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Examining cultural structures and functions in biology.

Richelle L TannerNeena GroverMichelle L AndersonKatherine C CrockerShuchismita DuttaAngela M HornerLoren E HoughTalia Y MooreGail L RosenKaitlin Stack WhitneyAdam P Summers
Published in: Integrative and comparative biology (2021)
Scientific culture and structure organize biological sciences in many ways. We make choices concerning the systems and questions we study. Our research then amplifies these choices into factors that influence the directions of future research by shaping our hypotheses, data analyses, interpretation, publication venues, and dissemination via other methods. But our choices are shaped by more than objective curiosity-we are influenced by cultural paradigms reinforced by societal upbringing and scientific indoctrination during training. This extends to the systems and data that we consider to be ethically obtainable or available for study, and who is considered qualified to do research, ask questions, and communicate about research. It is also influenced by the profitability of concepts like open-access-a system designed to improve equity, but which enacts gatekeeping in unintended but foreseeable ways. Creating truly integrative biology programs will require more than intentionally developing departments or institutes that allow overlapping expertise in two or more subfields of biology. Interdisciplinary work requires the expertise of large and diverse teams of scientists working together-this is impossible without an authentic commitment to addressing, not denying, racism when practiced by individuals, institutions, and cultural aspects of academic science. We have identified starting points for remedying how our field has discouraged and caused harm, but we acknowledge there is a long path forward. This path must be paved with field-wide solutions and institutional buy-in: our solutions must match the scale of the problem. Together, we can integrate-not reintegrate-the nuances of biology into our field.
Keyphrases
  • public health
  • high resolution
  • big data
  • machine learning
  • current status