_blank">https://commons.wikimedia.org/w/index.php?curid=33124418</a>
The BioScience Talks podcast features discussions of topical issues related to the biological sciences.
Scientists have long debated the best methods to achieve sound findings. In recent decades, hypothesis-driven frameworks have been enshrined in textbooks and school courses, with iterative and inductive approaches often taking a back seat. However, the advent of big data poses a challenge to the established dogma, as large data sets often require broad collaborations and make traditional hypothesis-driven approaches less tractable.
For this episode of BioScience Talks, we spoke with Michigan State University professors Kevin Elliott, Kendra Cheruvelil, Georgina Montgomery, and Patricia Soranno. Their interdisciplinary work, described in the journal BioScience, highlights the changing scientific landscape, in which large data sets and new computational methods encourage a more iterative approach to science. However, the authors are quick to note that despite the newness of the technology, the reinvigorated approaches are anything but: The debate over iterative and hypothesis-driven science has raged all the way back to Darwin, and beyond.
To hear the whole discussion, visit this link for this latest episode of the Bioscience Talks podcast.
###
Journal
BioScience